DV-Hop localization based on Distance Estimation using Multinode and Hop Loss in WSNs

TL;DR

This paper tackles two core issues in range-free WSN localization: how to leverage multi-anchor connectivity to constrain distance estimation and how to select among multiple Euclidean-distance solutions. It introduces distance estimation using multinode (DEMN) to compute an expected distance $E_{dis_{i,k}}$ via cross-domain information, and a hop loss that prefers solutions whose predicted hops align with real hops, both integrated into a NSGA-II multi-objective optimization framework. The combination yields substantial performance gains over baselines, with DEMN narrowing the search space and Hop Loss improving solution selection, as demonstrated across random and structured network topologies. The work thus provides a robust, scalable approach to improve localization accuracy in wireless sensor networks and offers directions for handling more complex cross-domain distributions in future deployments.

Abstract

Location awareness is a critical issue in wireless sensor network 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 multinode (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.

Figures (7)

• Figure 1: An illustration of WSNs. Anchor nodes ($a_1, a_2$ and $a_3$) and unknown nodes ($u_1, u_2, u_3,$ and $u_4$).
• Figure 2: Distance estimation using multinode. ${a}_{i}$ and ${a}_{j}$: two distinct anchor nodes, ${u}_{k}$: an unknown node, ${o}_{l}$: an ordinary node, $D_1$ and $D_2$: a part of the cross domain, $m$: the hop count, $R$: communication radius, $d_{i,j}$: the distance between node ${a}_{i}$ and node ${a}_{j}$.
• Figure 3: Design principle of hop loss. ${u}^{{pred}_{1}}_{k}$ and ${u}^{{pred}_{2}}_{k}$: two predicted locations of node ${u}_{k}$, ${u}^{gt}_{l}$: the ground truth location of node ${u}_{l}$, ${a}_{i}$ and ${a}_{j}$: two distinct anchor nodes.
• Figure 4: An example of the real and its predicted hops. ${Hop}^{real}$: the real hops, ${Hop}^{pred}$: the predicted hops.
• Figure 5: Data sets of different shape test networks. "$\star$": an anchor node, "$\bullet$": an unknown node.