ABSTRACT
Wireless Sensor Networks (WSNs) are arising from the proliferation of Micro-Electro-Mechanical Systems (MEMS) technology as an important new area in wireless technology. They are composed of tiny devices which monitor physical or environmental conditions such as temperature, pressure, motion or pollutants, etc. Moreover, the accuracy of individual sensor node readings is decisive in WSN applications. Hence, detecting nodes with faulty sensors can strictly influence the network performance and extend the network life-time. In this paper, we propose a new approach for faulty sensor node detection in WSNs based on Copula theory. The obtained experimental results on real datasets collected from real sensor networks show the effectiveness of our approach.1
- Ian F Akyildiz, Weilian Su, Yogesh Sankarasubramaniam, and Erdal Cayirci. 2002. Wireless sensor networks: a survey. Computer networks 38, 4 (2002), 393--422. Google ScholarDigital Library
- Jinran Chen, Shubha Kher, and Arun Somani. 2006. Distributed fault detection of wireless sensor networks. In Proceedings of the 2006 workshop on Dependability issues in wireless ad hoc networks and sensor networks. ACM, 65--72. Google ScholarDigital Library
- Intel Lab Data. 2004. (2004). http://db.lcs.mit.edu/labdata/labdata.htmlGoogle Scholar
- Paul Deheuvels. 1979. La fonction de dépendance empirique et ses propriétés. Un test non paramétrique d'indépendance. Acad. Roy. Belg. Bull. Cl. Sci. (5) 65, 6 (1979), 274--292.Google Scholar
- William F Eddy. 1977. A new convex hull algorithm for planar sets. ACM Transactions on Mathematical Software (TOMS) 3, 4 (1977), 398--403. Google ScholarDigital Library
- Christian Genest and Anne-Catherine Favre. 2007. Everything you always wanted to know about copula modeling but were afraid to ask. Journal of hydrologic engineering 12, 4 (2007), 347--368.Google ScholarCross Ref
- Shuo Guo, Heng Zhang, Ziguo Zhong, Jiming Chen, Qing Cao, and Tian He. 2014. Detecting faulty nodes with data errors for wireless sensor networks. ACM Transactions on Sensor Networks (TOSN) 10, 3 (2014), 40. Google ScholarDigital Library
- Shah Ahsanul Haque, Mustafizur Rahman, and Syed Mahfuzul Aziz. 2015. Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare. Sensors 15, 4 (2015), 8764--8786.Google ScholarCross Ref
- Zhiping Kang, Honglin Yu, Qingyu Xiong, and Haibo Hu. 2014. Spatial-temporal correlative fault detection in wireless sensor networks. International Journal of Distributed Sensor Networks 2014 (2014).Google Scholar
- Safdar Abbas Khan, Boubaker Daachi, and Karim Djouani. 2012. Application of fuzzy inference systems to detection of faults in wireless sensor networks. Neurocomputing 94 (2012), 111--120. Google ScholarDigital Library
- Farinaz Koushanfar, Miodrag Potkonjak, and Alberto Sangiovanni-Vincentelli. 2003. On-line fault detection of sensor measurements. In Sensors, 2003. Proceedings of IEEE, Vol. 2. IEEE, 974--979.Google ScholarCross Ref
- Farid Lalem, Rahim Kacimi, Ahcène Bounceur, and Reinhardt Euler. 2016. Boundary node failure detection in wireless sensor networks. In Networks, Computers and Communications (ISNCC), 2016 International Symposium on. IEEE, 1--6.Google ScholarCross Ref
- Roger B Nelsen. 2007. An introduction to copulas. Springer Science & Business Media. Google ScholarDigital Library
- Marek Omelka, Irène Gijbels, Noël Veraverbeke, and others. 2009. Improved kernel estimation of copulas: weak convergence and goodness-of-fit testing. The Annals of Statistics 37, 5B (2009), 3023--3058.Google ScholarCross Ref
- R Development Core Team. 2011. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ ISBN 3-900051-07-0.Google Scholar
- Ludger Rüschendorf. 2009. On the distributional transform, Sklar's theorem, and the empirical copula process. Journal of Statistical Planning and Inference 139, 11 (2009), 3921--3927.Google ScholarCross Ref
- M Sklar. 1959. Fonctions de répartition à n dimensions et leurs marges. Université Paris 8.Google Scholar
- Weili Wu, Xiuzhen Cheng, Min Ding, Kai Xing, Fang Liu, and Ping Deng. 2007. Localized outlying and boundary data detection in sensor networks. Knowledge and Data Engineering, IEEE Transactions on 19, 8 (2007), 1145--1157. Google ScholarDigital Library
Recommendations
Faulty Data Detection in Wireless Sensor Networks Based on Copula Theory
BDAW '16: Proceedings of the International Conference on Big Data and Advanced Wireless TechnologiesWireless Sensor Networks (WSNs) are a powerful instrument for monitoring and recording physical phenomena. Very often the quality of the sensed data collected by sensor nodes is affected by noise and errors, events, and malicious attacks. Also, the ...
Relay Node Placement in Wireless Sensor Networks
A wireless sensor network consists of many low-cost, low-power sensor nodes, which can perform sensing, simple computation, and transmission of sensed information. Long distance transmission by sensor nodes is not energy efficient since energy ...
A Faulty Node Detection Algorithm based on Spatial-temporal Cooperation in Wireless Sensor Networks*
Wireless Sensor Networks (WSNs) consist of a large number of nodes with limited resources and widely used in social production and life, especially in harsh environments and real-time applications. The common sensor nodes transmit and send the sensed ...
Comments