A decentralized and service-based approach to proactively correlating stream data

Han, Yanbo; Liu, Chen; Su, Shen; Zhu, Meiling; Zhang, Zhongmei

doi:10.29268/iciot.2016.0014

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…Based on our event capturing algorithm, we can capture the abnormal status of each device. In this section, we will introduce the dynamic service collaboration for correlating sensor streams ← the difference of their records' numbers; (5) ← getLongerSeries( , ); (6) ← getShorterSeries( , ); (7) ← interpolation( , ); (8) else (9) ← ; (10) ← ; (11) end if (12) ⟨ , ⟩ ← aligned data series from ⟨ , ⟩ based DTW (13) calculate correlation coefficient cor of ⟨ , ⟩; (14) if…”

Section: Dynamic Collaboration Among Servicesmentioning

confidence: 99%

“…But ad hoc association and streaming-oriented queries can hardly be reflected. Following the thought of software-defined sensors, we proposed a service abstraction called proactive data service in our previous works [11,12]. With proactive data service, the sensor events generated directly by sensors can be transformed into multiple service events, which reveal more meaningful information.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Liu

Zhang

et al. 2018

Wireless Communications and Mobile Computing

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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.

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Section: Dynamic Collaboration Among Servicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

Liu

Zhang

et al. 2018

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The servitization of sensor streaming data provides a discrete approach to realize sensor streaming data processing based on collaboration among various services. In our previous work (Zhang et al , 2017; Han et al , 2016b), a service abstraction called proactive data service (PDS) was proposed, which can improve the value density of sensor streaming data by creating PDS, and support flexible service collaboration facing dynamic sensor streaming data.…”

Section: Introductionmentioning

confidence: 99%

A real-time discovery method for vehicle companion via service collaboration

Zhang,

Hu,

Hou

et al. 2023

IJWIS

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Purpose Vehicle companion is one of the most common companion patterns in daily life, which has great value to accident investigation, group tracking, carpooling recommendation and road planning. Due to the complexity and large scale of vehicle sensor streaming data, existing work were difficult to ensure the efficiency and effectiveness of real-time vehicle companion discovery (VCD). This paper aims to provide a high-quality and low-cost method to discover vehicle companions in real time. Design/methodology/approach This paper provides a real-time VCD method based on pro-active data service collaboration. This study makes use of dynamic service collaboration to selectively process data produced by relative sensors, and relax the temporal and spatial constraints of vehicle companion pattern for discovering more potential companion vehicles. Findings Experiments based on real and simulated data show that the method can discover 67% more companion vehicles, with 62% less response time comparing with centralized method. Originality/value To reduce the amount of processing streaming data, this study provides a Service Collaboration-based Vehicle Companion Discovery method based on proactive data service model. And this study provides a new definition of vehicle companion through relaxing the temporal and spatial constraints for discover companion vehicles as many as possible.

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A Service-Based Declarative Approach for Capturing Events from Multiple Sensor Streams

Zhang

Liu

et al. 2018

Service-Oriented Computing

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A decentralized and service-based approach to proactively correlating stream data

Cited by 11 publications

References 9 publications

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

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

A real-time discovery method for vehicle companion via service collaboration

A Service-Based Declarative Approach for Capturing Events from Multiple Sensor Streams

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