Wireless sensor networks are widely used in smart environments to capture and detect the activities of human beings, and achieving reliable transmission between sensor nodes has become one of the main challenges of practical applications. This paper presents a scheme for path planning that is designed to achieve optimal coverage by using active nodes to periodically fill in the blank areas and to replace the failed nodes. This approach can effectively avoid uneven energy consumption while maintaining complete link states. Meanwhile, the curl field of the nodes is used to model the effects of the residual energy and the distance between nodes, thereby effectively relaxing the requirements on the spatial positions of the nodes. Experiments show that in the case of directional transmission, the proposed method demonstrates better performance than other algorithms in terms of the network lifecycle, coverage, and transmission reliability. This method can effectively address the problem of cross-node failure along the transmission paths in complex and dynamic networks.

The localization problem of target nodes remains unresolved, especially in large-scale and complex environments. In this paper, we propose a particle centroid drift (PCD) algorithm to reduce the distance errors between nodes and obtain the particle aggregation region by using the drift vector. First, we use the particle quality prediction function to obtain the particles in a high-likelihood region. The high-quality particles have high probability in the calculation, which can increase the number of effective particles and enable avoiding particle degradation. Then, the centroid drift vector is used to make the particle distribution similar to the actual reference distribution. Experiments are conducted on state-space models: the local movement where 55% nodes are moving and the globe movement where 100% nodes are moving. The results show that the proposed algorithm has low estimation errors, a good tracking effect and an acceptable time complexity.

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