  The advent of Internet of Things (IoT) has bring a new era in communication
technology by expanding the current inter-networking services and enabling the
machine-to-machine communication. IoT massive deployments will create the
problem of optimal power allocation. The objective of the optimization problem
is to obtain a feasible solution that minimizes the total power consumption of
the WSN, when the error probability at the fusion center meets certain
criteria. This work studies the optimization of a wireless sensor network (WNS)
at higher dimensions by focusing to the power allocation of decentralized
detection. More specifically, we apply and compare four algorithms designed to
tackle Large scale global optimization (LGSO) problems. These are the memetic
linear population size reduction and semi-parameter adaptation (MLSHADE-SPA),
the contribution-based cooperative coevolution recursive differential grouping
(CBCC-RDG3), the differential grouping with spectral clustering-differential
evolution cooperative coevolution (DGSC-DECC), and the enhanced adaptive
differential evolution (EADE). To the best of the authors knowledge, this is
the first time that LGSO algorithms are applied to the optimal power allocation
problem in IoT networks. We evaluate the algorithms performance in several
different cases by applying them in cases with 300, 600 and 800 dimensions.

Neural and Evolutionary Computing