Nallusamy, International Journal of Advanced Engineering Technology E-ISSN

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1 Research Paper THREE STAGE GA BASED HYBRID ROUTING ALGORITHM FOR SOLAR POWERED WIRELESS SENSOR NETWORKS R. Nallusamy Address for Correspondence Professor, Department of Computer Science and Engineering, Nandha College of Technology, Erode, India ABSTRACT Wireless Sensor Networks (WSNs) play an important role in monitoring and collecting data from difficult geographical terrains. The inherent constraints such as limited battery life, memory and less processing capability of the sensors make the routing of WSNs a tedious task. This paper proposes the utilization of solar energy with a view to extend the life of the networks in WSNs perspective. The existing routing algorithms in WSNs are quite complex in nature and many of them use data-centric based concept. Almost all the algorithms consume considerable amount of time for data aggregation. The route convergence time is also a critical factor in WSNs due to energy constraints existing in them. To avoid such types of drawbacks, this paper proposes a simple location based hierarchical, straightforward point-to-point SP routing for solar powered WSNs. By using energy efficient clustering and routing concepts, the energy consumption and computational overhead will be considerably reduced. After deploying the sensors in the field, the nodes can be grouped into small sized clusters. The routing overhead will further be reduced with the grouping of sensors into small sized network topologies. One node will act as a Cluster Head (CH) for each cluster. The nodes can communicate through CH, if any event occurs. A three phase Genetic Algorithm (GA) with k-means clustering is proposed for clustering and routing in WSNs. After clustering is over, initial feasible routes are generated for each cluster by using a new algorithm called Basic Solution Algorithm. The Shortest Path (SP) route from source node to CH within the cluster is calculated using GA. The three phase GA also provides efficient solution for routing in WSNs with a fast convergence rate. KEYWORDS: Wireless sensor networks, Genetic algorithm, k-means clustering, Solar energy and routing in sensor networks. INTRODUCTION The Shortest Path based network problems such as point-to-point SP problem are complex and Nondeterministic Polynomial-time hard (NP-hard) combinatorial optimization problems. These problems are typically associated with complex and dynamic systems with uncertain parameters. It is difficult and time-consuming to derive mathematical formulation based solutions for such type of problems and therefore, they are considered to be infeasible. The nature inspired algorithms such as Artificial Intelligence (AI) heuristics and soft computing based concepts are suitable to such type of problems. Shortest Path Problem: Many optimization problems amount to finding a sub-graph of certain type with minimum weight in a weighted graph. One such is the Shortest Path Problem (SPP). For example, from a road map network connecting various towns, determining the shortest route between two specified towns in the network, one must find, in a weighted graph, a path of minimum weight connecting two vertices s and t; the weights represent distances by road between directly-linked towns, and are therefore, non-negative. LITERATURE REVIEW Genetic Algorithms GAs imitate the concepts of natural selection by a process of randomized data exchange between chromosomes. By using this concept, they are able to solve a range of complex problems which cannot be solved easily and efficiently by other approaches. Because GAs were inspired by the behavior of natural systems, the terminology used to describe them is a mix from both biological and computer fields (Goldberg 2006). First step in GA is encoding in the form of chromosomes. After encoding, GA uses the operations such as selection, cross over and mutation. Wireless Sensor Networks A sensor network is a network of a large number of sensor nodes which are densely deployed either inside the field or very close to it. A WSN is a network of spatially distributed autonomous wireless computing devices to cooperatively monitor physical or environmental conditions using sensors. WSNs are used to collect data from physically challenging environments. The information of events can be detected, collected, processed and sent to a control room or sink by the sensors deployed in WSNs. The tiny nodes in WSNs are equipped with substantial processing capabilities of combining the data with adjacent nodes, compressing the data, intelligent gathering and processing of sensed data, understanding and controlling the processes inherent to the system. A major technical challenge for WSNs, however, lies in the node energy constraint and its limited computing resources (Jeong et al 2008). Energy consumption is a dominant factor in the design of large-scale sensor networks. Since these constraints are highly specific for sensor networks, new improved power sources, wireless ad-hoc networking and efficient routing techniques are required. By providing improved power sources such as renewable (solar) energy, it would solve many of the aforementioned constraints (Jiang et al 2005, Kansal and Srivastava 2005). Solar Powered Wireless Sensor Networks Voigt et al (2003) proposed to utilize solar power in WSNs establishing a topology where some nodes can receive and transmit packets without consuming the limited battery resources dynamically. The solar cells can be utilized to power the sensors as well as to charge the batteries during node idle periods. The stored battery energy can be used to power the nodes during the time periods when sunlight is unavailable. Polastre et al (2005) presented an extremely long duration solar power subsystem for the most recent wireless sensor network mote Telos. The solar energy harvesting WSN called Heliomote was proposed by Lin et al (2005). A battery-less wireless sensor network system that uses the combination of an electric double layer capacitor, equipped with a small solar cell as its energy sourcewas proposed by Minami et al (2005). The authors, however, have not reported any results of a long-term deployment. Taneja et al (2008) provided an empirical and mathematical analysis of two leading competitors (Heliomote and Trio) and developed taxonomy for

2 the micro-solar design space, identifying key components, design choices, interactions, difficulties and trade-offs. Routing Challenges in WSNs Due to the following reasons, routing in WSNs is very challenging compared to other wireless networks like MANET. First, due to the huge number of sensor nodes, it is very difficult to use a global addressing scheme for the sensor nodes as the overhead of address maintenance is high. Therefore, conventional Internet Protocol (IP) based protocols may not be suitable for WSNs.Second, in contrast to other communication networks, in WSNs, almost all applications use the flow of sensed data from many sensor nodes to a single Base Station (BS).Third, all the sensor nodes have the major constraints in terms of energy, processing and storage capacities. Fourth, in most of the WSN applications, sensor nodes are generally stationary after deployment except for, may be, a few mobile nodes. Fifth, location awareness of the sensor nodes is essential in WSNs since data collection is normally based on the location. Due to the aforementioned constraints, various algorithms have been proposed for the routing problem in WSNs (Al-Karaki and Kamal 2004). Routing Protocols in WSNs Recent advancements in WSN have led to many new routing protocols specifically designed for sensor networks where efficient energy utilization is an essential consideration (Akkaya and Younis 2005, 2005a). The various routing algorithms are broadly classified as (Al-Karaki and Kamal 2004), Address Centric (AC) Protocol: Each sensor independently sends data along the shortest path to Cluster Head. Data Centric (DC) Protocol: The sensors send data to the sink, but routing sensors en-route look at the content of the data and perform some form of aggregation function on the data originating at multiple sources to remove redundancy. Clustering in Solar Powered Wireless Sensor Networks Clustering is required to reduce the routing complexity and overhead and for effective energy efficient communication between sensors. From the literature, the various existing clustering algorithms for WSNs are: 1) Linked cluster algorithm 2) Adaptive clustering 3) Hierarchical control clustering 4) Energy efficient hierarchical clustering 5) Hybrid energy-efficient distributed clustering and 6) Attribute-based clustering (Abbasi and Younis 2007). Voigt et al (2004) proposed to extend LEACH, a well-known cluster-based protocol for sensor networks to become solar-aware. The simulation results show that making LEACH solar-aware significantly extends the lifetime of sensor networks. The visual-based wireless sensor networks have been implemented in several different fields such as environment monitoring, military applications, and robotic applications (Fan et al 2007). A solar cell recharging model and a layered clustering model were proposed by them to deal with the restricted energy consumption under the consideration of visual quality. Routing in Solar Powered Wireless Sensor Networks Ye et al (2003) proposed an energy conserving protocol called Probing Environment and Adaptive Sleeping (PEAS). PEAS keeps only necessary nodes active and puts the rest into sleepy mode to conserve energy. The solar aware routing allows the routing only through solar powered nodes (Voigt et al 2003). This will save the energy of the battery powered nodes. Theyproposed two protocols to perform solar aware routing. The results show that the first protocol is more suitable for small WSNs while the second protocol performs better on larger WSNs. Corke et al (2007) discussed hardware design principles for long term solar powered WSNs and straightforward non-energy aware protocols. They presented data from a long-term deployment that illustrated the use of solar energy and rechargeable batteries to achieve 24x7 operations for over two years. Lattanzi et al (2007) discussed the problem of optimal routing for energy harvesting WSNs. They provided a concept for assessing the energy efficiency of routing algorithms of WSNs whose nodes drain power from the environment. Al-Karaki et al (2009) proposed heuristic algorithms for routing in WSNs. They presented Grid-based Routing and Aggregator Selection Scheme (GRASS) with exact as well as heuristic approaches to find the minimum number of aggregation points while routing data to the BS such that the network lifetime is maximized. Yang et al (2009) formulated the problem of energy efficient routing for detection in a WSN under the Neyman-Pearson detection criterion, which related to both the energy consumption and detection performance in routing. Matrouk and Landfeldt (2009) proposed Routing based on Energy Temperature Transformation (RETT), RETT-gen, a scalable energy-efficient clustering and routing protocol designed for WSNs. Shortest Path Routing using Genetic Algorithms Yussof et al (2009) proposed a parallel GA for solving the SPR problem. This algorithm is developed and run on Message Passing Interface (MPI) cluster. Based on experimental result, there is a tradeoff between computation time and the result accuracy. However, for the same level of accuracy, the parallel algorithm can perform much faster compared to its non-parallel counterpart. Cheng and Yang (2009), Yang et al (2010) proposed GAs with immigrants and memory schemes to solve the dynamic SP routing problem in MANET. The algorithm adopts topological changes of the network and produces efficient solutions. There are several GAs that address different kinds of routing problems. Those approaches are beyond the scope of this paper. THREE STAGE ROUTING IN SOLAR POWERED WSNs As per literature, so far, the GA and other nature inspired algorithms based SP routing for WSNs have been studied only by a few researchers. The algorithm proposed by Al-Karaki et al (2009) is the only GA based routing algorithm in WSNs. They have applied GA based aggregation and routing algorithm concepts for WSN. The GA has been used in combination with other algorithms and therefore computation complexity is more. The battery life of the sensors is directly linked with their computation time. To provide energy for sensors from environment, solar powered WSN is the suitable solution. The majority of the algorithms are not concentrated on the convergence time for the

3 optimized route calculation. For real time communications such as battle field monitoring an optimal SP has to be computed within a very short period of time i.e. in milli/micro seconds. The existing algorithms do not provide these kinds of real time requirements due to their complex nature. A simple clustering and SP routing within the cluster will save the route convergence time as well as computation time. Another advantage is that these algorithms are not sensitive to network size and topology. When sensor nodes are organized in clusters, they could use either single hop or multi-hop mode of communication to send their data to their respective cluster heads (Mhatre and Rosenberg 2004). Multihop communication is expected to consume less power than the traditional single hop communication. Building a straightforward SP in WSN not only avoids wastage of energy, but also incurs less interference in other transmission when fewer nodes are involved in the transmission. Therefore, this section assumes a simple fixed solar powered WSN and straightforward multi-hop SP routing to find the energy efficient routing in WSNs. Proposed Routing Algorithm for WSNs This paper proposes three stage intelligent techniques for the routing in solar powered WSNs. This paper proposes a new simple location based algorithm for routing without aggregation. The proposed routing algorithm is based on the hierarchical routing concepts in WSNs. All the sensor nodes deployed in the field can collect their positional information and the distance of their neighbors. After collecting the positional information of sensors, the sensor nodes are grouped into a number of small size clusters by using k-means clustering algorithm. For each cluster, a CH is selected randomly. Then the heuristic initial solution based GA is applied for routing between source sensor and the CH in WSN. Grouping the sensors into small sized clusters will make the routing easy and also save the energy of the sensors due to less number of hops and reduced computation overhead. Now topology of each cluster is created and information of the nodes and various link weights are created. Within each cluster, GA based SP computing technique is applied. Basic Solution Algorithm based initial solutions are created and these will be provided to GA as initial population. Then standard GA operations are used to find the optimized SP route between source and destination nodes (cluster head) of WSN. Procedure of the Proposed Algorithm Step1: Initialize information of WSN nodes and their locations. Step2: Apply k-means clustering and create k number of small sized clusters. For each cluster do the following. Step 3: Initialize information table of network topology for each cluster. Step 4: Do genetic encoding and generate the initial populations using basic solution algorithm according to the topology information table with source and destination nodes. Step 5: Step 6: Calculate the fitness of the initial population. Select individual chromosomes by using proposed fitness assignment. Step 7: Perform crossover between selected chromosomes according to the proposed crossover method. Step 8: Step 9: Step 10: Step 11: Analyze the individual chromosome after crossover operation, remove infeasible chromosomes by using a repair function. Perform mutation according to the proposed mutation method. Generate new populations. If stopping criteria is met, output result, and the algorithm would be finished, otherwise, go to step 5. ROUTING IN WSN USING PROPOSED THREE PHASE GA Assumptions For calculation purpose, it is assumed in this paper that after clustering, the clusters contain fixed number of nodes such as 20, 25, 30, 35, 40, 45 or 50. The links are assumed to be symmetrical. To demonstrate the algorithm, only a single cluster is taken up for the computation of SP route between source sensor and CH. The solar energy based sensors are assumed to be deployed in WSNs. To demonstrate the algorithms in simple manner, two dimensional (2D) WSNs are assumed. k-means Clustering Clustering of the sensor nodes makes the route calculations much simpler. The search space and the computation time for the solution get increased as the number of sensor nodes (N) increases. In order to reduce the mathematical complexity, N should be reduced and this is achieved by clustering. For each cluster one node will act as a CH. The nodes can communicate to the CH and the CH can communicate to the BS as well as to other CHs. Here, the sensor nodes are clustered into small groups by using k-means clustering as explained in the previous sections. Figure 1 shows the 2D diagram of an example sensor network with randomly deployed sensors with six clusters after clustering the sensors using k-means clustering algorithm. Genetic Algorithm Genetic Encoding: After clustering, the routing has to be performed within each cluster. Encoding is the most fundamental operation in GA. A chromosome of the proposed GA consists of sequences of positive integers that represent the identification number of sensors through which a routing path passes from source to destination based on network topological information table of the network. Figure 2 shows the simple example for genetic encoding. Each locus (gene) of the chromosome represents an order of a sensor in a routing path. S and D represent the source and destination sensors respectively. N 1, N 2, N 3 and N 4 represent the intermediate sensor nodes between S and D. Initial Sub-population: Heuristic initialization is proposed in this paper for initial population. A large initial population size is quite useful, but it demands excessive costs in terms of both memory and time. The modified Shrink Wrap Algorithm (SWA) is used here and named as Basic Solution Algorithm(Ganesh and Narendran 2007a, 2007b). Based on the network topological information table, the initial populations are created based on feasible valid paths between source and destination, where the chromosomes consist of a sequence of sensor nodes that are in the path from sender to CH.

4 Figure 1 Example WSN with Clusters The algorithm used to generate the initial paths is as follows: 1. Start from the source node. 2. Choose a sensor node that has the lowest angle with a higher probability from the sensor nodes that are connected to the current source node. If two nodes have same angle then select a node by using the shortest distance. If only Euclidean distances are given then select a node that has the shortest distance. 3. If the selected sensor node has not been visited before, then mark that sensor node as the next node in the path. Otherwise, find another sensor node. 4. If all the neighboring sensor nodes have been already visited, go back to step Otherwise, repeat from step 2 by making the next node as the current source node. 6. Repeat the procedure until the CH is found. Fitness Function: The fitness function value of a particular chromosome shows that how good the solution is for that chromosome. Based on this information a set of chromosomes that will participate in the creation of the next generation of solution will be selected (Ahn and Ramakrishna 2002, Yussof et al 2009, 2009a). The fitness function in the SP routing problem of a WSN is obvious because the SP computation amounts to finding the minimal cost route between source and CH. The fitness function should completely reflect the objective function given in Equation (1). Therefore, the fitness function is defined as follows: 1 (1) f = i x i -1 C s i (q),s i (q +1) q=1 where f i represents the fitness function value of the i- th chromosome in the sub-population, x i is the length Figure 2 Encoding of the i-th chromosome, s i (q) indicates the gene (sensor node) of the q-th locus in the i-th chromosome, s i (q+1) indicates gene of the (q+1)th position, and C is the edge cost between sensor nodes. This would give a higher fitness function value for shorter routes. Selection: The reproduction or selection operation is used to select the chromosomes for cross over operation. From the literature, among the various selection schemes, the tournament selection method without replacement is perceived as an attempt to keep the selection noise as low as possible. Typically, the pairwise tournament is used. That is the tournament size is two. The selection pressure increases as the tournament size increases. Therefore, the pairwise tournament selection without replacement is employed for the proposed GA. Crossover: To generate better child chromosomes, crossover operation is used. Two parent chromosomes are selected based on the selection operation described above. Crossover is performed on the two selected parent chromosomes. Physically, in the SP routing problem of WSNs, crossover can be done by swapping each partial route of two selected parent chromosomes in such a manner that the offspring produced by the crossover generates two child chromosomes. The route connects the source node to an intermediate node is the one partial route and the route between the intermediate node to the destination node is the another partial route. The crossover between two high fitness function value chromosomes picked by the selection gives more probability of generating offspring having better route quality. Here, a single point crossover scheme with multiple crossover points is proposed for the GA.In the proposed scheme, two parent chromosomes selected for crossover operation must have at least one common sensor node except for source and destination nodes. The common node is the crossover point. The locus (position) of the

5 common genes in the parent chromosomes may be different. In other words, the crossover does not depend on the position of sensor nodes in routing paths. If more than one common node exists, one of them will be randomly chosen with equal probability. The chosen node is called the crossover point. Mutation: After crossover, the population undergoes mutation. Each chromosome produced by the crossover operation has a small chance to be mutated based on the mutation probability, ρ. Most of the researchers proposed only a small probability for mutation (Ahn 2006). For all the experiments, the value for ρ is set to For each chromosome that is selected for mutation based on the mutation probability, any one of the intermediate nodes is selected randomly as a mutation point. Once the mutation point is selected, by performing mutation a new child chromosome will be generated. The nodes placed in between the mutation point and the CH are changed. By using the WSN topological information table, one of the nodes, connected directly to the mutation point, is selected randomly as the first node of the new partial-route. To avoid repetition of sensor nodes, the nodes already placed in the partial route between source node and the mutation point should be deleted from the database. This will avoid inclusion of the same node twice in the new child chromosome. The upper partial route between the source node and the mutation point will be retained in the new child chromosome after mutation. Repair Function: As mentioned earlier, during crossover operation there is a possibility for generation of infeasible chromosomes that can violate the constraints, forming loops in the routes. It must be noted that none of the chromosomes of the initial population or after the mutation is infeasible because when once a node is chosen, it is excluded from the candidate nodes forming the rest of the path. A simple repair function is used in the proposed GA. The nodes placed within the loops are removed from the chromosomes to create viable routes. Stopping Criterion: In GA, various termination conditions can be used to stop the algorithm. The GA may be terminated after a fixed number of iterations or if the fitness value of the entire population converges to the same value. In this proposed algorithm, the GA is terminated if it reaches the fixed number of iterations. IMPLEMENTATION, RESULTS AND DISCUSSION After clustering the sensor nodes, the SP routing algorithm is applied for each cluster. Initially, a cluster with 20 nodes is assumed for the purpose of route calculation using the proposed GA. The results of the proposed GA are compared with Ahn s and Munemoto s GAs and Dijkstra s algorithms by using computer simulations. All the simulations were performed with MATLAB 7.0 on Pentium IV processor with 2.8 GHz speed and 1 GB RAM. Initial population creation, selection, crossover, mutation and repair function concepts are used as explained in the previous sections. The algorithm is terminated when the number of iterations reached or all the chromosomes have converged to the same solution. To demonstrate the performance of the proposed three phase GA, a weighted wireless sensor network topology with 20 nodes is assumed as shown in Figure 3.For all the simulations, the population size is taken to be the same as the number of nodes in the cluster of the WSN. Figure 3 shows the shortest path in bold line found by the proposed GA for the given source destination pair. The weight of the shortest path is 181. Figure 3: Given Network with Shortest Path To compare the performance of proposed and the existing algorithms, various randomly selected network topologies are used. The link weights are assigned randomly. The link weights are normalized (0,1) and for nonexistent links very high link weights (for example 1000) are assumed. Figure 4 compares the objective function values returned by the proposed and other existing algorithms. The figure shows that the proposed GA converges to stable state faster than other algorithms. The heuristic initialization based initial population helps the proposed GA to reach the stable state faster. The Figure 4 also shows that the route generated by the hybrid GA coincides with the route generated by Dijkstra s algorithm. The results show that the performance of the proposed GA is better than that of other algorithms. Figure 4: Comparison of Results (for 25 nodes) To analyze the convergence performance of the proposed and existing algorithms further with different WSN topologies, networks with nodes, and randomly assigned normalized link costs were analyzed. The proposed algorithm performs well to larger size WSNs also. Next, the convergence time of the proposed GA is compared with that of the standard GA and Dijkstra s algorithm. Convergence performance of the proposed algorithm is analyzed in terms of the average time required to reach the stable and viable solutions. The proposed GA and the existing algorithms are compared based on the total algorithm execution time required to find a viable solution of the same average quality. The results are shown in Figure 5. From the figure, it is easy to understand that the convergence rate of the proposed algorithm is higher than that of the standard GA for various size WSN topologies.

6 The convergence performance of the proposed GA and the Dijkstra s algorithm is given in Figure 6. The figure shows that the computation time of the proposed GA does not get increased significantly with increase in WSN size. Figure 5: Convergence Diagram for Proposed GA Convergence time in sec. Dijkstra Propo Number of nodes Figure 6: Convergence Diagram for Dijkstra and Proposed GA The formula for energy consumption of a cluster in WSN is given by Heinzelman et al (2000). Energy consumption of transmitting data: E s = E el * v + Єamp * v * d 2 (2) where, vis the amount of data to be transmitted (bit), d is the distance between source and CH, E el is the energy consumption to do data transmission (nj/bit), Єamp is the energy consumption constant used to expand radio coverage (nj/(bit*m 2 )). The energy consumption for receiving data and the energy consumption for route computation for a cluster are assumed as E R and E C respectively. Total energy consumption (E) of each cluster is given in Equation (3). E = ΣE R + ΣE S + ΣE C (3) From Equation (2), it is easy to understand that the power consumption is directly proportional to the square of the distance between the source and the sink. The proposed GA is concentrated on finding the shortest distance route between source and CH for each cluster. The route computation time is also reduced in the proposed GA. Therefore, the proposed GA helps to reduce the energy consumption of the sensors in the clusters. CONCLUSION A three stage hybrid GA for solving the SP routing problem has been demonstrated. By using efficient clustering and routing concepts the battery and computation overhead will be reduced. The simple clustering like k-means clustering has proved to be effective as it is able to group the nodes into clusters in an optimal manner with reduced convergence time. The BSA provides the initial population with variable length chromosomes to the proposed GA. By using the heuristic based initial population, the GA operators generate the final route for the given WSN. Simulation results indicate that the proposed algorithm is insensitive to changes in WSN topologies in respect of both route optimality and convergence time.the convergence performance of the proposed GA is better than those of the existing GA and Dijkstra s algorithm. GA routing introduces the concept of using sub-optimal paths randomly at times to reduce and distribute energy consumption in routing thus increasing the lifetime of the network. The current routing protocols in WSNs optimize only the limited capabilities of the nodes and the application specific nature of the networks. But they do not consider security. It is important to consider the security aspect of the WSN as these protocols have not been designed with the view of security as goal. 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