An Energy Efficient WSN Using Genetic Algorithm

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1 An Energy Efficient WSN Using Genetic Algorithm Neema Subash Teena Abraham Dillmol Thankachan PG Student, Dept ECE Asst. Professor, Dept ECE PG Student, Dept ECE MBITS, Nellimattom MBITS, Nellimattom MBITS, Nellimattom Abstract-An optimization methodology for self-organizing, adaptive wireless sensor network design and energy management is considered, taking into account energy-conservation characteristics. We use genetic algorithms as the optimization tool of the developed system. The design characteristics optimized by the genetic algorithm system include the status of sensor nodes (whether they are active or inactive), network clustering with the choice of appropriate clusterheads and finally the choice between two signal ranges for the simple sensor nodes. We show that optimal sensor network designs constructed by the genetic algorithm system incorporate energy-conservation characteristics. Energy management is optimized to guarantee maximum life span of the network without lack of the network characteristics that are required by the specific application. Index Terms Wireless Sensor Networks,Genetic Algorithm, Energy Conservation, Optimal design I. INTRODUCTION Fig. 1 Multi-gateway clustered sensor network Wireless sensor networks are composed of a large Energy consumption is one of the most important challenges in the number of low power sensor nodes which can sense the environment and communicate the information gathered from the monitored field through wireless links. Such tiny sensor nodes are equipped with data processing and communication capabilities.wsn have been widely used in both military and civillian applications such as target tracking, surveillance and security management. The sensing circuitry measures parameters from the environment surrounding the sensor and is transformed into an electric signal. Such signal is processed to obtain some properties about objects located and/or events happening in the vicinity of the sensor. The sensed data is then sent via radio transmitter to the base station either directly or through a data concentration center(a gateway or cluster head). A sensor network having a single gateway can cause overload to gateway with the increase in sensors density and cause severe limitations in communication. In addition, for covering a wider area of interest the single-gateway architecture is not scalable. To allow the system to cover a large area of interest without degrading the service, network clustering is usually design of WSNs. Signal processing and communication are the main source of energy dissipation in sensors, which greatly depends on the distance between the source and destination of a communication link. Since sensors are battery-operated, keeping the sensor active all the time will exhaust the battery rapidly and reduce the lifetime of the network. Therefore, optimal organization and management of the sensor network is very crucial in order to perform the desired function. One of the most powerful heuristics, which is appropriate to apply in our optimization problem, is based on Genetic Algorithms (GAs).GA is a search technique used in computing to find true or approximate solutions to optimization and search problems.ga is an optimization technique to improve the performance of CH election procedure. The primary goal is to find the optimal operation mode of each sensor so that energy consumption of the network is minimized. The ultimate objective of this research is to find a dynamic sequence of operation modes for each sensor, i.e. a sequence of WSN designs, which will lead to maximization of network lifetime in terms of number of data used by involving multiple gateways, as depicted in Fig.1. The collection (measuring) cycles. This is achieved by connectivity of the network follows the cluster-based architecture, where single-hop communication occurs between sensors of a cluster and a selected CH sensor that collects all information gathered by the other sensors in its cluster. Energy implementing the algorithm repeatedly in order to develop a dynamic network design that adapts to new energy-related information concerning the status of the network after each measuring cycle or at predefined time intervals

2 II. RELATED WORKS Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In [2], Energy-Efficient Communication Protocol for Wireless Microsensor Networks we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. The conventional protocols of direct transmission, minimum-transmission-energy, multihop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster base stations (CH) to evenly distribute the energy load among the sensors in the network. In LEACH, the nodes organize themselves into local clusters, with one node acting as the local base station or CH. If the CHs were chosen a priori and fixed throughout the system lifetime, as in conventional clustering algorithms, it is easy to see that the unlucky sensors chosen to be cluster-heads would die quickly, ending the useful lifetime of all nodes belonging to those clusters. Thus LEACH includes randomized rotation of the high-energy cluster-head position such that it rotates among the various sensors in order to not drain the battery of a single sensor. In addition, LEACH performs local data fusion to compress the amount of data being sent from the clusters to the base station, further reducing energy dissipation and enhancing system lifetime. Sensors elect themselves to be local cluster-heads at any given time with a certain probability. These CH nodes broadcast their status to the other sensors in the network. Each sensor node determines to which cluster it wants to belong by choosing the cluster-head that requires the minimum communication energy. Once all the nodes are organized into clusters, each CH creates a schedule for the nodes in its cluster. This allows the radio components of each non-ch node to be turned off at all times except during its transmit time, thus minimizing the energy dissipated in the individual sensors. Once the CH has all the data from the nodes in its cluster, the CH node aggregates the data and then transmits the compressed data to the base station. Since the base station is far away in the scenario we are examining, this is a high energy transmission. However, since there are only a few CHs, this only affects a small number of nodes. In [3], authors proposed an efficient method based on genetic algorithms (GAs).Long communication distances between sensors and a sink (or destination) in a sensor network can greatly drain the energy of sensors and reduce the lifetime of a network. By clustering a sensor network into a number of independent clusters using a GA, we can greatly minimize the total communication distance, thus prolonging the network lifetime. Once CHs are selected, each regular node connects to its nearest CH. Each node in a network is either a CH or a member of a CH. Each regular node can only belong to one CH. Each CH collects data from all sensors within its cluster and each head directly sends the collected data to the sink. In [4], authors showed how Genetic algorithms can be useful in enhancing the performance of clustering algorithms in mobile ad hoc networks. Here we propose to optimize WCA such that the CHs(dominant set) is minimized while load in the network is evenly balanced among the clusters. In order to have a smaller numher of CHs, each CH must serve the maximum possible number of nodes within their clusters. By balancing the nodes among the clusters, we also assure that the lifetime of individual nodes will be increased accordingly as none of the nodes will use their processing and/or battery power more than necessary. The goal of GA is to choose the one with the lowest fitness value to be the best chromosome in that population for that generation. As Elitist model of GA is used, the index of the chromosome in the population will be saved to pass on the next generation as the genetic algorithm performs crossover, mutation and replacement. The problem formulation along with the parameters are mapped to individual chromosomes as input to the genetic algorithmic technique. Encoding the individual chromosomes is an essential part of the mapping process; each chromosome contains information about the CHs and the members thereof, as obtained from the original WCA. The genetic algorithm then uses this information to obtain the best solution (chromosome) defined by the fitness function. The proposed technique is such that each CH handles the maximum possible number of mobile nodes in its cluster in order to facilitate the optimal operation of the medium access control WAC) protocol. Moreover, the loads among clusters are more evenly balanced by a factor of ten. In [5], Heinzelman et al consider a homogeneous clustered network in which each cluster head collects data from its one hop neighbors, aggregates the gathered data, and transmits it directly to the remote base station. The cluster heads are periodically rotated for efficient load balancing. All the nodes in the network are identical, and there is no multihop communication. In this scenario, the authors provide results supporting their idea that even though the cluster heads have the highest energy drainage rate, periodic rotation ensures good load balancing and hence a higher lifetime. In order to minimize the total energy spent in the network, the required number of cluster heads has to scale as the square root of the total number of sensor nodes. Here cluster head rotation requires that all the nodes be capable of performing data aggregation as well as long range transmissions to the remote base station. This results in extra hardware complexity in all the nodes. III. WSN MODELING The salient features of the proposed WSN are the following: A square grid of 30 by 30 length units is constructed and sensors are placed in all 900 junctions of the

3 grid, so that the entire area of interest is covered. Optimal organization and management of the sensor network is very crucial in order to perform the desired function with an acceptable level of quality and to maintain sufficient sensors' energy to last for the duration of the required mission. Mission-oriented organization of the sensor network enables the appropriate selection of only a subset of the sensors to be turned on and thus avoids wasting the energy of sensors that do not have to be involved. Sensors are identical and may be either active or inactive. They are assumed to have power control features allowing manual or automatic adjustment of their transmit power through the base station. In this way, they are capable of transmitting in one of three supported signal ranges. Provided that a sensor is active, it may operate as a CH transmitting at an appropriate signal range(ch sensor) that allows the communication with the remote base station (sink), or it may operate as a regular sensor transmitting at either high or low-signal range (HSR/LSR sensor, respectively). We consider a cluster-based network architecture. There are several sophisticated clustering methodologies in the literature of WSNs towards energy saving. However, our work tackles the energy saving issue through the optimization of the operating modes of sensors, thus a simple approach of clustering sensors in regular operating modes with their closest CH sensor is adopted for the formation of clusters in the network. The use of clusters for transmitting data to a base station leverages the advantages of small transmit distances for most nodes, requiring only a few nodes to transmit far distances to the base station. Clustering can greatly reduce communication costs of most nodes because they only need to send data to the nearest CH, rather than directly to a sink that may be further away. Consequently, sensors are divided into clusters and in each cluster a sensor is chosen to act as a CH. Crossover is a switching technique used for CH selection in the cluster. If the distance between the CH s is below the threshold value, the CH of the second cluster is switched to its neighbor HSR. The CH remains the same if the distance between the two exceeds the threshold value. All sensors in regular operating modes in a cluster communicate directly (one-hop) with the closest CH and this is how clusters are formed. CH communicate directly with the remote base station (single-hop transmission). It is assumed that communication between CHs and the base station can always be achieved when required and that the base station is able to communicate with every sensor in the field, meaning that every sensor is capable of becoming a CH at some point. In addition, it is assumed that traffic load is uniformly distributed among sensors in regular operating modes. Since CHs have to handle all traffic generated by and destined to the cluster, they have to transmit, receive and process a much larger amount of traffic than regular sensors. CHs need to perform long range transmissions to the base station, data collection and aggregation at specific periods including some computations, as well as coordination of MAC within a cluster. The power consumed by CH will be high since it has to handle the traffic and collect data from its neighbours in the cluster. The problem becomes more complex in the cases of multi-hop transmissions, where CHs need to cover distances that are usually much greater than the regular sensors transmission range. Although the analysis of this operation is out of the scope of this work, the clear result is that CHs experience high energy consumption and exhaust their energy resources more quickly than regular sensors do. IV. FORMULATION OF GA The methodology and formulation of GAs for some specific application incorporates following steps: A. WSN Representation The variables that are included in the WSN representation are those that give all the required information so that the performance of a specific network design can be evaluated. These variables are the placement of the active sensors of the network, the operation mode of each active sensor, that is, whether it is a CH or a regular sensor, and in the case of a regular sensor, whether transmitting at either high or low-signal range (HSR/LSR sensor, respectively). The status of CH, HSR, LSR and inactive sensors are represented by 1,2,3and 4 respectively in the code. B. Fitness Function Fitness function is a weighting function that measures the quality and the performance of a specific sensor network design. This function is maximized by the GA system in the process of evolutionary optimization. A fitness function must include and correctly represent all or at least the most important parameters that affect the performance of the WSN design. C. Genetic Operators And Selection Mechanism During each successive generation, a proportion of the existing population is selected to breed a new generation. Individual solutions are selected through a fitness-based process, where fitter solutions (as measured by a fitness function) are typically more likely to be selected. Certain selection methods rate the fitness of each solution and preferentially select the best solutions. Other methods rate only a random sample of the population, as this process may be very time-consuming. The adopted selection mechanism was the roulette wheel selection scheme. The probability of selecting some individual to become a parent for the production of the next generation was proportional to its fitness value. The most common type of crossover is single point crossover. In single point crossover, you choose a locus at which you swap the remaining alleles from on parent to the

4 other. This is complex and is best understood visually. The children take one section of the chromosome from each parent. The point at which the chromosome is broken depends on the randomly selected crossover point. This particular method is called single point crossover because only one crossover point exists. Sometimes only child 1 or child 2 is created,but oftentimes both offspring are created and put into the new population. Crossover does not always occur, however. Sometimes, based on a set probability, no crossover occurs and the parents are copied directly to the new population. V. DYNAMIC OPTIMAL DESIGN ALGORITHM Genetic algorithms (GA) is an optimization technique to improve the performance of CH election procedure. Long communication distances between sensors and a sink (or destination) in a sensor network can greatly drain the energy of sensors and reduce the lifetime of a network. By clustering a sensor network into a number of independent clusters using a GA, we can greatly minimize the total communication distance, thus prolonging the network lifetime.gas are defined as search algorithms that use the mechanics of natural selection and genetics such as reproduction, gene crossover, mutation as their problem-solving method. The algorithm consisted of two parts: the Optimal Design Algorithm (ODA), which is applied to a set of sensors with specific battery capacities, and the Dynamic Optimal Design Algorithm (DODA), which updates the battery capacities of the sensors and reapplies the optimal design algorithm accordingly. Pseudo code of the ODA is defined as follows. CH of the first cluster is fixed and the distance between CH of the first and second cluster is found. If the distance is below the threshold value, the CH of the second cluster is switched to its neighbor HSR and then this HSR will act as the CH for the particular cluster. The CH remains the same if the distance between the two exceeds the threshold value. The flowchart of the algorithm is shown in Fig 2. Pseudocode of the DODA is defined as follows. Apply ODA while WSN is alive evaluate battery capacities and update Re-apply ODA to sensors with updated battery capacities Set population size M and max of generations G Generate random initial population of M WSN designs Assign battery capacity to each individual in the population Set CH,HSR,LSR and inactive sensors to each individual Crossover the 2 individuals if the distance is below the threshold Store the 2 output offspring Replace old population with new offspring to form current population The deployment of sensor nodes is the first step in establishing a sensor network. Since sensor networks contain a large number of sensor nodes, the nodes must be deployed in clusters. Crossover is a switching technique used for CH selection in the cluster. We find the distance between the node and its neighbours in each clusters and calculate the average distance for each node in the cluster. The node having smallest average distance is assumed to be the CH. The HSR, LSR and inactive sensors are then randomly assigned to the nodes. The

5 Start VI. SIMULATION RESULT Set the number of nodes Position the nodes to form clusters Calculate average distance for nodes in the cluster Assign CH to node having smallest average CH remains the same Assign HSR,LSR and inactive sensors Distance between CH s are found No Crossover If distance threshold Switching Stop Ye Yes Fig. 3 Simulation result before switching We have generated a sample sensor network with 50 sensor nodes. These sensors are randomly distributed on a small region and positioned to form 5 clusters. The CH,HSR,LSR and inactive sensors are assigned as mentioned. Initially the node with minimum average distance acts as CH for that cluster. The distance is calculated as d=sqrt((x1-x2)ᴧ2)+(y1-y2)ᴧ2)) where (x1,y1) is the x and y position of first node and (x2,y2) is the x and y position of second node. From the above figure, node 8,10,20,39,49 act as CH which has the minimum average distance in the clusters containing them. For the cluster I, status of the nodes is fixed throughout the measuring cycle. The distance between CH of cluster I and II,II and III,III and IV,IV and V is found. If the distance is below the threshold value, the CH of cluster II ie node 10 is switched to one of its neighbor HSRs. Here the neighbor HSRs are node 14,16 and 15.The CH is switched to the HSR having smallest average distance ie to the node 16.This switching is shown in the figure 4. Fig. 2 Flowchart of ODA The steps of battery capacities update and reapplication of the optimal WSN design algorithm are performed during data collection. The life span of the network, which is referred to as WSN is alive in the pseudocode, defines the application time of the dynamic algorithm. The network, i.e. the set of sensors in the field, is considered to be alive if the set of sensors with battery capacities above zero is such that some operational WSN can be designed

6 Fig. 4 Simulation result after switching The performance of the Genetic algorithm in the design optimization of wireless sensor network is compared to the performance of Explosion method that floods the query to nodes within a specific circular region in the network. Experimental results show that our proposed method reduce energy consumption and traffic in comparison with existing method. In the design of WSNs, a major and probably most important challenges is that the energy resources are significantly limited than in wired networks. Communication is usually the main source of energy dissipation in sensors, which greatly depends on the distance between the source and destination of a communication link. Recharging or replacing the battery of the sensors in the network is difficult or impossible, causing severe limitations in the communication and processing time between all sensors in the network. The transmission power of CH,HSR and LSR are 0.9,0.5 and 0.2 respectively. For a sensor network with 50 sensor nodes, the number of CH,HSR and LSR are 5,16,15 respectively and the energy consumed by the nodes are 5 x 0.9=4.5,16 x 0.5=8,15 x 0.2=3 which makes a total of 15.5.Similarly we consider a network of 25,75 and 100 nodes and the energy consumed are 11.8,23,30.5 respectively. In the existing method, the nearest Nodes that are clustered together leverages the advantages of small transmit distances for most nodes, requiring only a few nodes to transmit far distances to the base station. All communication to (from) each sensor node is carried out through its corresponding master node. Clustering can greatly reduce traffic because they only need to send data to the nearest cluster-head, rather than directly to a sink that may be further away. This is shown in Fig 6. Fig. 5 Energy consumption of proposed(marked in red line) and existing method(marked in green line) node from the query point floods the query to nodes within a specific circular region, and each node receiving the query replies with information on itself, which means the energy consumed by each nodes are the same and therefore the energy consumption will be high. This is shown in the Fig 5.In the figure x-axis represents the number of nodes and y-axis represent the energy consumed by the nodes. Fig. 6 a)time-delay of exiting method b)time-delay of proposed method VII. CONCLUSION An algorithm for the optimal design and dynamic adaptation of application-specific WSNs, based on the evolutionary optimization properties of genetic algorithms is used. A fixed wireless network of sensors of different operating modes was considered on a grid deployment and the GA system decided which sensors should be active, which ones should operate as CHs and whether each of the remaining

7 active normal nodes should have high or low-signal range. During optimization, parameters of network connectivity, energy conservation were taken into account so that an integrated optimal WSN was designed. From the evolution of network characteristics during the optimization process, we can conclude that it is preferable to operate a relatively high number of sensors and achieve lower energy consumption for communication purposes than having less active sensors with consequently larger energy consumption for communication purposes. In addition, GA generated designs compared favorably to random designs of sensors. Uniformity of sensing points of optimal designs was satisfactory, while connectivity constraints were met and operational and communication energy consumption was minimized. We also showed that dynamic application of the algorithm in adaptive WSN design can lead to the extension of the network s life span. The algorithm showed sophisticated characteristics in the decision of sensors activity/inactivity schedule as well as the rotation of operating modes (CH or regular sensor with either high or low-signal range), which led to considerable energy conservation on available battery resources. REFERENCES [1] Adaptive design optimization of wireless sensor networks using genetic algorithms, Informatics Laboratory, Agricultural University of Athens, 75 Iera Odos street, Athens 11855, Greece. [2] Energy-Efficient Communication Protocol for Wireless Microsensor Networks by Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan Massachusetts Institute of Technology Cambridge, MA [3] S. Jin, M. Zhou, A.S. Wu, Sensor network optimization using a genetic algorithm, in: 7th World Multiconference on Systemics, Cybernetics and Informatics, Orlando, FL, [4] D. Turgut, S.K. Das, R. Elmasri, B. Turgut, Optimizing clustering algorithm in mobile ad hoc networks using genetic algorithmic approach, in: IEEE GLOBECOM 02, Taipei, Taiwan, November [5]. V. Mhatre, C. Rosenberg, D. Kofman, R. Mazumdar, N.Shroff, A minimum cost heterogeneous sensor network with a lifetime constraint, IEEE Trans. Mobile Comput. 4 (1) (2005) [6] A. Trigoni, Y. Yao, A. Demers, J. Gehrke, R. Rajaraman,WaveScheduling: energy-efficient data dissemination for sensor networks, in: Proc. Int. Workshop on Data Management for Sensor Networks (DMSN), in conjunction with VLDB, [8] S. Ghiasi, A. Srivastava, X. Yang, M. Sarrafzadeh, Optimal energy aware clustering in sensor networks, Sensors 2 (2002) [9] V. Rodoplu, T.H. Meng, Minimum energy mobile wireless networks, IEEE J. Select. Areas Commun. 17 (8) (1999)