Grey Wolf Optimizer-based Decentralized Service Discovery in the Internet of Things Applications

Abstract

Introduction: The Internet of Things (IoT) has emerged as a significant technology in recent years, wherein each object is equipped with sensors and applications that provide functionality through services. Due to the increasing benefits of heterogeneous objects with constrained resources in high environments, traditional service discovery approaches become impractical for dynamic IoT networks. Therefore, service discovery poses a considerable challenge for the Internet of Things. Methods: This paper introduces a novel decentralized discovery algorithm based on the Grey Wolf Optimizer (GWO) for IoT services. GWO is a recent metaheuristic in swarm intelligence designed to solve combinatorial optimization problems. Results: simulation results indicate that GWO achieves high discovery success with minimal steps required for service discovery. Conclusion: Our approach maintains its performance and exhibits good scalability as the number of objects increases in the decentralized approach for IoT.