Maximizing UAV fog deployment efficiency for critical rescue operations: A multi-objective optimization approach

In disaster scenarios and high-stakes rescue operations, integrating Unmanned Aerial Vehicles (UAVs) as fog nodes has become crucial. This integration ensures a smooth connection between affected populations and essential health monitoring devices, supporting the Internet of Things (IoT). Integrating UAVs in such environments is inherently challenging, where the primary objectives involve maximizing network connectivity and coverage while extending the networks lifetime through energy-efficient strategies to serve the maximum number of affected individuals. In this paper, we decomposed our problem into two subproblems. Connectivity, coverage subproblem and network lifespan optimization subproblem. For the Connectivity and coverage subproblem, the optimal connectivity and coverage are optimized by strategically deploying UAV fog nodes in a manner that maximizes network coverage, thoughtfully considering the distinctive constraints that emerge in these high-pressure situations where lives are at stake. We shape our UAV fog deployment problem as a multi-objective optimization and introduce a specialized UAV fog deployment algorithm tailored specifically for UAV fog nodes deployed in rescue missions. For the network lifespan subproblem, after determining the optimal connectivity and coverage of UAV nodes and users within the entire network, the network lifespan optimization subproblem is greatly simplified and efficiently solved via a one-dimensional swapping method. After conducting thorough experiments using our proposed architecture across diverse scenarios, our method consistently surpasses existing approaches. It effectively tackles issues like restricted connectivity and potential node failures. This advancement in deployment efficiency notably enhances rescue operations, enabling us to aid the maximum number of affected individuals swiftly during critical and time-pressing situations.