Abstract
Reliable and accurate localization of objects is essential for many applications in wireless networks. Especially for large-scale wireless sensor networks (WSNs), both low cost and high accuracy are targets of the localization technology. However, some range-free methods cannot be combined with a cooperative method, because these range-free methods are characterized by low accuracy of distance estimation. To solve this problem, we propose a hard decision-based cooperative localization method. For distance estimation, an exponential distance calibration formula is derived to estimate distance. In the cooperative phase, the cooperative method is optimized by outlier constraints from neighboring anchors. Simulations are conducted to verify the effectiveness of the proposed method. The results show that localization accuracy is improved in different scenarios, while high node density or anchor density contributes to the localization. For large-scale WSNs, the hard decision-based cooperative localization is proved to be effective.
Highlights
Location awareness is rapidly becoming an essential feature of many commercial, public service, and military wireless networks [1,2,3]
Zaidi et al [20,21] considered that the per hop length (PHL) between different nodes might be greatly different in anisotropic wireless sensor networks (WSNs), resulting in a large error in distance estimation
We propose a cooperative localization model for wireless sensor networks in range-free situations
Summary
Location awareness is rapidly becoming an essential feature of many commercial, public service, and military wireless networks [1,2,3]. Zaidi et al [20,21] considered that the per hop length (PHL) between different nodes might be greatly different in anisotropic WSNs, resulting in a large error in distance estimation They proposed a novel range-free localization algorithm and derived its average location estimation error in closed form. Shen et al [29] established the fundamental limits of wideband cooperative location-aware networks and provided a geometrical interpretation of equivalent Fisher information (EFI) for cooperative networks This approach helps succinctly derive fundamental performance limits and their scaling behaviors and to treat anchors and agents in a unified way from the perspective of localization accuracy. To improve the accuracy of distance estimation, a novel weight allocation way is proposed to offset the deviation from the multi-hop method based on the neighboring anchors.
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