Minimised Jaya Algorithm-based Structure optimisation for Heterogeneous Wireless Sensor Networks

Extensive research has been carried out in regard to homogenous Wireless Sensor Networks, subject to various constraints such as the available energy resources for sensor nodes. However, research for optimum cluster head selection and structure optimisation for heterogenous WSN has been quite wanting, especially considering the fact that heterogenous WSNs are more realistic compared to their homogenous counterparts and should be looked at with a greater sense of curiosity. It is found that despite making the fitness function converge, the genetic algorithm fails to optimise it fully, which is achieved by the proposed algorithm that is a modified version of the Jaya algorithm. The algorithm used in this study is successfully able to improve the network longevity by 52.979% and stability period by 43.149%. This is but one criterion of evaluation, however, it performs better in other criteria as well, like energy conservation, fitness convergence etc.