Mohamed-Ali Moussa
ABSTRACT
In this paper, we focus on collecting data and detecting anomalies in Wireless Sensor Networks (WSNs) while optimizing the use of sensor computational and energetic resources. Recently, Compressive Sensing (CS)-based solutions had been the subject of extensive studies for the design of efficient data gathering solutions in WSNs. However, existing CSbased approaches are very sensitive to outlying values and do not offer a proper tool to deal with the presence of anomalies. Moreover, CS data gathering schemes are based only on the spatial correlation pattern between sensory data and ignore an important feature which is the temporal correlation between sensor readings. This paper introduces a novel CS-based data gathering solution that allows to integrate the spatial and temporal correlation features into the data recovering process. Furthermore, the proposed approach is built in such a way that it also allows to detect and correct eventual anomalies. We propose a general formulation of data gathering and anomaly detection problem as a tractable convex optimization problem on the Hilbert space of data measurements and anomalies. Besides, we design a new class of primal-dual algorithms to solve the resulting optimization problem. We evaluate the efficiency of our method by running extensive simulations on two real datasets. We demonstrate that the proposed algorithm achieves good data recovery and anomaly detection performance and outperforms the main state-of-the-art technique addressing the same problem.
- I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. 2002. Wireless sensor networks: a survey. Computer Networks 38 (2002), 393--422. Google Scholar
Digital Library
- J.-F Cai, E. J. Cand'es, and Z. Shen. 2010. A Singular Value Thresholding Algorithm for Matrix Completion. SIAM Journal on Optimization 20, 4 (March 2010), 1956--1982. Google ScholarDigital Library
- Emmanuel J. Candes, Justin K. Romberg, and Terence Tao. 2006. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics 59, 8 (2006), 1207--1223. Google ScholarCross Ref
- J. Cand'es and M. B. Wakin. 2008. An Introduction To Compressive Sampling -- A sensing/sampling paradigm that goes against ... 25 (Mar. 2008), 21--30.Google Scholar
- L. Chong, W. Feng, S. Jun, and W. C. Chang. 2009. Compressive Data Gathering for Large-Scale Wireless Sensor Networks. In ACM Mobicom. Association for Computing Machinery, 145--156.Google Scholar
- P. L. Combettes and J.-C. Pesquet. 2008. A proximal decomposition method for solving convex variational inverse problems. Inverse Problems 24, 6 (Dec. 2008). Google ScholarCross Ref
- P. L. Combettes and J.-C. Pesquet. 2010. Proximal Splitting Methods in Signal Processing. In Fixed-Point Algorithms for Inverse Problems in Science and Engineering, Burachik R.S. Combettes P.L. Elser V. Luke D.R.; Wolkowicz H. (Eds.) Bauschke, H.H. (Ed.). Springer, New York, 185--212.Google Scholar
- P. L. Combettes and J.-C. Pesquet. 2011. Primal-dual splitting algorithm for solving inclusions with mixtures of composite, lipschitzian, and parallel-sum type monotone operators. Set-Valued and Variational Analysis 20, 2 (june 2011), 307--330. Google ScholarCross Ref
- Ni. Komodakis and J-C. Pesquet. 2015. Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large- Scale Optimization Problems. IEEE Signal Processing Magazine 32, 6 (Nov. 2015), 31--54. Google ScholarCross Ref
- R. Kwitt, P. Meerwald, and A. Uhl. 2009. Color-image watermarking using multivariate power-exponential distribution. In IEEE International Conference on Image Processing. Cairo, Egypt, 4245 --4248. Google ScholarCross Ref
- S. R. Madden, M. J. Franklin, J. M. Hellerstein, and W. Hong. 2005. TinyDB: An Acquisitional Query Processing System for Sensor Networks. ACM transactions on database systems 30, 1 (March 2005), 122--173. Google ScholarDigital Library
- A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, and J. Anderson. 2002. Wireless Sensor Networks for Habitat Monitoring. In ACM International Workshop on Wireless Sensor Networks and Applications. New York, NY, USA, 88--97. Google ScholarDigital Library
- Y. Marnissi, A. Benazza-Benyahia, E. Chouzenoux, and J-C. Pesquet. 2013. Generalized multivariate exponential power prior for wavelet-based multichannel image restoration. In IEEE International Conference on Image Processing. Melbourne, Australia, 2402--2406. Google ScholarCross Ref
- L. Mo, Y. He, Y. Liu, J. Zhao, S. Tang, X. Li, and G.i Dai. 2009. Canopy Closure Estimates with GreenOrbs: Sustainable Sensing in the Forest. ACM SENSY, Berkeley, CA, USA, 1--6.Google Scholar
- Z. Yang, Li, and Y Liu. 2007. Sea Depth Measurement with Restricted Floating Sensors. In IEEE RTSS. Tucson, AR, USA, 469 --478.Google Scholar
Index Terms
- Spatio-Temporal Compressive Sensing Technique for Data Gathering and Anomaly Detection in Wireless Sensor Networks
Recommendations
Compressive data gathering with low-rank constraints for Wireless Sensor networks
In this paper, a novel compressive data gathering with low-rank constraints is proposed for efficient data gathering and accurate recovery in wireless sensor networks. The proposed method utilizes both the low-rank feature of the data matrix by ...
Accurate compressive data gathering in wireless sensor networks using weighted spatio-temporal compressive sensing
The high number of transmissions in sensor nodes having a limited amount of energy leads to a drastic decrease in the lifetime of wireless sensor networks. For dense sensor networks, the provided data potentially have spatial and temporal correlations. ...
A Chain-Based Data Gathering Protocol Under Compressive Sensing Framework for Wireless Sensor Networks
ICCIS '13: Proceedings of the 2013 International Conference on Computational and Information SciencesThis paper proposes an efficient data gathering protocol for large scale wireless sensor networks by using the compressive sensing technology. All sensor nodes construct a chain by a greedy algorithm. Data packets are transmitted through this chain from ...
Comments