英语论文网

information war.
There is a special motivation to the approach to the problem too. The existing methods [1-6] of
detecting the jamming conditions are node-centric, where the complete data collection, processing and
decision making is done by individual nodes to arrive at a crisp decision of ‘jammed’ or ‘not jammed’.
This approach is not suitable in a hostile environment because, like wounded soldiers and damaged
equipment who/which are graded according to the severity of the casualty/damage, the nodes too must
be graded with different jamming indices as per the severity of jamming affecting them. This would
make the employment and deployment of the WSN nodes, like any other battle resource, economic and
flexible to suit different stages of the information warfare. The other serious drawback of the existing
approaches is that the complete processing and decision making is done at the node level. This is not
practicable as the WSN nodes are resource-starved and that because nodes may not be able to
communicate with others during jamming. Therefore, the authors are motivated to change the approach
from decision-making being node-centric to base-station-centric and from the decision being crispcentric
to fuzzy-centric.
1.2. Paper Layout
We begin with the discussion of various types of existing jamming attack models in Section 2, such
as those from Xu [1], Muraleedharan [2], and Cakiroglu [3]. We then discuss the military classification
of jammers, and finally, select our own models relevant to our study from the plethora of existing ones.
Section 3 is the study of different possible metrics used for jamming detection and selection of some of
them for our work. In Section 4, we analyze the effectiveness and suitability of the existing jamming
detection methods, as relevant to the WSN in an information war environment. Section 5 is the
Sensors 2010, 10
3446

 

Acknowledgements
Parts of the work of the first two authors are partially supported by Council for Scientific and
Industrial Research (CSIR), India, Ref. No. 22(0477)/09/EMR-II, which the authors
gratefully acknowledge.
References and Notes
1. Xu, W.; Trappe, W.; Zhang, Y.; Wood, T. The feasibility of launching and detecting jamming
attacks in wireless networks. MobiHoc ’05: In Proceedings of the Sixth ACM International
Symposium on Mobile ad hoc Networking and Computing, Alexandria, VA, USA, 07-07
November 2005; pp. 46-57.
2. Rajani, M.; Lisa, A.O. Jamming attack detection and countermeasures in wireless sensor network
using ant system. Available online: https://www.cognitiveintelligence.com/documents/SPIE
2006.pdf/ (accessed on 12 October 2009 at 1:15 PM).
3. Çakiroǧlu, M.; Özcerit, A.T. Jamming detection mechanisms for wireless sensor networks. In
Proceedings of the 3rd International Conference on Scalable Information Systems, Vico Equense,
Italy, 04-06 June 2008.
4. Kristopher, W.R.; Ahmed, S.; George, D. Using standard deviation in signal strength detection to
determine jamming in wireless networks. Available online: https://kreese.net/scr/content/
documents/SSDModel.reese.pdf/ (accessed on 07 October 2009 at 1:30 PM).
Sensors 2010, 10
3478
5. Mario, S.; Boris, D.; Srdjan, C. Detection of reactive jamming in sensor networks; ETH Zurich
D-INFK Technical Report 634; ETH: Zurich, Switzerland, August 2009.
6. Behrouz, A.F.; Sophia, 本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。