Issue No. 02 - Feb. (2013 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2012.105
Yun Wang , Bradley University, Peoria
Weihuang Fu , University of Cincinnati, Cincinnati
Dharma P. Agrawal , University of Cincinnati, Cincinnati
In a Wireless Sensor Network (WSN), intrusion detection is of significant importance in many applications in detecting malicious or unexpected intruder(s). The intruder can be an enemy in a battlefield, or a malicious moving object in the area of interest. With uniform sensor deployment, the detection probability is the same for any point in a WSN. However, some applications may require different degrees of detection probability at different locations. For example, an intrusion detection application may need improved detection probability around important entities. Gaussian-distributed WSNs can provide differentiated detection capabilities at different locations but related work is limited. This paper analyzes the problem of intrusion detection in a Gaussian-distributed WSN by characterizing the detection probability with respect to the application requirements and the network parameters under both single-sensing detection and multiple-sensing detection scenarios. Effects of different network parameters on the detection probability are examined in detail. Furthermore, performance of Gaussian-distributed WSNs is compared with uniformly distributed WSNs. This work allows us to analytically formulate detection probability in a random WSN and provides guidelines in selecting an appropriate deployment strategy and determining critical network parameters.
Wireless sensor networks, Intrusion detection, Sensors, Gaussian distribution, Analytical models, Mathematical model, Equations, wireless sensor network, Gaussian distribution, intrusion detection, network deployment, uniform distribution, sensing range
W. Fu, Y. Wang and D. P. Agrawal, "Gaussian versus Uniform Distribution for Intrusion Detection in Wireless Sensor Networks," in IEEE Transactions on Parallel & Distributed Systems, vol. 24, no. , pp. 342-355, 2013.