Abstract—The Disaster Area Wireless Mesh Mobile Ad Hoc Network (DAWN-MANETs) face many challenges such as: moving topology, lack of pre-determined infrastructure and vulnerable to a variety of attacks which make it quite difficult in establishing the reliable and secure communication. Although current Ad hoc On-Demand Distance Vector (Secure-AODV) and AI-based MANET schemes are designed for addressing the security or adaptability problems only and do not have a disaster-aware decision-making model in place. In this paper, we present an integrated Artificial Intelligence-Internet of Things (AI-IoT) secure routing framework for DAWN-MANETs. The proposed approach successfully integrates management of IoT-assisted situational awareness and AI-based decision-making engine along with secure AODV routing protocol. This approach assures adaptive and secured routing under dynamic and adverse disaster situations. As network and environmental data can be monitored and analyzed in real-time, the approach can dynamically calculate routing paths which best balancing between reliability and security under the stochastic environment. Experimental results demonstrate that the proposed framework significantly improves the important network performance parameters as compared to traditional AODV and Secure-AODV protocols. The robustness of packet delivery, communication delay and security against attack scenarios have been well improved. With these strengths, it is believed that the integration disaster awareness and intelligent decision-making into secure routing protocols can greatly enhance the robustness and fitness for DAWN-MANETs. Despite focusing on simulation-based validation in the present research, the framework provides a scalable basis for actual application in disaster responses.
 
Keywords—secure routing, Disaster Area Wireless Mesh Mobile Ad Hoc Network (DAWN-MANETs), disaster response, Artificial Intelligence (AI), Internet of Things (IoT), AODV protocol, Mobile Ad Hoc Networks (MANETs)


Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).