Computational Intelligence based WSN lifetime extension with maximizing the disjoint Set K-Cover
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Abstract
In this work-life enhancement of wireless sensor network has been considered through discovering the large ‘K’ number of disjoint set covers. Each disjoint set sensor covered all the targets. Hence rather than keeping active all the sensor nodes, only activating the sensors of a cover while keeping other covers sensors in sleep mode can increase the life span by K fold approximately. This approach also provides the saving of energy and time by eliminating the processing of redundant information from sensors. Evolutionary computation-based computational intelligence approach, Genetic algorithm, and Differential evolution have been applied over the different configurations of the sensor network. A local operator has also integrated to make the solution feasible. The facility of integer encoding of solution in the Genetic algorithm has given the benefit in finding more number of disjoint covers in compared to Differential evolution which carried the solution exploration fundamentally over continuous value region.
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