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

Abstract

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.

Double Q‐learning‐based adaptive trajectory selection for energy‐efficient data collection in wireless sensor networks

“…To prioritize trustworthiness mainly for mission-critical WSNs, a Q-learning based routing protocol is proposed in Keum and Ko. 16 Its two main parameters are efficient energy management and detecting malicious nodes. Since static sinks causes sink hole issues, Krishnan and Lim 28 proposed a routing protocol for mobile sink based on Q-learning.…”

“…In Kim et al, 15 Q‐learning is used to build a dynamic packet scheduling policy when the traffic condition changes dynamically and the data transmission pattern cannot be acquired. To prioritize trustworthiness mainly for mission‐critical WSNs, a Q‐learning based routing protocol is proposed in Keum and Ko 16 . Its two main parameters are efficient energy management and detecting malicious nodes.…”

“…WSNs and IoT technologies have both experienced significant use of these optimization algorithms for tasks such as CH selection, routing, coverage control and other similar tasks 12 . Over the past few decades, WSNs and IoTs has gained widespread recognition as a potentially game‐changing technology, and academics have proposed a plethora of routing algorithms for increasing network lifetime while simultaneously decreasing energy consumption 13–16 . Researchers have come up with different algorithms for clustering and routing, such as the optimization algorithm (heuristic and meta‐heuristic), fuzzy logic, and so on 14 .…”

“…12 Over the past few decades, WSNs and IoTs has gained widespread recognition as a potentially game-changing technology, and academics have proposed a plethora of routing algorithms for increasing network lifetime while simultaneously decreasing energy consumption. [13][14][15][16] Researchers have come up with different algorithms for clustering and routing, such as the optimization algorithm (heuristic and meta-heuristic), fuzzy logic, and so on. 14 Also, modern learning methods that are based on artificial intelligence (AI) are proposed for solving complex optimization problems in a dynamic way.…”

DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing

“…Deep reinforcement learning (DRL) algorithms are successfully applied to complex high-dimensional problems, mainly due to the use of deep neural networks (DNN) for function approximations [1]. Researchers have applied DRL algorithms for various problems in mobile ad-hoc networks (MANETs), e.g., for minimizing average or worst-case end-to-end delay in routing problems [2], [3] and routing path optimization [4]. In [5], it is shown that the DRL-based DeepCQ+ algorithm outperforms the state-of-the-art robust routing for dynamic networks (R2DN) [6].…”