Adaptive Sparse Sensor Network Target Coverage Algorithm Based on Edge Computing
Ocean exploration is the key to ocean development,and how to quickly and efficiently achieve underwater target detec-tion is a problem that must be solved for ocean exploration.Based on this,an adaptive sparse sensing network target coverage op-timization algorithm based on edge computing is proposed to efficiently accomplish underwater target detection with fewer sen-sing nodes.Firstly,the energy balance of the sensing network is optimized by adding an energy factor to protect the nodes with lower energy during the node movement through the Ad Hoc mobile energy optimization strategy mechanism.Secondly,an Ad Hoc greedy detection mechanism is proposed to achieve the detection of unknown areas with minimum cost and fast target cove-rage.Finally,using the virtual force-based adaptive connectivity mechanism,the connectivity of the sparse self-organized network is ensured by increasing the virtual gravitational range to solve the disconnection problem during the node movement.Simulation results show that the proposed algorithm is able to provide fast and durable target detection coverage with a smaller number of mobile sensors,with better performance compared to the comparison algorithms.
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