Abstract

A surge in sensor data volume has exposed the shortcomings of cloud computing, particularly the limitation of network transmission capability and centralized computing resources. The dynamic intervention among sensor streams also brings challenges for IoT applications to derive meaningful information from multiple sensor streams. To handle these issues, this paper proposes a service‐based method with fog computing paradigm based on our previous service abstraction, which can capture meaningful events from multiple sensor streams. In our service abstraction, we utilize correlation analysis method to capture events as variations of correlation among sensor streams. Facing inconsistent frequency and shift of correlation, we propose a Dynamic Time Warping‐ (DTW‐) based algorithm to obtain sensor streams’ lag‐correlation. For adaptively aggregating related events from different services, we also propose an event routing algorithm to assist the composition of cascaded events through service collaboration. This paper reports the tryout use of our method in Chinese power grid for detecting abnormal situations of power quality. Through a series of experiments based on real sensor data in power grid, we verified that our method can reduce the network transmission and computing resource with high accuracy.

Highlights

  • A Service-Based Method for Multiple Sensor Streams Aggregation in Fog ComputingZhongmei Zhang ,1,2,3 Chen Liu, Shouli Zhang, Xiaohong Li, and Yanbo Han

  • With the rapid development of Internet of Things (IoT), numerous sensors are deployed for event monitoring, ecological observation, and so forth, which produce an overwhelming amount of stream data [1]

  • Few works attach enough importance on what software abstractions should be fit to put on a fog/edge node and how to program them. It is very important for an IoT application to build with the fog computing paradigm

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Summary

A Service-Based Method for Multiple Sensor Streams Aggregation in Fog Computing

Zhongmei Zhang ,1,2,3 Chen Liu, Shouli Zhang, Xiaohong Li, and Yanbo Han. The dynamic intervention among sensor streams brings challenges for IoT applications to derive meaningful information from multiple sensor streams. To handle these issues, this paper proposes a service-based method with fog computing paradigm based on our previous service abstraction, which can capture meaningful events from multiple sensor streams. We utilize correlation analysis method to capture events as variations of correlation among sensor streams. Facing inconsistent frequency and shift of correlation, we propose a Dynamic Time Warping- (DTW-) based algorithm to obtain sensor streams’ lag-correlation. Through a series of experiments based on real sensor data in power grid, we verified that our method can reduce the network transmission and computing resource with high accuracy

Introduction
Scenario
Overview of Our Service-Based Method
Event Capturing in Proactive Data Service
Dynamic Collaboration among Services
Evaluation
Experiment Setup
Related Works
Full Text
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