Dynamic Resource Management in IoT Ecosystems using Reinforcement Learning: Towards Scalable and Efficient Solutions

Unprecedented growth of IoT has resulted in massive and sophisticated interconnected networks, applicative variety, and varying resource demands for connected devices. Scalability that depends on resource availability and system reliability and costs when used in such settings are paramount. This paper aims at investigating dynamic resource management in IoT environment with reinforcement learning as the fundamental approach. Because RL techniques can learn the policy of the best interaction through the defined environment, they also create effective solutions for the adaptive and dynamic nature of IoT networks. To this end, we suggest a novel, scalable solution based on Reinforcement Learning to manage the distribution of computational, communication, and energy in a large number of heterogeneously connected IoTs. These include real-time decision making and a capacity for dynamic levels of employment, a further avoidance of inefficiency. The effectiveness of our framework is then tested on several IoT use cases such as smart city, Industrial IoT, and edge computing environment. Performance analysis reveals that the proposed approaches yield lower latency, lower energy consumption, and higher system throughput when compared to standard heuristic approaches. Based on the findings of this study, it is clear that RL-based solutions enable the IoT systems to escape some of the worst impacts of scalability and efficiency issues in order to cause a shift towards formulating more intelligent, self-organized MLMRM strategies that are in line with the future of IoT extension.