Abstract

Sensor location awareness is a critical issue in internet of things applications. For more accurate location estimation, the two issues should be considered extensively: 1) how to sufficiently utilize the connection information between multiple nodes and 2) how to select a suitable solution from multiple solutions obtained by the Euclidean distance loss. In this paper, a DV-Hop localization based on the distance estimation using multi-node (DEMN) and the hop loss in WSNs is proposed to address the two issues. In DEMN, when multiple anchor nodes can detect an unknown node, the distance expectation between the unknown node and an anchor node is calculated using the cross domain information and is considered as the expected distance between them, which narrows the search space. When minimizing the traditional Euclidean distance loss, multiple solutions may exist. To select a suitable solution, the hop loss is proposed, which minimizes the difference between the real and its predicted hops. Finally, the Euclidean distance loss calculated by the DEMN and the hop loss are embedded into the multi-objective optimization algorithm. The experimental results show that the proposed method gains 86.11% location accuracy in the randomly distributed network, which is 6.05% better than the DEM-DV-Hop, while DEMN and the hop loss can contribute 2.46% and 3.41%, respectively.