Underwater Technology 37.2 by SUT

Vol. 37 32 No. No. 232 2020 2014 Vol.

UNDERWATER TECHNOLOGY

ISSN 1756 0543

A Personal View... Donâ&#x20AC;&#x2122;t fence me in!

Simon Hems

Reliability of cluster head Node based upon parametric constraints in underwater acoustic sensor networks

Gaurav Sharma, Shilpi Harnal, Neha Miglani, and Savita Khurana

Hierarchical classification of time series data aggregation in underwater wireless sensor networks

Durairaj Ruby and Jayachandran Jeyachidra

Book Review 3D Recording and Interpretation for Maritime Archaeology

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UNDERWATER TECHNOLOGY Editor Dr MDJ Sayer Scottish Association for Marine Science Assistant Editor E Azzopardi SUT Editorial Board Chairman Dr MDJ Sayer Scottish Association for Marine Science Gavin Anthony, GAVINS Ltd Dr MA Atamanand, National Institute of Ocean Technology, India LJ Ayling, Maris International Ltd Commander Nicholas Rodgers FRMetS RN (Rtd) Prof Ying Chen, Zhejiang University Jonathan Colby, Verdant Power Neil Douglas, Viper Innovations Ltd, Prof Fathi H. Ghorbel, Rice University G Griffi ths MBE, Autonomous Analytics Prof C Kuo FRSE, Emeritus Strathclyde University Dr WD Loth, WD Loth & Co Ltd Craig McLean, National Ocean and Atmospheric Administration Dr S Merry, Focus Offshore Ltd Prof Zenon Medina-Cetina, Texas A&M University Prof António M. Pascoal, Institute for Systems and Robotics, Lisbon Dr Alexander Phillips, National Oceanography Centre, Southampton Prof WG Price FRS FEng, Emeritus Southampton University Dr R Rayner, Sonardyne International Ltd Roland Rogers CSCi, CMarS, FIMarEST, FSUT Dr Ron Lewis, Memorial University of Newfoundland Prof R Sutton, Emeritus Plymouth University Dr R Venkatesan, National Institute of Ocean Technology, India Prof Zoran Vukić, University of Zagreb Prof P Wadhams, University of Cambridge Cover Image (top): zoonar.com/syrist Cover Image (bottom): Steve Crowther Cover design: Quarto Design/ kate@quartodesign.com

Society for Underwater Technology Underwater Technology is the peer-reviewed international journal of the Society for Underwater Technology (SUT). SUT is a multidisciplinary learned society that brings together individuals and organisations with a common interest in underwater technology, ocean science and offshore engineering. It was founded in 1966 and has members in more than 40 countries worldwide, incIuding engineers, scientists, other professionals and students working in these areas. The Society has branches in Aberdeen, London and South of England, and Newcastle in the UK, Perth and Melbourne in Australia, Rio de Janeiro in Brazil, Beijing in China, Kuala Lumpur in Malaysia, Bergen in Norway and Houston in the USA. SUT provides its members with a forum for communication through technical publications, events, branches and specialist interest groups. It also provides registration of specialist subsea engineers, student sponsorship through an Educational Support Fund and careers information. For further information please visit www.sut.org or contact: Society for Underwater Technology 2 John Street, London WC1N 2ES e info@sut.org

Scope and submissions The objectives of Underwater Technology are to inform and acquaint members of the Society for Underwater Technology with current views and new developments in the broad areas of underwater technology, ocean science and offshore engineering. SUT’s interests and the scope of Underwater Technology are interdisciplinary, covering technological aspects and applications of topics including: diving technology and physiology, environmental forces, geology/geotechnics, marine pollution, marine renewable energies, marine resources, oceanography, salvage and decommissioning, subsea systems, underwater robotics, underwater science and underwater vehicle technologies. Underwater Technology carries personal views, technical papers, technical briefings and book reviews. We invite papers and articles covering all aspects of underwater technology. Original papers on new technology, its development and applications, or covering new applications for existing technology, are particularly welcome. All papers submitted for publication are peer reviewed through the Editorial Advisory Board. Submissions should adhere to the journal’s style and layout – please see the Guidelines for Authors available at www.sut.org.uk/journal/default.htm or email elaine.azzopardi@sut.org for further information. While the journal is not ISI rated, SUT will not be charging authors for submissions.

in more than 40 countries worldwide, including over 190 Corporate Members of the Society.

Disclaimer and copyright The Society does not accept responsibility for the technical accuracy of any items published in Underwater Technology or for the opinions expressed in such items. The copyright of any paper published in the journal is retained by the author(s) unless otherwise stated. All authors are supplied with a PDF version of their papers once published. Authors are encouraged to make the PDF version of their papers free to download from their own websites.

Open Access Underwater Technology is available as Open Access. PDF versions of all published papers from Underwater Technology may be accessed via ingentaconnect at www. ingentaconnect.com/content/sut/unwt. All issues from Volume 20 (1995) onwards are available as Open Access. The Society for Underwater Technology also encourages Underwater Technology authors to make their papers available online on their personal and/or institutional websites for Open Access. Through this arrangement, the Society supports the Open Access policy not only in the UK (the Research Councils UK (RCUK) policy) but also the drive towards Open Access in other countries.

Abstracting and indexing Underwater Technology is included in Emerging Sources Citation Index. Additional abstracting and indexing services include American Academy of Underwater Sciences (AAUS) E-Slate; Aquatic Sciences and Fisheries Abstracts (Biological Sciences and Living Resources; Ocean Technology, Policy and Non-Living Resources; and Aquatic Pollution and Environmental Policy); Compendex; EBSCO Discovery Service; Fluidex; Geobase; Marine Technology Abstracts; Oceanic Abstracts; Scopus; and WorldCat Discovery Services.

Subscription Subscription to the print version of Underwater Technology is available to non-members of the Society at the following rates per volume (single issue rates in brackets). Prices are given in GBP. Accepted methods of payment are cheque or credit card (MasterCard and Visa). Foreign cheques must be in GBP and drawn on a British bank otherwise a currency conversion surcharge is incurred. UK subscription Overseas subscription

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Underwater Technology is also available in electronic format via ingentaconnect as Open Access. To subscribe to the print version of the journal or for more information please email Elaine Azzopardi at elaine.azzopardi@sut.org

Publication and circulation Underwater Technology is published in March, July and November, in four issues per volume. The journal has a circulation of 2,400 copies to SUT members and subscribers

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A Personal View...

doi:10.3723/ut.37.037 Underwater Technology, Vol. 37, No. 2, pp. 37–38, 2020

Don’t fence me in! A warning: this Personal View may not be entirely environmentally friendly. I don’t care (the COVID-19 lockdown made me do it!). It is motivated by reflections whilst stuck in the splendid isolation that a COVID-19 world has imposed on all of us and thoughts about what I miss, which is inevitably driven by reminiscences of experiences. It turns out I have itchy feet. I am not, I don’t think, an immediately obvious choice to write a piece such as this for Underwater Technology. I am a lawyer, working in the City of London. I grew up in the middle of Yorkshire, then Kent, and mostly in Cheshire before going to university and law school in Sheffield. I have lived a largely land-locked existence, and far from seeking out the sea, in my free time I prefer the mountains – heading for, and scaling, rock, snow and ice rather than the depths of the oceans. But there is a thread running through me and (I have no doubt) all who are reading this, which is captured in the words of Dennis Merritt Jones: ‘We were born to be free, to expand our horizons by going where we have never gone before, and not to hang out in the relative comfort and safety of the nest, the known. There is a place within us that is courageous beyond our human understanding; it yearns to explore beyond the boundaries of our daily life’. Exploration: the desire to go further, discover more, work out the mysteries of the world and take excitement and pleasure along

the way – and then to use what we experience to learn from it, and try again to go even further. My relationship with the sea and all that is done on, in, under and to it only really began when I started my legal career. I joined Ince & Co, the pre-eminent firm for all things related to maritime law, as a trainee and quickly became involved in disputes concerning projects related to the upstream offshore oil and gas industry. Projects in this industry have pretty much been my entire legal focus, both in the nearly 21 years I spent at Ince and now at McGuire Woods, and it has been a joy. The arrival of lawyers on the scene is rarely welcomed by those who are seeking to innovate and push the boundaries of what is possible. We come armed with the tools to temper risk, impose responsibility, slow things down and take away the rewards of the brave few who are striving to achieve ‘new’. Hardly the stuff of heroes. I would argue, however, that our role in the progress of exploring the underwater world is a crucial one, and one which facilitates rather than hinders or prevents. We lawyers are also curious. The legal aspects of the work we handle is our expertise, and makes us qualified to help commercial and technical people navigate the legal hurdles and impositions that exist. Things need to be safe, which means regulation. Wrongs need to be righted, and that often means litigation. But the technical subject matter underlying the project disputes we are requested to help with provides colour and context to our work, and delving into that

After studying law and criminology at the Universities of Shefﬁeld and Utrecht, Simon joined Ince & Co as a trainee solicitor in 1999. He became a partner in 2009 and led the Energy and Infrastructure practice for 3½ years. He has now joined the London ofﬁce of McGuire Woods, where he continues to practice as a dispute resolution specialist with a focus on advising and representing players in the offshore oil and gas industry. Simon’s bread and butter is engineering, construction and offshore installation project disputes, but he also has extensive experience of offshore casualties, London market and protection and indemnity insurance matters, and disputes under charter parties for the provision of offshore services, principally pipelay, subsea equipment installation and diving.

detail helps us to understand our clients, their business and the world they operate in. More than anything, it is also the fun part. Looking back over my career to date, highlights, which I could never have envisioned sitting in a Sheffield classroom, include: • Understanding how walrus migration interferes with the ability to perform a seismic survey in arctic waters; • Dealing with the dismantling and cleaning of a vessel’s saturation diving system, including the bell, after the gas lines became contaminated with oil; 37

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Simon Hems. Don’t fence me in!

• Handling the fall-out from pipe lay operations in the Baltic Sea that damaged a subsea wreck of historic importance; • Getting to grips with the furious power of a subsea well blow-out and the effort required to tame it; • The importance of hydrogen, or rather the havoc it can wreak, having dealt with a number of cases involving the need to fix microscopic hydrogeninduced stress cracking in component parts, some of which were installed hundreds of feet below the waves; • Dealing with damage caused to subsea infrastructure in the Gulf of Mexico by some of the strongest hurricanes the world has ever seen; and • Helping a client put in place the legal framework that enabled it to try to find the missing Malaysian Airlines flight MH-370 somewhere in the vast, inhospitable expanse between Australia and Antarctica. It is my belief that the best lawyers take an interest beyond the law and get to know the people and businesses they are dealing with, which leads to an understanding of the significance of the work and what the right solution needs to be. When something goes wrong in the offshore industries, losses

are not small. The greatest losses are counted in lives. The financial losses, and who is responsible for them, often lead to arguments on a ‘bet the company’ scale. I do not want these companies to fail, possibly for largely selfish reasons. I love the people in these industries whom I have the privilege to work with, and I am constantly fascinated by the technological developments that are achieved. I want to see what happens next. I want to see how deep we can go, how much more we can discover about the oceans. I want to find out if we can ever use the ocean’s power effectively to achieve a form of energy transition, without being so foolish as to think we can ever tame the sea. And, yes, I want the oil and gas industry to continue its quest to develop resources in increasingly difficult environments, at least until feasible alternatives can properly flourish. So, as lawyers, we do try to impose measures that will manage the risks those operating offshore are exposed to. Yes, we will hold people accountable for wrongs that are committed and try to ensure behaviour continues in a particular direction. And yes, that does mean we play a part in slowing things down, because we don’t want you, the explorers, to fail – especially as

we try to survive and come out of the effects of COVID-19. I, for one, unapologetically cannot wait to get on an aeroplane to see my friends and clients in the places important to me, like Houston, Aberdeen, Paris, Mexico, parts of Africa, or wherever the work takes me. A lot of companies will not make it, but if everyone does what they can to help the rest survive, then the future is still exciting. For those directly involved, who are pushing these boundaries and taking the risks, I salute them as modern day explorers living by the ethos espoused by T S Eliot: ‘We shall not cease from exploration, and the end of all our exploring will be to arrive where we started and know the place for the first time’. Frankly, I hope we never arrive where we started, but let us keep striving for it when the freedom to do so returns. If my role helps in any way to keep the journey going, then it will be my privilege to keep doing it. By the time this article is published, I shall have moved on from Ince & Co to start a new adventure of my own at McGuire Woods: still based in London, still exploring the subsea world through the issues my clients face and doing what I can to help them keep going. I cannot wait.

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Technical Paper

doi:10.3723/ut.37.039 Underwater Technology, Vol. 37, No. 2, pp. 39â&#x20AC;&#x201C;52, 2020

Reliability of cluster head node based upon parametric constraints in underwater acoustic sensor networks Gaurav Sharma*, Shilpi Harnal, Neha Miglani and Savita Khurana Department of Computer Science and Engineering, Seth Jai Parkash Mukand Lal Institute of Engineering and Technology, Radaur, Haryana, India Received 8 February 2020; Accepted 4 June 2020

Abstract Underwater wireless sensor network (UWSN) has emerged as one of the most popular network technologies owing to its applicability to offshore searches, and underwater monitoring and exploration applications. It has been shown to be useful in the ďŹ elds of investigations and surveillance, and in assisting with and offering solutions to water-based calamities. Reliability in the underwater environment has caused researchers to direct attention towards improving the overall efďŹ ciency and energy utilisation of the network. In the present paper, reliable node quester (RNQ) algorithm has been formulated to calculate the node reliability for numerous parameters such as success rate, transmission time, and the affordability, congestion and stability of the nodes. The present paper highlights the data-forwarding mechanism of the nodes to enhance overall network reliability by (i) reducing the packet drop rate; (ii) increasing the packet delivery ratio; and (iii) minimising the energy consumption. Simulation results further support the proposed strategy by ensuring the network lifespan and detection accuracy. Keywords: underwater sensor network, node reliability, packet delivery rate, energy consumption, acoustic communications

1. Introduction Underwater wireless sensor network (UWSN) has attracted attention as the subspace of wireless sensor networks. It defines novice application domains in order to establish an underwater network that relies on mobile sensor nodes for communication and monitoring of the underlying system (Yadav and Tomar, 2013). UWSN comprises low-cost acoustic nodes deployed in the monitoring regions (Yang and Sikdar, 2008). Sensor nodes are battery-powered,

but are difficult to recharge (Chitre et al., 2008). Thus, there are memory and energy constraints with underwater sensor nodes. Data transmission takes place by sending packets to intermediate nodes if source and destination nodes are not adjacent to each other, and all nodes work on the same range of transmission. UWSN results in multi-hop and self-organised system of networks while implementing data storage, processing, collection and wireless communication in a collaborative manner (Peng et al., 2009). The communication network created under water relies on the cooperation and synchronisation of various sensor nodes that make use of bidirectional acoustic links between them (Lui et al., 2012). Each node works separately to send, forward and receive messages; i.e. there is no impact of existing nodes on the current node. Collected data by underwater sensor nodes is relayed to the surface beacon, which is either static or dynamic on the water surface, and rigged out with radio frequency (RF) and acoustic modems via wireless acoustic links (Peng et al., 2007). Moreover, the satellite is used to transmit and forward the information from the surface station to the command center which is positioned ashore. Communication network between various command centres rigged onshore is formed via satellite. UWSNs cover a large number of domains such as offshore exploration, ocean sampling, distributed tactical surveillance (Cheng et al., 2008) and oceanographic data collection (Chitre et al., 2008; Sozer et al., 2000). Unlike terrestrial sensor networks, underwater sensor networks face different challenges, making it more challenging to create

* Contact author. Email address: gaurav13@gmail.com

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Sharma et al. Reliability of cluster head node based upon parametric constraints in underwater acoustic sensor networks

and design wireless communication protocols for UWSN (Su et al., 2010). The mechanism used for communications in UWSNs is different to that of terrestrial sensor networks. Terrestrial sensor networks utilise highfrequency electromagnetic waves for sending and forwarding data. However, as electromagnetic waves cannot stretch over a long distance in underwater scenarios owing to a high absorption rate, this method cannot be used in UWNSs (Peng et al., 2009). While UWSN uses large amounts of energy and power consumption for data transmission (Van Kleunen et al., 2012; Senel et al., 2015), it is difficult to satisfy this energy need as batteries cannot be recharged frequently. The sensor nodes are also vulnerable to failure as a result of corrosive reactions (Akyildiz et al., 2005; Cui et al., 2005). Other challenges facing USWN include lower packet delivery ratio, high packet drop rate and reduced network lifetime. In order to enhance network lifetime, node reliability is an important requirement in underwater acoustic networks (Zenia et al., 2016; Yavuz and Ning, 2012). Therefore, the node must follow the defined protocols of the network, leading to trustbuilding among the acoustic nodes. The nature of trust is asymmetric and qualitative (Xu et al., 2015). A node which does not follow the specified protocols is considered an errant node and is immediately discarded from the network to guarantee error-free communication among the remaining nodes. Thus, the reliability-based framework is key to efficient and effective communication between the sensor nodes. The mechanism of the reliability-based framework involves analysing the neighbouring nodes, and evaluating and calculating reliability factors of sent and forwarded data packets. The value of reliability factors of the data packets is calculated, and the overall value of the sum represents the trustworthiness of considered neighbour nodes. In order to obtain an efficient and reliable system, reliable factor parameters should be considered to yield a trustworthy network with well-organised and speedy data transmission. The reliability of collected data at the sink node relies on data packets gathered from all respective nodes, rather than an individual report or status of the data packet from a specific source node, and reliable event detection becomes difficult if the available transport mechanism is not reliable. Moreover, an end-to-end packet delivery mechanism does not work well with the underwater network. Thus, a reliable event transport mechanism is needed (Han et al., 2016). Numerous techniques and methodologies have been devised and discussed to enhance the reliability

of the network in a harsher underwater environment. Many approaches to enhance overall reliability involve a trade-off with parametric measures such as energy efficiency, node reliability and overall system performance. The present study outlines an approach involving: (i) detection of individual node reliability; (ii) minimised packet drop rate; (iii) imbibing energy constraints at the initial stage itself; and (iv) enhanced network lifespan. In the present paper, Section 2 discusses related work; Section 3 presents the problem statement and underlying concepts; and Section 4 illustrates the reliable node quester (RNQ) pseudo-code in detail. In order to provide an in-detail understanding of the proposed strategy, an illustrative example is provided in Section 5. The comparative study of the RNQ algorithm with existing approaches and experimental verifications are summarised in Section 6, and Section 7 offers conclusions and future scope.

2. Related work The reliability of a system or network plays a crucial role in increasing overall network efficiency and performance. Numerous researchers have explored the reliability issues that should be considered. Han et al. (2016) developed the collaborative secure localisation trust (CSLT) model to guarantee location security. The model divides the localization process into five sub-processes namely, trust value of anchor nodes, filtering and selecting the reference nodes, trust value of selected reference nodes, initial localisation, and secondary localisation of nodes. The attack-resistant trust model based on multidimensional trust metrics (ARTMM) for underwater sensor networks (UWA-SNs) was proposed by Vennila and Madhura (2016). This model considered multidimensional trust metrics, as well as communication and energy levels. In order to predict the packet loss, an autoregressive integrated moving average (ARIMA) model was employed in the system. For wireless sensor networks, a trust management framework was introduced by Geetha and Chandrasekaran (2014) for the identification of various trust factors and parameters that impact the trust value. The data distribution strategy proposed by Ren et al. (2013) examines forward and backward secrecy, and the reliability of the data packet being sent or received. Rezvani et al. (2015) focused on modifying and improving iterative filtering (IF) techniques. An improvement was attained by embedding numerous parameters based on error rates such as bias and variance in wireless sensor

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networks, yielding a robust network while increasing the accuracy level and convergence rate. Further, a trust assessment scheme using the fuzzy logic concept was presented by Jadidoleslamy et al. (2016) for both direct and indirect trust. The drawback of this scheme is increased overhead for big networks, as every node needed to audit its neighbouring nodesâ&#x20AC;&#x2122; behaviour. Golgiri and Javidan (2016) targeted congestion overhead and developed congestion control protocol that enhanced network lifespan and increased network reliability. A framework was modelled for assimilation of the network service to achieve quality services for varied and inconsistent traffic priority. Fault-tolerant routing (FTR) protocol was modelled by Xu and Liu (2011). The FTR protocol designed was energy efficient, and showed an increased adaptability level, reduced end-to-end delay and high packet delivery rate. Energyefficient adaptive hierarchical and robust architectureenhanced (EDETA-e) protocol worked on more than one sink node, which further enhanced fault tolerance. Ayaz et al. (2012) investigated data transmission efficiency and designed an algorithm that estimated data packet size. 2H-ACK (two-hop acknowledgment) model was utilised in the proposed algorithm, where the back-up of the data packet is taken as two copies and preserved without exerting burden on immediately operative system resources. The authors also outlined the relationship between network parameters such as the size of the data packet, network throughput and bitwise error rate (BER). Wang et al. (2016) presented a data transport protocol that provides reliability and network efficiency in the underwater network using network coding and hybrid automatic repeat request (NCHARQ). This protocol balanced the tradeoff between energy utilisation and throughput parameters. Xie et al. (2010) presented a segmented data reliable transport (SDRT) protocol to assure reliable data transmission in UWA-SNs. SDRT is a hybridisation of forward error correction (FEC) and automatic repeat request (ARQ) mechanisms. To make data packet encoded, random forward error correction code and erasure code were utilised, and the packet was transmitted block-by-block through multi-hopping. Unlike conventional approaches, SDRT protocol maximised channel utility and simplified the protocol handling mechanism.Â Liu et al. (2010) proposed mechanisms at bit level and packet level based on redundancy scheme targeting reliable data transfer. It was concluded

that the broadcast nature allows redundancy utility owing to communication between cooperative nodes. An adaptive redundancy transport protocol (ARRTP) was developed for two types of topologies (random and regular) which led to increased transmission rate and efficient energy consumption. Goyal et al. (2020) proposed the security scheme for secure data aggregation in the deployed UWSN network. The authors presented the scheme with improvised reliability and data delivery ratio, even with an increasing number of attackers. The performance of the scheme showed better results in comparison to other state-of-art techniques. Goyal (2019) analysed the possibility of machine learning methodologies for data agglomeration in the underwater network. Various aspects were considered and discussed, with possibilities for further exploration in the field. Wei and Kim (2014) proposed a reliability-based algorithm to detect the route with low latency. By using this route, the sender can deliver the data in the shortest time. A hello packet is sent to every neighbouring node and computes its time; the neighbour node that takes less delivery time in the path is declared as the node to use. In this way, the route is traced with low latency. The path which leads to the delivery of data with less latency is followed by all other nodes. Khasawneh et al. (2018) implemented a reliabilitybased algorithm that considered three parameters in order to select the destinations: link information, depth and energy. The algorithm performs better in latency, energy consumption, lifetime of the network and packet delivery. Another reliability-based algorithm proposed by Wahid et al. (2014) uses different routing metrics based on link quality parameters and distance between the source and destination nodes. The node that is nearer in the water is taken as next forwarder owing to its link quality and better remaining energy. The proposed algorithm consumes less energy and latency compared to others. Calculating the reliability of acoustic nodes remains an important step in establishing overall system reliability. Therefore, the present study aimed to devise a technique that calculates the acoustic node reliability with a method that considers the reliable node before sending the data packet, rather than considering the node randomly, thus leading to increased throughput and an energy-efficient system. In order to achieve successful data transmission while considering the node reliability individually, the reliable node quester (RNQ) algorithm is used in the present paper. This scheme considers parametric constraints to enhance the efficiency of the overall system.

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Sharma et al. Reliability of cluster head node based upon parametric constraints in underwater acoustic sensor networks

3. Problem statement and preliminaries 3.1. Problem statement The acoustic nodes in an underwater environment work as a cluster to monitor a pre-specified region. In such clusters, there could exist some nodes whose actions and behaviour hamper the overall network efficiency. Such nodes are known as malicious nodes, or attacker nodes. They might attack other acoustic nodes of the network or be responsible for delivering false information, thus hindering network operations. If such nodes exist in the network, the network performance might be challenged as a result of their malfunctioning behaviour. It is therefore essential to create reliable communication among nodes for assurance of correct data at the receiver end. The proposed approach of the present study uses a reliable node quester thus calculates the reliability value of the nodes to validate different acoustic nodes in UWSM. 3.2. WorkďŹ&#x201A;ow model The reliability of an UWSN depends on the reliability of its sensor nodes. The prime factor responsible for the reliability of any sensor node is its battery life. Thus, the proposed approach initially checks the battery life of all nodes in the network. If the battery life of a node is less than 10%, the node cannot be considered as a reliable node for further communications. All such nodes are added to the dead list, and the network administrator is informed to replace the battery or take the necessary measure. For all nodes with sufficient battery life, the reliability computational algorithm prepares a reliability matrix by calculating the reliability factor for each ith node based on the following parameters: (i) success rate of node i (SNi); (ii) transmission time of node i (TNi); (iii) node affordability (NAi); (iv) node congestion value (CNi); and (v) node stability (NSi). 3.2.1. Success rate of node i (SNi) The success rate for a node in UWSN is calculated on the number of packets successfully transferred by that node to other nodes, and the total number of packets received by that node for further transmission in the network. The success rate is directly proportional to the node reliability, i.e. the higher the success rate, the more reliable the node is for communication. The success rate for node i (represented as SNi) is calculated as: SNi = PSi / PRi,

The resulting value of success rate for ith node will be in the range 0 to 1. 3.2.2. Transmission time of node i (TNi) As the conditions are harsher in UWSN in comparison to traditional wireless sensor networks, packet loss is possible owing to high error probability, even after successful arrival of acknowledgment from the receiver. For instance, if a sender node S1 sends a packet P1 to receiver node R1, after successful receipt of the packet P1 at R1 the receiver node R1 sends back an acknowledgment to sender node S1. After the successful arrival of acknowledgment at the sender end, the sender node can delete the packet. However, the receiver node R1 might not be able to forward the packet to another node and might have died because of low battery. Thus, in this scenario, there can be a complete irrecoverable loss of packets. The threeway handshaking mechanism of transmission control protocol (TCP) is not feasible for such a situation. Therefore, to improve the reliability during transmission using the proposed scenario, the receiver node R1 will not send the acknowledgment until it finds another node or next hop for forwarding the packet. Thus, according to the proposed scenario, the transmission time for a node i (i.e. TNi), as shown in Fig 1, will depend on the propagation time (TP) and the time required to locate the next forwarding hop (TNH): TNi = 2 Ă&#x2014; TPi + TNH,

(2)

where TPi is propagation time and TNH is next forwarding hop identification time. 3.2.3. Node affordability (NAi) In UWSN, the node affordability factor for any ith node is defined as the ratio between the number of times the node was available for the packets transmission (Nt) and the total number of attempts made to transfer the packets (Nc) through that particular node in the network: NAi = NTi / NCi,

(3)

(1)

where, PSi is number of data packets successfully transmitted; and PRi is number of packets received.

Fig 1: Node transmission time

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where NTi is number of times node is available and NCi is number of attempts made to access the node.

3.2.4. Node congestion value (CNi ) The node congestion value (CNi) uses the implicit signaling mechanism in which the sender node performs analysis for the acknowledgment time. In UWSN, nodes forward the information towards the sink nodes in upper layers after sensing the information. These sink nodes are responsible for processing data packets for various applications. Thus, if the value of acknowledgment time for a node is greater than the threshold value (maximum time limit), then it is assumed that the network is congested and the sender node should slow down the transmission of packets. If CNi is higher than the maximum acceptable value, then the node is not considered as a reliable node, as the network congestion will increase if it is considered for further communications; CNi value is therefore set to 0. If CNi is lower than the threshold value, then the node is not congested, and is therefore considered to be reliable. CNi was calculated by considering threshold acknowledgment time and actual acknowledgment time. The congestion value of a node i is initialised as: If ((TACKi / AACKi) < 1) Then Node is congested and set CNi = 0 Else Node is not congested and Set CNi = TACKi / AACKi End IF,

(4)

where TACKi is threshold acknowledgment (ACK) time of Ni and AACKi is actual ACK time of Ni. 3.2.5. Node stability (NSi ) As a result of the multi-layer structure and dynamicity of nodes in UWSN, it is necessary to calculate the stability of each node before approaching it for further communication in the network. In the scenario of a node having a transmission range (R) in the network, as depicted in Fig 2, the node traverses from a position (Xr,Yr) to (Xn,Yn) in a given time frame covering a distance (d). When a node movement occurs, the movement stability of the node varies, as it is directly proportional to its previous position. The distance covered by the node i in an instance of time (T) is represented as DTi , and can be measured as:

Fig 2: Node movement

DTi = √ ((Xn−Xr)2 + (Yn−Yr)2)

(5)

Based on the movement and distance covered by the node at every time window, the node stability factor can be estimated by: If (DTi >= 0 and DTi <= R/2) Then NSi = 1- (DTi / (R/2)) Else NSi = 0 End IF

(6)

In Equation 6, NSi varies in the range 0 to 1. When the distance covered by the node DTi increases at some instances of time, then the stability value of node decreases proportionally at the same time. The R can be replaced by ‘R/4’ or ‘R/8’ if a higher degree of movement stability is required.

3.3. Parameters mapping to node’s reliability (RELNi) All parameters discussed in Section 3.2 are used with the respective weighting value assigned to all the factors individually. The weighting value assigned to all factors are analysed on the basis of judgment and past experiences, and falls in the range 0 to 1. Based on these parameters, the reliability index value for ith node is given by: RELNi = 0.8 × (SNi) + 0.9 × (NTi) + 0.5 (7) × (NAi) + 0.8 ×(CNi) + 0.6* ×(NSi) Each parameter is assigned with a weight value within the range 0 to 1: • Success rate of node i (SNi) is assigned with a weighting of 8; • Transmission time of node i (TNi) is assigned with a weighting of 9; • Node affordability (NAi) is assigned with a weighting of 5;

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• Node congestion value (CNi) is assigned with a weighting of 8; and • Node stability (NSi) is assigned with a weighting of 6. Since an overall reliability value should not be biased towards any specific parameter, and in order to put forward all parameters in the reliability equation at the same level, all the parameters are normalised. The value of RELNi will fall in the range 0 to 1. The closer the value of RELNi is to 1, the more reliable is the node. Thus, the final obtained value will be considered stable for establishing reliable communication across the underwater network.

4. Flowchart and pseudo code for RNQ algorithm The alive and dead nodes in UWSN should be identified in the initial stage. The steps of the proposed algorithm should then be repeated for each ith node to compute the parameters discussed in section 3.2 of the present paper. These factors will then be applied to estimate the reliability of each alive node in the network. The reliability factor of each node will be obtained in RELNi when executing the proposed technique. The reliability value will be stored in an array RELN [N], or the reliability value of all the N nodes considered initially. All parameters will be aggregated with their assigned weighting value to compute the reliability equation RELNi, and the higher the value of RELNi , the higher is the reliability of the respective node. The benefit of the proposed scheme is that at the time of transmission, the node would forward the packet based on its reliability index rather than randomly, thus producing an energy-efficient and optimised sequence for the node transmission. The step-by-step procedure of the proposed scheme is depicted in Fig 3. The reliable_node_quester algorithm: input is: node_list (N), NL, EAi, NEi, TACKi, AACKi, PSi, PRi, TNi, PSi, PRi, TNi, TPi, TNH, NTi, NCi 2, and output is: list of all the all the reliable nodes among existing nodes (RELN). The steps involved are: 1 Initialise the new_list (NL) and rel_node_list (RELN) to 0. 2 For each i from 1 to N: // Repeat for each i th node in the UWSN. a) If (NEi >= 10* EAi) Then Declare node i as alive; Add node i to NL Else

Node i is dead; Discard the node i and do not update NL [end of for loop] 3 Repeat while (NL! = empty) Success rate, SNi = PSi / PRi Node availability, NTi = 2 × TPi + TNH Node affordability, NAi = NTi / NCi If ((TACKi / AACKi) < 1) Then Node is congested and set CNi = 0 Else Node is not congested and set CNi = TACKi / AACKi End IF e) If (DTi > = 0 and DTi < = R/2) Then Node stability, NSi = 1- (DTi / (R/2)) Else Node stability, NSi = 0 End IF f) RELNi = 0.8 ×(SNi) + 0.9 ×(NTi) + 0.5 ×(NAi) + 0.8 ×(CNi) + 0.6 ×(NSi) [End of while loop]

a) b) c) d)

[End of simulation] 4 Obtain list of reliable nodes.

5. Illustrative example In a system model with an underwater network of 30 acoustic nodes (alive) located at random positions in a pre-defined region, the node positions are not fixed and may change at any time. The nodes lying in the range of 10 m–15 m from the node Ni will be considered as neighbouring nodes. There are a few nodes in the system model that have battery life of less than 10 %, and will therefore not be considered for data forwarding. Fig 4 shows the diagrammatic representation of alive and dead nodes in the underwater environment. Abbreviations list: AACKi: actual ACK time of Ni CNi: congestion value DTi: distance (d) covered by the node i in an instance of time (T) EAi: energy assigned to node i N: total number of nodes in UWSN NAi: node affordability NCi: number of attempts made to access the node NEi: present energy level of node i NL: list of nodes satisfying battery constraints NSi: node stability NTi: number of times node is available PSi: number of data packets successfully transmitted PRi: number of packets received RELNi: reliability of alive node i SNi: success rate of node i TNH: next forwarding hop identification time TACKi: threshold ACK time of Ni TNi: transmission time of node i TPi: propagation time (Xr, Yr): previous position of node i (Xn,Yn): present position of node i

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Dead nodes with battery < 10%

Alive nodes with battery >10%

N1 N3

N24

N23

N22

N13 N25 N20

N26

N12

N6 N27

N15

N29

N11

N19

N28

N16

N14

N17 Nd

N21

N18 N10

Fig 4: System model comprising sensor nodes in underwater environment

Fig 3: Flowchart for reliable node quester

To calculate the reliability of a node, values for the five parameters of success rate, transmission time, node affordability, node congestion and nodes stability need to be calculated. Table 1 represents the values of these five parameters, along with the reliability value (node reliability) of all existing nodes, based on

the calculated values of these parameters. The values are normalised in the range 0 to 1. The closer the value of node reliability to 1, the more reliable the node will be. The graphical representation of the calculated parametric values for the sensor nodes is shown in Fig 5. Fig 6 shows the node reliability. Node N7 has the highest reliability with the value of 0.75719693, and node N23 has the lowest reliability value of 0.20589535. The remaining nodes have intermediate values between the range 0 to 1. The data packet needs to be transmitted from node N1 to N21, where N1 is the source node and N21 is the receiver node. Only the 30 alive sensor nodes are considered; the remaining dead nodes are discarded. As the underwater environment is dynamic, the positions of nodes shown at initial phase are not consistent; the positions may or may not be the same at the next time stamp. After the data reception by node N1, it is forwarded to the next neighbouring node (the neighboring node for the current node lies in the diameter of 50 m from the considered node). The data-forwarding concept is shown in Fig 7 (aâ&#x20AC;&#x201C;f) for the time T1 to T6. At time T1, N1, the sender node, will search for its neighbouring nodes (N23, N5, N2 and N3). Based on the reliability value (given in Table 1), the node that is considered next for the data forwarding is that with the highest reliability value (N5, with reliability value of 0.40455065). After reaching N5, the nodes might have displaced from their previous locations; Fig 7(b) shows the shuffled positions of the nodes, and the next receptor node selection accordingly. The process was repeated until the data packet reached the receiver node (N21), which took place at timestamp T6. Table 2 depicts the current node and neighbouring nodes with calculated reliability values. Based on 45

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Table 1: Calculated values of parameters for all the nodes Node

N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11 N12 N13 N14 N15 N16 N17 N18 N19 N20 N21 N22 N23 N24 N25 N26 N27 N28 N29 N30

Success rate

0.32640318 0.40195939 0.44844365 0.11149613 0.28976621 0.18123454 0.84557045 0.67700312 0.4146016 0.82518423 0.64862771 0.19446521 0.76926342 0.93431879 0.75818089 0.00653931 0.85164349 0.82535831 0.66529132 0.69068879 0.30348089 0.93210318 0.16897199 0.1511329 0.89320178 0.05391176 0.95378376 0.06170123 0.39107509 0.06085291

Transmission time

0.71214367 0.37199248 0.17535799 0.42132616 0.03949759 0.34422005 0.63189212 0.21923916 0.90382269 0.16232342 0.61588137 0.76324488 0.51194725 0.52225596 0.57890942 0.93240026 0.45453781 0.92843433 0.18295013 0.29903428 0.39424609 0.55196469 0.48020178 0.86756866 0.56038835 0.47202325 0.71722389 0.67799572 0.17994359 0.83946513

Node affordability

0.02835 0.62989 0.2985 0.60393 0.77205 0.62075 0.36786 0.54993 0.0763 0.16423 0.18842 0.6184 0.56796 0.35305 0.99728 0.78841 0.64453 0.34514 0.08223 0.23908 0.11344 0.91479 0.16748 0.00858 0.1516 0.01325 0.89044 0.77538 0.5757 0.81253

the reliability value, the priority of the neighbouring nodes was decided, and the node with highest priority was selected for the data forwarding. Thus, when the data packet is transmitted by considering the reliability value, this results in reduced costs, packet drop rate and energy consumption.

6. Experimental veriﬁcation To obtain the efficacy and efficiency of the proposed model, the deployment of 50 UWSNs was carried out randomly. The results were obtained by simulating and experimenting using network simulator ns2. The range considered for sending the packet varied from 50–250 Kb as per requirement. The simulation parameters considered are shown in Table 3 with the accepted ranges.

6.1. Performance metrics The present paper examines node reliability in order to improve the overall network efficiency. In order to achieve the results of reduced delay and

Node congestion

0.01304 0.41205 0.31759 0.07773 0.29274 0.60542 0.94657 0.43965 0.52368 0.83089 0.24165 0.94007 0.57537 0.43251 0.36961 0.51736 0.27057 0.05408 0.83967 0.79973 0.42278 0.01668 0.04818 0.79539 0.07386 0.05275 0.26151 0.32041 0.41365 0.81993

Node stability

0.76984 0.07093 0.28338 0.28558 0.948 0.75491 0.89928 0.59892 0.43067 0.53535 0.7639 0.69469 0.26135 0.6815 0.17175 0.49722 0.79166 0.76937 0.77016 0.47818 0.4646 0.13302 0.08596 0.94336 0.37169 0.38293 0.22924 0.81861 0.71559 0.46588

Node reliability

0.38571156 0.3731956 0.30275671 0.27885706 0.40455065 0.47289995 0.75719693 0.47915439 0.51683648 0.52063159 0.5052946 0.64460107 0.54923564 0.59692148 0.56248307 0.54189306 0.58447702 0.60370457 0.51995492 0.51886505 0.353142 0.49805746 0.20589535 0.58564952 0.43800391 0.20737012 0.61125136 0.49853941 0.42303632 0.59609478

error rate, numerous performance parameters have been considered: • Packet delivery ratio: calculated as the number of packets successfully transmitted to the total packets sent. The higher value is anticipated in order to obtain improved performance. • Packet drop rate: the number of packets dropped midway owing to battery loss, weak connections or high traffic. The rate must be minimised to achieve high reliability. • Network lifespan: the total time until the last remaining node dies. This should be extended as far as possible. • Overall energy utilisation: the energy consumed by all sensor nodes for the packet transmission from the sender end to receiver end. This should be done in such a way that the network should remain alive for a longer duration. • Detection accuracy: the ratio of correctly detected attack count to the total attack attempts made. If the detection accuracy is high, it indicates the optimised overall performance of the network.

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Success rate Transmission time Node’s affordability

1.0

Node’s congestion Node’s stability 0.8

Range

0.6

0.4

0.2

0.0 N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11 N12 N13 N14 N15 N16 N17 N18 N19 N20 N21 N22 N23 N24 N25 N26 N27 N28 N29 N30

Nodes in network

Fig 5: Graphical representation of sensor nodes with their parametric values

In order to perform comparative analysis, the proposed approach is compared with the performance parameters of the existing approaches: CSLT model (Han et al., 2016) and trust model for cluster head validation (TMCHV; Goyal et al., 2017). The underlying concept for TMCHV targets trust, but it fails to take into consideration node reliability in terms of node stability and node congestion. CSLT model relies on the concept of data packet loss and packet error rate, but has problems with node reliability.

The reliability value for any model can be ascertained in underwater sensor networks if attackers or malicious nodes are considered. Thus, in order to attain simulated results, attackers or malicious nodes have been included using network simulator tool ns2, with the nodes varying in count in the range of 1 to 5. The simulation results, along with comparative analysis with the existing approaches of CSLT model and TMCHV model, are described below.

0.8

0.7

Node reliability

0.6

0.5

0.4

0.3

0.2

0.1

0.0

N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11 N12 N13 N14 N15 N16 N17 N18 N19 N20 N21 N22 N23 N24 N25 N26 N27 N28 N29 N30

Nodes in network

Fig 6: Graphical representation of sensor nodes’ reliability value

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Sharma et al. Reliability of cluster head node based upon parametric constraints in underwater acoustic sensor networks

Fig 7: Acoustic nodes position at different time intervals

6.1.1 Packet delivery ratio Packet delivery ratio demands that the reliable nodes forward the data packet correctly to the receiver. In the proposed scheme, every next node considered for the data transmission is based on the reliability value, resulting in the substantial packet delivery ratio. Though the count ratio for the proposed scheme is significant, slight downfall can be seen when the number of attacker nodes is increased. However, in comparison to existing approaches, the delivery ratio is comparatively high in all situations. Fig 8 shows the graphical representation

for the packet delivery ratio for both the proposed and existing schemes. 6.1.2. Packet drop rate The reliability of the network can be deduced from packet drop count. In the previous performance metric, it was observed that the packet delivery ratio is significant using RNQ algorithm, and would therefore yield the reduced packet drop count on its own, since packet delivery ratio directly correlates to reliability. Similarly, the packet drop count has inverse relation to reliability. Since the proposed

Table 2: Node selection for data transmission Time instance t1

Current node N1 (Sender node)

Neighbouring nodes

Priority order of neighbouring nodes

Selected node for routing

N23 N5 N2 N3

0.20589535 0.40455065 0.3731956 0.30275671

4 1 2 3

N25 N6 N14

0.43800391 0.47289995 0.59692148

3 2 1

N14

N15 N17

0.56248307 0.58447702

2 1

N17

N8 N4 N10

0.47915439 0.27885706 0.52063159

2 3 1

N10

N9 N18

0.51683648 0.60370457

2 1

N18

N21 Other nodes

Not required Not required

N21 (Receiver node)

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Table 3: Simulation parameters Parameter

Value

Number of nodes in the network Network size (L × W) Simulation time Antenna type Channel capacity Packet size Initial energy Sensor nodes deployment Sensing range (R) Transmission range Transmission power Receiving power

50 100 M2 × 100 M2 50 s Omni antenna 2 Mbps 50– 250 bytes 1000 J Random deployment 100 m 10– 20 m 2.0 W 0.5 W

scheme focused on packet transmission by reliable nodes, this resulted in fewer packet drops when simulated with varying number of malicious nodes. Though increased number of malicious nodes raised the packet drop count to some extent, the

simulated results showed better results when compared to existing approaches, as shown in Fig 9. 6.1.3. Network lifespan The lifespan of the network depends on parameters such as overall efficiency and the battery life of nodes. The major benefit of the proposed scheme is that both these factors are considered as per requirement. The constraint of battery life is addressed in the initial stage, and the packet forwarding to the nodes satisfies all the reliability constraints. Moreover, the foundation of the proposed scheme is reliable node, and overall efficiency is considered at every point in time. Therefore, the simulated results ensured an enhanced lifetime of the network. Even when compared with existing techniques, the proposed technique outperformed the existing approaches by providing an increased network lifespan. However, when the attacker count was raised, it negatively impacted the lifespan, but the decrease

Fig 8: Packet delivery ratio with varying number of attacker nodes

Fig 9: Packet drop count with increasing number of attackers

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Sharma et al. Reliability of cluster head node based upon parametric constraints in underwater acoustic sensor networks

Fig 10: Network lifetime with increasing number of attackers

rate was linear rather than exponential, and remained higher in comparison to existing techniques. Fig 10 graphically presents the average network lifespan for the various approaches. 6.1.4. Overall energy utilisation The energy utilisation must be minimised as far as possible in order to keep the network alive for longer durations. Therefore, the proposed scheme considered the factor of battery life in the initial phase so that nodes with low battery lives would not be considered, as they might die during operation. This benefitted the overall network by reducing the energy consumption of the acoustic nodes. Since the nodes with low battery were discarded before starting the packet transmission process, this avoided energy wastage. The overall energy utilisation by the system for an increasing number of attackers is shown in Fig 11.

6.1.5. Detection accuracy Fig 12 shows the results for the proposed model, as well as the TMCHV and CSLT models. Detection accuracy results are precisely depicted for all three schemes. For the detection of malicious nodes in the simulated environment, it is crucial to calculate the reliability value of each node. Since the proposed model targeted calculation of nodes reliability by considering numerous parameters, it is able to yield effective detection of malicious nodes when compared with the existing approaches. The filtration of reliable nodes makes it trivial to discard the malicious nodes. Nonetheless, the probability of selection of reliable nodes is increased in the proposed scheme, thus leading to raised detection accuracy. The graphical representation in Fig 12 also suggests that the detection accuracy increases when the number of attackers is increased.

Fig 11: Energy utilisation for increasing number of attacker nodes

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Fig 12: Detection accuracy with increasing number of attackers/malicious nodes

Although the proposed scheme was shown to be effective in the considered simulation environment, it was considered for varying simulation environments to measure its performance. The proposed technique was also considered for a larger area with a node count of 500, test area of 5000 m2 × 5000 m2 and increased transmission range. When simulated in varied conditions, the results were satisfactory; there was no negative effect observed on the overall network performance and efficacy.

7. Conclusion and future scope In the proposed scheme, the node reliability has been estimated while considering the parametric constraints in the underwater sensor network. A varying number of nodes could be present in the underwater network, and the proposed approach aimed to choose the most reliable nodes among all existing neighbouring nodes. Moreover, energy constraints have been satisfied by discarding nodes with low battery life in the initial stage. The approach may be summarised as: (i) inculcation of parametric constraints to attain a reliable node for effective data packet forwarding mechanism; (ii) improved network lifespan and reduced energy consumption; and (iii) adequate detection of malicious nodes or attackers, thereby enhancing the overall performance and efficiency of the network. The proposed scheme varies from conventional approaches, as the reliability has been estimated by inducing numerous parameters, namely success rate, transmission time, node affordability, node congestion and node stability. Simulation results further validate the improved performance when compared with existing approaches.

The scope of the proposed scheme is to make the UWSN more reliable. However, as this is not the only concern for UWSN, future scope could include other areas such as scalability and bandwidth issues.

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doi:10.3723/ut.37.053 Underwater Technology, Vol. 37, No. 2, pp. 53–64, 2020

Technical Paper

www.sut.org

Hierarchical classiﬁcation of time series data aggregation in underwater wireless sensor networks Durairaj Ruby* and Jayachandran Jeyachidra Department of Computer Science and Applications Periyar Maniammai Institute of Science & Technology, Tamilnadu, India Received 31 August 2019; Accepted 12 May 2020

Abstract

LTSP

leveraging time series prediction

Environmental fluctuations are continuous and provide opportunities for further exploration, including the study of overground, as well as underground and submarine, strata. Underwater wireless sensor networks (UWSNs) facilitate the study of ocean-based submarine and marine parameters details and data. Hardware plays a major role in monitoring marine parameters; however, protecting the hardware deployed in water can be difficult. To extend the lifespan of the hardware, the inputs, processing and output cycles may be reduced, thus minimising the consumption of energy and increasing the lifespan of the devices. In the present study, time series similarity check (TSSC) algorithm is applied to the real-time sensed data to identify repeated and duplicated occurrences of data for reduction, and thus improve energy consumption. Hierarchical classification of ANOVA approach (HCAA) applies ANOVA (analysis of variance) statistical analysis model to calculate error analysis for realtime sensed data. To avoid repeated occurrences, the scheduled time to read measurements may be extended, thereby reducing the energy consumption of the node. The shorter time interval of observations leads to a higher error rate with lesser accuracy. TSSC and HCAA data aggregation models help to minimise the error rate and improve accuracy.

mean of square

MS-HCAA

mean of square hierarchical classiﬁcation of ANOVA analysis

predicted value

precision

RMSE

root-mean-square error

SCEA

similarity checking error analysis

S-HCAA

similar hierarchical ANOVA analysis

sum of square

SST

sea surface temperature

TAO

tropical atmosphere ocean

TRITON

TRIangle Trans-Ocean Buoy Network

TSSC

time series similarity check

UWSN

underwater wireless sensor network

Keywords: data aggregation, time series, similarity checking, hierarchical classiﬁcation, energy consumption, accuracy. Acronyms list: ANOVA

analysis of variance

CRH

completely randomised hierarchical

Ds-HCAA

dissimilar hierarchical classiﬁcation of ANOVA analysis

HCAA

hierarchical analysis

classiﬁcation

ANOVA

classiﬁcation

1. Introduction Vital information at the appropriate time is not only essential but also a requisite for the security and safety of society and nations. The criticality and sensitivity of information demand a high level of accuracy, and precise, concise data for analysis and interpretation. Deployment of the sensor node of both homogenous and heterogeneous patterns involves hierarchical arrangement under water. In a hierarchical clustering structure, the sensor nodes change their positions under water, leading to balanced, unbalanced and partially balanced categories according to the node, link and path issues. This results in duplication and repetition of data collected. To restrict these issues, an efficient data aggregation scheme is needed to improve the

* Contact author. Email address: rubymca@pmu.edu

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Ruby and Jeyachidra. Hierarchical classification of time series data aggregation in underwater wireless sensor networks

data gathering and increase the lifespan of the sensor nodes. This relies on the sensor and its deployment at various domains of data acquisition. The sensor networks are used in various domains such as underground, under water, space exploration, terrestrial, etc. (Harb et al., 2017). Generally, sensor networks are used to monitor the environment, habitats, forest fire detection, volcanic activity (Qian et al., 2012), as well as water conditions (Peach and Yarali, 2013). For real-time applications, the deployment of sensors in any domain collects a large number of readings about the environment and forwards these to the sink node efficiently. The readings may be in the form of structured, unstructured or semi-structured representations of the environment. Owing to real-time monitoring, the sink node receives the repeated occurrences of readings frequently, consuming energy for data collection and data transmission. To minimise the consumption of energy, a proper data aggregation process is essential to help reduce redundancy before the final set of data is forwarded to the destination node. To improve the utilisation of the energy level of the sensor, better and effective deployment of sensor node, as well as data aggregation practice, are required. Emerging research has been shown to be relevant to the cluster-based transmission to control the battery depletion of the sensor node. The readings are transmitted from the sensor node to sink node through the data aggregation node or cluster head. An assorted clustering approach is used to minimise the power usage of the cluster head that extends the lifespan of the sensor node.

To improve the energy usage, attempts have been carried out in the clustering process routing the packets in various ways (Alkindi et al., 2018; Ahmed et al., 2018): vector-based routing (Mazinani et al., 2018), energy-based routing (Hou et al., 2018), identification of optimal clustering (Yadav and Kumar, 2017) and optimising the cluster size (Wang et al., 2014). In the ocean environment, sensor nodes face issues related to how data is forwarded and received between nodes and paths. To avoid these issues, classification needs to be performed with underwater wireless sensor network (UWSN). The structures of node classification are varied depending on the energy loss and link failure. Consequently, the node identifies an incorrect path to the sink node. Independent nodes have been identified that are not able to send or receive any data because of an incorrect path, resulting in substantial loss of data. The level of nodes in a hierarchical clustering architecture is assumed to be a nested classification. Fig 1 shows the hierarchical classification of nodes under water. It depicts that the sensor node (X) collects the sensed data, forwards it to the aggregator node (Y), and the aggregator nodes then forward the received packets to the sink node. Finally, the packets reach the sink node (Z). This process of sampling and sub-sampling from each node is known as nested or hierarchical classification. The present study examines the nature of sea surface temperature (SST) readings on a large scale with UWSN deployment in TAO (tropical atmosphere ocean)/TRITON (TRIangle TransOcean Buoy Network) projects.

Sink node (Z)

Z(YX)

Data aggregation node (Y)

Y(X)

Sensor node P

(pressure) Sink node

Sensors (X)

Underwater Sea surface

Data aggregation node Terrestrial space

Fig 1: Hierarchical classiďŹ cation of nodes underwater

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In TAO, various types of sensors deployed in the Central Pacific basin are used to measure atmospheric and oceanographic data such as winds, relative humidity, air temperature, sea surface temperature, salinity, density, subsurface temperature, seawater pressure, fixed depth currents and speed. SST readings from a one-year period and measured at 1.5 m water depth, have been analysed in the present study for further processing. The time series similarity check (TSSC) algorithm applied to the sensed temporal reading, and the reduction in the capacity of readings with various rounding options within the given interval of time, were analysed. Hierarchical classification of ANOVA analysis (HCAA) statistical model analyses was used on the sensed temporal data to substantiate the changes in temperature based on various environmental factors. The error ratio was calculated with sensed temporal data and replication identified in the sensed temporal data. The present paper aims to detail this process. Section 2 describes research that forms the background for the proposed work, Section 3 explains the hierarchical classification, Section 4 represents a proposed methodology of the data aggregation process, Section 5 presents the results and analysis of the proposed work, and Section 6 offers conclusions and addresses future work.

2. Related work In UWSN, communication cost is greater than computation cost when forwarding the packets to the destination safely (Bhandari et al., 2017). To conserve the battery energy of each node, an effective data aggregation technique is needed. Yahya et al. (2018) examine the efficient sink mobility model to improve the lifespan of the network and throughput ratio. Compressed sensing data aggregation has been proposed by Wang et al. (2016) and Fazel et al. (2011) to improve the consumption of energy. Anuradha and Srivatsa (2018) and Lin et al. (2015) describe the energy efficient cluster architecture for better transmission of data. Harb et al. (2015) show the similarity aggregation process to avoid the overhead of the cluster head. Huang et al. (2010) identified the optimal way of choosing the cluster head. Tran and Oh (2014) describe round-based clustering to avoid packet collision during transmission between sensor node and cluster head. Tran et al. (2013) introduce the similarity functions to reduce the redundancy from the collected data. Goyal et al. (2016) offer the intra- and inter-cluster approach to reduce collision among the clusters. Rahman et al. (2018) present the routing algorithm to avoid horizontal

routing of the packet and overhead of the cluster head. Hou et al. (2018) use a new cluster algorithm to balance the energy level of the sensor node. Yu et al. (2016) note the entire network is layered into uneven clusters, and the radius of nodes involve heterogeneous communication, to improve the network lifetime and energy level. Han et al. (2013) summarise the deployment of nodes in three ways: static, self-adjustment and movementassisted for lossless data communication. Liu (2011) project that the static deployment algorithm assigns the sensor nodes in a fixed position by predefined calculation, which cannot move under water in order to avoid collision and identify the cluster shapes showing improvement in full coverage and connectivity. Bhandari et al. (2017) conclude that the high sampling rate decreases accuracy and is analysed with the interpolation and extrapolation approach. Li and Wang (2011) compare the original sensed data with forecast data using the ARIMA model and data aggregation process performed by varying the time interval, and demonstrate the low prediction error rate. Khalil et al. (2016) outline the possible trends in SST until the year 2100. Ruby and Jeyachidra (2018) examine the review-based description of various clustering processes and the disadvantages associated with various data aggregation methods. Lionello et al. (2012) note that SST is affected by gas emissions of greenhouses. Shaltout and Omstedt (2014) address recent SST trends and forthcoming developments for the Mediterranean Sea. Parry et al. (2007) ascertain that global SST has increased drastically during the 21st century. For enhancing the utilisation of the energy of the sensor node, the semaphore-based data aggregation algorithm was applied in the data aggregation node and cluster head, and similar readings were identified to extend the lifespan of the sensor (Ruby and Jeyachidra, 2019). Rahul Saha et al. (2019) define the data aggregator used for checking the authentication issues, which can minimise the overhead of communication. The TSSC algorithm is used to find the natural redundancy of raw data from each sensor node to be eliminated. Multiple operations on the raw data were performed to avoid the duplication of data and forwarding of deduplication of data to the destination.

3. Hierarchical classiďŹ cation of ANOVA (HCAA) approach Every node in the cluster is nested with one another at all levels. The packets are forwarded to different levels in the cluster. In hierarchical clustering, the sensor nodes are nested with data

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Ruby and Jeyachidra. Hierarchical classification of time series data aggregation in underwater wireless sensor networks

aggregator nodes, and the data aggregator nodes in turn are nested with a sink node. Sometimes the node modified in each level is well adjusted, but unstable and incompletely balanced owing to energy loss in a particular node or link failure in a cluster. According to a hierarchical classification of designs, the SST readings are nested with date and time series, and hence called the hierarchical classification of SST (seasonality, time series). With the analysis of data, the design of the experiment is based on the analysis of variance (ANOVA) model showing the change in the temperature differs as a result of seasonality and time series only. The HCAA approach with completely randomised design (CRD) SST applied in the temporal data is represented by Equation 1. In the following equations, p represents the various methods applied for data aggregation from m1 to mn; m denotes the seasonality ranging from d1 to dn; and r indicates the time series value varies from t1 to tn: yiju = + αi + β j i ) + εu(ijij ) ,

The sum of square (ssx) owing to τ and the mean square msy(x) of τ depend on the time interval and season wise it is determined by: (x ) =

msy s (x ) =

ccf − ssx

ssy(x ) p(m )

cf =

∑∑∑y

iju

i =1 j =1 u =1

rpm

(2)

where the total number of observations analysed by the data aggregation method is denoted by p; the number of time intervals is represented by r; and the number of days is denoted by m. The sum of square (ssx) owing to τ and the mean square (msx) of τ for the time interval is calculated by: ssx =

1 p ∑y rm i =1

msx =

ssx p −1

ccf

(3)

(4)

(6)

ssd = ∑ ∑ ∑ y

ccf

i =1 j =1 u =1

(7)

The sum of square and mean of square error measure that the value τ in line with the time and season wise is calculated as follows: m

sse = ∑ ∑ ∑ y iju 2 − ssx − ssy(x ) i =1 j =1 u =1

where i = 1,2,3….,p; j = 1,2,3…..m; and u = 1,2,…..,r; yiju is a packet received successfully by the sink node forwarded from the sensor node via the data aggregator node; μ is the trend of extended environment changes; αi is the effect of temperature on season wise; and βj(i ) is the effect of temperature on time series wise. εu(ij ) is an error owing to time series (uth) in receiving the temperature (seasonality, time series), and is distributed as N(0, σε2) and independent of αi and βj(i ). The correction factor (cf ) is calculated by the mean of the sum of the square of sea surface temperature (τ) value stored (yiju) for the period t1 to tn:

(5)

The total sum of the square is ssd, resulting from the individual reading of τ with the degrees of freedom pm(r-1):

(1)

1 p m ∑∑y r i =1 j =1

mse =

sse pm(r − )

(8) (9)

The predicted value (pr) is calculated by msx for mse msy s (x ) x and for y(x). The tabulated F-value is mse found with degrees of freedom of F(p-1),pmq(r-1) and Fp(m-1),pmq(r-1) with 5 % significant level. If the predicted value (pr) is less than the tabulated value F, then the hypothesis is accepted; otherwise, it needs to be revised.

4. Data aggregation scheme The real-time oceanographic data and surface meteorological data are used for monitoring, forecasting and understanding the climate change of the underwater environment. The sea surface temperature, salinity, sea subsurface temperature and water pressure, as well as time-stamp information, are collected every ten minutes from the floating buoys deployed under water. An improved data aggregation algorithm is applied with the collected real-time sensed data for the conservation of energy by sensing the error and redundant data. The duplication readings are identified from the collected data, and energy consumed for sensing and forwarding the reading to the sink node are also calculated. Fig 2 depicts the TSSC process. The data aggregation node gathers readings from the sensor node and sends these to the sink node. The

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check and type check code assigned for the sensed data is represented below:

Sink node

File

• The qc value is ‘e’, and represents the error data sensed by the sensor. It can be identified with the negative value received by the information centre, and the corresponding tc value is 1. • The qc stores the ‘r’ and represents the frequent occurrences of unique data. It is compared with previous frequent value, and the tc value is 2. • The qc assigned with ‘o’ represents the remaining data sensed by the sensor, and the tc value is 3.

Read d,t,sst,qc,tc

fr={d,t,sst,qc,tc}

Yes

efr={d,t,sst,qc,tc}

qc Yes

rfr={d,t,sst,qc,tc}

qc Yes

R(sst,3), R(sst,2), R(sst,1)

sst(i)=sst(j)

dfr={d,t,sst,qc,tc

Yes

Each frame consists of parameters such as time, sea surface temperature, quality check code, type check code and distance. The algorithm selects the temperature (ssti,), quality check (qci,), type check (tci,) and distance (di) and assigns it to the frame fo, ∀ ∈1 ∈ n , where i is the number of observations collected. The entire data set is segmented into smaller data sets according to the quality check. The data set is stored in the frame named fo. The quality check (qc) is retrieved from each record of the frame fo. The data in the frame fo is then converted into four frames termed as fe, fr, fs,fd. The error data set is represented as fe, and the frequent occurrences of the unique data set are stored in fr. The similar data set is saved in fs and the dissimilar data set is represented in fd with various precision options. The data set stored in the corresponding frame is listed below:

sfr={d,t,sst,qc,tc}

Fig 2: Flow representation of TSSC

quality check code and type check code from the readings are assigned to a particular reading which is then saved in the repository. The readings are collected from the repository and segmented according to the quality check. Similar and dissimilar readings identified with various precision ranges are stored in the different frames.

4.1. Algorithm time series similarity check (TSSC) To improve the lifespan of the sensor node, the TSSC model is proposed for analysing the sensor data and the statistical expression used to find the error analysis of the sensed data. This algorithm converts the data set collected from the TAO project into frames. Based on the sea surface temperature values sensed by the sensors (sst), the quality check codes (qc) are assigned. The type check code (tc) consists of integer value assigned to each temperature based on quality check code. The quality

• The qc = ‘e’ is the error data from the fo is stored in the frame fe • The qc = ‘r’ is the frequent unique data from the frame fo transmitted into the frame fr. • The qc = ‘o’ is the data set retrieved from the frame fo. The data set is transmitted into either fs or fd depending on the rounding option of the precision value. The frequent unique data is appended with fs. The precision value of the sea surface temperature provided by the TAO project is in the form of (00.000 °C ). Further, fs and fd are converted into subframes after applying rounding up functions, and the precision value is (0.00 °C ) and (0.0 °C ). 4.1.1. Algorithm 1: TSSC (ti, ssti, qci, tci, di) 1 2 3 4 5 6 7

fo←〈〈 i i i for i ∈ 1..n do qc← qc i ∈ f o if qc=’e’ then fe←〈〈 i i i else if qc=’r’ then fr←〈〈 i i i

〉, ∀i ∈ 1..n

〉

〉 57

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Ruby and Jeyachidra. Hierarchical classification of time series data aggregation in underwater wireless sensor networks

8 9 10 11 12 13 14 15 16 17 18 19 20 21

else if qc=’o’ then sst ← sstti ∈ f o ssc←{round(sst,p) | p ≤ 3} for j ∈ 1..n do ssn←sstj if (ssc = ssn) then fs←〈 j j j j j else fd←〈〈 j j j j j〉 End if fs←append(fr) End if percent(fo, fe, fr ,fs, fd) return

Count( f ) r←1..n, n ∈ eof ( f )

s=∑ ri

return s

i =1

The algorithm similarity checking error analysis is used to find the average of the error data of similar and dissimilar data sets in the frame fs and fd. HCAA algorithms are applied for the data sets fs and fd. After applying the HCAA algorithm for similar frame fs of each month, the calculated values are tabulated in Table 1 in the form of S-HCAA. The dissimilar frame fd of every month is presented in Table 2 in the form of dissimilar hierarchical classification of ANOVA analysis (Ds-HCAA). The values shown in Table 3 are derived by the algorithm similarity checking error analysis (SCEA). The error values e1, e2..en for the data set S-HCAA are retrieved, and the average of error is found and stored in μ1. Similarly, the average error for Ds-HCAA is assigned to μ2. The total number of months from September 2017 to August 2018 is represented for the variables ‘n’ and ‘m’. The average similar data set error ea is calculated by subtracting the average value μ2 from μ1.

The percentage function (percent) of the frames fe, fr, fs, fd is calculated by the percentage of data that belongs to the data set amongst the original data set in the frame fo. The no, ne, nr, nd, ns are represented by the number of records in the particular data set, and is calculated by the count ( f ) function. It is used to count the number of records in each frame. The percentage of pe, pr, pd, ps is calculated by the number of records in the relevant frame to the number of records in the original data set no. Percent ( fo, fe, fr, fs, fd) 1 2 3 4 5 6 7 8

4.1.2. Algorithm 2: SCEA (similarity checking error analysis)

no←count( fo) ne←count( fe) pe←( e o )× 100 nr←count( fr);nd←count( fd); ns←count( fs) pr←( r o )×100 pd←( d o )×100 ps←( s o )×100 return

Read e1,e2,e3….en from S-HCAA

μ1←∑ e i / n

Read e1,e2,e3….em from Ds-HCAA

μ2←

i n

i =1

j m

∑e

j =1

ea←(μ1 - μ2)

Table 1: Calculated values for S-HCAA Month

Time series Sum of square (SS)

Mean of square (MS)

Predicted value (PV)

Season F value (Table)

Sum of square (SS)

F value (Table)

Mean error in percentage

3.976

0.0462

0.1355

Mean of Predicted square value (PV) (MS)

Sep 2017

79.09

5.835

0.0158

53.89

Oct 2017

70.54

5.854

0.01558

111

110.63

9.180

0.00246

0.1205

Nov 2017

96.12

7.262

0.00707

182

182.1

13.761

0.00021

0.1324

Dec 2017

53.48

3.690

0.0548

1.157

0.2822

0.1450

Jan 2018

11.5

0.872

0.350

444

443.7

33.580

7.31e-09

0.132

Feb 2018

42.3

2.836

0.0922

681

681.1

45.721

1.56e-11

0.149

Mar 2018

101

101.02

7.746

0.0054

34.37

2.635

0.1046

0.134

Apr 2018

86.95

6.859

0.0089

109

108.88

8.588

0.00340

0.1268

May 2018

33.31

2.285

0.1307

27.26

1.869

0.1716

0.1458

June 2018

166

7.50e-12

0.102

July 2018

93.56

6.132

0.0133

10.88

0.713

0.3984

0.1526

Aug 2018

35.30

3.578

0.0586

4.31

0.837

0.5088

0.0987

166.3

16.278

5.56e-05

482

16.17

481.7

47.153

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Table 2: Calculated values for Ds-HCAA Month

Time series

Season

Mean of square (MS)

Predicted F-value value (PV) (Table)

Sep 2017

32.5

32.46

18.125

Oct 2017

58.9

58.94

37.982

1.05e-09

24.6

24.59

Nov 2017

14.8

14.758

8.685

0.00329

22.5

22.500

3.106

6.844

Sum of square (SS)

2.3e-05

8.0

F-value (Table)

Mean error in percentage

0.0343

0.0179

15.846

7.39e-05

0.0155

13.240

0.00029

0.01699

Mean of Predicted square value (PV) (MS)

Sum of square (SS)

8.05

4.493

Dec 2017

6.8

0.0784

14.6

14.596

6.625

0.0103

0.02203

Jan 2018

34.2

34.20

26.609

2.94e-07

90.7

90.70

70.568

2.0e-16

0.0129

Feb 2018

48.7

48.67

48.309

5.54e-12

283.4

283.44

281.341

2.0e-16

0.0101

Mar 2018

23.4

23.433

15.296

9.84e-05

27.08

27.792

18.142

2.25e-05

0.01532

Apr 2018

38.4

38.36

25.922

4.24e-07

13.6

13.62

9.205

0.00248

0.0148

May 2018

29.08

29.750

19.980

8.64e-06

19.8

June 2018

42.96

39.11

5.3e-10

283.9

July 2018

67.5

76.54

45.562

2.42e-11

Aug 2018

45.8

45.82

26.61

3.06e-07

0.02 41.9

19.834 283.92 0.04 41.86

13.320 258.50 0.929 24.30

0.000275

0.01489

2.0e-16

0.0110

0.865

0.0148

9.78e-07

0.0172

Table 3: Comparison of error analysis μ with S-HCAA and Ds-HCAA Months

μ of S-HCAA

μ of Ds-HCAA

Error analysis – SCEA

Sep – 17 to Aug – 18

0.131192

0.015285833

0.881769

TSSC has been used to analyse the frequently received similar and dissimilar sensed data for a one-year period. To increase the lifespan of the sensors, the sensing duration must be increased, therefore reducing the number of times readings are senses and consequently the consumption of energy is minimised. The SCEA algorithm is needed to measure the error rate of similar and dissimilar data sets derived from S-HCAA and Ds-HCAA.

5. Result analysis The oceanographic temporal data set has been analysed, and the duplications are identified with TSSC algorithm. Temporal readings over a oneyear period are explored from September 2017 to

August 2018. Oceanographic data are continuously changing based on factors such as trend, time series, season, depth of water and random shock. The trend factor explains the deterministic propensity such as long-term global warming, regularly monitoring temperature variations, seasonal stand for the winter/summer seasons and random shock from the effects of short-term changes. Table 4 shows the temporal data set over the one-year period investigated with TSSC algorithm, and presents the obtained results of the percentage of redundant entry, error data received and number of similar and dissimilar data measured. The R(1) represents the rounding up of one digit precision of the sea surface temperature. R(2) represents the rounding up of two digit precision of

Table 4: Percentage of observed data analysed with TSSC Months Percentage

Sep 2017

Oct 2017

Nov 2017

Dec 2017

Jan 2018

Feb 2018

Mar 2018

Apr 2018

May 2018

June 2018

July 2018

Aug 2018

Redundant observations

0.47

0.41

0.57

0.60

0.59

0.45

0.42

0.31

0.67

0.54

0.30

Error observations

0.92

0.83

0.92

1.02

0.95

1.08

0.92

0.88

0.99

0.67

1.01

0.64

Dissimilar observation Similar observation

R(1)

0.35

0.44

0.33

0.39

0.43

0.60

0.34

0.35

R(2) R(3)

2.82 19.99

3.27 22.08

2.78 20.77

2.84 15.33

3.38 25.51

5.23 35.46

2.99 21.54

2.84 23.58

0.47 3.93 24.62

0.60 4.57 32.50

0.40 3.32 24.15

0.64 4.48 30.51

R(1)

99.65

99.56

99.67

99.61

99.57

R(2) R(3)

97.18 80.01

96.73 77.92

97.08 79.23

97.16 84.67

96.62 74.49

99.40 94.77 64.54

99.44 97.01 78.46

99.65 97.16 76.42

99.53 96.07 77.63

99.40 95.43 67.50

95.10 96.45 75.85

99.03 94.85 69.49

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40.00 35.00 30.00 25.00 20.00

Dsim-R(1) Dsim-R(2)

15.00

DSim-R(3)

10.00 5.00

Aug-18

Jul-18

Jun-18

May-18

Apr-18

Mar-18

Feb-18

Jan-18

Dec-17

Nov-17

Oct-17

0.00 Sep-17

Error percentage of disiimilar data

Ruby and Jeyachidra. Hierarchical classification of time series data aggregation in underwater wireless sensor networks

Month

Fig 3: Comparison of dissimilar data with various precision

100.00 90.00 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00

Si-R(1) Si-R(2)

Month

Fig 4: Comparison of similar data with various precision

Aug-18

Jul-18

Jun-18

May-18

Apr-18

Mar-18

Feb-18

Jan-18

Dec-17

Nov-17

Oct-17

Si-R(3)

Sep-17

Error percentage of siimilar data

the sea surface temperature. R(3) measures the three digit precision of the same parameter. The similar and dissimilar data observed with various precision (p) options by the TSSC algorithm are represented. Fig 3 compares the dissimilar readings and shows the comparison of the percentage of dissimilar samples with various precision ranges from one to three. The percentage of dissimilar sea surface temperature (τ) measurements increased for every month with the precision (p) of (0.000 °C) compared with p(0.0 °C). An average of 24.57 % of dissimilar τ with the p(0.000 °C), 3.5 % of dissimilar τ with the p(0.00 °C) and 1 % of dissimilar τ with the p(0.0 °C) is identified by TSSC algorithm. An average of 21.07 % of dissimilar τ with the p(0.000 °C) is increased and compared with (0.00 °C). The additional storage space needed in the repository is increased when the occurrences of dissimilar data are collected, and more bandwidth is required for communication when extending the number the precision. Fig 4 depicts the percentage of similar samples with various precision options. It shows the

percentage of similar sea surface temperature (τ) measurements during the one-year period from September 2017 to August 2018 a ten minute measurement interval. The percentage of similar sea surface temperature (τ) readings collected decreased every month in line with the precision (p) of (0.000 °C) compared with p(0.0 °C). An average of 99.13 % of similar τ with the p(0.0 °C), 96.38 % of similar τ with the p(0.00 °C) and 75.52 % similar τ with the p(0.000 °C) are derived with the TSSC algorithm. As the precision value increases, the accuracy of the data is increased, but the similar data collection is reduced. An average of 23.61 % of similar data observed is decreased in the p(0.000 °C) compared with p(0.0 °C). The energy needed for the receiver, transmitter in the sensor node and forwarding the readings to the sink node is maximised when the length of the readings is increased. Table 5 shows the mean value of analysis of original and duplication observations with various rounding options. 99.55 % of duplication samples analysed with TSSC in the p(0.0 °C). 96.3 % of duplication samples are found with p(0.00 °C) and 67.7 % of duplication samples identified with p(0.000 °C) options. Table 1 displays the calculated values of S-HCAA derived for every month from the HCAA statistical analysis model. The SS and mean of square (MS) values of time series are derived from Equation 4, the season wise values are achieved from Equation 6, and the percentage of mean error values are formulated from Equation 9. From Tables 1 and 2, the predicted values shown in the table are greater than the F-value, showing that the sea surface temperature varies with multiple parameters. In S-HCAA, the mean error percentage values are between 0.09 and 0.15. The percentage of error is less than the minimum number of similar readings deposited in the month. Table 2 displays the calculated values of DsHCAA derived for every month from the HCAA statistical analysis model. The SS and MS values of time series are derived from Equation 4. The season wise values are attained from Equation 6, and the percentage of mean error values are calculated with Equation 9. The percentage of mean error values are derived from Ds-HCAA approach in the range of 0.010 and 0.022. The dissimilar readings are lower in the particular month, which produces a higher mean error percentage. Fig 5 shows the comparison of error percentage variation with month wise sensed data. It represents that the redundant samples for every month in the S-HCAA increases along with the error percentage of Ds-HCAA. The error percentage of the Ds-HCAA

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Table 5: Comparison of mean analysis for various categories of observation Month

Mean Value of (μ) Observa- error Dissimilar tions observation r(1)

No. of observations 4212.58 Sep-17 to Aug-18

38.25

18.50

Similar r(1)

% of Mean Value of (μ) Similar Dissimilar Similar r(1) r(2) r(2)

4175.75

99.55

is drastically decreased when the duplication of samples is identified and segregated from the original data set. An average of 13.11 % of error rate was observed in S-HCAA and 1.53 % of error rate was attained in Ds-HCAA. The variation of 11.59 % indicates that a large number of similar and error readings are stockpiled in S-HCAA data set, showing that the Ds-HCAA has outperformed the S-HCAA. Table 3 highlights the comparison of the mean value of error analysis derived from the S-HCAA and Ds-HCAA. The error analysis is found with SCEA. It indicates that 88 % of error analysed in HCAA is due to redundant data recorded from the sensed ones. Table 6 represents the prediction error values of Ds-HCAA that are calculated and compared with leveraging time series prediction (LTSP) by varying the data samples. The samples were refined with TSSC algorithm, and the final data are analysed in HCAA and compared with LTSP (Li and Wang, 2011). The percentage of prediction error increases whenever the sample size is increased in both LTSP and Ds-HCAA. The error rate is decreased by increasing the sample size in DsHCAA. An average of 40.45 % of the prediction error of redundant data is reduced in Ds-HCAA compared with LTSP. Fig 6 presents the results of prediction error analysis between LTSP and Ds-HCAA. It shows that

147.58

% of Mean Value of (μ) Similar Dissimilar Similar r(2) r(3) r(3)

4062.00 96.3

1029.83

3191.00

% of Similar r(3)

67.7

the error rate in Ds-HCAA increases for smaller samples and decreases drastically when the sample sizes are increased. Compared with LTSP, 35.5 % of error rate in the 250 samples and 77.30 % of error rate in the 500 samples are decreased using the DsHCAA. In Ds-HCAA, the error rate is minimised to 32.50 % in the 500 samples, compared to that from the 250 samples, demonstrating that when the sample size is increased, the error rate is decreased in Ds-HCAA. The mean error values are shown in Table 7, with one-week samples taken at various intervals: 10, 20, 30, 60, 90, 120 and 240 minutes. These were verified through TSSC and analysed in HCAA. The tabulated values are compared against root-meansquare-error (RMSE) linear (Bhandari et al. (2017). Compared with the RMSE linear approach, the mean of square hierarchical classification of ANOVA analysis (MS-HCAA) error rate is drastically decreased with an increase in sampling intervals. The average error rate of 82.57 % is decreased in MS-HCAA model because the data set is examined in the TSSC algorithm, which removes the redundancy and error observations. Fig 7 highlights the mean error analysis between RMSE linear (Bhandari et al., 2017) and MS-HCAA. It reveals that the mean error rate in MS-HCAA is small up to 60 minutes, and thereafter constantly increases with higher sampling intervals. In RMSE linear, the error rate is high, and is continuously maximised in line with different sizes of higher order samples.

0.18

Error percentage

0.16

Table 6: Prediction error values of LTSP and Ds-HCAA

0.14 0.12 0.1 0.08

S-HCAA

0.06

Ds-HCAA

0.04 0.02 0

Month

Fig 5: Comparison of error percentage

Samples

LTSP

Ds-HCAA

50 100 150 200 250 300 350 400 450 500

0.03 0.055 0.07 0.082 0.09 0.1 0.105 0.115 0.128 0.13

0.04 0.05 0.066 0.0626 0.058 0.04896 0.0425 0.0367 0.0326 0.0295

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Ruby and Jeyachidra. Hierarchical classification of time series data aggregation in underwater wireless sensor networks

0.14

LTSP

0.12

Ds-HCAA

Prediction error

0.1 0.08 0.06 0.04 0.02 0 0

100

200

300 400 Samples

500

600

Fig 6: Evaluation of prediction error

Table 7: Mean error values of RMSE linear with MS-HCAA Sampling interval (mins)

RMSE linear

MS-HCAA

10 20 30 60 90 120 240

0.0528 0.0755 0.0937 0.1335 0.1566 0.1720 0.2162

0.003908 0.00264 0.009345 0.01209 0.03819 0.05687 0.07479

0.35

Prediction error

0.3 0.25 0.2 RMSE Linear

0.15

MS-HCAA

0.1 0.05 0 0

100

150

200

250

300

Interval (minutes)

Fig 7: Comparison of mean error

Fig 7 illustrates the comparison of RMSE linear and MS-HCAA. It shows that 93 %â&#x20AC;&#x201C;90 % of an error rate is decreased in MS-HCAA during the 10, 20, 30 and 60 minute time intervals, and 76 %â&#x20AC;&#x201C;65 % of an error rate is minimised through MS-HCAA during the 90, 120 and 240 minute time intervals. In MS-HCAA, the 24 % error rate is reduced during the observations of 120 minutes compared with the 30 minute time intervals.

of data over a one-year period mined from under water reveals that the error and redundancy of data collected and transmitted severely affect the life of the power source of the sensor. The present study has resulted in the formation of algorithms that minimise wasteful duplications, thereby enhancing the life of sensors. After the estimation and evaluation of the sensed data, and based on the error analysis, it is concluded that the number of occurrences with the same measurements can be minimised by extending the interval. Such data aggregation process extends the energy level of the sensor node and minimises the storage space. The enlargement and enhancement evaluation for extending the lifespan of the nodes had predominated the data over the one-year period, using the application of TSSC and HCAA. The results were compared with that of the existing studies with RMSE linear as suggested by Bhandari et al. (2017), and LTSP as discussed by Li and Wang (2011). It was observed that similar data was 75.5 % while the dissimilar data was 24.5 %. It was also noted that the data derived by HCAA has an error of 88.17 % when examined with TSSC owing to repeated observations with Ds-HCAA. Hence, the data over the one-year period was split into smaller data sets within the time frame. It was subjected to simultaneous and parallel applications of RMSE linear and LTSP algorithms that reduced the error to 40.45 % and 82.57 %, respectively. The outcomes indicate that as the number of sampling and duration increase, energy consumption is reduced. The HCAA statistical model highlights that the sea surface temperature varies depending on environmental factors for 88 % of repeated occurrences of measurements; 38.25 % of error occurrences were found during the present study between September 2017 and August 2018. From the analysis, the authors conclude that in order to minimise the consumption of energy and maximise the lifespan of the nodes, the duration of the time interval should be increased and sampling sizes improved. While the present study analyses the mined data from one location only, in future aggregation schemes could be applied to measure worldwide oceanographic and meteorological data.

6. Conclusion The study of submarine data requires the judicious deployment of sensors that should have a maximum lifespan for the economic and effective operation of data mining. The critical exploration

Acknowledgement The authors thank the TAO project office of NOAA/PMEL for allowing the use of the TAO data in the present study.

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Underwater Technology Vol. 37, No. 2, 2020

References Ahmed M, Salleh M and Channa MI. (2018). CBE2R: Clustered-based energy efficient routing protocol for underwater wireless sensor network. International Journal of Electronics 105: 1916–1930. Alkindi Z, Alzeidi N, Arafehand B and Touzene A. (2018). Performance evaluation of grid based routing protocol for underwater wireless sensor networks under different mobility models. International Journal of Wireless & Mobile Networks 10: 13–25. Anuradha D and Srivatsa SK. (2018). Reliable and energy efficient cluster-based architecture for underwater wireless sensor networks. International Journal of Mobile Network Design and Innovations 8: 27–35. Bhandari S, Bergmann N, Jurdak R and Kusy B. (2017). Time series data analysis of wireless sensor network measurements of temperature. Sensors 17: 1221. Fazel F, Fazel M and Stojanovic M. (2011). Random access compressed sensing for energy-efficient underwater sensor networks. IEEE Journal on Selected Areas in Communications 29: 1660–1670. Goyal N, Mayank D and Verma AK. (2016). Energy efficient architecture for intra and inter cluster communication for underwater wireless sensor networks. Wireless Personal Communication 89: 687–707. Han G, Zhang C, Shu L, Sun N and Li Q. (2013). A survey on deployment algorithms in underwater acoustic sensor networks. International Journal of Distributed Sensor Networks 9: 1–11. Harb H, Makhoul A and Couturier R. (2015). An enhanced K-means and ANOVA-based clustering approach for similarity aggregation in underwater wireless sensor networks. IEEE Sensors Journal 15: 5483–5493. Harb H, Makhoul A, Laiymani D and Jaber A. (2017). A distance-based data aggregation technique for periodic sensor networks. ACM Transactions on Sensor Networks 13: Article 32. Hou R, He L, Hu S and Luo J. (2018). Energy-balanced unequal layering clustering in underwater acoustic sensor networks. IEEE Access 6: 39685–39691. Huang CJ, Wang YW, Lin CF, Chen YT, Chen HM, Shen HY, Chen YJ, Chen IF, Hu KW and Yang DX. (2010). A selfhealing clustering algorithm for underwater sensor networks. Cluster Computing 14: 91–99. Khalil I, Atkinson PM and Challenor P. (2016). Looking back and looking forwards: Historical and future trends in sea surface temperature (SST) in the Indo-Pacific region from 1982 to 2100. International Journal of Applied Earth Observation and Geoinformation 45: 14–26. Li G and Wang Y. (2011). An efficient data aggregation scheme leveraging time series prediction in wireless sensor networks. International Journal of Machine Learning and Computing 1: 373–377. Lin H, Wei W, Zhao P, Ma X, Zhang R, Liu W, Deng T and Peng K. (2015). Energy-efficient compressed data aggregation in underwater acoustic sensor networks. Wireless Networks 22: 1985–1997. Lionello P, Gacic M, Gomis D, Garcia-Herrera R, Giorgi F. Planton S, Trigo R, Theocharis A, Tsimplis M, Ulbrich U and Xoplaki E. (2012). Program focuses on the climate of the Mediterranean region. Eos 93: 105–106. Liu L. (2011). Deployment algorithm for underwater sensor networks in the ocean environment. Journal of Circuits, Systems and Computers 20: 1051–1066.

Lu Y, Zhang T, He E and Comsa IS. (2018). Self-learning based data aggregation scheduling policy in wireless sensor networks. Journal of Sensors 2018: Article ID 9647593. Mazinani SM, Yousefi H and Mirzaie M. (2018). A vector based routing protocol in underwater wireless sensor networks. Wireless Personal Communications 100: 1569– 1583. Parry M, Canziani O, Palutikof J, Linden P and Hanson C. (2007). Climate change 2007: impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press, 987 pp. Available at https://www.ipcc.ch/site/assets/ uploads/2018/03/ar4_wg2_full_report.pdf, last accessed <20 June 2020>. Peach C and Yarali A. (2013). An overview of underwater sensor networks. In: Proceedings of the 9th International Conference on Wireless and Mobile Communications (ICWMC’13), 31–36. Qian H, Sun P and Rong Y. (2012). Design proposal of selfpowered WSN node for battle field surveillance. Energy Procedia 16: 753–757. Rahman Z, Hashim F, Rasid MFA and Othman M. (2018). Totally opportunistic routing algorithm (TORA) for underwater wireless sensor network. PLoS ONE 13: e0197087. Ruby D and Jeyachidra J. (2018). Architecture, routing protocols and data aggregation in underwater wireless sensor networks – A review based description. International Journal of Applied Engineering Research 13: 6325– 6331. Ruby D and Jeyachidra J. (2019). Semaphore based data aggregation and similarity findings for underwater wireless sensor networks. International Journal of Grid and High Performance Computing 11: 59–76. Saha R, Kumar G, Rai MK, Thomas R and Lim S. (2019). Privacy ensured e-healthcare for fog-enhanced IoT based applications. IEEE Access 7: 44536–44543. Shaltout M and Omstedt A. (2014). Recent sea surface temperature trends and future scenarios for the Mediterranean Sea. Oceanologia 56: 411–443. Souiki S, Hadjila M and Feham M. (2015). Fuzzy based clustering and energy efficient routing for underwater wireless sensor networks. International Journal of Computer Networks & Communications (IJCNC) 7: 33–44. Tran KTM and Oh SH. (2014). UWSNs: A round-based clustering scheme for data redundancy resolve. International Journal of Distributed Sensor Networks 10: Article ID 383912. Tran KTM, Oh SH and Byun JY. (2013). Well-suited similarity functions for data aggregation in cluster-based underwater wireless sensor networks. International Journal of Distributed Sensor Networks 9: Article ID 645243. Wang D, Xu R, Hu X and Su W. (2016). Energy-efficient distributed compressed sensing data aggregation for cluster-based underwater acoustic sensor networks. International Journal of Distributed Sensor Networks 12: Article ID 8197606. Wang F, Wang L, Han Y, Liu B, Wang J and Su X. (2014). A study on the clustering technology of underwater isomorphic sensor networks based on energy balance. Sensors 14: 12523–12532.

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Yahya A, Islam SU, Akhunzada A, Ahmed G, Shamshirband S and Lloret J. (2018). Towards efficient sink mobility in underwater wireless sensor networks. Energies 11: 1471. Yadav S and Kumar V. (2017). Optimal clustering in underwater wireless sensor networks: Acoustic, EM and FSO

communication compliant technique. IEEE Access 5: 12761â&#x20AC;&#x201C;12776. Yu S, Liu S and Jian P. (2016). A high efficiency uneven cluster deployment algorithm based on network layered for event coverage in UWSNs. Journal of Sensors 16: 1â&#x20AC;&#x201C;18.

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3D Recording and Interpretation for Maritime Archaeology Edited by John K McCarthy, Jonathan Benjamin, Trevor Winton and Wendy van Duivenvoorde Published by Springer

Hardcover edition, 2019 ISBN 978-3-030-03634-8 237 pages

This volume is the result of a workshop held in 2016 in Adelaide, South Australia by the UNESCO UNITWIN Network for Underwater Archaeology in collaboration with Flinders University Maritime Archaeology Program. The theme of the workshop was 3D Modelling and Interpretation for Underwater Archaeology. Participants expressed the need for ‘stronger communication and collaboration between maritime archaeologists working in the areas of 3D applications’, hence the papers in this publication aim to fulfil this concern, and simultaneously contribute towards the network’s objectives. The authors of this A4-sized hardback book collated fourteen research papers presented at the 2016 workshop. The selection demonstrates the quick-paced 3D advances that are being made worldwide within the discipline and the future directions

emerging from 3D technologies for surveying, analysing and dissemination. The papers highlight both challenges and opportunities encountered during the application of technologies, with some authors elaborating on the need of consistency, the lack of which has been the result of insular research undertaken by specialists and various subdisciplines. Moving forward, these separate nodules of research need to be combined in order to progress, and this collection of papers is an opportune starting point. The book is divided into fourteen papers, plus an index. Each paper delivers on the theme of 3D recording methods and/or interpretation of data collected, supplemented with tables, graphs and colour illustrations. The first paper, The Rise of 3D in Maritime Archaeology, by the editors, provides an overarching introduction to the shift from representing in 2D to 3D, and the adoption of 3D recording, analysis and interpretation techniques that, as the authors state, have become part of the maritime archaeologist’s toolbox. The need to capture data rapidly, especially in underwater environments, has led to a shift from manual recording of sites to 3D capture as the first choice of survey method. Standardisation of workflows is crucial when utilising 3D technologies, reducing duplication and unnecessary effort. The paper by Mark Shortis on Camera Calibration Techniques for Accurate Measurement Underwater provides a review of the different approaches to calibrating underwater camera systems in a theoretical and

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Book Review

doi:10.3723/ut.37.065 Underwater Technology, Vol. 37, No. 2, pp. 65–66, 2020

practical way, including the accuracy, reliability, validation and stability of underwater camera system calibrations. The author provides further reading for anyone who wishes to delve into the complexities of calibration and applying the technique appropriately. The application of photogrammetry has now become a frequently adopted technique to record archaeological material underwater, allowing for the extraction of measurements from photographs. This method was applied in three case studies in the Mediterranean (Chapters 4, 5 and 9) in order to record the archaeological sites and in situ material culture. All three projects had their own challenges and each exploited the results in a unique way: the Gnalić shipwreck project exploited the results to create VR GNALIĆ, allowing viewers to move around the site and interact with components of the wreck, as well as enable the process of excavation; the Xlendi shipwreck project produced 3D outputs for dissemination; and the underwater photogrammetry of Anfeh in Lebanon allowed for the monitoring of the site as a means to mitigate against loss of material via looting. 3D visualisation is further explored in Chapter 12, where the authors present the opportunity to create a 3D visual of HMS Falmouth, a town-class light cruiser which sunk during the First World War. This was created by combining the multibeam echosounder survey of the wreck, with photogrammetry and laser scanning of the original builder’s model. This was a

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McCarthy et al. 3D Recording and interpretation for maritime archaeology

successful project, as it achieved its principle aim of sharing the story of the wreck. It also made the site more accessible to the wider public, generating further resources and co-products. The application of photogrammetry goes beyond the need to utilise this as an end to surveying an archaeological site or feature. Its application with converging technologies is highlighted in a number of contributions that demonstrate meaningful results using both simple approaches and more complex methods. Chapter 7 discusses how information modelling can be applied to highly complex historic ships. The work is based on HMS Victory, a conservation project that utilised laser scanning and photogrammetry for the survey of the extant historic vessel. This 3D data was integrated with heritage information through information modelling for effective ongoing conservation management. The use of building information model (BIM), an application used in the heritage sector, to create the Victory information model, allowed for better management of conservation works throughout the lifetime of the project. Chapter 8 demonstrates the potential for applying procedural modelling to change the way we approach historical ship reconstruction. In terrestrial archaeology, computer-aided modelling offers a quick 3D image of past reconstructions and a means to analyse archaeological data. The authors have transferred this application to nautical archaeology by creating a prototype from the hull timbers of a sixteenthcentury European merchant ship. The developed prototypes were then evaluated for their usefulness and effectiveness through the scantlings of a set of timbers from the Belinho 1 shipwreck.

The study clearly established the potential of procedural models to explore research hypotheses, which the authors intend to pursue. The use of sub-bottom profiler (SBP) acoustic technology is discussed in Trevor Winton’s paper (Chapter 10). The research was carried out utilising in situ experimentation of shallow-buried oak and pine timber beams at different burial depths, and comparative in situ wreck material belonging to the James Matthews. The field survey results allowed the author to extract the capabilities of parametric SBP for in situ management purposes, and by utilising complementary tools, to identify and characterise shallow buried archaeological material. Another terrestrial technique applied in a shallow maritime archaeological context is discussed in Chapter 11. Electrical resistivity tomography (ERT) was applied to survey the Crowie, a barge wrecked near the town of Morgan in South Australia. ERT was successful owing to the low resistivity values of the external metal cladding forming the barge hull, which was recognisable in the geophysical survey. This alternative surveying method is ideal for buried targets – which are not picked up by laser scanning or photogrammetry - and also material located within turbid or shallow waters. The significance of legacy data is demonstrated in Green’s paper on Legacy Data in 3D: The Cape Andreas Survey (1969-1970) and Santo António de Tanná Expeditions (198-1979). Green explores the potential for using old photographs to generate 3D data, highlighting the opportunities presented by reassessing previous data sources. The paper examines the data collection

methodology along with reprocessing of the data and outcomes. The case study of HMCS/ HMAS Protector (Chapter 6) also utilises archival photographs to enhance the archaeological and laser scanning survey of the former Australian warship. This combination of datasets allowed the archaeologists to identify gradual variations to Protector’s hull throughout its lifetime. The use of iconographic data has the potential to aid in the interpretation of historic shipwrecks or abandoned vessels, enabling subtle changes to be identified. The integration of data from aerial and underwater methods is highlighted in Chapter 14, which showcases maritime landscapes in 3D. Here, the authors focus on merging technologies, including underwater photogrammetry, aerial photographs and Lidar datasets. Apart from enhancing the archaeological interpretation of sites, these methods allow for the creation of visual and experiential environments through 3D visualisations. This is further explored in Chapter 13, where a virtual reality (VR) simulation of an entire ‘maritime landscape’ was designed. The Beacon Virtua VR was made accessible through an online platform, creating an interactive experience to a general audience. Enriched datasets through the application of diverse technologies has given a spectrum of options to the maritime archaeological community. In the last decade, the fluency in 3D working practices has vastly developed, and seems likely to continue to progress, as demonstrated throughout the case studies in this book. (Reviewed by Stephanie Said, maritime archaeologist with Wessex Archaeology Ltd, UK)

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Society for Underwater Technology International multidisciplinary learned society This non-aligned membership-based organisation seeks to further the dissemination of knowledge and lessons learned in the underwater environment through networking, events and publications

Its membership covers the following activity areas: defence diving and manned submersibles environmental forces marine policy marine renewable energies ocean resources offshore site investigation and geotechnics salvage and decommissioning

For further information For events, membership, publications or general enquires, contact: e info@sut.org e events@sut.org

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Society for Underwater Technology

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SUT sponsors UK and overseas students (studying in the UK and abroad) at undergraduate and MSc level who have an interest in marine science, technology and engineering. Students are supported who are studying subjects such as:

Offshore and Ocean Technology Subsea Engineering Oceanography Marine Biology Ship Science and Naval Architecture Meteorology and Oceanography The SUT annual awards are £2,000 per annum for an undergraduate, and £4,000 for a one-year postgraduate course. (Part-time postgraduate studies funding available.) As one of the largest non-governmental sources of sponsorship, the SUT has donated grants totaling almost half a million pounds to over 270 students since the launch of the fund in 1990.

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