A TRUST-BASED ROUTING IN IoT ENVIRONMENT USING REINFORCEMENT LEARNING: application for the well-being of people

Abstract

Many application domains gain considerable advantages with the Internet of Things(IoT) network. It improves our lifestyle towards smartness like smart cities, smart health, smart home, smart vehicle, smart grid, etc. The ubiquity of IoT permits numerous heterogeneous smart devices interconnected through the internet to provide smart services. IoT devices are mostly resource-constrained regarding memory, processing capacity, battery, etc. So, it is highly susceptible to security attacks. Traditional security mechanisms can not apply to these devices due to their restricted resources. A trust-based security mechanism plays an important role to ensure security in the IoT environment because it consumes only fewer resources. Thus it is most essential to evaluate the trustworthiness among IoT devices. The proposed model improves trusted routing in the IoT environment by detecting and isolating malicious nodes. This model uses Reinforcement Learning (RL) where the agent learns the behavior of the node and isolates the malicious nodes to improve the network performance. The proposed work focuses on IoT with the Routing Protocol for Low power and Lossy network(RPL) and counters the black hole attack. The simulation results show that the proposed RLTrust model provides better performance than the existing one.