Fast and Efficient Online Selection of Sensors for Transmitter Localization
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The increase in cost and usage of RF spectrum has made it increasingly necessary to monitor its usage and protect it from unauthorized use. A number of prior studies have designed algorithms to localize unauthorized transmitters using crowdsourced sensors. To reduce the cost of crowdsourcing, these studies select the most relevant sensors a priori to localize such transmitters. In this work, we instead argue for online selection to localize such transmitters. Online selection can lead to more accurate localization using limited number of sensors, as compared to selecting sensors a priori, albeit at the cost of higher latency. To account for the trade-off between accuracy and latency, we add a constraint on the number of selection rounds. For the case where the number of rounds is equal to the number of selected sensors, we propose a heuristic based on Thompson Sampling and show using trace-driven simulation that it provides 23\% better accuracy compared to a number of proposed baseline algorithms. For restricted number of rounds, we show that using conventional parallel version of the modified Thompson Sampling which selects equal number of sensors in each round results in a substantial reduction in accuracy. To this end, we propose a strategy of selecting decreasing number of sensors in subsequent rounds of the modified Parallel Thompson Sampling. Our evaluation shows that the proposed heuristic leads to only 3 % reduction in accuracy in contrast to 22 % using modified Parallel Thompson Sampling, when we select 50 sensors in 20 rounds.