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A survey on behavioral pattern mining from sensor data in Internet of Things

journal contribution
posted on 14.07.2021, 00:55 by MD Mamunur RashidMD Mamunur Rashid, Joarder Kamruzzaman, Mohammad M Hassan, Sakib Shahriar Shafin, Md Zakirul A Bhuiyan
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.

History

Volume

8

Start Page

33318

End Page

33341

Number of Pages

24

eISSN

2169-3536

Publisher

IEEE

Additional Rights

CC BY 4.0

Peer Reviewed

Yes

Open Access

Yes

Acceptance Date

04/02/2020

External Author Affiliations

Federation University; King Saud University, Saudi Arabia; Islamic University of Engineering and Technology, Bangladesh; Fordham University, USA

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Journal

IEEE Access