Abstract
One interesting scenario in personal positioning involves an energy-conscious mobile user who tries to obtain estimates about his positions with sufficiently high confidence while consuming as little battery energy as possible. Besides obtaining estimates directly from a position measuring device, the user can rely on extrapolative calculations based on a user movement model and a known initial estimate. Because each measuring probe usually incurs a substantially higher cost than the extrapolative calculation, the objective is to minimize the overall cost of the measurement probes.
Assuming that the user moves at a normally-distributed velocity, we consider two scenarios which differ in the probing devices used. In the first scenario, only one probing device is used. In this case, the aim is to minimize the total number of probes required. In the second scenario, two types of positioning devices are given, where one type of devices offers a higher positioning precision, but also at a greater probing cost. In this case, the aim is to choose an optimal combination of probes from the two types of devices.
For both scenarios, we present algorithms for determining the minimum-cost probing sequences. The algorithms are computationally efficient in reducing the searching space of all possible probing sequences. Our approach is based on Kalman Filtering theory which allows to integrate estimates obtained from the measurements and the extrapolative calculations. The variances in the estimates can provably stay below the specified level throughout the journey.
To the best of our knowledge, these results appear to be the first that uses a mathematically rigorous approach to minimize the probing cost while guaranteeing the quality of estimates in personal positioning.
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References
Ashbrook, D., Starner, T.: Learning significant locations and predicting user movement with gps. In: International Symposium on Wearable Computing, pp. 101–108 (2002)
Bhattacharya, A., Das, S.K.: Lezi-update: An information-theoretic approach to track mobile users in pcs networks. In: Mobile Computing and Networking, pp. 1–12 (1999)
Chung, K.L.: Elementary Probability Theory with Stochastic Processes. Springer-Verlag, New York (1974)
D’Roza, T., Bilchev, G.: An overview of location-based services. BT Technology Journal 21(1), 20–27 (2003)
Hoffmann-Wellenhof, B., Lichtenegger, H., Collins, J.: GPS: Theory and Practice, 3rd edn. Springer, New York (1994)
Kalman, R.E.: A new approach to linear filtering and prediction problems. Transactions of the ASME - Journal of Basic Engineering 82, 35–45 (1960)
Kourogi, M., Kurata, T.: Personal positioning based on walking locomotion analysis with self-contained sensors and a wearable camera. In: Proc. of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2003, pp. 287–292 (July 2003)
Manesis, T., Avouris, N.: Survey of position location techniques in mobile systems. In: Proceedings of the 7th international conference on Human computer interaction with mobile devices and services, Salzburg, Austria, vol. 111, pp. 291–294 (2005); ISBN:1-59593-089-2
Negenborn, R.: Robot localization and kalman filters- on finding your position in a noisy world. Master’s thesis, Institute of Information and Computing Sciences, Utrecht University (September 2003), http://www.negenborn.net/kal_loc/thesis.pdf
Petzold, J., Bagci, F., Trumler, W., Ungerer, T.: Hybrid predictors for next location prediction. In: Ma, J., Jin, H., Yang, L.T., Tsai, J.J.-P. (eds.) UIC 2006. LNCS, vol. 4159, pp. 125–134. Springer, Heidelberg (2006)
Roy, A., Das, S., Basu, K.: A predictive framework for location-aware resource management in smart homes. IEEE Transactions on Mobile Computing 6(11), 1270–1283 (2007)
Rudolph, L., Fang, H., Hsu, W.J.: Adaptive learning/prediction of mobile user’s location via historical records of gps and cell id information. Technical Report NTUTR-07-01, Singapore-MIT Alliance of Nanyang Technological University, Singapore (June 2007), http://www.mit.edu/~fanghui/loc/kfpredict.pdf
Salcic, Z., Chan, E.: Mobile station positioning using gsm cellular phone and artificial neural networks. Wireless Personal Communications 14(3), 235–254 (2000)
Trevisani, E., Vitaletti, A.: Cell-id location technique, limits and benefits: An experimental study. In: Proceedings of the Sixth IEEE Workshop on Mobile Computing Systems and Applications (WMCSA 2004), pp. 51–60 (2004)
Welch, G., Bishop, G.: An introduction to the kalman filter, http://www.cs.unc.edu/~welch
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Fang, H., Hsu, WJ., Rudolph, L. (2008). Controlling Uncertainty in Personal Positioning at Minimal Measurement Cost. In: Sandnes, F.E., Zhang, Y., Rong, C., Yang, L.T., Ma, J. (eds) Ubiquitous Intelligence and Computing. UIC 2008. Lecture Notes in Computer Science, vol 5061. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69293-5_37
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DOI: https://doi.org/10.1007/978-3-540-69293-5_37
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