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Cost-aware compressive sensing for networked sensing systems

Xu, L; Hao, X; Lane, ND; Liu, X; Moscibroda, T; (2015) Cost-aware compressive sensing for networked sensing systems. In: Nath, S and Krishnamachari, B and Rowe, A and Zhao, F, (eds.) Proceedings of the 14th International Conference on Information Processing in Sensor Networks. (pp. pp. 130-141). Association for Computing Machinery: Seattle, Washington. Green open access

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Abstract

Compressive Sensing is a technique that can help reduce the sampling rate of sensing tasks. In mobile crowdsensing applications or wireless sensor networks, the resource burden of collecting samples is often a major concern. Therefore, compressive sensing is a promising approach in such scenarios. An implicit assumption underlying compressive sensing - both in theory and its applications - is that every sample has the same cost: its goal is to simply reduce the number of samples while achieving a good recovery accuracy. In many networked sensing systems, however, the cost of obtaining a specific sample may depend highly on the location, time, condition of the device, and many other factors of the sample. In this paper, we study compressive sensing in situations where different samples have different costs, and we seek to find a good trade-off between minimizing the total sample cost and the resulting recovery accuracy. We design CostAware Compressive Sensing (CACS), which incorporates the cost-diversity of samples into the compressive sensing framework, and we apply CACS in networked sensing systems. Technically, we use regularized column sum (RCS) as a predictive metric for recovery accuracy, and use this metric to design an optimization algorithm for finding a least cost randomized sampling scheme with provable recovery bounds. We also show how CACS can be applied in a distributed context. Using traffic monitoring and air pollution as concrete application examples, we evaluate CACS based on large-scale real-life traces. Our results show that CACS achieves significant cost savings, outperforming natural baselines (greedy and random sampling) by up to 4x.

Type: Proceedings paper
Title: Cost-aware compressive sensing for networked sensing systems
Event: IPSN '15, 14th International Conference on Information Processing in Sensor Networks, Seattle, Washington, April 13 - 16, 2015
ISBN-13: 9781450334754
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/2737095.2737105
Publisher version: http://dx.doi.org/10.1145/2737095.2737105
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Crowdsensing, compressive sensing, resource-efficiency.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1503683
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