A Reliable and Secure Cluster-Routing Framework for Drone-Assisted Disaster Management in Smart Cities

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Terrestrial communication infrastructure is vulnerable to disasters, hindering smart city emergency response.
• Existing solutions lack adaptiveness, security, and energy efficiency for post-disaster scenarios.

Purpose of the Study:

• To introduce the Weighted Average Yo-Yo-based Clustering and Routing (WAY-CR) scheme for adaptive, secure, and energy-efficient drone-assisted post-disaster network recovery.
• To enhance emergency response coordination in smart cities through a robust communication network.

Main Methods:

• Developed a novel WAY-based metaheuristic optimizer integrating Yo-Yo Motion for improved cluster head selection.
• Implemented a secure communication model using Paillier Homomorphic Cryptosystem (PHC) and trust evaluation for drone authentication.
• Introduced Trust-Aware Boltzmann Path Selection for adaptive, secure, and energy-efficient routing paths.
• Formulated a multi-objective optimization model to balance cost, energy, trust, stability, and coverage.

Main Results:

• WAY-CR demonstrated superior performance over state-of-the-art Flying Ad Hoc Network (FANET) baselines.
• Achieved significant improvements in energy efficiency, cluster stability, trust accuracy, and end-to-end packet delivery.
• Validated the scheme's resilience and scalability in simulated post-disaster smart-city environments.

Conclusions:

• The WAY-CR scheme offers a resilient, scalable, and secure solution for drone-assisted emergency response networks in smart cities.
• The proposed methods effectively address challenges posed by disaster-induced communication disruptions.
• WAY-CR enhances network performance critical for post-disaster coordination and recovery efforts.