Abstract
Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput.
Highlights
The wireless sensor network (WSN) contains large number of sensor nodes monitored are controlled by the base station (BS)
To improve the network longevity clustering model is constructed in heterogeneous WSN (HWSN) network as well as genetic algorithm-based optimized clustering (GAOC) protocol is introduced with multiple data sinks model called
Evolutionary algorithms has many classification, genetic algorithm is one among algorithms manyselection classification, geneticItalgorithm onemain among them themEvolutionary which is inspired by thehas natural approach
Summary
The wireless sensor network (WSN) contains large number of sensor nodes monitored are controlled by the base station (BS). A major process behind the sensor nodes are data transmission, sensing the devices and computation with limited consumption of power factor as well as it is non-rechargeable in nature. Due to these drawbacks there is still a research gap to increase the energy efficiency and to augment network lifetime [1]. In this paper CPU/FPGA heterogeneous architecture scheduling problem is studied and the author proposed two genetic algorithm based approaches to overcome this issue which are the MPSoC and makespan (minimize the schedule length) [27]. Hybrid crossover technique is the core idea of the algorithm which is mainly used to reduce the computation cost [30]
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