Distributed Abnormal Activity Detection in Binary WSNs

Distributed abnormal activity detection approach (DetectingAct), which employs the computing and storage resources of these sensor nodes, was proposed to detect abnormal activity under binary sensor network. In DetectingAct, activity was defined as the combination of trajectory and duration, while abnormal activity was defined as the activity whose deviation between normal activities, i.e. repetitive activities, is big enough. Firstly, DetectingAct found the normal activity patterns through duration-dependent frequent pattern mining algorithm (DFPMA), which adopted unsupervised learning instead of supervised learning. Secondly, the distributed knowledge storage mechanism (DKSM) was introduced to store the mined patterns in each node. Finally, Distributed abnormal activity detection algorithm (DAADA), which was based on the clustering analysis, was introduced to compare the present activity with normal activity patterns to determine the possibility of the current activity being abnormal. The feasibility, real-time property and accuracy of the approach were evaluated by experiments. The average detect distance reaches 78.2% and the accuracy is 96.9%.