Trust-Based Intelligent Routing Protocol with Q-Learning for Mission-Critical Wireless Sensor Networks

Sensors

Young-Bae Ko¹  DooHo Keum² 

[1] Department of AI Convergence Network, Ajou University, Suwon 16499, Korea;LIG Nex1 Company Ltd., Seongnam 13488, Korea;

关键词: mission-critical wireless sensor network;    trust-based routing;    Q-learning;    reinforcement learning;    QoS;   

DOI  :  10.3390/s22113975

来源: DOAJ

【 摘 要 】

Mission-critical wireless sensor networks require a trustworthy and punctual routing protocol to ensure the worst-case end-to-end delay and reliability when transmitting mission-critical data collected by various sensors to gateways. In particular, the trustworthiness of mission-critical data must be guaranteed for decision-making and secure communications. However, it is a challenging issue to meet the requirement of both reliability and QoS in sensor networking environments where cyber-attacks may frequently occur and a lot of mission-critical data is generated. This study proposes a trust-based routing protocol that learns the trust elements using Q-learning to detect various attacks and ensure network performance. The proposed mechanism ensures the prompt detection of cyber threats that may occur in a mission-critical wireless sensor network and guarantees the trustworthy transfer of mission-critical sensor data. This paper introduces a distributed transmission technology that prioritizes the trustworthiness of mission-critical data through Q-learning results considering trustworthiness, QoS, and energy factors. It is a technology suitable for mission-critical wireless sensor network operational environments and can reliably operate resource-constrained devices. We implemented and performed a comprehensive evaluation of our scheme using the OPNET simulator. In addition, we measured packet delivery rates, throughput, survivability, and delay considering the characteristics of mission-critical sensor networks. The simulation results show an enhanced performance when compared with other mechanisms.

【 授权许可】

Unknown