Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-based Continuous Event Processing

Many pervasive applications, such as activity recognition or remote wellness monitoring, utilize a personal mobile device (aka smartphone) to perform continuous processing of data streams acquired from locally-connected, wearable, sensors. To ensure the continuous operation of such applications on a...

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Main Authors: MISRA, Archan, LIM, Lipyeow
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Language:English
Published: Institutional Knowledge at Singapore Management University 2011
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Online Access:https://ink.library.smu.edu.sg/sis_research/1357
https://ink.library.smu.edu.sg/context/sis_research/article/2356/viewcontent/mdm11_eecep.pdf
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spelling sg-smu-ink.sis_research-23562019-02-11T06:35:09Z Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-based Continuous Event Processing MISRA, Archan LIM, Lipyeow Many pervasive applications, such as activity recognition or remote wellness monitoring, utilize a personal mobile device (aka smartphone) to perform continuous processing of data streams acquired from locally-connected, wearable, sensors. To ensure the continuous operation of such applications on a battery-limited mobile device, it is essential to dramatically reduce the energy overhead associated with the process of sensor data acquisition and processing. To achieve this goal, this paper introduces a technique of "acquisition-cost" aware continuous query processing, as part of the Acquisition Cost-Aware Query Adaptation (ACQUA) framework. ACQUA replaces the current paradigm, where the data is typically streamed (pushed) from the sensors to the smartphone, with a pull-based asynchronous model, where the phone retrieves appropriate blocks of sensor data from individual sensors, only when the stream elements are judged to be relevant to the query being processed. We describe algorithms that dynamically optimize the sequence (for complex stream queries with conjunctive and disjunctive predicates) in which such sensor data streams are retrieved by the phone, based on a combination of the communication cost and selectivity properties of individual sensor streams. Simulation experiments indicate that this approach can result in 70% reduction in the energy overhead of continuous query processing, without affecting the fidelity of the processing logic. 2011-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1357 info:doi/10.1109/MDM.2011.76 https://ink.library.smu.edu.sg/context/sis_research/article/2356/viewcontent/mdm11_eecep.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Software Engineering
spellingShingle Software Engineering
MISRA, Archan
LIM, Lipyeow
Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-based Continuous Event Processing
description Many pervasive applications, such as activity recognition or remote wellness monitoring, utilize a personal mobile device (aka smartphone) to perform continuous processing of data streams acquired from locally-connected, wearable, sensors. To ensure the continuous operation of such applications on a battery-limited mobile device, it is essential to dramatically reduce the energy overhead associated with the process of sensor data acquisition and processing. To achieve this goal, this paper introduces a technique of "acquisition-cost" aware continuous query processing, as part of the Acquisition Cost-Aware Query Adaptation (ACQUA) framework. ACQUA replaces the current paradigm, where the data is typically streamed (pushed) from the sensors to the smartphone, with a pull-based asynchronous model, where the phone retrieves appropriate blocks of sensor data from individual sensors, only when the stream elements are judged to be relevant to the query being processed. We describe algorithms that dynamically optimize the sequence (for complex stream queries with conjunctive and disjunctive predicates) in which such sensor data streams are retrieved by the phone, based on a combination of the communication cost and selectivity properties of individual sensor streams. Simulation experiments indicate that this approach can result in 70% reduction in the energy overhead of continuous query processing, without affecting the fidelity of the processing logic.
format text
author MISRA, Archan
LIM, Lipyeow
author_facet MISRA, Archan
LIM, Lipyeow
author_sort MISRA, Archan
title Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-based Continuous Event Processing
title_short Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-based Continuous Event Processing
title_full Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-based Continuous Event Processing
title_fullStr Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-based Continuous Event Processing
title_full_unstemmed Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-based Continuous Event Processing
title_sort optimizing sensor data acquisition for energy-efficient smartphone-based continuous event processing
publisher Institutional Knowledge at Singapore Management University
publishDate 2011
url https://ink.library.smu.edu.sg/sis_research/1357
https://ink.library.smu.edu.sg/context/sis_research/article/2356/viewcontent/mdm11_eecep.pdf
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