1. GAUSSIAN VERSUS UNIFORM DISTRIBUTION FOR INTRUSION
DETECTION IN WIRELESS SENSOR NETWORKS
ABSTRACT:
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.
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