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research-article

Selection of Sensors for Efficient Transmitter Localization

Published: 16 August 2021 Publication History

Abstract

We address the problem of localizing an (unauthorized) transmitter using a distributed set of sensors. Our focus is on developing techniques that perform the transmitter localization in an efficient manner, wherein the efficiency is defined in terms of the number of sensors used to localize. Localization of unauthorized transmitters is an important problem which arises in many important applications, e.g., in patrolling of shared spectrum systems for any unauthorized users. Localization of transmitters is generally done based on observations from a deployed set of sensors with limited resources, thus it is imperative to design techniques that minimize the sensors’ energy resources. In this paper, we design greedy approximation algorithms for the optimization problem of selecting a given number of sensors in order to maximize an appropriately defined objective function of localization accuracy. The obvious greedy algorithm delivers a constant-factor approximation only for the special case of two hypotheses (potential locations). For the general case of multiple hypotheses, we design a greedy algorithm based on an appropriate auxiliary objective function—and show that it delivers a provably approximate solution for the general case. We develop techniques to significantly reduce the time complexity of the designed algorithms by incorporating certain observations and reasonable assumptions. We evaluate our techniques over multiple simulation platforms, including an indoor as well as an outdoor testbed, and demonstrate the effectiveness of our designed techniques—our techniques easily outperform prior and other approaches by up to 50-60% in large-scale simulations and up to 16% in small-scale testbeds.

References

[1]
A. Bhattacharya, A. Chakraborty, S. R. Das, H. Gupta, and P. M. Djurić, “Spectrum patrolling with crowdsourced spectrum sensors,” IEEE Trans. Cogn. Commun. Netw., vol. 6, no. 1, pp. 271–281, Mar. 2020. 10.1109/TCCN.2019.2939793.
[2]
M. Khalediet al., “Simultaneous power-based localization of transmitters for crowdsourced spectrum monitoring,” in Proc. 23rd Annu. Int. Conf. Mobile Comput. Netw. (MobiCom), Oct. 2017, pp. 235–247. 10.1145/3117811.3117845.
[3]
A. Nikaet al., “Empirical validation of commodity spectrum monitoring,” in Proc. 14th ACM Conf. Embedded Netw. Sensor Syst. CD-ROM (SenSys), Nov. 2016, pp. 96–108. 10.1145/2994551.2994557.
[4]
A. Chakraborty, M. S. Rahman, H. Gupta, and S. R. Das, “SpecSense: Crowdsensing for efficient querying of spectrum occupancy,” in Proc. IEEE Conf. Comput. Commun. (INFOCOM), May 2017, pp. 1–9. 10.1109/infocom.2017.8057113.
[5]
V. Cevher, P. Boufounos, R. G. Baraniuk, A. C. Gilbert, and M. J. Strauss, “Near-optimal Bayesian localization via incoherence and sparsity,” in Proc. IEEE Comput. Soc. Int. Conf. Inf. Process. Sensor Netw. (IPSN), Apr. 2009, pp. 205–216.
[6]
A. Bhattacharya, C. Zhan, H. Gupta, S. R. Das, and P. M. Djuric, “Selection of sensors for efficient transmitter localization,” in Proc. IEEE Conf. Comput. Commun. (INFOCOM), Jul. 2020, pp. 2410–2419. 10.1109/INFOCOM41043.2020.9155230.
[7]
C. Zhan, H. Gupta, A. Bhattacharya, and M. Ghaderibaneh, “Efficient localization of multiple intruders in shared spectrum system,” in Proc. 19th ACM/IEEE Int. Conf. Inf. Process. Sensor Netw. (IPSN), Apr. 2020, pp. 205–216. 10.1109/IPSN48710.2020.00025.
[8]
R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. Hoboken, NJ, USA: Wiley, 2012.
[9]
P. K. Penumarthiet al., “Multirobot exploration for building communication maps with prior from communication models,” in Proc. Int. Symp. Multi-Robot Multi-Agent Syst. (MRS), Dec. 2017, pp. 90–96. 10.1109/mrs.2017.8250936.
[10]
F. Zafari, A. Gkelias, and K. K. Leung, “A survey of indoor localization systems and technologies,” IEEE Commun. Surveys Tuts., vol. 21, no. 3, pp. 2568–2599, 3rd Quart., 2019. 10.1109/COMST.2019.2911558.
[11]
NSF Workshop on Spectrum Measurements Infrastructure, Assoc. Comput. Mach., New York, NY, USA, 2016, pp. 22–23.
[12]
W. Sun, M. Xue, H. Yu, H. Tang, and A. Lin, “Augmentation of fingerprints for indoor WiFi localization based on Gaussian process regression,” IEEE Trans. Veh. Technol., vol. 67, no. 11, pp. 10896–10905, Nov. 2018. 10.1109/TVT.2018.2870160.
[13]
A. Chakraborty, L. E. Ortiz, and S. R. Das, “Network-side positioning of cellular-band devices with minimal effort,” in Proc. IEEE Conf. Comput. Commun. (INFOCOM), Apr. 2015, pp. 2767–2775. 10.1109/ INFOCOM.2015.7218669.
[14]
G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher, “An analysis of approximations for maximizing submodular set functions—I,” Math. Program., vol. 14, no. 1, pp. 265–294, Dec. 1978. 10.1007/BF01588971.
[15]
J. Kleinberg and E. Tardos, Algorithm Design. Chennai, India: Pearson, 2006.
[16]
S. Khuller, A. Moss, and J. S. Naor, “The budgeted maximum coverage problem,” Inf. Process. Lett., vol. 70, no. 1, pp. 39–45, 1999. 10.1016/S0020-0190(99)00031-9.
[17]
R. Ayyalasomayajulaet al., “Deep learning based wireless localization for indoor navigation,” in Proc. 26th Annu. Int. Conf. Mobile Comput. Netw. (MobiCom), Apr. 2020, pp. 1–14. 10.1145/3372224.3380894.
[18]
Joblib. (Oct. 2018). Joblib/Joblib. [Online]. Available: https://github.com/joblib/joblib
[19]
S. K. Lam, A. Pitrou, and S. Seibert, “Numba: A LLVM-based Python JIT compiler,” in Proc. 2nd Workshop LLVM Compiler Infrastruct. HPC, 2015, pp. 1–6. 10.1145/2833157.2833162.
[20]
K. A. Chamberlin and R. J. Luebbers, “An evaluation of Longley–Rice and GTD propagation models,” IEEE Trans. Antennas Propag., vol. AP-30, no. 6, pp. 1093–1098, Nov. 1982. 10.1109/TAP.1982. 1142958.
[21]
N. Patwari. (Sep. 2007). CRAWDAD Dataset Utah/CIR (V. 2007-09-10). [Online]. Available: https://crawdad.org/utah/CIR/20070910
[22]
(2018). RTL-SDR (RTL2832U) and Software Defined Radio News and Projects. Accessed: Oct. 18, 2018. [Online]. Available: https://www.rtl-sdr.com/
[23]
A. Krause, A. Singh, and C. Guestrin, “Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies,” J. Mach. Learn. Res., vol. 9, no. 2, pp. 235–284, 2008.
[24]
A. Yassinet al., “Recent advances in indoor localization: A survey on theoretical approaches and applications,” IEEE Commun. Surveys Tuts., vol. 19, no. 2, pp. 1327–1346, 2nd Quart., 2017. 10.1109/COMST.2016.2632427.
[25]
P. Bahl and V. N. Padmanabhan, “RADAR: An in-building RF-based user location and tracking system,” in Proc. IEEE Conf. Comput. Commun., 19th Annu. Joint Conf. IEEE Comput. Commun. Soc. (INFOCOM), vol. 2, Mar. 2000, pp. 775–784. 10.1109/INFCOM.2000.832252.
[26]
R. Liu, T.-N. Do, U.-X. Tan, and Y. Chau, “Fusing similarity-based sequence and dead reckoning for indoor positioning without training,” IEEE Sensors J., vol. 17, no. 13, pp. 4197–4207, Jul. 2017. 10.1109/JSEN.2017.2706303.
[27]
R. Liu, C. Yuen, T.-N. Do, M. Zhang, Y. L. Guan, and U.-X. Tan, “Cooperative positioning for emergency responders using self IMU and peer-to-peer radios measurements,” Inf. Fusion, vol. 56, pp. 93–102, Apr. 2020. 10.1016/j.inffus.2019.10.009.
[28]
Y. Sun, J. Chen, C. Yuen, and S. Rahardja, “Indoor sound source localization with probabilistic neural network,” IEEE Trans. Ind. Electron., vol. 65, no. 8, pp. 6403–6413, Aug. 2018. 10.1109/TIE.2017.2786219.
[29]
R. K. Martin and R. Thomas, “Algorithms and bounds for estimating location, directionality, and environmental parameters of primary spectrum users,” IEEE Trans. Wireless Commun., vol. 8, no. 11, pp. 5692–5701, Nov. 2009. 10.1109/TWC.2009.090494.
[30]
H. Rowaihyet al., “A survey of sensor selection schemes in wireless sensor networks,” Proc. SPIE, vol. 6562, May 2007, Art. no.
[31]
H. Wang, K. Yao, G. Pottie, and D. Estrin, “Entropy-based sensor selection heuristic for target localization,” in Proc. 3rd Int. Symp. Inf. Process. Sensor Netw. (IPSN), 2004, pp. 36–45. 10.1145/984622.984628.
[32]
Y. Chen, S. H. Hassani, A. Karbasi, and A. Krause, “Sequential information maximization: When is greedy near-optimal?” in Proc. 28th Conf. Learn. Theory, vol. 40, Jul. 2015, pp. 338–363. [Online]. Available: http://proceedings.mlr.press/v40/Chen15b.html
[33]
A. Krause and C. Guestrin, “Near-optimal nonmyopic value of information in graphical models,” in Proc. 21st Conf. Uncertainty Artif. Intell. (UAI), Arlington, VA, USA, 2005, pp. 324–331.
[34]
D. Golovin and A. Krause, “Adaptive submodularity: Theory and applications in active learning and stochastic optimization,” J. Artif. Intell. Res., vol. 42, no. 1, pp. 427–486, Nov. 2011.
[35]
J. A. Magliacane. (2008). SPLAT! A Terrestrial RF Path Analysis Application for Linux/Unix. [Online]. Available: https://www.qsl.net/kd2bd/splat.html

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  • (2023)Real-Time CRLB Based Antenna Selection in Planar Antenna ArraysIEEE Transactions on Wireless Communications10.1109/TWC.2022.322288522:6(4043-4056)Online publication date: 1-Jun-2023

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        cover image IEEE/ACM Transactions on Networking
        IEEE/ACM Transactions on Networking  Volume 30, Issue 1
        Feb. 2022
        473 pages

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        IEEE Press

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        Published: 16 August 2021
        Published in TON Volume 30, Issue 1

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        • (2023)Real-Time CRLB Based Antenna Selection in Planar Antenna ArraysIEEE Transactions on Wireless Communications10.1109/TWC.2022.322288522:6(4043-4056)Online publication date: 1-Jun-2023

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