Abstract
As the density of wireless, resource-constrained sensors grows, so does the need to choreograph their actions across both time and space. Recent advances in ultra-wideband RF communication have enabled accurate packet timestamping, which can be used to precisely synchronize time. Location may be further estimated by timing signal propagation, but this requires additional communication overhead to mitigate the effect of relative clock drift. This additional communication lowers overall channel efficiency and increases energy consumption. This article describes a novel approach to simultaneously localizing and time synchronizing networked mobile devices. An Extended Kalman Filter is used to estimate all devices’ positions and clock errors, and packet timestamps serve as measurements that constrain time and overall network geometry. By inspection of the uncertainty in our state estimate, we can adapt the number of messages sent in each communication round to balance accuracy with communication cost. This reduces communication overhead, which decreases channel congestion and power consumption compared to traditional time of arrival and time difference of arrival localization techniques. We demonstrate the performance and efficiency of our approach using a real network of custom RF devices and mobile quadrotors.
- James Aspnes, David Goldenberg, and Yang Richard Yang. 2004. On the computational complexity of sensor network localization. In Algorithmic Aspects of Wireless Sensor Networks, Sotiris Nikoletseas and Jose Rolim (Eds.). Springer, 32--44. http://www.cs.yale.edu/homes/aspnes/papers/localization-abstract.html.Google Scholar
- Pratik Biswas and Yinyu Ye. 2004. Semidefinite programming for ad hoc wireless sensor network localization. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks. ACM, 46--54. Google ScholarDigital Library
- Bitcraze. 2015. Bitcraze CrazyFlie 2.0. Retrieved https://www.bitcraze.io/.Google Scholar
- Sundeep Prabhakar Chepuri, Geert Leus, and A van der Veen. 2012. Joint localization and clock synchronization for wireless sensor networks. In Proceedings of the 46th Asilomar Conference on Signals, Systems and Computers (ASILOMAR’12). IEEE, 1432--1436.Google ScholarCross Ref
- A. A. D’Amico, L. Taponecco, and U. Mengali. 2013. Ultra-wideband TOA estimation in the presence of clock frequency offset. IEEE Transactions on Wireless Communications 12, 4 (April 2013), 1606--1616.Google ScholarCross Ref
- D. Dardari, A. Conti, U. Ferner, A. Giorgetti, and M. Z. Win. 2009. Ranging with ultrawide bandwidth signals in multipath environments. Proceedings of the IEEE 97, 2 (Feb 2009), 404--426.Google ScholarCross Ref
- DecaWave. 2016. DecaWave DW1000 IR-UWB. Retrieved from http://www.decawave.com/products/dw1000.Google Scholar
- Benoit Denis, Jean-Benoît Pierrot, and Chadi Abou-Rjeily. 2006. Joint distributed synchronization and positioning in UWB ad hoc networks using TOA. IEEE Transactions on Microwave Theory and Techniques 54, 4 (2006), 1896--1911.Google ScholarCross Ref
- Satyam Dwivedi, Alessio De Angelis, Dave Zachariah, and Peter Händel. 2015. Joint ranging and clock parameter estimation by wireless round trip time measurements. Computing Research Repository abs/1501.05450 (2015). http://arxiv.org/abs/1501.05450Google Scholar
- Jeremy Elson, Lewis Girod, and Deborah Estrin. 2002. Fine-grained network time synchronization using reference broadcasts. Special Interest Group on Operating Systems Review 36, SI (Dec. 2002), 147--163. Google ScholarDigital Library
- Bernhard Etzlinger, Folker Meyer, Henk Wymeersch, Franz Hlawatsch, Gunter Muller, and Andreas Springer. 2014. Cooperative simultaneous localization and synchronization: Toward a low-cost hardware implementation. In Proceedings of the 8th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014). IEEE, 33--36.Google ScholarCross Ref
- Saurabh Ganeriwal, Ram Kumar, and Mani B. Srivastava. 2003. Timing-sync protocol for sensor networks. In Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys’03). ACM, New York,138--149. Google ScholarDigital Library
- Mohammad Reza Gholami, Sinan Gezici, and Erik G Strom. 2013. TDOA based positioning in the presence of unknown clock skew. IEEE Transactions on Communications 61, 6 (2013), 2522--2534.Google ScholarCross Ref
- Fredrik Gustafsson and Fredrik Gunnarsson. 2003. Positioning using time-difference of arrival measurements. In Proceedings of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 6. IEEE, VI--553.Google ScholarCross Ref
- Bill Jackson and Tibor Jordán. 2005. Connected rigidity matroids and unique realizations of graphs. Journal of Combinatorial Theory, Series B 94, 1 (2005), 1--29. Google ScholarDigital Library
- Benjamin Kempke, Pat Pannuto, and Prabal Dutta. 2015. PolyPoint: Guiding indoor quadrotors with ultra-wideband localization. In Proceedings of the 2nd International Workshop on Hot Topics in Wireless (HotWireless’15). ACM, New York, 16--20. Google ScholarDigital Library
- Richard Knoblauch, Martin Pietrucha, and Marsha Nitzburg. 1996. Field studies of pedestrian walking speed and start-up time. Transportation Research Record: Journal of the Transportation Research Board 1538 (1996), 27--38.Google ScholarCross Ref
- Manikanta Kotaru, Kiran Joshi, Dinesh Bharadia, and Sachin Katti. 2015. Spotfi: Decimeter level localization using wifi. In Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication. ACM, 269--282. Google ScholarDigital Library
- Anton Ledergerber, Michael Hamer, and Raffaello D’Andrea. 2015. A robot self-localization system using one-way ultra-wideband communication. In Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 3131--3137.Google ScholarCross Ref
- Guoqiang Mao, Barış Fidan, and Brian D. O. Anderson. 2007. Wireless sensor network localization techniques. Computer Networks: The International Journal of Computer and Telecommunications Networking 51, 10 (July 2007), 2529--2553. Google ScholarDigital Library
- Miklós Maróti, Branislav Kusy, Gyula Simon, and Ákos Lédeczi. 2004. The flooding time synchronization protocol. In Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems (SenSys’04). ACM, New York, 39--49. Google ScholarDigital Library
- J. Medvesek, A. Symington, A. Trost, and S. Hailes. 2015. Gaussian process inference approximation for indoor pedestrian localisation. Electronics Letters 51, 5 (2015), 417--419.Google ScholarCross Ref
- N. Patwari, J. N. Ash, S. Kyperountas, A. O. Hero, R. L. Moses, and N. S. Correal. 2005. Locating the nodes: Cooperative localization in wireless sensor networks. IEEE Signal Processing Magazine 22, 4 (July 2005), 54--69.Google ScholarCross Ref
- Morgan Quigley, Ken Conley, Brian P. Gerkey, Josh Faust, Tully Foote, Jeremy Leibs, Rob Wheeler, and Andrew Y. Ng. 2009. ROS: An open-source robot operating system. In Proceedings of the ICRA Workshop on Open Source Software.Google Scholar
- Ali H. Sayed. 2011. Adaptive Filters. John Wiley 8 Sons.Google Scholar
- Ali H. Sayed, Alireza Tarighat, and Nima Khajehnouri. 2005. Network-based wireless location: Challenges faced in developing techniques for accurate wireless location information. Signal Processing Magazine, IEEE 22, 4 (2005), 24--40.Google ScholarCross Ref
- Longfei Shangguan, Zheng Yang, Alex X. Liu, Zimu Zhou, and Yunhao Liu. 2015. Relative localization of RFID tags using spatial-temporal phase profiling. In Proceedings of the 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI’15). USENIX Association, Oakland, CA, 251--263. Google ScholarDigital Library
- F. Sivrikaya and B. Yener. 2004. Time synchronization in sensor networks: A survey. IEEE Network 18, 4 (July 2004), 45--50. Google ScholarDigital Library
- Affan Syed and John Heidemann. 2006. Time synchronization for high latency acoustic networks. In Proceedings of the IEEE Infocom. IEEE.Google ScholarCross Ref
- Andrew Symington and Niki Trigoni. 2012. Encounter based sensor tracking. In Proceedings of the 13th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc’12). ACM, New York, 15--24. Google ScholarDigital Library
- J. Tiemann, F. Schweikowski, and C. Wietfeld. 2015. Design of an UWB indoor-positioning system for UAV navigation in GNSS-denied environments. In Proceedings of the 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN). 1--7.Google Scholar
- Deepak Vasisht, Swarun Kumar, and Dina Katabi. 2016. Decimeter-level localization with a single WiFi access point. In Proceedings of the 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI’16). USENIX Association, Santa Clara, CA, 165--178. Google ScholarDigital Library
- P. Zhang and Q. Wang. 2014. On using the relative configuration to explore cooperative localization. IEEE Transactions on Signal Processing 62, 4 (Feb 2014), 968--980. Google ScholarDigital Library
- Jun Zheng and Yik-Chung Wu. 2010. Joint time synchronization and localization of an unknown node in wireless sensor networks. IEEE Transactions on Signal Processing 58, 3 (2010), 1309--1320. Google ScholarDigital Library
Index Terms
- SLATS: Simultaneous Localization and Time Synchronization
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