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.

In this paper, we have proposed and designed DPHK (data prediction based on HMM according to activity pattern knowledge mined from trajectories), a real-time distributed predicted data collection system to solve the congestion and data loss caused by too many connections to sink node in indoor smart environment scenarios (like Smart Home, Smart Wireless Healthcare and so on). DPHK predicts and sends predicted data at one time instead of sending the triggered data of these sensor nodes which people is going to pass in several times. Firstly, our system learns the knowledge of transition probability among sensor nodes from the historical binary motion data through data mining. Secondly, it stores the corresponding knowledge in each sensor node based on a special storage mechanism. Thirdly, each sensor node applies HMM (hidden Markov model) algorithm to predict the sensor node locations people will arrive at according to the received message. At last, these sensor nodes send their triggered data and the predicted data to the sink node. The significances of DPHK are as follows: (a) the procedure of DPHK is distributed; (b) it effectively reduces the connection between sensor nodes and sink node. The time complexities of the proposed algorithms are analyzed and the performance is evaluated by some designed experiments in a smart environment.

In this paper, we have proposed and designed a realtime distributed predicted data collection system-DCTP (Data Collection based on Trajectory Prediction according to Knowledge mined from trajectories) to solve the congestion and data loss caused by too many connections to sink node in indoor Smart Environment scenarios (like Smart Home, Smart Wireless Healthcare and so on). DCTP predicts and sends predicted data of the sensor nodes which people is going to pass at one time instead of sending the triggered data in several times. Firstly, our system applies data mining to get the knowledge of transition probability among sensor nodes from the historical binary motion data. Secondly, each sensor node stores the corresponding knowledge based on a special storage mechanism. Thirdly, each triggered sensor node predicts the next destinations people will arrive at according to the received message using HMM algorithm. At last, the sensor node sends its triggered data and the predicted data to the sink node. The significances of DCTP are as follows: (a) the procedure of DCTP is distributed; (b) it effectively reduces the connection between sensor nodes and sink node. The time complexities of the proposed algorithms are analyzed and the performance is evaluated by some designed experiments in a Smart Environment.

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