Reliable and secure routing is required in mission‐critical wireless sensor networks (MC‐WSNs), but currently trust‐based protocols such as ATRP are limited by their inability to adapt effectively to attack intensity and their fixed trust weighting. In this paper, an evaluation of the algorithmic Mission‐Critical Trust‐Based Intelligent Routing Protocol (MC‐TIRP) is validated with its use of Q‐learning to aid in adaptive assessment of trust, multipath prioritization, and energy‐conscious routing choices. MC‐TIRP combines local and global trust measurements to provide dynamic detection of malicious nodes in denial‐of‐service, gray hole, and on−off attacks. OPNET simulations of 100 nodes were run to measure packet delivery ratio (PDR), end‐to‐end delay, throughput, and survivability. The findings indicate that MC‐TIRP can maintain around 50 percent PDR in the face of 40 percent malicious nodes, which is better than ATRP, TQR, and AOTDV in providing 1825 percent increased PDR and 15 percent reduced delay. These results indicate that MC‐TIRP is a viable routing framework to improve QoS, resilience, and energy efficiency of mission‐critical WSNs.
Mustafa et al. (2026) studied this question.
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