The main intent of this paper is to implement the stability-aware energy-efficient clustering protocol in WSN.This paper plans to derive a multi-objective function with the constraints like energy,distance,delay,stabi...The main intent of this paper is to implement the stability-aware energy-efficient clustering protocol in WSN.This paper plans to derive a multi-objective function with the constraints like energy,distance,delay,stability period,and intents to attain the objective by developing a new well-performing meta-heuristic algorithm called Opposition-based Elephant Herding Optimisa-tion(O-EHO).The objective function diminishes the energy consumption of sensor nodes by optimum selection of cluster heads that leads to maintain the energy balance between the nor-mal nodes.In this way,there is a remarkable enhancement in the performance parameters such as throughput,stability period,and network lifetime.It is proved that the network lifetime is enhanced by the stability period and thus it is considered as the most significant parameter.The experimental analysis proves the competitive performance of the proposed model over other heuristic methods.展开更多
文摘The main intent of this paper is to implement the stability-aware energy-efficient clustering protocol in WSN.This paper plans to derive a multi-objective function with the constraints like energy,distance,delay,stability period,and intents to attain the objective by developing a new well-performing meta-heuristic algorithm called Opposition-based Elephant Herding Optimisa-tion(O-EHO).The objective function diminishes the energy consumption of sensor nodes by optimum selection of cluster heads that leads to maintain the energy balance between the nor-mal nodes.In this way,there is a remarkable enhancement in the performance parameters such as throughput,stability period,and network lifetime.It is proved that the network lifetime is enhanced by the stability period and thus it is considered as the most significant parameter.The experimental analysis proves the competitive performance of the proposed model over other heuristic methods.
