Nature-Inspired Meta-Heuristic Algorithms for Resource Allocation in the Internet of Things

Abstract

The Internet of Things (IoT) is a paradigm-shifting concept that helps realize an acquisition, processing, and analytical global network, digitizing tangible entities to enhance efficiency and safety in various smart cities, healthcare, and Industry 4.0 domains. However, whereas IoT scales, with several heterogeneous devices and diverse, varied capabilities with service demands, cloud resource management in IoT usually faces the challenge of intricate complexity in efficiently allocating resources despite the demand for varied quality of service (QoS). Hence, this paper systematically reviews nature-inspired metaheuristic algorithm applications in IoT resource allocation for solving NP-hard problems. We summarize recent advances in metaheuristic methods, including comparisons against traditional methods. We also discuss practical feasibility and scaling issues in real-world IoT scenarios. Further, we have highlighted a few gaps in the current literature and provided recommendations on specific topics for future research, thereby indicating how to develop scalable, efficient resource allocation solutions to meet IoT's ever-evolving demands.