Abstract
In wireless sensor networks, organizing nodes into clusters, finding routing paths and maintaining the clusters are three critical factors that significantly impact the network lifetime. In this paper, using a chaotic genetic algorithm, a clustering routing protocol combined with these three features called CRCGA is proposed to improve the network energy efficiency and load balancing. In CRCGA, the chaotic genetic algorithm is used to select the best cluster heads (CHs) and to find the optimal routing paths by coding them into a single chromosome simultaneously. Chaotic genetic operators based on a novel fitness function considering minimum energy consumption and load balancing along with new determination conditions make the algorithm converge quickly. Besides, an adaptive round time considering energy and load balancing is presented to maintain the clusters so as to further reduce energy consumption. Simulation results indicate that CRCGA is better than LEACH, GECR, OMPFM and GADA-LEACH in terms of convergence speed, energy efficiency, load balancing, network throughput and lifetime.
Highlights
With the rapid development of information technology, wireless sensor networks (WSNs) are widely used in military, disaster prevention, space exploration and in environmental monitoring, intelligent transportation and smart home by means of various sensors built in nodes [1], [2]
As a result the convergence speed of CRCGA is increased by 7.95%, 15.96% and 29.09% respectively compared to GECR, GADA-Low-energy adaptive clustering hierarchy (LEACH) and OMPFM
The results indicate that adaptive round time can affect the network energy efficiency
Summary
With the rapid development of information technology, wireless sensor networks (WSNs) are widely used in military, disaster prevention, space exploration and in environmental monitoring, intelligent transportation and smart home by means of various sensors built in nodes [1], [2]. Fuzzy logic [9], particle swarm optimization [8], ant colony optimization [20], [26], genetic algorithm [10], [13], [17], [20] are utilized to find the optimal routing paths for each CH so as to achieve balanced energy consumption of CHs and increased network lifetime They can alleviate hot spot problem caused by hop-by-hop routing to some extend as other unequal clustering routing methods by adjusting cluster size, selecting vice CHs, and controlling hop counts and so on [12], [15], [24], [27]. An improved multi-hop clustering routing protocol using a chaotic genetic algorithm (CRCGA) is proposed to minimize the network energy consumption and to balance the network load To this end, a chaotic genetic algorithm is used to find the optimal CHs and routing paths simultaneously, in which chaotic selection, crossover and mutation operations are adopted to avoid local optimum and to improve the convergence speed.
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