Anomalous Node Detection in Socially-Aware Mobile IoT Networks Using Q-Learning

Rapid growth in demand for Internet of Things (IoT) applications has driven advancements in network tech-nologies. Mobile IoT applications, including tracking, intelli-gent wearables, and autonomous vehicles, necessitate high data throughput, privacy, low-latency data transfer, and prolonged network lifetime. In every IoT application, many sensor devices collect data from physical objects and send the acquired information to the access point (AP). A secure connection between IoT nodes and the AP is crucial for optimal data routing, transmission reliability, and network lifetime in IoT networks. Additionally, it is essential to detect and remove anomalous nodes to protect the network from unwanted intrusion. This work proposes a socially-aware anomalous node detection and data routing strategy, representing the mobile IoT network as a dynamic graph. A novel social relationship index (SRI)-factor is computed based on current and predicted future node encounters to identify and remove the imposture nodes. The network graph is then dynamically updated. Moreover, an optimization problem is formulated to maximize the network lifetime with respect to data transmission latency, optimum routing path, and energy budget. The proposed optimization problem is solved using the Q-learning framework. Q-learning-based routing algorithm adapts to the changes of the dynamic graph by continuously updating the Q-values on the basis of feedback. Finally, numerical results demonstrate a significant performance improvement compared to existing methods.