Fog Computing Architecture-Based Data Acquisition for WSN Applications

• Published in: China communications Vol. 14; no. 11; pp. 69 - 81
• Main Authors: Zhang, Guangwei, Li, Ruifan
• Format: Journal Article
• Language: English
• Published: China Institute of Communications 01-11-2017; Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; Engineering Research Center of Information Networks, Ministry of Education, Beijing 100876, China%Engineering Research Center of Information Networks, Ministry of Education, Beijing 100876, China; School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
• Subjects: abnormal data; Computer architecture; Data acquisition; data filtering; Data models; Edge computing; fog computing; intrusion tolerance; Monitoring; Real-time systems; Wireless sensor networks; WSN; data filtering; fog computing; WSN; abnormal data; intrusion tolerance
• ISSN: 1673-5447
• DOI: 10.1109/CC.2017.8233652

Efficient and effective data acquisition is of theoretical and practical importance in WSN applications because data measured and collected by WSN is often unreliable, such as those often accompanied by noise and error, missing values or inconsistent data. Motivated by fog computing, which focuses on how to effectively offload computation-intensive tasks from resource-constrained devices, this paper proposes a simple but yet effective data acquisition approach with the ability of filtering abnormal data and meeting the real-time requirement. Our method uses a cooperation mechanism by leveraging on both an architectural and algorithmic approach. Firstly, the sensor node with the limited computing resource only accomplishes detecting and marking the suspicious data using a light weight algorithm. Secondly, the cluster head evaluates suspicious data by referring to the data from the other sensor nodes in the same cluster and discard the abnormal data directly. Thirdly, the sink node fills up the discarded data with an approximate value using nearest neighbor data supplement method. Through the architecture, each node only consumes a few computational resources and distributes the heavily computing load to several nodes. Simulation results show that our data acquisition method is effective considering the real-time outlier filtering and the computing overhead.