Distributed Abnormal Activity Detection in Smart Environments

Abstract

The abnormal activity detection in smart environments has experienced increasing attention over years, due to its usefulness in pervasive applications. In order to meet the real-time needs and overcome the high costs and privacy issues, this paper proposes distributed abnormal activity detection approach (DetectingAct), which employs the computing and storage resources of simple and ubiquitous sensor nodes, to detect abnormal activity in smart environments equipped with wireless sensor networks (WSN). In DetectingAct, activity is defined as the combination of trajectory and duration, and abnormal activity is defined as the activity which deviates greater enough from those normal activities. DetectingAct works as follows. Firstly, DetectingAct finds the normal activity patterns through duration-dependent frequent pattern mining algorithm (DFPMA), which adopts unsupervised learning instead of supervised learning. Secondly, the distributed knowledge storage mechanism (DKSM) is introduced to store the mined patterns in each node. Then, the current triggered sensor adopts distributed abnormal activity detection algorithm (DAADA), in which the clustering analysis plays a critical role, to compare the present activity with normal activity patterns, by calculating the similarity between them. The feasibility, real-time property, and accuracy of the DetectingAct algorithm are evaluated using both simulation and real experiments case studies.

Modeling Users’ Data Traces in Multi-Resident Ambient Assisted Living Environments

“…In Ref. 6, a distributed abnormal activity detection approach, called DetectingAct, is proposed. In DetectingAct, an activity is defined as the combination of traces of sensor activations and their durations, and an abnormal activity is defined as the one which deviates from the normal routines.…”

“…[2][3][4][5][6][7][8] This can be performed by modeling traces of users' sensory data. Residents' behaviors, and in turn, their traces can be effectively modeled by Markov models (MMs).…”

A context-aware model for human activity prediction and risk inference in actions

“…The work of Wang et al (2014) presents a distributed approach that employs the computing and storage resources in each node of a wireless sensors network (WSN) to detect abnormal activities. A normal activity is defined as the combination of duration and trajectory, and an abnormal activity is defined by the authors as an activity which has a trajectory and a duration that are significantly different from a normal activity.…”

Hidden State Conditional Random Field for Abnormal Activity Recognition in Smart Homes

“…In 2011, Jakkula et al [14] also proposed recognizing abnormal activities based on a One-class Support Vector Machine (One-class SVM), and later Lotfi et al [15] proposed recognizing abnormal activities in smart homes based on a clustering and neural networks method, but neither both of them can model the temporal relationships between activities. In 2014, Wang et al [16] defined abnormal activity as an activity which deviates greatly enough from those normal activities, and recognized abnormal activities using a distributed abnormal activity detection approach which employs the computing and storage resources of simple and ubiquitous sensor nodes, but they cannot deal with abnormal activities which deviate only a little from those normal activities. In the same year, Zhao et al [5] proposed a Markov Chains Model-based method to classify abnormal sequences by analyzing the probability distribution of the spatiotemporal activity data, but the sensors used were only infrared sensing tubes.…”

“…To assess a new activity, we often compare it with normal activities [16,30]. Thus, a model that can compare normal activity and new activity is important for abnormal activity recognition.…”