Simulated annealing and genetic algorithm-based hybrid approach for energy-aware clustered routing in large-range multi-sink wireless sensor networks. (11th September 2020)

• Record Type: Journal Article
• Authors: Kavitha, A.; Velusamy, R. Leela
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 35:Number 2(2020)
• Page Start: 96
• Page End: 116
• Publication Date: 2020-09-11
• Subjects: wireless sensor networks -- WSNs -- clustered routing -- energy aware routing -- genetic algorithm -- GA -- simulated annealing -- multi-sink WSN -- energy efficient routing; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

Abstract

In recent years, the researchers have got progressively more interest in wireless sensor networks (WSNs) as it plays a vital role in numerous applications. WSNs have strict resource constraints such as limited bandwidth, limited energy, limited processing capability, and limited memory. Among them, the most critical one is limited energy. So, energy efficiency has got significant importance in the WSNs. Energy consumption is mainly due to computation and communication between sensor nodes and the base station. Energy consumption for communication is more when compared to computation. Hence it is indispensable to focus on designing an energy-efficient routing for WSN. In this paper, a cluster-based routing using simulated annealing and genetic algorithm-based hybrid (SAGA-H) approach has been proposed. The proposed approach is explained and simulated using MATLAB. Further, the results observed have been compared with existing GA-based approach with respect to network lifetime, the number of packets sent to BS and sink, and average residual energy. From the simulation results, it is observed that the proposed approach outperforms the existing GA-based approach.