A Hierarchical Genetic Algorithm for Spatio-Temporal Coverage Optimization in Communication Networks under Emergency Monitoring Scenarios
Large-scale sudden environmental incidents often disrupt network infrastructures such as fiber cables and base stations. Establishing a special self-organizing network for emergency monitoring data transmission is essential for effective disaster response. Utilizing spatial analysis techniques,this paper guides the emergency communication vehicle to a strategically advantageous position. Building on this,considering factors such as the distribution of emergency personnel,communication performance,and climate conditions,this research intelligently deploys communication drones and establishes communication links to form an emergency terrestrial-aerial communication network using a hybrid hierarchical genetic algorithm. This includes designing a spatio-temporal hierarchical chromosome matrix,a comprehensive fitness evaluation model,and a co-evolutionary mechanism to achieve adaptive transmission of monitoring data. In the event of unexpected changes such as the addition or failure of communication resources or adjustments to communication requirements,the algorithm adaptively updates the deployment positions of emergency communication resources and communication links. This method was validated and analyzed using a tailings pond in Xinjiang as an experimental area. The deployment solutions were visualized on a 3D Earth platform,and the fitness convergence ability,dynamic adjustability,and implementation effectiveness were evaluated. The algorithm demonstrated an approximate 30％ improvement in fitness after 260 iterations,showing a higher convergence speed and improvement amplitude compared to other five algorithms such as random search. The communication nodes achieved comprehensive coverage and balanced distribution of monitoring points under different scenarios within the endurance period,meeting the requirements for on-site emergency monitoring response,thereby proving the algorithm's reliability and feasibility.
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