Range-free localization algorithms in wireless sensor networks

Luo, Xiangyun; Liu, Ye; Long, Chengzhi; Luo, Jianping

doi:10.1117/12.2021191

Cited by 1 publication

(2 citation statements)

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“…To keep up with the trend, some eminent ideas have been put forward to solve tracking with WSNs [7]- [9]. Traditionally, WSNs-based localization algorithms can be divided into two categories: range-free methods and range-based methods [10]. The latter is defined by protocols that use point-to-point distance estimates (range) or angle estimates for calculating location [11], [12], and the former makes no use of such information.…”

Section: Introductionmentioning

confidence: 99%

“…The latter is defined by protocols that use point-to-point distance estimates (range) or angle estimates for calculating location [11], [12], and the former makes no use of such information. Created with less range information, a range-free method, however, is more suitable for large-scale network localization applications, for the range-based one requires specific statistics and more expensive hardware or careful system calibration [10], [13], [14]. In addition, the range-free strategy has been extensively studied in many algorithms such as the RSD [15], DV-Hop [16], APIT [17], and REP [18].…”

Section: Introductionmentioning

confidence: 99%

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Context-Centric Target Localization with Optimal Anchor Deployments

Zhang

Huang

et al. 2015

2015 IEEE 23rd International Conference on Network Protocols (ICNP)

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Localization proves to be a promising application of wireless sensor networks. Although a considerable number of algorithms have been designed for low-overhead and highaccuracy localization, problems remain to be tackled such as the way to use anchor-deploying. In this paper, we present a mechanism for range-free localization called Enhanced Map Segmentation (EMS) to deploy and segment the map where precise indoor localization is required. Despite the limits of environmental noise, sensing irregularity, received signal strength (RSS) variation and other unavoidable factors, EMS can be reliable by improving the quality of map segmentation. This paper will present and analyze the enhancing method by a series of simulations. In addition, to deal with ambiguous context positions that confounds the localization, this paper ameliorates the segmentation with context conception mentioned in [1] by statistical methods. In fact, a well-organized deployment and a context-based decision mechanism can make such a layer of abstraction more reliable and compatible.

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