Abstract
WSNs have been used in different sectors of applications such as industrial, environmental, and social due to the progress of technology and the necessity. Because the network's sensors are restricted by battery power, network operations are important. The life extension of a wireless sensor network has been explored in this study by locating a large number of disjoint set coverings. All of the targets were covered by each separate group of sensors. Instead of maintaining all sensor nodes in operation, the only way to prolong the service life by about K times is to use the sensors of one cover while the sensors of the other covers are in sleep mode. This approach saves both energy and time by processing useful data and reducing duplicate data coming from different sensors in a region. Different configurations of sensor networks have been tested using an evolutionary computation-based computer intelligence technique, as well as a genetic algorithm and differential evolution. To make the solution possible, a local operator has been incorporated. With integer encoding of solutions in genetic algorithm performs better than differential evolution in finding a good number of disjoint set covers. Over a continuous search space, DE is highly efficient, but its efficiency has been hampered by integer transformation.
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Shanthi, D.L. (2022). Maximization of Disjoint K-cover Using Computation Intelligence to Improve WSN Lifetime. In: Raj, J.S., Shi, Y., Pelusi, D., Balas, V.E. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-19-2894-9_17
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