Hole Location and Repair Algorithms for Heterogeneous Wireless Sensor Networks

In heterogeneous wireless sensor networks, hole repair is the core task of ensuring network coverage and connectivity. The key is to accurately locate the best coverage points and efficiently schedule mobile nodes for patching. To this end, this paper proposes an optimal hole coverage point positioning algorithm based on gradient descent method and priority mechanism, and uses the positioning results to guide mobile nodes to perform path planning through deep reinforcement learning algorithm. First, the boundaries of the hole area are positioned, the hole area is reconstructed, the optimal coverage point is positioned using the gradient descent method, and the hole repair priority algorithm is proposed to sort the repair tasks. Secondly, a mobile node scheduling framework based on deep reinforcement learning is built. This framework integrates dual deep Q networks and priority experience replays, so that mobile nodes can not only effectively avoid obstacles during repair, but also achieve trade-offs and coordination between multiple optimization goals such as path length, energy consumption and time. The simulation experiment results show that the use of the hole positioning and repair algorithm proposed in this paper can effectively improve the overall coverage quality of the network and expand the network life cycle.