Resource Discovery in the Internet of Things Based on Learning Automata - Qhanbari, Mehdi; Akbari Torkestani, Javad; Taghipour, Sara (Management Strategies and Engineering Sciences, 2026)
The Internet of Things (IoT) comprises many devices and produces huge amounts of data that need efficient methods of resource finding. Resource discovery is required to locate and use devices, sensors, services, and data in IoT networks. Traditional approaches, however, have limitations in terms of scalability, efficiency, and adapting to changing environments. In this study, we present a novel model of resource discovery in IoT based on learning automata for efficiency, scalability, and energy efficiency. The approach integrates the Pastry DHT with Bloom filters for fast and large-scale resource discovery. It uses hashing mechanisms for better identification and learning automata for reinforcement learning to make resource access adaptive to real-time feedback. Large-scale simulation shows great improvement in data access speed, energy consumption, and cost over traditional methods. This paper presents an efficient, scalable, and versatile solution for IoT resource discovery, surmounting key challenges and enabling effective applications in smart homes, smart cities, and industrial automation. This research has important implications for IoT applications that require low latency, scalability, and energy efficiency. For instance, the 17.14% decrease in latency at 60 requests allows for real-time data requests in smart city traffic applications. Likewise, the 40% energy savings at 80 requests allows battery-operated healthcare devices to last longer. In comparison to other methods, such as Liu and Deng (2024), CoAP, and mDNS, the proposed method of mapping data in a hierarchical way achieved the best time efficiency and energy efficiency results, representing a groundbreaking solution for smart cities, healthcare, and any industrial IoT deployments.