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- Bhattacharya, Arani, et al.
(författare)
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Selection of Sensors for Efficient Transmitter Localization
- 2020
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Ingår i: IEEE INFOCOM 2020 - IEEE conference on computer communications. - : IEEE. ; , s. 2410-2419
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Konferensbidrag (refereegranskat)abstract
- We address the problem of localizing an (illegal) 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 illegal 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.
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| 2. |
- Zhan, Caitao, et al.
(författare)
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Efficient Localization of Multiple Intruders in Shared Spectrum System
- 2020
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Ingår i: 2020 19Th ACM/IEEE International Conference On Information Processing In Sensor Networks (Ipsn 2020). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 205-216
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Konferensbidrag (refereegranskat)abstract
- We address the problem of localizing multiple intruders (unauthorized transmitters) using a distributed set of sensors in the context of a shared spectrum system. In contrast to single transmitter localization, multiple transmitter localization (MTL) has not been thoroughly studied. In shared spectrum systems, it is important to be able to localize simultaneously present multiple intruders to effectively protect a shared spectrum from malware-based, jamming, or other multi-device unauthorized-usage attacks. The key challenge in solving the MTL problem comes from the need to "separate" an aggregated signal received from multiple intruders into separate signals from individual intruders. Furthermore, in a shared spectrum paradigm, presence of an evolving set of authorized users (e.g., primary and secondary users) adds to the challenge. In this paper, we propose an efficient algorithm for the MTL problem based on the hypothesis-based Bayesian approach called MAP. Direct application of the MAP approach to the MTL problem incurs prohibitive computational and training cost. In this work, we develop optimized techniques based on MAP with significantly improved computational and training costs. In particular, we develop a novel interpolation method, ILDW, which helps minimize the training cost. We generalize our techniques via online-learning to the setting wherein there may be a set of dynamically-changing authorized users present in the background. We evaluate our developed techniques on large-scale simulations as well as on small-scale indoor and outdoor testbeds. Our experiments demonstrate that our technique outperforms the prior approaches by significant margins, i.e., error up to 74% less in large-scale simulations and 30% less in real-world testbeds.
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