Optimizing fog device deployment for maximal network connectivity and edge coverage using metaheuristic algorithm

Abstract

Fog computing emerged to address the limitations and challenges of traditional Cloud computing, particularly in handling real-time, heterogeneous, and latency-sensitive applications. However, the spread of Fog computing devices across the network introduces various challenges, especially concerning device connectivity and ensuring sufficient coverage to fulfil users’ requests. To maintain network operability, Fog Device Deployment (FDD) must effectively consider two crucial factors: connectivity and coverage. Network connectivity relies on FDD, determining the physical network topology, while coverage determines the accessibility of the Internet of Things (IoT) or edge devices. Both these objectives significantly impact the network performance and guarantee the network's Quality of Service (QoS). However, determining an optimal FDD method that reduces computation and communication overhead, and provides high network connectivity and coverage, is challenging. In this work, we propose an FDD algorithm that effectively connects the Fog devices for internal communication and covers maximum edge devices to entertain the requests. Firstly, FDD is formulated as a multi-objective optimization problem and then, an emerging metaheuristic Jaya Algorithm (JA) is applied to optimize the multi-objective function. The suitability of the JA, for the FDD problem, is substantiated by its rapid convergence and better computational complexity when contrasted with other contemporary population-based algorithms. In conclusion, the performance of the proposed method is assessed across a spectrum of benchmark-generated instances, each reflecting distinct Fog scenarios. The experimental outcomes showcase the proposed method's remarkable promise, especially when compared against state-of-the-art methodologies.