An area coverage algorithm for wireless sensor networks based on differential evolution

• Published in: International journal of distributed sensor networks Vol. 14; no. 8; p. 155014771879673
• Main Authors: Qin, Ning-ning, Chen, Jia-le
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.08.2018; Wiley
• Subjects: differential evolution; Wireless sensor networks; node subset of coverage; minimal cost; maximal lifetime
• ISSN: 1550-1477; 1550-1329; 1550-1477
• DOI: 10.1177/1550147718796734

Lifetime requirements and coverage demands are emphasized in wireless sensor networks. An area coverage algorithm based on differential evolution is developed in this study to obtain a given coverage ratio ε . The proposed algorithm maximizes the lifetime of wireless sensor networks to monitor the area of interest. To this end, we translate continuous area coverage into classical discrete point coverage, so that the optimization process can be realized by wireless sensor networks. Based on maintaining the ε-coverage performance, area coverage algorithm based on differential evolution takes the minimal energy as optimization objective. In area coverage algorithm based on differential evolution, binary differential evolution is redeveloped to search for an improved node subset and thus meet the coverage demand. Taking into account that the results of binary differential evolution are depended on the initial value, the resulting individual is not an absolutely perfect node subset. A compensation strategy is provided to avoid unbalanced energy consumption for the obtained node subset by introducing the positive and negative utility ratios. Under the helps of those ratios and compensation strategy, the resulting node subset can be added additional nodes to remedy insufficient coverage, and redundancy active nodes can be pushed into sleep state. Furthermore, balance and residual energy are considered in area coverage algorithm based on differential evolution, which can expand the scope of population exploration and accelerate convergence. Experimental results show that area coverage algorithm based on differential evolution possesses high energy and computation efficiencies and provides 90% network coverage.