Reduce Effect of Dependent Malicious Sensor Nodes in WSNs using Pairs Counting and Fake Packets
Keywords:
Wireless Sensor Networks (WSN), Wireless Sensor Networks, dependent malicious sensor nodes, Dependent Malicious Sensor Nodes, detection and prevention methods, Detection and Prevention methodsAbstract
In this paper, we propose a new technique for the enhancement of target detection in Wireless Sensor Networks (WSNs) in which sensor nodes are responsible for taking binary decisions about the presence or absence of a given target and reporting the output to the fusion center. We introduce the algorithm; Fail Silent Pair (FSP) to calculate global decision in the fusion center. The FSP algorithm randomly distributes all sensor nodes into pairs then considers pairs of the same local decision. Also, we present new detection and prevention methods to reduce the effect of dependent malicious sensor nodes. The detection method is based on the deception of suspicious sensor nodes with fake packets to detect a subset of the malicious sensor nodes, as these nodes eavesdrop on other sensor nodes packets to use their local decisions as a reference to build an intelligent decision. While the prevention method allows the fusion center to correct local decisions of some malicious sensor nodes with identified strategies, assisting in the increase of the accuracy of global decisions. We introduce a mathematical analysis to verify our methods, in addition to simulation experiments to validate our technique.
References
Webster JG, Eren H. Measurement, instrumentation, and sensors handbook: spatial, mechanical, thermal,
and radiation measurement. CRC press; 2017.
Demirbas M. Wireless sensor networks for monitoring of large public buildings. 2005;.
Kim T, Kim IH, Sun Y, Jin Z. Physical layer and medium access control design in energy efficient
sensor networks: An overview. IEEE Transactions on Industrial Informatics. 2015;11(1):2-15.
Salahuddin MA, et al. Introduction to wireless sensor networks. In: Wireless sensor and mobile ad-hoc
networks. Springer; 2015. p. 3-32.
Buratti C, Conti A, Dardari D, Verdone R. An overview on wireless sensor networks technology and
evolution. Sensors. 2009;9(9):6869-6896.
Li J, Andrew LL, Foh CH, Zukerman M, Chen HH. Connectivity, coverage and placement in wireless
sensor networks. Sensors. 2009;9(10):7664-7693.
Spachos P, Hatzinakos D. Real-time indoor carbon dioxide monitoring through cognitive wireless
sensor networks. IEEE sensors journal. 2016;16(2):506-514.
Lara R, Ben´ıtez D, Caaman˜o A, Zennaro M, Rojo-A´ lvarez JL. On real-time performance evaluation of
volcano-monitoring systems with wireless sensor networks. IEEE Sensors Journal. 2015;15(6):3514-
Antonopoulos A, Verikoukis C. Misbehavior detection in the Internet of Things: A network-codingaware
statistical approach. In: Industrial Informatics (INDIN), 2016 IEEE 14th International Conference
on. IEEE; 2016. p. 1024-1027.
Zhang Y, He S, Chen J. Data gathering optimization by dynamic sensing and routing in rechargeable
sensor networks. IEEE/ACM Transactions on Networking. 2016;24(3):1632-1646.
Dˆamaso A, Freitas D, Rosa N, Silva B, Maciel P. Evaluating the power consumption of wireless sensor
network applications using models. Sensors. 2013;13(3):3473-3500.
Duriˇsi´c MP, Tafa Z, Dimi´c G, Milutinovi´c V. A survey of military applications of wireless sensor
networks. In: Embedded Computing (MECO), 2012 Mediterranean Conference on. IEEE; 2012. p.
-199.
Yu Y, Li K, Zhou W, Li P. Trust mechanisms in wireless sensor networks: Attack analysis and countermeasures.
Journal of Network and computer Applications. 2012;35(3):867-880.
Anwar RW, Bakhtiari M, Zainal A, Abdullah AH, Qureshi KN, Computing F, et al. Security issues and
attacks in wireless sensor network. World Applied Sciences Journal. 2014;30(10):1224-1227.
Wang Y, Attebury G, Ramamurthy B. A survey of security issues in wireless sensor networks. 2006;.
Althunibat S, Antonopoulos A, Kartsakli E, Granelli F, Verikoukis C. Countering Intelligent-
Dependent Malicious Nodes in Target Detection Wireless Sensor Networks. IEEE Sensors Journal.
;16(23):8627-8639.
Curiac DI, Banias O, Dragan F, Volosencu C, Dranga O. Malicious node detection in wireless sensor
networks using an autoregression technique. In: Networking and Services, 2007. ICNS. Third
International Conference on. IEEE; 2007. p. 83-83.
Plastoi M, Volosencu C, Banias O, Tudoroiu R, Curiac DI, Doboli A. Integrated System for Malicious
Node Discovery and Self-destruction inWireless Sensor Networks. International Journal on Advances
in Networks and Services Volume 2, Numbers 2&3, 2009. 2009;.
Di Pietro R, Mancini LV, Soriente C, Spognardi A, Tsudik G. Catch me (if you can): Data survival
in unattended sensor networks. In: Pervasive Computing and Communications, 2008. PerCom 2008.
Sixth Annual IEEE International Conference on. IEEE; 2008. p. 185-194.
Hiregoudar S, Manjunath K. Effective Malicious Node Detection and Data Fusion under Byzantine
Attacks. 2017;.
Pires W, de Paula Figueiredo TH, Wong HC, Loureiro AAF. Malicious node detection in wireless
sensor networks. In: Parallel and distributed processing symposium, 2004. Proceedings. 18th international.
IEEE; 2004. p. 24.
Zurawski R. Embedded systems handbook. CRC press; 2005.
Additional Files
Published
Issue
Section
License
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.