Intrusion detection in a KK-Gaussian distributed wireless sensor network

Random sensor deployment is of crucial importance for intrusion detection applications using a wireless sensor network (WSN) in hostile and dangerous environments. A uniform random WSN fails to detect a moving intruder if it starts inside the network domain and close to the target. A Gaussian distributed WSN cannot effectively detect the intruder if it starts from the network boundary. In view of this, this paper introduces a novel kk-Gaussian deployment strategy to leverage the advantages of both uniform and Gaussian random sensor deployment for efficient and effective intrusion detection. The key idea is to employ multiple deployment points in the area of interest and a subset of the total sensors are deployed around each deployment point following a Gaussian distribution and form a kk-Gaussian distributed WSN. Is the kk-Gaussian deployment strategy always better than the uniform and Gaussian deployment strategy and how much better for intrusion detection in WSNs is therefore a must-answer question. This work explores the intrusion detection problem in a kk-Gaussian distributed WSN under the multi-level probabilistic sensing models theoretically and by simulations. Matching between the analytical results and simulation outcomes validates the correctness of the modeling and analysis, and the effectiveness of the proposed approach is demonstrated by comparing with the counterpart uniform and Gaussian sensor deployment strategies under the considered scenarios. This work provides insights into random sensor deployment for efficient intrusion detection.

▸ A novel kk-Gaussian WSN is proposed and modeled for intrusion detection. ▸ A probabilistic detection model is integrated into the modeling and analysis. ▸ Intrusion detection probability is derived and validated by simulations. ▸ Comparisons with uniform and Gaussian WSNs. ▸kk-Gaussian WSN outperforms its counterparts in considered scenarios.