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Bayesian Network Based Fault Tolerance in Distributed Sensor Networks

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A Distributed Sensor Network (DSN) consists of a set of sensors that are interconnected by a communication network. DSN is capable of acquiring and processing signals, communicating, and performing simple computational tasks. Such sensors can detect and collect data concerning any sign of node failure, earthquakes, floods and even a terrorist attack. Energy efficiency and fault-tolerance network control are the most important issues in the development of DSNs. In this work, two methods of fault tolerance are proposed: fault detection and recovery to achieve fault tolerance using Bayesian Networks (BNs). Bayesian Network is used to aid reasoning and decision making under uncertainty. The main objective of this work is to provide fault tolerance mechanism which is energy efficient and responsive to network using BNs. It is also used to detect energy depletion of node, link failure between nodes, and packet error in DSN. The proposed model is used to detect faults at node, sink and network level faults (link failure and packet error). The proposed fault recovery model is used to achieve fault tolerance by adjusting the network of the randomly deployed sensor nodes based on of its probabilities. Finally, the performance parameters for the proposed scheme are evaluated.
Rocznik
Tom
Strony
44--52
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
autor
  • Department of Information Science and Engineering, Basaveshwar Engineering College, Karnataka, India
autor
  • Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology, Karnataka, India
Bibliografia
  • [1] S. S. Iyengar, T. Ankit, and R. R. Brooks, “An overview”, in Distributed Sensors Network”, S. S. Iyengar and R. R. Brooks, Eds. Chapman & Hall/CRC, 2004.
  • [2] B. B. Lokesh and N. Nalini, “Energy aware based fault tolerance approach for topology control in distributed sensor networks”, Int. J. High Speed Netw., vol. 18, no. 3, pp. 197–210, 2012.
  • [3] I. Ben-Gal, “Bayesian networks”, in Encyclopedia of Statistics in Quality and Reliability, F. Ruggeri, R. Kenett, and F. Faltin, Eds. Wiley, 2007.
  • [4] R. E. Neapolitan, Learning Bayesian Networks [Online]. Available: http://www.cs.technion.ac.il/_dang/books/Learning%20Bayesian%20Networks
  • [5] I. Saha, L. K. Sambasivan, R. K. Patro, and S. K. Ghosh, “Distributed fault-tolerant topology control in static and mobile wireless sensor networks”, in Proc. 2nd Int. Conf. Commun. Syst. Softw. Middlew. COMSWARE 2007, Bangalore, India, 2007, pp. 1–8.
  • [6] J. Liu and L. Baochun, “Distributed topology control in wireless sensor networks with asymmetric links”, in Proc. IEEE Global Telecommun. Conf. GLOBECOM 2003, San Francisco, CA, USA, 2003, vol. 3, pp. 1257–1262.
  • [7] R. Arroyo-Valles, A. G. Marques, J. J. Vinagre-Diaz, and J. CidSueiro, “A Bayesian decision model for intelligent routing in sensor networks”, in Proc. 3rd Int. Symp. Wirel. Commun. Syst. ISWCS 2006, Valencia, Spain, 2006, pp. 103–107.
  • [8] P. Lilia and H. Qi, “A survey of fault management in wireless sensor networks”, J. Netw. Syst. Managem., vol. 15, no. 2, pp. 171–190, 2007.
  • [9] C. Mihaela, Y. Shuhui, and W. Jie, “Fault-tolerant topology control for heterogeneous wireless sensor networks”, in Proc. IEEE 4th Int. Conf. Mob. Adhoc and Sensor Sys. MASS 2007, Pisa, Italy, 2007, pp. 1–9.
  • [10] K. Bhaskar and I. Sitharama, “Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks”, IEEE Trans. Comp., vol. 53, no. 3, pp. 241–250, 2004.
  • [11] M. Mohammad, C. Subhash, and A. Rami, “Bayesian fusion algorithm for inferring trust in wireless sensor networks”, J. of Netw., vol. 5, no. 7, pp. 815–822, 2010.
  • [12] C. Mihaela, Y. Shuhui, and W. Jie, “Algorithms for fault-tolerant topology control for heterogeneous wireless sensor networks”, IEEE Trans. Parall. Distrib. Syst., vol. 19, no. 4, pp. 545–558, 2008.
  • [13] N. Ababneh, A. Viglas, H. Labiod, and N. Boukhatem, “ECTC: Energy efficient topology control algorithm for wireless sensor networks”, in Proc. 10th IEEE Int. Symp. World of Wirel., Mob. Multim. Netw. & Workshops WOWMOM 2009, Kos Island, Greece, 2009.
  • [14] A. Abolfazl, D. Arash, K. Ahmad, and B. Neda, “Fault detection and recovery in wireless sensor network using clustering”, Int. J. Wirel. & Mob. Netw., vol. 3, no. 1, pp. 130–138, 2011.
  • [15] H. Xiaofeng, C. Xiang, L. L. Errol, and S. Chien-Chung, Fault-tolerant relay node placement in heterogeneous wireless sensor networks”, IEEE Trans. Mob. Comput., vol. 9, no. 5, pp. 643–656, 2010.
  • [16] J. L. Bredin, E. D. Demaine, M. T. Hajiaghayi, and D. Rus, “Deploying sensor networks with guaranteed fault tolerance”, IEEE/ACM Trans. Netw., vol. 18, no. 1, pp. 216–228, 2010.
  • [17] R. H. Abedi, S. Ghani, and S. Haider, “Selection of cluster heads in wireless sensor networks using bayesian network”, in Proc. Int. Conf. Comp., Electr. Sys. Sci., and Engin. ICCESSE 2010, Venice, Italy, 2010.
  • [18] S. Nishant and S. Upinderpal, “A location based approach to prevent wormhole attack in wireless sensor networks”, Int. J. Adv. Res. Comp. Sci. Softw. Engin., vol. 4, no. 1, pp. 840–845, 2014.
  • [19] H. Taub and D. L. Schilling, Principles of Communication Systems. Columbus, OH, USA: McGraw-Hill, 1986.
  • [20] G. Miao, N. Himayat, abd G. Y. Li, “Energy efficient link adaptation in frequency-selective channels”, IEEE Trans. Commun., vol. 58, no. 2, pp. 545–554, 2010.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3377641d-cb15-4760-bdb3-3921eff5e728
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