Decentralized localization in wireless sensor networks

Al-Hattab, Mohammad; Takruri, Maen; Attia, Hussain; Al-Omari, Huthaifa

doi:10.1109/icecta.2017.8252071

Cited by 3 publications

(1 citation statement)

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“…Though SpringLoc does not require any offline measurements, it does need to know the location of the transceiver nodes. Future work could include performing self-localization [53][54][55] on the nodes themselves, while following a deployment strategy that could be realistically followed by end users. This paper only investigated single entity localization; multi-entities is left for future work.…”

Section: Discussionmentioning

confidence: 99%

SpringLoc: A Device-Free Localization Technique for Indoor Positioning and Tracking Using Adaptive RSSI Spring Relaxation

et al. 2019

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Device-free localization (DFL) algorithms using the received signal strength indicator (RSSI) metrics have become a popular research focus in recent years as they allow for location-based service using commercial-off-the-shelf (COTS) wireless equipment. However, most existing DFL approaches have limited applicability in realistic smart home environments as they typically require extensive offline calibration, large node densities, or use technology that is not readily available in commercial smart homes. In this paper, we introduce SpringLoc and a DFL algorithm that relies on simple parameter tuning and does not require offline measurements. It localizes and tracks an entity using an adaptive spring relaxation approach. The anchor points of the artificial springs are placed in regions containing the links that are affected by the entity. The affected links are determined by comparing the kernel-based histogram distance of successive RSSI values. SpringLoc is benchmarked against existing algorithms in two diverse and realistic environments, showing significant improvement over the state-of-the-art, especially in situations with low-node deployment density. INDEX TERMS Device-free localization (DFL), histogram distance, indoor positioning systems (IPS), smart homes, spring-relaxation.

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Section: Discussionmentioning

confidence: 99%