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Wireless Communications and Mobile Computing
Volume 2017 (2017), Article ID 6345316, 11 pages
Research Article

Compressed RSS Measurement for Communication and Sensing in the Internet of Things

1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
2State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
3Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China
4Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA

Correspondence should be addressed to Yanchao Zhao

Received 29 April 2017; Accepted 6 July 2017; Published 7 August 2017

Academic Editor: Feng Wang

Copyright © 2017 Yanchao Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The receiving signal strength (RSS) is crucial for the Internet of Things (IoT), as it is the key foundation for communication resource allocation, localization, interference management, sensing, and so on. Aside from its significance, the measurement process could be tedious, time consuming, inaccurate, and involving human operations. The state-of-the-art works usually applied the fashion of “measure a few, predict many,” which use measurement calibrated models to generate the RSS for the whole networks. However, this kind of methods still cannot provide accurate results in a short duration with low measurement cost. In addition, they also require careful scheduling of the measurement which is vulnerable to measurement conflict. In this paper, we propose a compressive sensing- (CS-) based RSS measurement solution, which is conflict-tolerant, time-efficient, and accuracy-guaranteed without any model-calibrate operation. The CS-based solution takes advantage of compressive sensing theory to enable simultaneous measurement in the same channel, which reduces the time cost to the level of (where is the network size) and works well for sparse networks. Extensive experiments based on real data trace are conducted to show the efficiency of the proposed solutions.