Fossel: Efficient Latency Reduction in Approximating Streaming Sensor Data

Fatima Abdullah, Limei Peng and Byungchul Tak
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Fatima Abdullah: School of Computer Science and Engieering, Kyungpook National University, Daegu 41566, Korea
Limei Peng: School of Computer Science and Engieering, Kyungpook National University, Daegu 41566, Korea
Byungchul Tak: School of Computer Science and Engieering, Kyungpook National University, Daegu 41566, Korea

Sustainability, 2020, vol. 12, issue 23, 1-21

Abstract: The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network traffic which leads to heavily delayed network operations. In streaming data analytics, the ability to obtain real time data insight is crucial for computational sustainability for many IoT enabled applications such as environmental monitors, pollution and climate surveillance, traffic control or even E-commerce applications. However, such network delays prevent us from achieving high quality real-time data analytics of environmental information. In order to address this challenge, we propose the Fo g S ampling Node Sel ector (Fossel) technique that can significantly reduce the IoT network and processing delays by algorithmically selecting an optimal subset of fog nodes to perform the sensor data sampling. In addition, our technique performs a simple type of query executions within the fog nodes in order to further reduce the network delays by processing the data near the data producing devices. Our extensive evaluations show that Fossel technique outperforms the state-of-the-art in terms of latency reduction as well as in bandwidth consumption, network usage and energy consumption.

Keywords: sensor data; sampling; fog computing; streaming data; real time analytics; optimal node selection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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