Galactic swarm‐improved whale optimization algorithm‐based resource management in Internet of things

Abstract

SummaryInternet of things (IoT) is the reliable alternative among the networking technologies for achieving high performance with improved potentialities of flexible adoptions, data exchanges, resource allocations, and system controls. The existing IoT suffers from the limitations of resource allocation ranging between complicated service provisioning environments and networking service quality mismatching. IoT environment needs to handle the resource allocation issue for attaining satisfactory degree of quality of experience (QoE) that maps multiple resources to gateways. This problem of mapping multiple resources to gateways belongs to the class of NP‐complete problem and can be ideally solved through intelligent metaheuristic algorithms. In this paper, galactic swarm‐improved whale optimization algorithm‐based resource management (GSIWOA‐RM) scheme is proposed for efficient mapping of multiple resources to gateways in IoT. It specifically utilizes galactic swarm optimization algorithm (GSOA) for establishing global control with inherited multiple adaptive cycles of exploitation and exploration during resource allocation. It further prevents the limitations of early convergence in the exploitation phase by utilizing the evolution‐based whale optimization algorithm (WOA) that aids in better balance between exploitation and exploration. The simulation results of the proposed GSIWOA‐RM scheme confirm a better throughput of 28.32% with minimized delay and energy consumptions of 19.24% and 21.82%, when compared to the baseline resource management schemes.