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The Wireless Localization Matching Problem
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DOI：10.1109/JIOT.2017.2723013.png)
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Abstract
En 中文
We propose new approaches toward wireless localization of devices belonging to the Internet of Things (IoT), specifically related to scenarios where the device positions are known a priori, however, the device IDs are not. These positions and device IDs therefore need to be matched using radio frequency positioning methods, which are more time and cost efficient as compared to manual installation. Immediate examples of real world applications include but are not limited to smart lighting and heating. We propose maximum-likelihood matching algorithms called MLMatch and MLMatch3D for resolving this problem based on measured received signal strength indicator values. Since the search space of node-to-position permutations grows factorially with the number of target devices, we propose several searching methods including mixed integer programming, linear programming relaxation to reduce computation time. The MLMatch3D algorithm further addresses the problem whereby nodes are located at multiple rooms and/or floors of a building. This algorithm first utilizes a graph partitioning method to determine in which room a node is located, followed by MLMatch for finding room specific positions corresponding to each node. In addition, this paper analyzes the stability of these algorithms with respect to different wireless fading models as well as compares the performance of these algorithms in various environments via numerical simulations. Finally, we report on experiments performed indoors and outdoors using up to 33 wireless devices in order to demonstrate the problem and validate our results.
Keywords:
Genetic algorithm (GA)
graph partitioning
linear programming (LP) relaxation
matching
maximum likelihood
mixed integer programming (MIP)
wireless localization
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