Toward a bio-inspired adaptive spatial clustering approach for IoT applications

Bio-inspired algorithms have demonstrated effective capabilities to solve Wireless Sensor Network (WSN) challenges. As sensors represent a main component in the emergent domain of Internet of Things (IoT), these algorithms are expected to perform also well in this field while adapting to contextual changes and optimizing the use of the limited resources. In this paper, we propose a new firefly-based clustering approach for IoT applications. Our approach includes a micro clustering phase during which Real-World Things (RWTs) compete and self-organize into clusters. These clusters are then polished during a macro-clustering phase where they compete to integrate small neighboring clusters. We extend our approach to allow the IoT clusters to self-adapt by hiring and/or firing RWTs depending on ongoing events and their expected impact on the network and its current deployment area. Initial simulations are showing promising results where the number of clusters tends to stabilize independently from the density of the network and the various communication ranges of RWTs. (c) 2017 Elsevier B.V. All rights reserved.

Publication year:2020

Keywords:Firefly approach, Bio-inspired algorithm, Wireless Sensor Network, Internet of Things, Micro clustering, Macro clustering, Attractiveness, Hiring candidates, Firing candidates

BOF-keylabel:yes

IOF-keylabel:yes

BOF-publication weight:6

CSS-citation score:2

Authors:International

Authors from:Higher Education

Accessibility:Closed