Fault Tolerance and Clustering Algorithm in WSN-A Review

IJSRD - International Journal for Scientific Research & Development| Vol. 4, Issue 05, 2016 | ISSN (online): 2321-0613

Fault Tolerance and Clustering Algorithm in WSN-A Review Mandeep Kaur1 Parul Garg2 Department of Computer Science & Engineering 1,2 GZS Campus College of Engineering & Tech. Bathinda, Punjab, India 1,2

Abstract— WSN is a wireless sensor network consisting of a large no of sensor nodes. These nodes are deployed in the network for its feature of monitoring. The process of gathering data and communication takes place from nodes to base stations. The base station is the sink to collect all the data. These sensor nodes have some limitations like limited energy in the form of in-built battery, storage capacity. A WSN can go through different types of failure like connectivity, link, node and malfunctions of nodes. WSN should be good at identifying the fault and put it right instead of discarding it. Energy constraint is one of the main problems that occur when sensors are deployed in the environment. So to create a network which consume less energy and has a long lifetime is a challenge. In this paper, fault tolerance (FT) techniques and clustering algorithm is discussed.

Key words: WSN, Fault, Energy, Fault Tolerance, Cluster, Cluster Head, Clustering Algorithms I. INTRODUCTION A wireless sensor network consists of so many sensor nodes, which are deployed in the environment [5]. The sensor nodes collect the information from the environment and transmit the data to the base station. Then the information is transmitted to the end user for its use. In the WSN, sensors are dynamic in nature as any sensor nods or links may fail at any point of operation. Replacing faulty nodes is not an easy task so fault tolerance or reliability measure is a very important research challenge that needs to be focused.

A. Fault Tolerance Fault in sensor network cannot be tolerated and recovered directly using the approaches employed with traditional wired or wireless network. In WSN failures occur more often than traditional networks, where client machines, servers and routers most of the times operate in normal conditions. Also, most of the approached used with traditional networks we do not consider device energy consumption an issue as they have a fixed power supply to their system. Various ways that may cause fault in sensor nodes. [13]Firstly, it is due to energy depletion of the sensor nodes that means when the node whole energy is consumed to answer different data request coming from users.

Secondly, it is due to sensor hardware failure which may include measurement, transmitting or receiving faults. The third one is due to fault in communication links between different sensor nodes. A monitor node is added to the sensor network, where it will detect the fault and report them to base station. In it mentions the type of fault occurred in the network and the monitor node maintains the health of the network. The monitor node transmits all the monitored traffic and dedicated monitors in a separate channel. A monitoring node can either be placed directly on a sensor node or inside the sensor network near to the sensor nodes. The basic need of a system is to make it work for as long as one can. While working it undergoes so many

failures. Failure can be of any form, it might be due to environment factors, hardware failure, any type of damage to the sensors, nodes are running out of power, software failure or any type of intended attack done to cause failure.

[12] FT ensures that the work done is reliable and has the ability to tackle any kind of fault. [12]FT mechanisms are classified as redundancy based, clustering based and deployment based. One of the efficient mechanisms of FT is clustering. It is an efficient method to save energy and control the network.

Energy consumption is the most important aspect to actuate the life of a WSN because sensor nodes work on in-built batteries. Sometimes energy optimization is more difficult in sensor networks because it involves not only degradation of energy consumption but also stretching the life of the network as more as possible. The optimization can be done by having energy consciousness in every facet of design and operation. This secures that energy awareness is also assimilated into groups of communicating sensor nodes and the whole network and not only in the individual nodes. To decrease the overall energy consumption of the sensor network, various types of protocols and algorithms have been studied. The lifetime of a sensor network can be enlarged significantly if the operating system, the application layer and the network protocols are designed to be energy aware.

B. Clustering Algorithm Cluster formation is the process to form clusters in the network. Each cluster consists of CH and cluster members.

Cluster member send data to the CH and CH forwards the data to the base station (BS). When the data is forwarded from one CH to another CH. The process is known as inter-cluster communication.[11] When the data transmission takes place from CH to BS. As shown in Fig 1.

Fig. 1: Communication b/w clusters The process is termed as intra-cluster communication. Clustering can be done in one hope or multi hop way. Members can directly send data to the CH or they may pass the data to the nearer cluster member. And then further that member will pass the data to Clustering can be done in one hope or multi hop way. Members can directly

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Fault Tolerance and Clustering Algorithm in WSN-A Review (IJSRD/Vol. 4/Issue 05/2016/093)

send data to the CH or they may pass the data to the nearer cluster member. And then further that member will pass the data to the CH. CH can be selected randomly or using cost function, or calculating the average energy and on that bases decide the CH probability.

II. RELATED WORK [10] In this Leach protocol is introduced to increase the life time of network is increased using clustering. [9]In this technique the cluster head exchange all its information with the neighbour CH. On failure of any CH, neighbour CH consult each other and negotiate among them to take over that cluster under them. [8]In CMATO author proposed the mechanism in which detection and recovery is done locally side by side while network is operational. [7] Author proposed a scheme which is based on cross-layer design of Mac layer. When the data is transmitted, sender waits for an acknowledgement. It relies with an ACK after a short inter frame space. If not, the channel remains silent. In this way, the cooperative node knows that the intended link fails and replies an ACK to the sender to become the next replay. It works to minimize the impact of packet loss. [6] Author has gone through various clustering algorithms like leach, heed.

And created a view why we require clustering. Clustering enhances the network in various forms like limits energy consumption, improves network life time. In this various factors are discussed which should be kept in mind so to design an effective clustering algorithm. [10] Leach-C is a centralized algorithm. [4] In this the author proposed an algorithm which consists of three protocols in order to provide scalability, energy efficient, multi level clustering, fault tolerance and load balancing. Author uses NS2 for the implementation. Leach-ked [3], in this author put forward the clustering technique of leach and then combines it with leach-C. This 5% implies that the CH number is fixed.

DFCA [2] is a distributed fault tolerant clustering algorithm addresses both issues of cluster formation based on the remaining energy of the nodes and fault tolerance when the CH dies. EFDCB [1] is extended form of FDCB algorithm.

III. VARIOUS FT AND CLUSTERING TECHNIQUES A. Low-Energy Adaptive Clustering Hierarchy This protocol randomly with equal probability selects cluster head to achieve energy balance.[14] The two main objective of this protocol is to increase network lifetime and second to reduce the energy consumption. Energy consumption is reduced by data aggregation process in which communication messages is lowered. It is probably first dynamic clustering protocol, still serving as base for various schemes. In this the nodes themselves take the random decision of selecting CH without any centralized control.

Each node gets an equal chance to become CH as the rounds keep on going. Leach can provide a better balance of energy by random rotation of CH. It provides scalability and enhances the network performance as a localized protocol.

[2] The main drawback of this algorithm can be taken as the possibility of selecting the node of low energy as CH. It has the probability to select every node as CH. The node with low energy when selected as CH may die more fastly because CH consumes more energy causing a fault in the network.

B. A Cluster-Member Based Fault Tolerant Mechanism [8]This scheme proposed a mechanism in which detection and recovery is done locally side by side while network is operational. The nodes monitor the link with the CH, at time of failure of CH. They detect the failure and re-select the CH for proper functioning of the network. It works in two phase Fault detection phase detects the fault as cluster head communicate with each other through beacon message so the cluster members send data to another CH when they don’t get an acknowledgement from the first CH which means a fault has occurred. The second phase is the recovery from fault. Every node is also linked to the neigbouring CH. Once its head is dead it sends data to the neighbouring CH or the newly formed CH. So the recovery is possible because it is in the range of two CH. This scheme gives a way to recover from medium error or the CH error.

C. Low Energy Adaptive Clustering Algorithm-Centralized [10] This is a centralized algorithm. This algorithm is the extended form of Leach. In this scheme Base station holds the main responsibility of cluster formation and choosing the CH. As the whole process is centrally operated by the BS so known as centralized process. Each node directly communicates with the base station thus a better and centralized view of network is created. Nodes reply to the BS there residual energy for the process of cluster formation. The CH is selected by the BS. Even in this way BS has a full control over the CH formation process. By this process performance is enhanced as the control lie in the hands of BS. D. Low-Energy Adaptive Clustering Hierarchy K-Mean Author put forward the clustering technique of leach and then combines it with leach-C. [3] Leach-ked operates in rounds. Each round consists of two stages cluster building stage and stable stage. CH is selected from the nodes as 5% are the cluster heads by using k-means. In First phase cluster geometric centre is calculated and residual energy, initial energy and distance between the nodes is considered. Once the CH is chosen process moves to second phase that is stable phase. CH sends all nodes notification of elected as the CH and ask them to send data for transmission. Once the nodes join the CH that is present in there range. They transmit data to the CH. Further cluster head forwards data to the BS by merging the transmitted data into packets. When compared the results its survival time is increased and death rate of nodes is slower then Leach and Leach-C. E. Distributed Fault Tolerant Clustering Algorithm This algorithm addresses both issues of cluster formation based on the remaining energy of the nodes and fault tolerance when the CH dies. It undergoes two phases steadystate phase and set-up phase. [2] Firstly the bootstrapping process takes place. In which the BS provides unique id’s to the nodes and gateways. BS forms the schedule and divides the process into multiple rounds and duration is fixed of each round. Gateway generates a hello message. The hello message holds the three main parameters like residual energy, distance from base station and its id. The sensor nodes that have no CH within their communication range come under uncovered set and generates help message for backup. DFCA also presents a distributed run time recovery of the faulty cluster members due to sudden failure of the

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Fault Tolerance and Clustering Algorithm in WSN-A Review (IJSRD/Vol. 4/Issue 05/2016/093)

CH. When the CH dies then the instead of disconnecting the whole cluster it moves to the neighbouring CH and transfer its data to neighbour CH. A sensor node can join to only one gateway.

thus by this it covers shortcomings of FDCB algorithm. It works in four phases cluster formation, selecting cluster head, to find faulty node and then provide fault free path for data transmission. In this algorithm CH has all the information regarding its area and the nodes connected.

When the connection between the CH and the Clustermember is lost due to mobility or fault. CH has whole knowledge of that node and without any delay it reestablishes the connection again.

F. Extended Fully Distributed Cluster-based Routing Algorithm This technique presents a distributed architecture in which each and every cluster member maintains a table holding parameters of quality of service. Cluster members, table and failure are calculated locally at the nearest site. [1] Only suffers minor disturbance in the path during recovery and

IV. COMPARATIVE ANALYSIS OF CLUSTERING AND FT TECHNIQUES

Parameter

Leach

Leach-C

Leach-KED

Good at

To inc life time

Better performance as BS forms cluster

Improve survival time then leach

Technique used

Clustering

Leach and centralized algorithm

Leach, Leach-C and K-mean algorithm

Drawbacks

Randomly select low energy node as CH

Data send to BS so energy gets consumed in this process

Little slower than the algorithms used

Probability/Rand omly

Random

Cost-based

Distributed

Centralized

Distributed

Distributed

Distributed

Distributed

Limited

Limited

NO

NO

Limited

Yes

1-hop

1-hop

1-hop

Multi-hop

Multi-hop

Multi-hop

Variable

Variable

Fixed

variable

Variable

Variable

CH selection Cluster process Node mobility Cluster topology Cluster count

CMATO Deal with permanent and medium faults Fault detection and recovery done by cluster members.

Multiple failure inc. load on newly CH Weight-based function

DFCA Fault tolerance and energy efficient LBCA and Fault tolerance clustering algorithms.

Load on neigbouring CH inc.

Cost-function

EFDCB Fault detection and fault management.

Extended FDCB and local repair.

Takes some time in reestablishment of path Average energy of nodes

Table 1: Comparative Analysis of Clustering and Ft Techniques [2] Azharuddin, Md, Pratyay Kuila, and Prasanta K. Jana.

"A distributed fault-tolerant clustering algorithm for V. CONCLUSION wireless sensor networks." In Advances in Computing, In this review paper we have gone through the concept of Communications and Informatics (ICACCI), 2013 wireless sensor networks .Various failure that can occur and International Conference on, pp. 997-1002. IEEE, 2013. can cause damage to WSN network. To make the network [3] Yunjie, Jia, Liu Ming, Zhu Song, and Dong Pengtao. "A work in case of any fault that means capacity of the network clustering routing algorithm based on energy and to tackle fault situation is termed as fault tolerance. To distance in WSN." In Computer Distributed Control and achieve FT, researches have come up with so many Intelligent Environmental Monitoring (CDCIEM), 2012 techniques. Some clustering and FT techniques are analyzed International Conference on, pp. 9-12. IEEE, 2012. and some of the good points of techniques are laid down [4] Kumar, Rajesh, and Uttam Kumar. "A hierarchal cluster with some drawbacks. Various parameters are laid down framework for wireless sensor network." In Advances like CH selection, Cluster Topology. From this we came to in Computing and Communications (ICACC), 2012 know about various research challenges that need to be International Conference on, pp. 46-50. IEEE, 2012. focused like in DFCA the load on neighbouring cluster head [5] Alkhatib, Ahmad Abed Alhameed, and Gurvinder increases. In future one can come up with a technique to Singh Baicher. "Wireless sensor network architecture." reduce this load further.

In International Conference on Computer networks and Communication Systems (ICNCS 2012), vol. 35, pp.

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