Congestion Aware Resource Allocation and Routing Protocol Approach Based on Metaheuristic Algorithm in IoT: A Survey

Abstract

The explosion of Internet of Things (IoT) devices imposes efficient resource allocation and routing protocols to achieve data transmission and also network performance. Traditional approaches struggle by the dynamic nature and congestion problems prevalent in IoT environments. This survey discovers the application of metaheuristic algorithms, encouraged by natural processes, as an auspicious approach for congestion-aware resource allocation and routing in IoT networks. We studied various metaheuristic algorithms, including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Jaya Optimization (JO), Walrus Beetle Optimization (WBO), and Tabu Search (TS). Each algorithm suggests unique strengths and weaknesses in terms of search strategy, solution representation, and computational complexity. There are still a number of research gaps in spite of tremendous advancements. These include handling device heterogeneity and interoperability, incorporating security and privacy measures, meeting a variety of QoS requirements, scaling for large-scale networks, real-time adaptability to dynamic conditions, energy efficiency for battery-powered devices, and adapting to dynamic network topologies. For metaheuristic algorithms to be successfully developed and used in congestion control for Internet of Things networks, these gaps must be filled.