OPTIMAL ENERGY CONSUMPTION TO EXTEND THE LIFETIME OF WIRELESS SENSOR NETWORKS

Recent advances in Wireless Sensor Networks (WSNs) enable us to develop minimum energy consumption clustering algorithms. The WSNs can be used for various applications like battle field, disaster areas and military applications. The most important issue in WSNs is the limited battery of sensor nodes. When sensor nodes are placed in a field, it is difficult to replace their batteries. To minimize the energy consumption of nodes, WSNs introduce various protocols and algorithms. Among the techniques, the genetic based energy efficient clustering algorithms are used to minimize the energy consumption of nodes and to extend the network lifetime. Genetic Algorithm (GA) is an adaptive method for search and optimize problems and the genetic process is based on biological organisms. Clustering is an effective way for reducing energy consumption of a sensor nodes as well as the cost of transmission. The proposed genetic algorithm based clustering protocol provide better efficiency in terms of delay, energy consumption. Here we simulated the proposed protocol by using network simulator ns2.34 and compared with other existing protocols.


INTRODUCTION
Wireless Sensor Networks become more important for many applications (Zhu et al., 2009). Figure 1 shows a sensor network having large number of sensor nodes deployed very close to each other. In WSNs, sensor nodes have limited battery, when it is placed in the specific field. It is difficult to replace their batteries or to supply additional energy (Guo et al., 2011). If a node consumes completely its energy, whole network gets disconnected. High population of nodes and minimum node energy consumption are managed by clustering mechanism. Data aggregation is used to eliminate data redundancy and minimize the communication overload.
The position of the sensor nodes does not need to be pre-determined (Akyildiz et al., 2002). Routing algorithms having self-organizing capabilities and consist of different sensors namely seismic, low sampling rate magnetic, thermal, visual, infrared and acoustic sensors. WSNs were used in various applications like military applications, environment monitoring and detecting wild life in a dense forest area. Designing of sensor nodes having some constraints like the small size, low weight, energy consumption, multi functional, communicate with short distance and low price and minimum transmission cost. Most known applications are target tracking, habit monitoring, surveillance and security (Bandyopadhyay and Coyle, 2003). Cluster based approach is useful for environment monitoring. WSN is the combination of wireless communication and environmental perception. It is a special form of wireless ADHOC network. This can construct the network without any infrastructure. Energy efficient routing algorithms are mainly divided in to the following categories.
(1) Reduction of the communication energy consumption by adopting multi-hop transmission strategy.
(2) Balancing the network load by adopting the cluster-based routing protocols and optimizing the location of cluster head. (3) Adopting the sleep and wake-up mechanism to avoid the unnecessary energy consumption. The energy of a node is very limited; the maximum lifetime of WSN plays an important role to design the routing protocols. The efficient routing protocol plays an important role for packet transmission and also considers the Network balance.

GENETIC ALGORITHM
This section provides basic idea on GAs and defines its terms and describes how a simple GA works. Genetic algorithms are search algorithms based on natural selection and recombination process (Guo et al., 2011). They attempt to find a solution to the problem at hand by manipulating a population of candidates. The population is evaluated and the best solutions are selected to reproduce to form the next generation. In number of generations, good traits dominate the total population, resulting in better quality of the solutions. The basic ideas in GAs are Darwinian evolution. We follow Goldberg (1989) and restrict the notion of a building block to the shortest schemata that contributes to the global optimum.

RELATED WORK
Genetic algorithm based clustering protocols Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm for clustering protocol was proposed by Heinzelman et al. (2000). In LEACH the network area is divided in to several clusters and each cluster has a Cluster Head (CH) and cluster members transmit data to their own sink (Guo et al., 2011). Extension of LEACH is known as LEACH-C protocol. Younis and Fahmy (2004) proposed a protocol namely HEED. In TEEN and APTEEN protocols, the network area is divided in to several clusters and among the clusters, one node will be introduced as a head of the cluster and the task of these cluster heads are to collect the data sent from the nodes of the cluster members, mixing the data and forward those data to the sink. LEACH protocol is hierarchical and self-organizing cluster-based approach for monitoring applications based on Time Division Multiple Accesses (TDMA). New CHs are selected after specific time intervals. A node can be reselected only after all the remaining nodes have been elected. Lindsey and Raghavendra (2002) proposed PEGASIS is also an extension of LEACH. This routing protocol is having base station with short distance communication nodes from the geopolitical locations of nodes (Handy et al., 2002). Bandyopadhyay and Coyle (2003) described a multilevel hierarchical clustering algorithm to minimize the energy consumption and obtain using stochastic geometry. Zhu et al. (2009) proposed DEEC based on the ratio between residual energy of each node and the average energy of the network CH is selected. Selection of CH in a network is done based on the initial and residual energy of the nodes. The nodes having high initial and residual energy become a CH. The new protocol that improves set-up phase of LEACH protocol is called LEACH-Centralized (LEACH-C). Central control algorithm is used to form the clusters, may produce better cluster design by assigning the CH nodes throughout the network. Hussain et al. (2007) introduced the Hierarchical Cluster based Routing (HCR) protocol (Zahmatkesh and Yaghmaee, 2012;Matin and Hussain, 2006). All the nodes are self-organized in to clusters and each cluster is managed by a set of associates called head-set and CH selected by Round Robin technique. Hussain et al. (2007) improved the HCR protocol by using a heuristic-based approach. It uses GA to determine the cluster, CH and CH members and the transmission schedule (Hussain et al., 2007). Maraiya et al. (2011) describe Energy Efficient Cluster Head Selection Scheme for Data Aggregation (ECHSSDA) protocol. It is having steady-state phase to improve the life time of the network (Maraiya et al., 2011). Jin and Sonenshein (1994) also used GA for energy optimization in WSN (Zahmatkesh and Yaghmaee, 2012). Formation of predefined independent clusters to reduce the total minimum communication distance. Ferentinos et al. (2005) extended the attempts proposed by Jin and Sonenshein (1994) by improving the GA fitness function (Matin and Hussain, 2006). The work done is based on the optimization properties of genetic algorithms. Cluster based routing protocol to minimize square sum of the distance between the node and the cluster head, or the centrality of the node can be recognized as an important factor to selecting cluster heads (Zhu et al., 2008;Ferentinos et al., 2005).

PROPOSED SYSTEM
The proposed mechanism based an adaptive clustering protocol to determine the optimal thresholding probability for cluster formation with GA in WSNs.

Fig. 2. shows the flow of proposed mechanism
This mechanism consists of set-up and steady-state phases for each round in the protocol and an additional preparation phase before the beginning of the first round. Preparation phase prior to the set-up phase of the first round to gather information about node status, IDs and location and sends it to the BS, which determines the optimal probability to use in the CH selection mechanism. Once the base station received the messages from all nodes, then it searches for an optimal probability of nodes being cluster heads via a genetic algorithm by minimizing the total energy consumption required for completing one round in the sensor field. Figure 2 shows the flow of proposed algorithm:

Algorithm Steps for Genetic Leach
Step is defined such as syntax, creation of topography for nodes and nodes position.
Step 3: Trace object for NS and NAM: Trace object is created here were named as trace and NAM trace. Normally this file act as a history for the running program and in trace definition we define topology for X and Y for area.
Step 4: Create GOD: It is called as General Operational Directory whose main function is to packet forwarding and receiving, en-queue and dequeues. Here it stores the information about all nodes so this makes routing between nodes easier.

JCS
Step 5: Global node setting: Configuration of parameters is done in NAM window and bandwidth of MAC layer is also done and nodes are assigned to corresponding channels.
Step 6: Traffic model: For movement of nodes C.P and S.C file must be loaded. This file is already defined in TCL but for the movement of nodes at this stage it is loaded using puts command.
Step 7: Simulation ends: The end of stimulation time for nodes to stop communication is allocated here.
Step 8: Defining the graph values and execution of graph: Defining of graph is done here. X value and Y value for (X, Y) axis is defined according to movement of nodes. AWK format is used for generation of graph.
Step 9: Printing XY values: The values of trace fd, number of nodes, routing, CP, seed, propagation and antenna is printed by using puts command then the nodes start the simulation and command is created for running the program. Figure 3 shows the observation from the above simulation that the ch4 senses the data and then it sends the sensed data to its neighboring cluster head ch3 and then the data is being sent to gateway through CH3 Figure 4 shows all the data which are collected by the nodes are being delivered to the gateway and the gateway sends the aggregated data to the sink i.e., the base station in between this process the nodes are moved from one cluster to another cluster. Figure 5 shows the comparison graph between the earlier method and the proposed method is being done by comparing the packet drop during the transmission of data the proposed method performs better than the earlier method by reducing the packet loss in network. Figure 6 shows the packet delivery ratio and the speed of the node movements were aggregated in the earlier method the speed and the packet delivery ratio decreases step by step and then it goes to minimum speed but in the proposed method there is slight variation compared to the earlier method and achieves a steady speed and constant packet delivery. Figure 7 Shows the end to end delay in the earlier method is too high so the packet loss in the network will be high this situation is been handled carefully and the end to end delay is minimized in the proposed algorithm by reducing it. The energy of the nodes is maintained and the lifetime of the wireless sensor network is extended.    Figure 8 shows energy consumed by proposed mechanism is very less compared to the other protocol for 50 sec.

CONCLUSION
In proposed mechanism, to minimize the energy consumption of nodes introduced the genetic based energy efficient clustering algorithms. This Genetic algorithm is an adaptive method to search and optimize the energy consumption. The result shows Clustering is an effective way for reducing energy consumption of a sensor nodes as well as the cost of transmission. The proposed genetic algorithm based clustering protocol provide better efficiency in terms of delay, packet drop, packet delivery ratio.