Distance Based Energy Optimization through Improved Fitness Function of Genetic Algorithm in Wireless Sensor Network

• Published in: Studies in Informatics and Control Vol. 27; no. 4; pp. 461 - 468
• Main Authors: PANHWAR, Muhammad Aamir, DENG, ZhongLiang, KHUHRO, Sijjad Ali, HAKRO, Dil Nawaz
• Format: Journal Article
• Language: English
• Published: Bucharest National Institute for Research and Development in Informatics 2018
• Subjects: Genetic algorithms; Optimization; Sensors
• ISSN: 1220-1766; 1841-429X; 1841-429X
• DOI: 10.24846/v27i4y201810

For the last few decades, Wireless Sensor Networks (WSNs) has been drawing important considerations due to having application-specific characteristics. These WSNs are usually deployed in one of the following two manners: deterministic or random (ad hoc). In the ad hoc manner, the deployment is mostly subjected to a significant number of limitations such as limited bandwidth, routing failure, storage and computational constraints. The overall performance of the WSNs is determined by a robust routing scheme. Nevertheless, WSNs include prominent application parameters for routing such as energy usage and network longevity. Therefore, the routing scheme is the key element for the longevity and usability of WSNs. In the conventional WSNs, the routing design can be opted for the network longevity optimization, while, assuming all the other objectives to be the limitations are imposed on the optimization problem Genetic Algorithm (GA) performs the small-scale computation and large-scale computation as well. Performance of GA is robust in both small scale and large scale computations. The original GA is assumed with some modifications. In this paper, a GA based optimization in the stationary WSNs with the deployment of multiple sinks is proposed. It is assumed that the sensor nodes route the data towards the nearest sink through the multiple hops communication strategy. In our simulations results: routing is following the multiple hops to the sink by the optimized routing. Moreover, we’ve enhanced the Network lifespan. The proposed technique saved both the route distance through optimization and energy by routing the data through optimized neighbor sensor nodes.