Optimization Technique using Clustering to Prolong the Lifetime of Wireless Sensor Networks: A Review

Transcription

1 INTERNATIONAL JOURNAL OF R&D IN ENGINEERING, SCIENCE AND MANAGEMENT Vol.4, Issue 2, June 2016, p.p , ISSN X Optimization Technique using Clustering to Prolong the Lifetime of Wireless Sensor Networks: A Review Shikha Bhardwaj 1, Shefali Dhingra 2 1,2 Asstt. Prof., Deptt. of ECE., University institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India. ABSTRACT Wireless sensor networks (WSN) is a self organized network where tremendous growth has been observed since last decade. WSNs acts as an interface between real (physical) and virtual worlds. WSNs are composed of sensor nodes which sense the changes in external environment and sends the data to other nodes in the network or the sink (also called as Base Station). WSNs performance gets affected by the limited battery lifetime of sensor nodes. Many types of techniques and algorithms have been used to increase life time of a network. Clustering is an important technique when combined with power management algorithms/protocols can reduce the energy consumption of WSN. Improvements are being made day by day in basic clustering techniques and also protocols. In this paper basic clustering technique combined with various protocols /algorithms has been presented. Key Words: Wireless sensor network, Clustering technique, Lifetime of WSN 1. INTRODUCTION Wireless sensor networks (WSN), sometimes called wireless sensor and actuator networks (WSAN), are spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location. The more modern networks are bi-directional and also enable control of sensor activity. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance. Today these networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on. Wireless sensor networks consists of various densely deployed sensor nodes inside or very near to application area. Advancement in the field of micro-electro-mechanical-systems (MEMS) provides low cost small sized yet powerful sensor nodes that are capable of sensing, data processing and wireless communication and carry a limited power battery. Sensor nodes always work in collaboration to complete the task in time and to provide information accurately [7]. Available at : Page 248 R&D Publications

2 Fig 1Typical multihop wireless sensor network architecture (Reference: en.wikipedia.org/wiki/wireless_sensor_network) An important issue in sensor networks is power scarcity due to battery size and weight limitations. Mechanisms that optimize sensor energy utilization have a great impact on prolonging the network lifetime. Lifetime of wireless sensor networks depends upon battery power of nodes as every operation of node consumes energy, hence node goes out of energy. Harsh/remote application area makes it impossible to recharge or replace the battery of nodes. So, efficient energy consumption of nodes is the prime design issue for wireless sensor networks from the circuitry of sensor nodes to application level to network protocols. Naturally, grouping sensor nodes into clusters has been widely adopted by the research community to satisfy the above scalability objective and generally achieve high energy efficiency and prolong network lifetime in large-scale WSN environments. The corresponding hierarchical routing and data gathering protocols imply cluster-based organization of the sensor nodes in order that data fusion and aggregation are possible, thus leading to significant energy savings. In the hierarchical network structure each cluster has a leader, which is also called the cluster head (CH) and usually performs the special tasks called (fusion and aggregation). 2. MAIN OBJECTIVES AND DESIGN CHALLENGES OF CLUSTERING IN WSNs Hierarchical clustering in WSNs can greatly contribute to overall system scalability, lifetime, and energy efficiency. Hierarchical routing is an efficient way to lower energy consumption within a cluster, performing data aggregation and fusion in order decrease the number of transmitted messages to the BS. In addition to supporting network scalability and decreasing energy consumption through data aggregation, clustering has many other secondary advantages Hierarchical clustering is useful for applications that require scalability to hundreds or thousands of nodes. Scalability in this direction is required for load balancing and efficient resource utilization. Applications requiring efficient data aggregation (e.g., computing the maximum detected radiation (around a large area) also require clustering. Routing protocols can also employ clustering [3,4]. In Ref. [5], clustering was also proposed as a useful tool for efficiently pinpointing object locations. In addition to supporting network scalability and decreasing energy consumption through data aggregation, clustering has numerous other secondary advantages and corresponding objectives [6]. It can localize the route setup within the cluster and thus reduce the size of the routing table stored at the individual node. It Page 249

3 can also conserve communication bandwidth because it limits the scope of inter cluster interactions to CHs and avoids redundant exchange of messages among sensor nodes. Moreover, clustering can also stabilize the network topology at the level of sensors and thus cuts on topology maintenance overhead. Sensors would care only for connecting with their CHs and would not be affected by changes at the level of inter- CH tier. The CH can also implement optimized management strategies to further enhance the network operation and prolong the battery life of the individual sensors and the network lifetime. A CH can schedule activities in the cluster so that nodes can switch to the low-power sleep mode and reduce the rate of energy consumption. Furthermore, sensors can be engaged in a round-robin order and the time for their transmission and reception can be determined so that the sensors reties are avoided, redundancy in coverage can be limited, and medium access collision is prevented. WSNs also present several particular challenges in terms of design and implementation.similar challenges and design goals have also been faced earlier in the field of mobile ad hoc networks (MANETs), and naturally a lot of related ideas (considering clustering protocols etc.) have been borrowed from that field. In WSNs, the limited capabilities (battery power, transmission range, processing hardware and memory used, etc.) of the sensor nodes combined with the special location-based conditions met (not easily accessed in order recharge the batteries or replace the entire sensors) make the energy efficiency and the scalability factors even more crucial. Moreover, the challenge of prolonging network lifetime under the above restrictions is difficult to be met by using only traditional techniques. Consequently, it becomes unavoidable to follow alternative to more efficient protocols with a lot of differences compared to the ones designed for MANETs. Beyond the typical (however vital) challenges mentioned above (limited energy, limited capabilities, network lifetime) some additional important considerations in the design process of clustering algorithms for WSNs should be the following: (i) Cluster formation: The CH selection and cluster formation procedures should generate the best possible clusters (well balanced, etc.). However they should also preserve the number of exchanged messages and the total time complexity should (if possible) remain constant and independent to the growth of the network. (ii) Application Dependency: When designing clustering and routing protocols for WSNs, application robustness must be of high priority and the designed protocols should be able to adapt to a variety of application requirements. (iii) Secure communication: As in traditional networks, the security of data is naturally of equal importance in WSNs too.the ability of a WSN clustering scheme to preserve secure communication is ever more important when considering these networks for military applications. (iv) Synchronization: Slotted transmission schemes such as TDMA allow nodes to regularly schedule sleep intervals to minimize energy used. Such schemes require corresponding synchronization mechanisms and the effectiveness of this mechanisms must be considered. (v) Data aggregation: Because this process makes energy optimization possible it remains a fundamental design challenge in many sensor network schemes nowadays. However its Page 250

4 effective implementation in many applications is not a straightforward procedure and has to be furtheroptimized according to specific application requirements. 3. GENETIC ALGORITHM: AN INTRODUCTION In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover [8]. A genetic algorithm is an exploratory procedure that is often able to locate near optimal solutions to complex problems. To do this, it maintains a set of trial solutions (often called individuals), and forces them to evolve towards an acceptable solution. First, a representation for possible solutions must be developed. Then, starting with an initial random population and employing survival of-the-fittest and exploiting old knowledge in the gene pool, each generation s ability to solve the problem should improve. This is achieved through a four-step process involving evaluation, reproduction, recombination and mutation. Evaluation. The first step in each generation is the evaluation of the current chromosomes. This is the only step where the interpretation of the chromosome is used. Each chromosome in the population is decoded and evaluated on how well it solves the problem. This fitness measure is used in the next step to determine how many offspring will be generated from any particular chromosome. Reproduction. In the next step, a new population is created based upon the evaluation of the current one. For every chromosome in the current population, a number of exact copies are generated with the best chromosomes producing the most copies. This is the step that allows GA to take advantage of a survivalof-the-fittest strategy. Recombination. The previous step, reproduction, creates a population whose members currently best solve the problem; however, many of the chromosomes are identical and none are different from the previous generation. Remember, reproduction simply produces multiple copies of existing chromosomes. Recombination combines chromosomes from the population and produces new chromosomes that, while they did not exist in the previous generation, maintain many of the features of the previous generation. In natural evolution, recombination and reproduction occur in the same step. However, in GA they are often separated to facilitate experimentation with different methods. The most common method for recombination is crossover. Two individuals are randomly selected from the population and, governed by a specified crossover probability or rate, subsections of the two chromosomes are swapped about a randomly chosen crossover point. Figure 2 illustrates this operation. Page 251

5 BEFORE CROSSOVER AFTER CROSSOVER Fig 2: Crossover in GA (Ref: Genetic Algorithms by James F. Frenzel) Mutation. The last step in creating a new generation is motivated by the possibility that the initial population didn t contain all of the information necessary to solve the problem. Furthermore, it is possible that the individuals that produce no offspring may have had some information that is essential to the solution. The injection of new information into the population is called mutation. Here again, implementations vary; but, most randomly change a fixed number of bits every generation based upon a specified mutation probability. 4. GA PARAMETERS: The best values for mutation rate, crossover percentage, and other parameters are problem specific; researchers have even recommended using genetic algorithms to find the best combinations! However, certain generalizations can be made. If the population is too small, relative to the size of the search space, it will be difficult to effectively search the entire region. Second, large mutation rates tend to disrupt the steady improvement resulting from crossover and reproduction. Researchers have found that a population of 30 individuals, a crossover probability of 60%, and a mutation probability of 3% seems to be a good starting point. 5. OPTIMIZATION BY GENETIC ALGORITHM Genetic algorithms [1] are search and optimization algorithms based on the principles of natural evolution, which were first introduced by john Holland in Genetic algorithms also implement the optimization strategies by simulating evolution of species through natural selections. Genetic algorithm is generally composed of two processes. First process is selection of individual for the production of next generation and second process is manipulation of the selected individual to form the next generation by crossover and mutation techniques [2]. The selection mechanism determines which individual are chosen for reproduction and how many offspring each selected individual produce. The main principle of selection strategy is the better is an individual; the higher is its chance of being parent. 6. LEACH Low-energy adaptive clustering hierarchy ("LEACH") is a TDMA-based MAC protocol which is integrated with clustering and a simple routing protocol in wireless sensor networks (WSNs). The goal of Page 252

6 LEACH is to lower the energy consumption required to create and maintain clusters in order to improve the life time of a wireless sensor network. 6.1 Protocol explanation LEACH is a hierarchical protocol in which most nodes transmit to cluster heads, and the cluster heads aggregate and compress the data and forward it to the base station (sink). Each node uses a stochastic algorithm at each round to determine whether it will become a cluster head in this round. LEACH assumes that each node has a radio powerful enough to directly reach the base station or the nearest cluster head, but that using this radio at full power all the time would waste energy. Nodes that have been cluster heads cannot become cluster heads again for P rounds, where P is the desired percentage of cluster heads. Thereafter, each node has a 1/P probability of becoming a cluster head again. At the end of each round, each node that is not a cluster head selects the closest cluster head and joins that cluster. The cluster head then creates a schedule for each node in its cluster to transmit its data. All nodes that are not cluster heads only communicate with the cluster head in a TDMA fashion, according to the schedule created by the cluster head. They do so using the minimum energy needed to reach the cluster head, and only need to keep their radios on during their time slot. LEACH also uses CDMA so that each cluster uses a different set of CDMA codes, to minimize interference between clusters. 7. QBGA Queen Bee genetic algorithm also called as QEGA is an advanced algorithm over genetic algorithm. There are basically two main differences between the two. Firstly in GA parents are composed of n individuals and in QBGA parents are chosen from n/2 of individuals. Secondly in GA all individuals in GA are mutated with small mutation probability while in QBGA, only a part are mutated with normal mutation probability and rest with high mutation probability. The energy consumption can be further decreased by using QBGA in comparison to other algorithms and lifetime of the network can be increased. 8. LITERATURE REVIEW Vinay Kumar et.al,2011, In this paper the author describes that in WSN s in order to minimize the total energy consumed by a network, the paths for data transfer have to be selected. To support high scalability, sensor nodes are grouped into disjoint, non overlapping subsets called clusters. LEACH is an important algorithm which is used to increase the network lifetime. Also various descendants of LEACH can also be used to increase lifetime of a network. Z.Pooranian et.al,2011 This paper presents a new algorithm called Queen Bee to create energy efficient clusters in wireless sensor networks. This algorithm when used on clusters can reduce the energy consumption of the network. This algorithm when compared with previous algorithms can decrease the energy consumption.since in this algorithm only one mother is Queen Bee which is necessary and selected Page 253

7 for the reproduction of bees.also the number of marriages are much less as compared to Genetic Algorithm. So by using this technique diversity in children will be increased and pre mature divergence will be avoided. M. Aslam et.al,2012 The author in this paper presents energy efficient protocol which is an extension of Leach protocol and is known as extended Leach protocol. Also this paper also explains the some of the issues faced by Leach protocol and how these issues are resolved by extended Leach protocol. Various versions of Leach protocol like Multi hop Leach, M-Leach and Solar aware Leach have been discussed. Many characteristics of WSN like energy efficiency and throughput enhancement of network has been studied Qian Liao et.al, 2013, The author presents energy balanced clustering algorithm based on LEACH protocol to extend the entire lifetime of a network. This protocol takes node s residual energy and location information into account, improves optimal cluster head selection strategy and selects the optimal cluster head based on cost function. This algorithm considers the residual energy and distance factors, improves cluster head election and strategy of non cluster head nodes selecting the optimal cluster head. This improved algorithm is better than LEACH and can balance the network energy consumption and can prolong the network life time. Vipin Pal,et.al,2015 In this paper the author presents an optimization technique for cluster head selection based on Genetic Algorithm to prolong the lifetime of wireless sensor networks. As lifetime of WSN depends upon battery power of nodes, hence to prolong lifetime of nodes is an important issue. Cluster head selection is an important issue in performance of clustering algorithms with load balancing of network. To achieve load balancing, clustering algorithm rotate the role of cluster head among the nodes, so cluster head selection is an important issue. A centralized cluster head selection scheme based on Genetic Algorithm selects the head according to the residual energy and takes care of trade off between inter and intra communication distance. 9. CONCLUSION We have identified and collected information regarding weaknesses in WSNs which is mainly due to limited time span of battery life. Various techniques can be used to save life span of nodes in WSNs among which one of the technique is clustering. Clustering optimizes the cluster head selection by using different algorithms.algorithms like Genetic Algorithm, LEACH-GA, Extended LEACH, QBGA can be used based on the common concept of clustering. These algorithms when used in combination with any of energy saving technique like clustering can increase the lifetime of battery, thus increasing the total lifetime of a Wireless sensor Network. Although many improvements can be done in future to implement a technique which not only provides optimal cluster head solutions but also increase the energy efficiency in WSN. REFERENCES [1] J. Holland, Adaptation in natural and artificial systems, University of Michigan press, Ann Arbor, Page 254

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