A Dynamic Weighted Trilateration Algorithm for Indoor Localization Using Dual-Band WiFi

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WiFi based indoor and semi-indoor localization techniques are essential components of indoor location-based services. Calibration-free techniques for WiFi signal strength based indoor localization can help make indoor localization systems scalable, cost-effective and easy to deploy. However, distance estimation errors and environmental factors affect the accuracy of non-calibration solutions like trilateration significantly, and addressing the accuracy issue is critical. To help improve accuracy, localization over dual-band WiFi (IEEE 802.11n) which uses both 2.4 GHz and 5 GHz bands is a potential alternative. This paper proposes a novel adaptive, weighted trilateration technique that uses the behavior of these two bands under different conditions. An iterative heuristic approach based on the characterization of the behavior of the bands is employed to determine the most likely position of a smart phone. Additionally optimization strategies are applied to improve the time complexity of this approach. Experiments conducted in different indoor environments show that our approach performs better than other non-calibration signal strength based approaches in terms of accuracy, and also reduces the worst case error. © 2019, Springer Nature Switzerland AG.

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cited By 0; Conference of 17th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2019 ; Conference Date: 16 May 2019 Through 17 May 2019; Conference Code:225899