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Consensus-Aware Sociopsychological Trust Model for Wireless Sensor Networks

Published:26 July 2016Publication History
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Abstract

Security plays a vital role in Wireless Sensor Networks (WSN) for providing reliability to the network. In WSN, where nodes, in addition to having their inbuilt capability of sensing, processing, and communicating data, also possess certain risks. These risks expose them to attacks and bring in many security challenges. Many researchers are engaged in developing innovative design paradigms to address security issues by developing trust management systems. In WSN, trust is important for the establishment of cooperation among the sensor nodes. The article presents a sociopsychological model for detecting fraudulent nodes in WSN. The three factors, viz. ability, benevolence, and integrity, are used for the computation of trust. Furthermore, the article provides a novel consensus-aware sociopsychological approach to deal even in the presence of higher number of fraudulent nodes than benevolent nodes. The proposed work has been implemented in the LabVIEW platform and extensive simulations were carried out to study its performance. Additionally, it is experimentally evaluated on a testbed of size 16 nodes to obtain results that demonstrate the accuracy and robustness of the proposed model.

References

  1. Jamal N. Al-Karaki and Ahmed E. Kamal. 2004. Routing techniques in wireless sensor networks: A survey. Wireless Commun. 11, 6, 6--28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Th. Arampatzis, John Lygeros, and S. Manesis. 2005. A survey of applications of wireless sensors and wireless sensor networks. In Proc. International Symposium on Intelligent Control. 719--724.Google ScholarGoogle Scholar
  3. Idris M. Atakli, Hongbing Hu, Yu Chen, Wei Shinn Ku, and Zhou Su. 2008. Malicious node detection in wireless sensor networks using weighted trust evaluation. In Proc. Spring Simulation Multiconference. 836--843. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Valentina Baljak, Kenji Tei, and Shinichi Honiden. 2012. Classification of faults in sensor readings with statistical pattern recognition. In Proc. International Conference on Sensor Technologies and Applications, 6, 270--276.Google ScholarGoogle Scholar
  5. Kavitha Bhaskaran, Joan Triay, and Vinod M. Vokkarane. 2011. Dynamic anycast routing and wavelength assignment in WDM networks using ant colony optimization (ACO). In Proc. International Conference on Communications. 1--6.Google ScholarGoogle Scholar
  6. Antoine Bordes, Seyda Ertekin, Jason Weston, and Lon Bottou. 2005. Fast kernel classiers with online and active learning. J. Mach. Learn. Res. 6, 1579--1619. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Lon Bottou and Chih-Jen Lin. 2007. Support vector machine solvers. Large Scale Kernel Machines, 301--320.Google ScholarGoogle Scholar
  8. Haiguang Chen, Huafeng Wu, Xi Zhou, and Chuanshan Gao. 2007. Agent-based trust model in wireless sensor networks. In Proc. ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 8, 119--124. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Daniel-loan Curiac, Constantin Volosencu, Alex Doboli, Octa Vian Dranga, and Tomasz Bednarz. 2007. Discovery of malicious nodes in wireless sensor networks using neural predictors. WSEAS Trans. Comput. Res. 2, 1, 38--43.Google ScholarGoogle Scholar
  10. Jing Deng, Richard Han, and Shivakant Mishra. 2003. Enhancing base station security in wireless sensor networks. Technical Report CU-CS-951-03, Department of Computer Science, University of Colorado.Google ScholarGoogle Scholar
  11. Miroslav Fiedler. 1973. Algebraic connectivity of graphs. Czech. Math. J. 23, 2, 298--305.Google ScholarGoogle ScholarCross RefCross Ref
  12. Susan T. Fiske and Shelley E. Taylor. 2013. Social Cognition. McGraw-Hill, New York, NY, 2.Google ScholarGoogle Scholar
  13. Saurabh Ganeriwal and Mani B. Srivastava. 2004. Reputation-based framework for high integrity sensor networks. In Proc. 2nd ACM Workshop on Security of Ad Hoc and Sensor Networks (SASN’04). 66--77. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Michael Hogan. 2013. Learning together and the challenge of collaboration. Retrieved on 2 December, 2013 from http://www.psychologytoday.com/blog/in-one-lifespan/201309/learning-together-and-the-challenge-collaboration.Google ScholarGoogle Scholar
  15. Catholijn M. Jonker and Jan Treur. 1999. Formal analysis of models for the dynamics of trust based on experiences. In The 9th European Workshop on Modelling Autonomous Agents in a Multi-Agent World: MultiAgent System Engineering. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Harold H. Kelley. 1967. Attribution theory in social psychology. In Nebraska Symposium on Motivation, 15, 192--238.Google ScholarGoogle Scholar
  17. Cristobald de Kerchove and Paul Van Dooren. 2010. Iterative filtering in reputation systems. SIAM J. Matrix Anal. Appl. 31, 4, 1812--1834. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Javier Lopez, Rodrigo Roman, Isaac Agudo, and Carmen Fernandez-Gago. 2010. Trust management systems for wireless sensor networks: Best practices. Comput. Commun. 33, 9, 1086--1093. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Christhu Raj MR, Edwin Prem Kumar G, and Kartheek Kusampudi. 2013. A survey on detecting selfish nodes in wireless sensor networks using different trust methodologies. Int. J. Eng. Adv. Technol. 2, 3, 197--200.Google ScholarGoogle Scholar
  20. Bertram F. Malle. 2003. Attributions as behavior explanations: Toward a new theory. Unpublished Article, University of Oregon.Google ScholarGoogle Scholar
  21. Gomez Marmol, Felix, and Gregorio Martnez Perez. 2011. Providing trust in wireless sensor networks using a bio-inspired technique. Telecommun. Syst. 46, 2, 163--180. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Roger C. Mayer, James H. Davis, and F. David Schoorman. 1995. An integrative model of organizational trust. Acad. Manag. Rev. 20, 3, 709--734.Google ScholarGoogle ScholarCross RefCross Ref
  23. Yuxin Meng, Wenjuan Li, and Lam-for Kwok. 2013. Evaluation of detecting malicious nodes using Bayesian model in wireless intrusion detection. Network Syst Secur 7873, 40--53.Google ScholarGoogle ScholarCross RefCross Ref
  24. Russell Merris. 1994. Laplacian matrices of graphs: A survey. Lin. Algebr. Appl. 197, 143176.Google ScholarGoogle Scholar
  25. Mohammad Momani, K. Khalid Aboura, and Subhash Challa. 2007. RBATMWSN: recursive Bayesian approach to trust management in wireless sensor networks. In The Third International Conference on Intelligent Sensors, Sensor Networks and Information.Google ScholarGoogle ScholarCross RefCross Ref
  26. Mohammad Momani, Subhash Challa, and Rami Alhmouz. 2008. BNWSN: Bayesian network trust model for wireless sensor networks. In Proc. Mosharaka International Conference on Communications, Computers and Applications. 110--115.Google ScholarGoogle ScholarCross RefCross Ref
  27. Mohammad Momani and Subhash Challa. 2010. Survey of trust models in different network domains. Int. J. Ad Hoc Sens. Ubiq. Comput. 1, 3, 1--19.Google ScholarGoogle ScholarCross RefCross Ref
  28. Kevin Ni, Nithya Ramanathan, Mohamed Nabil Hajj Chehade, Laura Balzano, et al. 2009. Sensor network data fault types. ACM Trans. Sensor Networks 5, 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Reza Olfati-Saber, J. Alex Fax, and Richard M. Murray. 2007. Consensus and cooperation in networked multi-agent systems. Proc. IEEE, 95, 1, 215--233.Google ScholarGoogle ScholarCross RefCross Ref
  30. Luis M. Oliveira and Joel J. Rodrigues. 2011. Wireless sensor networks: A survey on environmental monitoring. J. Commun. 6, 2.Google ScholarGoogle ScholarCross RefCross Ref
  31. Al-Sakib Khan Pathan, Hyung-Woo Lee, and Choong Seon Hong. 2006. “Security in wireless sensor networks: Issues and challenges,” in proc. International Conference Advanced Communication Technology, 2, 8, 1043--1048.Google ScholarGoogle ScholarCross RefCross Ref
  32. Maurizio Porfiri and Daniel J. Stilwell. 2007. Stochastic consensus over weighted directed networks. In Proc. American Control Conference. 1425--1430.Google ScholarGoogle Scholar
  33. Kyle Porter, David Poole, Jacek Kisyski, Shinjiro Sueda, Byron Knoll, Alan Mackworth, Holger Hoos, Peter Gorniak, and Cristina Conati. 2013. Belief and decision networks tool. AISpace. Retrieved on 2 December, 2013 from http://aispace.org/bayes/.Google ScholarGoogle Scholar
  34. Heena Rathore and Sushmita Jha. 2013. Bio-inspired machine learning based wireless sensor network security. World Congress on Nature and Biologically Inspired Computing (NaBIC). 140--146.Google ScholarGoogle ScholarCross RefCross Ref
  35. Heena Rathore, Venkataramana Badarla, Sushmita Jha, and Anupam Gupta. 2014. Novel approach for security in wireless sensor network using bio-inspirations. In Proc. IEEE International Conference on Communication Systems and Networks (COMSNETS), 6, 1--8.Google ScholarGoogle ScholarCross RefCross Ref
  36. Heena Rathore and Venkataramana Badarla. 2014. Primary-secondary immune response adaptation for wireless sensor network. In Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). IEEE, Los Alamitos, CA, 164--166.Google ScholarGoogle ScholarCross RefCross Ref
  37. Yenumula B. Reddy. 2012. Trust-based approach in wireless sensor networks using an agent to each cluster. Int. J. Secur. Priv. Trust. 1, 1, 19--36.Google ScholarGoogle Scholar
  38. Paul Resnick, Ko Kuwabara, Richard Zeckhauser, and Eric Friedman. 2000. Reputation systems. Commun. ACM, 43, 45--48. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Mohsen Rezvani, Elisa Bertino, Sanjay Jha, and Aleksandar Ignjatovic. 2013. A robust iterative filtering technique for wireless sensor networks in the presence of malicious attacks. In Proc. SenSys ACM Conference on Embedded Networked Sensor Systems, 11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Mohsen Rezvani, Elisa Bertino, Aleksandar Ignatovic, and Sanjay Jha. 2014. Secure data aggregation technique for wireless sensor networks in the presence of collusion attacks. IEEE Transactions on Dependable and Secure Computing.Google ScholarGoogle Scholar
  41. Shigen Shen, Yue Guangxue, Cao Qiying, and Yu Fei. 2011. A survey of game theory in wireless sensor networks security. J. Networks 6, 3, 521--532.Google ScholarGoogle ScholarCross RefCross Ref
  42. Shigen Shen, Changyuan Jiang, Hua Jiang, and Lizheng Guo et al. 2013. Evolutionary game based dynamics of trust decision in WSNs. In Proc. International Conference on Sensor Network Security Technology and Privacy Communication System (SNS and PCS). 1--4.Google ScholarGoogle Scholar
  43. Satwant Singh and Usvir Kaur. 2013. Review of trust based methodologies in WSNs. Int. J. Sci. Res. 2, 11, 184--185.Google ScholarGoogle Scholar
  44. H. H. Soliman, Noha A. Hikalb, and Nehal A. Sakrb. 2012. A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks. 13, 2, 225--238.Google ScholarGoogle Scholar
  45. Jianping Song, Song Han, Aloysius K.Mok, Deji Chen, and Mark Nixon. 2007. Centralized control of wireless sensor networks for real-time applications. IFAC Proceedings Volumes, 40, 22, 25--32.Google ScholarGoogle ScholarCross RefCross Ref
  46. Alec Woo, Terence Tong, and David Culler. 2003. Taming the underlying challenges of reliable multihop routing in sensor networks. In Proc. 1st International Conference on Embedded Networked Sensor Systems. ACM, New York, NY, 14--27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Michael Wooldridge. 2002. An Introduction to Multiagent Systems. Wiley, New York, NY. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Guoxing Zhan, Weisong Shi, and Julia Deng. 2012. Design and implementation of TARF: A trust-aware routing framework for WSNs. IEEE Trans. Dependable and Secure Computing 9, 2, 184--197. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Liu Zhiyuan, Zhang Zhigang, Liu SongSong, and Ke YeQing. 2011. A trust model based on Bayes theorem in WSNs. In Proc. International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), 7, 1--4.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Transactions on Sensor Networks
      ACM Transactions on Sensor Networks  Volume 12, Issue 3
      August 2016
      304 pages
      ISSN:1550-4859
      EISSN:1550-4867
      DOI:10.1145/2976745
      • Editor:
      • Chenyang Lu
      Issue’s Table of Contents

      Copyright © 2016 ACM

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      New York, NY, United States

      Publication History

      • Published: 26 July 2016
      • Revised: 1 March 2016
      • Accepted: 1 March 2016
      • Received: 1 November 2014
      Published in tosn Volume 12, Issue 3

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