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Decentralized Kernel-Based Localization in Wireless Sensor Networks Using Belief Functions
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DOI：10.1109/JSEN.2019.2898106.png)
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Abstract
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Localization of sensors has become an essential issue in wireless networks. This paper presents a decentralized approach to localize sensors in indoor environments. The targeted area is partitioned into several sectors, each of which having a local calculator capable of emitting, receiving, and processing data. Each calculator runs a local localization algorithm, developed in a belief functions framework, using RSS fingerprinting database, to estimate the sensors zones. The fusion of all calculators estimates yields a final zone estimate. Various decentralized architectures are described, then compared with each other, and against the state-of-the-art. The experimental results using WiFi real measurements show the effectiveness of the proposed approach in terms of localization accuracy, processing time, and complexity.
Keywords:
Belief functions
decentralized data fusion
fingerprints
kernel density estimation
localization
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