Optimising the power using firework‐based evolutionary algorithms for emerging IoT applications. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Ali, Hafiz Munsub; Ejaz, Waleed; Lee, Daniel C.; Khater, Ismail M.
• Is Part Of: IET networks. Volume 8:Issue 1(2019)
• Page Start: 15
• Page End: 31
• Publication Date: 2019-01-01
• Subjects: swarm intelligence -- particle swarm optimisation -- integer programming -- genetic algorithms -- Internet of Things -- statistical testing -- power aware computing; statistical tests -- natural phenomenon -- load‐aware DFWA -- L‐DFWA -- cluster‐assisted internet of things network -- firework‐based evolutionary algorithms -- population‐based metaheuristic algorithms -- optimisation problem -- low‐complexity biogeography‐based optimisation algorithm -- discrete fireworks algorithm -- swarm intelligence‐based evolutionary algorithms -- nonbinary integer programming problem -- formulated mathematical model -- communicating nodes -- computational power -- CCNs–BSs -- IoT nodes–CCNs -- base stations -- cluster‐assisted IoT network -- IoT applications; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2018.5041
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract : 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.