Railway environmental monitoring is essential to ensure the safe operation of trains. WSN has great potential in environmental detection, space exploration, smart home, military and other fields. In this paper, The WSN monitoring network is built by deploying relay/sensor nodes along two sides of the rail, to realize the railway environmental monitoring. The problem of "energy hole" caused by uneven energy consumption of nodes in linear WSN is discussed, and a non-uniform distance correlation deployment strategy is designed. In the strategy, the rectangular monitoring region is divided into multiple triangular region. And, the relationship among the number of sensor nodes, the area of unit triangle, the distance between the triangle vertices (relay nodes) and the energy consumption of relay nodes is analyzed. The simulation results have showed that the proposed algorithm has the smaller energy consumption gap between relay nodes, compared with uniform and non-uniform uncorrelated deployment algorithms. It ensures the stability of the whole network energy consumption, and has better performance in prolonging the network lifetime.

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Aiming at the problem that the network lifetime declines in linear WSN powered by battery, node energy model with energy harvesting (EH) and prediction is constructed. Based on the model, the multi-sink WSN network is deployed in an optimal non-uniform way. The mathematical expressions of the average packet delivery ratio and throughput density of the optimal deployment strategy are deduced theoretically. The performances of the average packet delivery ratio, throughput density, network lifetime, and residual energy ratio are simulated in EH-WSN and WSN, as well as the ratio q and the signal-to-interference noise ratio (SINR) threshold θ. The results show that the lifetime of EH-WSN is prolonged by adjusting the ratio q and the SINR threshold θ, compared with WSN powered by battery. The proposed strategy has good scalability, suitable for the non-uniform deployment in linear EH-WSN, especially with a large number of nodes and a long distance.