STCDG: An Efficient Data Gathering Algorithm Based on Matrix Completion for Wireless Sensor Networks

• Published in: IEEE transactions on wireless communications 2013-02, Vol.12 (2), p.850-861
• Main Authors: Cheng, Jie, Ye, Qiang, Jiang, Hongbo, Wang, Dan, Wang, Chonggang
• Format: Article
• Language: English
• Subjects: Accuracy; Applied sciences; Compressed sensing; compressive sensing; data gathering; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Information, signal and communications theory; matrix completion; Minimization; Optimization; Sampling, quantization; Services and terminals of telecommunications; Signal and communications theory; Signal, noise; Stability analysis; Switching and signalling; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Telemetry. Remote supervision. Telewarning. Remote control; Thermal stability; Wireless sensor networks
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2012.121412.120148

Data gathering in sensor networks is required to be efficient, adaptable and robust. Recently, compressive sensing (CS) based data gathering shows promise in meeting these requirements. Existing CS-based data gathering solutions require that a transform that best sparsifies the sensor readings should be used in order to reduce the amount of data traffic in the network as much as possible. As a result, it is very likely that different transforms have to be determined for varied sensor networks, which seriously affects the adaptability of CS-based schemes. In addition, the existing schemes result in significant errors when the sampling rate of sensor data is low (equivalent to the case of high packet loss rate) because CS inherently requires that the number of measurements should exceed a certain threshold. This paper presents STCDG, an efficient data gathering scheme based on matrix completion. STCDG takes advantage of the low-rank feature instead of sparsity, thereby avoiding the problem of having to be customized for specific sensor networks. Besides, we exploit the presence of the short-term stability feature in sensor data, which further narrows down the set of feasible readings and reduces the recovery errors significantly. Furthermore, STCDG avoids the optimization problem involving empty columns by first removing the empty columns and only recovering the non-empty columns, then filling the empty columns using an optimization technique based on temporal stability. Our experimental results indicate that STCDG outperforms the state-of-the-art data gathering algorithms in terms of recovery error, power consumption, lifespan, and network capacity.