LAUSR

Optimising the overall power in a cluster-assisted internet of things (IoT) network is a challenging problem for emerging IoT applications. In this study, the authors propose a mathematical model for the cluster-assisted IoT network. The cluster-assisted IoT network consists of three types of nodes: IoT nodes, core cluster nodes (CCNs) and base stations (BSs). The objective is to minimise transmission, between IoT nodes (IoTs)-CCNs and CCNs-BSs, and computational power (at CCNs), while satisfying the requirements of communicating nodes. The formulated mathematical model is a integer programming problem. They propose three swarm intelligence-based evolutionary algorithms: (i) a discrete fireworks algorithm (DFWA), (ii) a load-aware DFWA (L-DFWA), and (iii) a hybrid of the L-DFWA and the low-complexity biogeography-based optimisation algorithm to solve the optimisation problem. The proposed algorithms are population-based metaheuristic algorithms. They perform extensive simulations and statistical tests to show the performance of the proposed algorithms when compared with the existing ones.