Proceedings International Conference On Advances In Engineering And Technology
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A Survey on Geographic Routing Relay Selection in Wireless Sensor Network Santosh Kumar K1, Dr.N.Duraipandian2 1. PG Scholar, Department of Computer Science and Engineering, Velammal Engineering College, Chennai, Tamilnadu, India. 2. Professor, Department of Computer Science and Engineering, Velammal Engineering College, Chennai, Tamilnadu, India. Email: santhosh2567@gmail.com1 , emailpandiandurai@gmail.com2
ABSTRACT: Geographic routing (or position-based routing) is the technique which employs position information of nodes while routing from the source to the sink. Geographic routing has been considered as a simple, effective and scalable routing protocol for designing a variety of applications ranging from mobility prediction, management of nodes and metrics such as hop count, power, relay selection, energy consuming, delay, etc. Most geographic routing algorithms use a greedy strategy for selecting the neighbor closest to the sink as a next hop. However, greedy forwarding fails in reaching a node that is closer to the sink than all its neighbors and so planar graph routing is adopted which guides the packet with guarantees delivery. Geographic routing algorithms exploit location information but the problem exist is convergecasting around connectivity holes and relay selection of each node. For resolving these issues, an alternative method termed ALBA_R was proposed along with enhanced relay selection mechanism in order to maximum the lifetime of a node.
I. INTRODUCTION A Wireless Sensor Networks (WSN) has been emerged as a promising area for research and scientific advancement. WSNs are greatly applied in many application domains such as surveillance, environmental monitoring, vehicular applications, defense applications, traffic systems, medical monitoring, precision agriculture, etc. WSN consist of nodes with sensing (measuring), computing, and wireless communications capabilities. Technology upgradation in WSN is the result of integration of sensor, transceiver, memory and microcontroller technologies on single unit called sensor node. The Sensor Node, a basic ISBN NO: 978 - 1503304048
element of WSN capable of observing(sensing) physical capabilities, process the monitored and received information and communicate the observed or processed information to the nearby sensor nodes to form a network of sensor nodes.
Fig.1 components of a sensor node
Many sensor nodes are randomly distributed over larger distance and each sensor node having data content were gathered in the sink. Through internet everyone can view the collected data in the network. As shown in Fig.1, it consists of three major components namely Sensing unit, processing unit, and Transmission unit. They include some additional components like position finding system, power supply and a mobilizer. Sensing unit composed of two subunits such as Sensors and Analog-to-Digital Converters (ADCs).The analog signals are measured by the sensors are digitized through an ADC and in turn fed into the processing unit (storage unit and a processor). The processor and its associated memory commonly RAM is used to manage the procedures that make the sensor node carry out its assigned sensing and collaboration tasks. In Transmission unit, the transceiver connects the node with the network and serves as the communication medium of the node. The power supply/battery is the most important component of the sensor node because it implicitly determines the lifetime of the entire network.
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A major evolution in communication technology has been the introduction of the Global Positioning System (GPS) which provides the location information and universal timing of a node. The recent development of a wireless sensor network has led to an innovative use of small sensory nodes which operate with a very low power in extreme environmental conditions. A group of small sensory nodes are randomly deployed in a sensor field. These nodes have the ability to organize themselves automatically and to detect the data content accurately.
II. LITERATURE REVIEW A New Contention-Based MAC Protocol for Geographic Forwarding in Ad Hoc and Sensor Networks [1]: Michele Zorzi referred that Geographic Random Forwarding (GeRaF) is based on the assumption that sensor nodes have a means to determine their location information, and that the positions of the final destination and of the transmitting node are explicitly included in each message. GeRaF is designed to integrate MAC message exchanges and the designation of the most convenient relay (from a geographic point of view). Thanks to awake/sleep cycles and to this cross–layer design, it is very energy– efficient. Moreover, it is simple and easy to implement on real nodes. However, it has some drawbacks, e.g., it cannot route around connectivity holes, and thus may not be able to deliver all messages in sparse networks (because of the physical absence of nodes). Also, it is not able to operate in dense traffic scenario (when congestion builds up). On the Effect of Localization Errors on Geographic Face Routing in Sensor Networks [2]: The reason for geographic routing protocols does not need to maintain per destination information and only neighbor location information is needed to route packets. Geographic routing protocols are very attractive choices for routing in sensor networks. Most geographic routing protocols use greedy forwarding for basic operations. Greedy forwarding is based on next forwarding hop is chosen to minimize the distance of the ISBN NO: 978 - 1503304048
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destination. It fails in dead-ends. Most geographic routing protocols use greedy forwarding for basic operations. In order to provide correct routing in the presence of dead ends, face routing has been introduced. GPSR is a geographic routing protocol for wireless networks that combines greedy forwarding and face routing. GPSR uses geographic hash table (GHT) system that hashes keys into geographic location and stores the key-value pair at the sensor node closest to the hash of its key.GHT uses mainly for geographic routing to the hash location. The applications are data centric storage and distributed indexing. Locating and Bypassing Holes in Sensor Networks [3]: In routing, connectivity holes cause difficulties in organizing the networks. Holes define the “hot spots” regions created by traffic congestion and sensor power shortage. A commonly used assumption in studying sensor networks is that sensors are uniformly densely distributed in the plane. However in system deployment, this assumption does not hold in general. Even if sensors nodes are distributed randomly, there are still regions with sensor density much lower than others. In practice, sensor networks usually have holes, i.e. regions without enough working sensors. An example of a large number of dead sensor nodes it creates a big hole in the network. A packet is forwarded to a 1-hop neighbor who is closer to the destination than the current sensor node. This process is repeated until the packet data reaches the destination, or the packet is stuck at a node when there is no neighbor to reach the destination. Here, holes define to be simple regions enclosed by a polygon cycle which contains all the nodes where local minima can appear. The information storage and Memory requirement are based on boundary node. The applications are avoiding network hot spots, supporting path migration. The applications are avoiding network hot spots, supporting path migration, information storage mechanisms. It can able to handle node failures, information storage and memory requirement. It uses TENT rule and BOUNDHOLE techniques to identify and build around holes. TENT rule requires each node to know its 1-hop neighbors locations. To help packets get out of stuck nodes, BOUNDHOLE to find the boundary of the hole.
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Efficient Non-Planar Routing around Dead Ends in Sparse Topologies Using Random Forwarding [4]:
ALBA-R: Load-Balancing Geographic Routing Around Connectivity Holes in Wireless Sensor Networks [7]:
Geographic forwarding in wireless sensor networks (WSN) has long suffered from the problem of bypassing "dead ends," i.e., those areas in the network where no node can be found in the direction of the data collection point (the sink). However previous scheme resolve these problems that rely on geometric techniques leading to the planarization of the network topology graph. In this paper, a alternative method to planarization is proposed, termed ALBA-R, which successfully routes packets to the sink transparently to dead ends. ALBA-R combines scheduling of awake or asleep nodes, channel access and geographic. By enhancing geographic routing along deadends with a mechanism that is capable of routing packets around connectivity holes. Through simulations results, it demonstrates that ALBA-R can provide insignificant overhead, and outperforms similar solutions with respect to all the metrics of interest investigated, especially specifying the benchmark for geographic routing protocols. .
ALBA-R features the cross-layer integration of geographic routing with contention-based MAC for relay selection and load balancing (ALBA), as well as a mechanism to detect and route around connectivity holes (Rainbow). ALBA and Rainbow (ALBA-R) together solve the problem of routing around a dead end without overhead-intensive techniques such as graph planarization and face routing. The Rainbow mechanism allows ALBA-R to efficiently route packets out of and around dead ends. Rainbow is resilient to localization errors and to channel propagation impairments. It does not need the network topology to be planar, unlike previous routing protocols. It is, therefore, more general than face routing-based solutions and is able to guarantee packet delivery in realistic deployments.
Localization Error-Resilient Geographic Routing for Wireless Sensor Networks [5]:
Routing is a process of determining a path between source and destination regarding the transmission of packet messages. When the sink is far away from the source or not in the range of source node, multi-hop technique is followed. In order to moving a packet of data from source to destination, intermediate sensor nodes have to relay their packets efficiently. As shown in Fig.2 depend on network structure, routing protocol that perform an end-to-end message delivery can be classified as flat-based routing, hierarchical-based routing, and geographic routing (location-based routing). In flat-based routing, all the nodes are treated as equal and ensure same roles or functionality. In hierarchical-based routing, all the nodes are assigned with differed roles and provide higher energy nodes for transmission as well as lower energy nodes for sensing. In location-based routing, node’s positions are extracted to route packet data i.e. Sensor nodes are addressed by means of their locations and their location were obtained by distance estimation, neighbor discovery, GPS etc.
This paper concerns the demonstration of the resilience to localization errors of ALBA-R, a protocol for geographic routing in wireless sensor networks (WSNs). In particular, it shows that a simple effective nodal coloring mechanism for handling nodal connectivity holes, ALBA-R achieves the further desirable benefit of being totally resilient to localization errors, which are unavoidable in WSNs. Through ns2-based simulations it explains that fundamental network parameters such as network density, and also independently of errors in nodal coordinate estimations as high as the node transmission radius, ALBA-R is successful in delivering all generated packets while incurring reasonable degradation for metrics such as route-length and end-to-end latency and still remaining and energy efficient protocol.
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III. PRELIMINARY A. Routing in WSN
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technique (GPS) or relative positioning. Geographical routing assumes that each node knows its own location and each source is aware of the location of its destination. Geographic routing protocols require only local information and thus are very efficient in wireless networks. Geographic Greedy Forwarding:
Fig.2 classification of routing protocols
Based on their protocol operations, routing protocols are categorized into negotiation-based routing, QoS-based routing, multipath-based routing, query-based routing and coherentbased routing. In negotiation-based routing, these protocols add top-level data descriptors to eliminate duplicate data transmissions through negotiation. In multipath-based routing, it increases fault tolerant capabilities that routes the data packet through a path. The path gets changed whenever a short path is discovered. In query-based routing, the target nodes transmit a query of data from a node in the network and a node with this data that matches the query sends back to the initial node. In QoS-based routing, this protocol ensures balance between energy consumption and data quality in the network. In coherent-based routing, sensor nodes send data to an intermediate node where necessary data can be aggregated and may be subject to minimum processing. Hence each node can reduce route cost in terms of energy consumption. Each of these routing schemes has the common objective of trying to get better throughput and to extend the lifetime of the sensor network. B. Geographic routing Geographic routing (also called position-based routing) is a routing principle that relies on geographic location information. It is defined only for wireless networks and based on the idea that the source sends a message to the geographic location of the destination instead of using the network address. In geographical routing, a sender uses the destination’s geographic location to deliver a message. Information of physical location might be determined by means of a global positioning ISBN NO: 978 - 1503304048
Fig.3 Greedy Forwarding
An important technique in geographic routing is greedy forwarding, in which each node should transfer packet by selecting the neighbor closest to the destination along the path until the packet reaches the destination as shown Fig.3. Greedy forwarding, however, fails in the presence of connectivity holes or dead-ends when a node that has no neighbors closer to the destination. A greedy forwarding can minimize the distance to reach the destination location but it cannot assure guarantee in delivery of messages. Planar Graph Routing: A widely adopted approach to solve this guaranteed message delivery is planar graph routing. Planar graph routing is a key concept for recovery from a local minimum situation. It can provide delivery guarantees using face routing used when greedy forwarding fails. In order to perform face routing, a planar connectivity graph for the network needs to be constructed and so a planarization algorithm is required to create the planar graph as shown in Fig.4. Face routing is integrated with greedy forwarding and is used as a way to overcome dead-ends when greedy forwarding fails. Greedy forwarding coupled with face routing is the common efficient approach of the currently proposed geographic protocols.
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Rainbow is the mechanism used by ALBA_R to deal with dead ends. An important feature for avoiding dead ends is that of allowing the nodes to forward packets away from the sink when a relay selection toward the sink cannot be found. To remember whether to search for relays in the direction of the sink, each node is labeled with different colors. Rainbow mechanism discovers a specific color of each node so that a possible route to the sink is determined.
Fig.4 Planar graph routing
C. Issues in geographic routing Some of the following issues while designing geographic routing schemes are (a)Routing around dead ends, (b) Flexibility to localization errors, (c)Relay selection process.
IV. OVERVIEW D. Relay Selection in Geographic routing Adaptive load balancing algorithm improves a convergecasting in WSNs that integrates awake/asleep schedules, Medium access control (MAC), load balancing, and back-to-back data transmissions. While transmission, it is necessary to ensure that a node which is awake or asleep or else usage of energy can get increases. A sensor node can forward packet from source to destination by sending RTS (Request-to-send) packet to all the neighbor nodes in order to ensure their availability of awake nodes. The sensor nodes which are available will report with clear-to-send (CTS) packet carrying information through which the sender can choose the best relay. A ‘best throughput’ can get performed through effective relay selection. Every upcoming relay is characterized by two parameters: the queue priority index (QPI), and the geographic priority index (GPI). The QPI is measured as, the requested number of packets to be transmitted in a burst is Nb, and the number of packets in the queue of an eligible relay is Q. The potential relay keeps a moving average M of the number of packets it was able to transmit back-to-back, without errors, in the last forwarding attempts. The QPI is then defined as min (Q+Nb)/M, Nq, where Nq is the maximum allowed QPI.
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E. Enhanced Relay Selection Scheme The relay selection scheme of ALBA_R [7] can fail in two cases: 1. If no node with any QPI is found 2. If the contention among nodes with the same QPI and GPI is not resolved within a maximum number of attempts. Both situations cause the sender to back off. It will lead to end to end delay also. To overcome that, we can send the RTS packets to all the one hop neighbor nodes to collect the QPI value as the reply instead of sending RTS with particular QPI value. After receiving QPI value, we can select the relay node based on the QPI value in the increasing order. If there is a tie, we can choose the relay node which is having lowest GPI value as shown in Fig.5. Thus the delay will be reduced by transmitting the RTS packets again and again until find out the node with particular QPI value.
Fig.5 System architecture
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V. CONCLUSION WSNs have seen tremendous developments in design and applications over the recent years. This speedy progress has resulted in the stress towards solving the hurdles that this area has to face. The area of WSN is thriving and every day new ideas are emerging. The positive benefits of this are quite obvious; such a technology will achieve fine granularity tracking of what is going on at far away and generally in inaccessible locations. A wireless senor network is the latest and fastest growing technology and is expected to revolutionize a wide range of applications in terms of its quality and availability in the near future. REFERENCES [1]M. Zorzi, “A New Contention-Based MAC Protocol for Geographic Forwarding in Ad Hoc and Sensor Networks,” Proc. IEEE Int’l Conf. Comm. (ICC ’04), vol. 6, pp. 3481-3485, June 2004. [2]K. Seada, A. Helmy, and R. Govindan, “On the Effect of Localization Errors on Geographic Face Routing in Sensor Networks,” Proc. IEEE/ACM Third Int’l Symp. Information Processing in Sensor Networks (IPSN ’04), pp. 71-80, Apr. 2004. [3]Q. Fang, J. Gao, and L.J. Guibas, “Locating and Bypassing Holes in Sensor Networks,” ACM Mobile Networks and Applications, vol. 11, no. 2, pp. 187-200, Apr. 2006.
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Sensor Networks,” Ad Hoc Networks, Special Issue on Cross-Layer Design in Ad Hoc and Sensor Networks, vol. 11, no. 2, pp. 654-671, Mar. 2013. [7]C.Petrioli, P.Casari, M.Zorzi, M.Nati, and S.Basagni, “ALBA-R: Load-Balancing Geographic Routing Around Connectivity Holes in Wireless Sensor Networks” IEEE Transactions on parallel and distributed systems, vol.25, no.3, March 2014. [8]S. Ru¨ hrup and I. Stojmenovic, “Optimizing Communication Overhead while Reducing Path Length in Beaconless Georouting with Guaranteed Delivery for Wireless Sensor Networks,” IEEE Trans. Computers, vol. 62, no. 12, pp. 2240-2253, Dec. 2013. [9] H. Frey, S. Ru¨ hrup, and I. Stojmenovic, “Routing in Wireless Sensor Networks,” Guide to Wireless Sensor Networks, S. Misra, I. Woungang, and S. C. Misra, eds., ch. 4, pp. 81112, Springer-Verlag, May 2009. [10] Fraser Cadger, Member, Kevin Curran, IEEE, Jose Santos and Sandra Moffett “A Survey of Geographical Routing in Wireless Ad- Hoc Networks “, IEEE Communications Surveys and Tutorials, Vol. PP, No. 99, pp: 133, 2012. [11] Rama Sundari Battula, O. S. Khanna “Geographic Routing Protocols for Wireless Sensor Networks: A Review”, International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 12, June 2013
[4]P. Casari, M. Nati, C. Petrioli, and M. Zorzi, “Efficient Non-Planar Routing around Dead Ends in Sparse Topologies Using Random Forwarding,” Proc. IEEE Int’l Conf. Comm. (ICC ’07), pp. 3122-3129, June 2007. [5]S. Basagni, M. Nati, and C. Petrioli, “Localization Error-Resilient Geographic Routing for Wireless Sensor Networks,” Proc. IEEE GLOBECOM, pp. 1-6, Nov./Dec. 2008.
[6]A. Camillo, M. Nati, C. Petrioli, M. Rossi, and M. Zorzi, “IRIS: Integrated Data Gathering and Interest Dissemination System for Wireless ISBN NO: 978 - 1503304048
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