ABSTRACT
The capability to support plethora of new diverse applications has placed Wireless Sensor Network (WSN) technology at threshold of an era of significant potential growth. In this paper, an attempt is made to analyze effectiveness of various available approaches toward pattern recognition in WSNs while introducing a novel method using a highly distributed associative memory technique called Graph Neuron (GN). The proposed approach not only enjoys from conserving the limited power resources of resource-constrained sensor nodes but also can be scaled effectively to address scalability issues which are of primary concern in wireless sensor networks. In addition, to overcome the issues of crosstalk available in the GN algorithm, Hierarchical Graph Neuron (HGN) an extended model of GN is presented which not only promises to deliver accurate results but also can be used for diverse types of applications in a multidimensional domain.
- M. Welsh, D. Malan, B. Duncan, T. Fulford-Jones, S. Moulton, "Wireless Sensor Networks for Emergency Medical are", GE Global Research Conference, Harvard University and Boston University School of Medicine, Boston, MA, March 2004.Google Scholar
- J. Rabaey, "Ultra low-power computation and communication enables ambient intelligence", In Proceedings of the Smart Objects Conference, Grenoble, 2003.Google Scholar
- J. M. Kahn, R. H. Katz, K. S. J. Pister, "Mobile networking for smart dust", In Proceedings of the ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom 99), Seattle, WA, August 17--19, 1999. Google ScholarDigital Library
- A. I. Khan, P. Mihailescu, "Parallel pattern recognition computations within a wireless sensor network", Proceedings of the 17th International Conference on Pattern Recognition, Volume 1, August 2004, Page(s): 777--780. Google ScholarDigital Library
- F. Jensen, "Bayesian Networks and Decision Graphs", Springer, New York, USA, 2001. Google ScholarDigital Library
- J. Pearl, "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference", Morgan Kaufmann, 1997. Google ScholarDigital Library
- C. Kruegel, D. Mutz, W. Robertson, F. Valeur, "Bayesian Event classification for intrusion detection", In Proceedings of the 19th Annual Computer Security Applications Conference, 2003, Page(s): 14--23 Google ScholarDigital Library
- J. Lawrence, "Introduction to Neural Networks", California Scientific Software Press, 1994 Google ScholarDigital Library
- D. Bolzoni, S. Etalle, P. Hartel, "a 2-Tier Anomaly-Based Network Intrusion Detection System", Fourth IEEE International Workshop on Information Assurance, April 2006, Page(s): 147--156 Google ScholarDigital Library
- T. Kohonen, "Self-Organizing Maps", Volume 30, Springer Series in Information Sciences, 1995. Google ScholarDigital Library
- G. Carpenter, S. Grossberg, "Adaptive Resonance Theory", the Handbook of Brain Theory and Neural Networks, 2003, Page(s): 87--90. Google ScholarDigital Library
- R. Jalili, F. Imani-Mehr, M. Amini, H. Shahriari, "Detection of Distributed Denial of Service Attacks using Statistical Preprocessor and Unsupervised Neural Networks", First Information Security Practice and Experience Conference, 2005. Google ScholarDigital Library
- A. Kulakov, D. Davcev, "Tracking of Unusual Events in Wireless Sensor Networks Based on Artificial Neural-Networks Algorithms", International Conference on Information Technology, Coding and Computing (ITCC'05), 2005. Google ScholarDigital Library
- A. I. Khan, "A Peer-to-Peer Associative Memory Network for Intelligent Information Systems", In Proceedings of the Thirteenth Australasian Conference on Information Systems, Vol. 1, 2002.Google Scholar
- E. Izhikevich, "Weakly Pulse-Coupled Oscillators, fm Interactions, Synchronization, and Oscillatory Associative Memory", IEEE Transactions on Neural Networks, 1999, Page(s): 508--526. Google ScholarDigital Library
- A. I. Khan, M. Isreb, R. S. Spindler, "A parallel distributed application of the wireless sensor network", In the Proceedings of the Seventh International Conference on High Performance Computing and Grid in Asia Pacific Region, July 2004, Page(s):81--88 Google ScholarDigital Library
- C. A. R. Hoare, "Communicating Sequential Processes", London, U.K. Prentice-Hall, 1985. Google ScholarDigital Library
- B. B. Nasution, A. I. Khan, "Hierarchical Graph Neuron Scheme for Real-Time Pattern Recognition", IEEE Transactions on Neural Networks, Volume 19, Issue 2, February 2008, Page(s): 212--229 Google ScholarDigital Library
Index Terms
- Graph neuron and hierarchical graph neuron, novel approaches toward real time pattern recognition in wireless sensor networks
Recommendations
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 ...
Sensor scheduling for p-percent coverage in wireless sensor networks
We study sensor scheduling problems of p-percent coverage in this paper and propose two scheduling algorithms to prolong network lifetime due to the fact that for some applications full coverage is not necessary and different subareas of the monitored ...
Integrated Connectivity and Coverage Techniques for Wireless Sensor Networks
MobiWac '16: Proceedings of the 14th ACM International Symposium on Mobility Management and Wireless AccessA wireless sensor network (WSN) consists of a group of energy-constrained sensor nodes with the ability of both sensing and communication, which can be deployed in a field of interesting (FoI) for detecting or monitoring some special events and then ...
Comments