Distance based Modelling and Optimization of Wireless Sensor Network Energy Consumption with Adaptive Clustering using Genetic Algorithm

Abstract

Wireless sensor network (WSN), as one of the most important technologies due to its wide variety of applications, consists of various densely deployed sensor nodes inside or very near to application area. WSNs work with several limitations related to resources like battery power, bandwidth, memory and etc. and hence node goes out of energy where it's impossible to recharge or replace the battery of nodes. It has been proved that, long communication distance between sensor nodes and base station (BS) can drain the energy. This paper proposes an approach to optimize the WSN energy consumption of nodes via optimizing the number of clusters that minimizes the transmission distance, for maximizing network lifetime. A genetic algorithm is proposed for sensor nodes clustering to find the optimal number of cluster heads that reduces the energy consumption. The proposed solution considers the communication distance, as a main factor, which is formulated as an objective function to be optimized for the mathematical model constrained by the number of cluster heads. The results were conducted using the proposed GA for different instances with different settings such as the population size, number of cluster-heads, and number of generations. The experimental results show that the algorithm achieved good results and it converges toward the optimal solution through the generations for the different instances. Moreover, the proposed approach reduces the energy consumption more efficient when compared with hierarchical clustering algorithm on minimizing the communicating distance. It is recommended to scale the algorithm to consider a trade-off between the total intra-cluster communication distance and total distance of cluster-heads to BS as a future work