A Gaussian Process Based Technique of Efficient Sensor Selection for Transmitter Localization

Aritrik Ghosh, Arani Bhattacharya

Abstract: Spectrum monitoring via crowdsourcing is a technique that promises to enable opportunistic spectrum access. Crowdsourcing aims to provide incentives to users to deploy a large number of cheap but potentially noisy sensors. The sensors all send their data to a fusion center, where typically some algorithms are used to remove the noise from the data. Such crowdsourced monitoring of spectrum has been shown to be feasible in practice in multiple studies. One of the key goals of such monitoring is to identify any users that are violating the protocols of accessing spectrum. While a number of crowdsourcing techniques to identify such violations have been proposed, a key challenge that remains is to minimize the cost of data consumption and energy of running the sensors. In this work, we propose sequential probing of sensors to accurately localize/identify such transmitters. We formulate this as a Gaussian Process multi-armed bandit problem, and use a widely known solution technique called Upper Confidence Bound to solve it. We next observe that such sequential probing incurs additional latency, and use batched selection of sensors in few rounds to reduce latency. We show that instead of naively selecting sensors in parallel batches, an intelligent technique of selecting sensors called Gaussian Process Adaptive Upper Confidence Bound (GP-AUCB) can lead to selection of sensors that can lead to more accurate localization. Finally, we show the tradeoff between accuracy of localization, latency incurred and number of selected sensors via simulations.