Abstract
Device-to-device relative positioning has been widely applied in modern Web-based systems such as COVID-19 mobile contact tracing, seamless access systems, mobile interactive gaming, and mobile e-Commerce. The legacy absolute positioning technologies are not suitable for device-to-device positioning attributed to their mobility and heterogeneity of devices. In this paper, we focus on the heterogeneity problem and propose Capo, the first calibration algorithm that enables the interaction among devices with different communication modes for relative positioning in heterogeneous systems. Capo optimizes the ranging results of low-precision devices in a collaborative network based on the ranging data from high-precision devices. The evaluation shows that Capo can significantly improve up to 26.56% of the positioning accuracy of the heterogeneous systems. Real use case study on COVID-19 contact tracing further shows that Capo significantly improves the accuracy of exposure notifications.
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Notes
- 1.
Since the centimeter-level error of high precision technology is negligible compared to the meter-level error of low precision technology, for the sake of simplicity, in this paper we assume a zero positioning error between high-precision devices, and \({d_i}\) represents the ranging distance generated using the signal attenuation model mentioned above. The case of non-zero positioning error, however, we leave it as future work.
- 2.
According to the \(RSSI_{i}\) equation, based on the scale of the distance and BLE’s RSSI-based ranging error of 2–10 m, we set \(\gamma = 5\) for BLE devices.
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Acknowledgements
This work is supported by NSFC Project (Grant No. 62202473, 62132011, 62132009), National Key R &D Program of China (Grant No. 2022YFB3105000), and PCL Future Regional Network Facilities for Large-scale Experiments and Applications (Grant No. PCL2018KP001).
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Wan, K., Wu, Z., Cao, Q., Zheng, X., Li, Z., Li, T. (2023). Capo: Calibrating Device-to-Device Positioning with a Collaborative Network. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_65
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