A Survey on Behavioral Pattern Mining from Sensor Data in Internet of Things

The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE

Efficient Pattern Mining for Wireless Sensor Networks Data

Wireless Sensor Networks generate a large amount of data in the form of streams. Mining association rules on the sensor data provides useful information for different applications. In this paper, a total from partial (TFP) tree based approach is used to generate the set of all association rules from data. Our experimental results show that TFP techniques perform better result in case of sparse dataset and significantly comparable as SP-tree approach for the dense dataset. Keywords: Association Rule Mining; Wireless Sensor Networks; Frequent Pattern

Outlier detection techniques for wireless sensor networks: A survey

In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree

Mining top-k regular episodes from sensor streams

International audienceThe monitoring of human activities plays an important role in health-care applications and for the data mining community. Existing approaches work on activities recognition occurring in sensor data streams. However, regular behaviors have not been studied. Thus, we here introduce a new approach to discover top-k most regular episodes from sensors streams, TKRES. The top-k approach allows us to control the size of the output, thus preventing overwhelming result analysis for the supervisor. TKRES is based on the use of a simple top-k list and a k-tree structure for maintaining the top-k episodes and their occurrence information. We also investigate and report the performances of TKRES on two real-life smart home datasets