Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams

There is a growing interest in applications that utilize continuous sensing of individual activity or context, via sensors embedded or associated with personal mobile devices (e.g., smartphones). Reducing the energy overheads of sensor data acquisition and processing is essential to ensure the succe...

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Main Authors: LIM, Lipyeow, MISRA, Archan, MO, Tianli
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Language:English
Published: Institutional Knowledge at Singapore Management University 2012
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Online Access:https://ink.library.smu.edu.sg/sis_research/1493
https://ink.library.smu.edu.sg/context/sis_research/article/2492/viewcontent/dapd12_AdaptiveDataAcqSmartphone.pdf
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spelling sg-smu-ink.sis_research-24922020-03-31T05:58:11Z Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams LIM, Lipyeow MISRA, Archan MO, Tianli There is a growing interest in applications that utilize continuous sensing of individual activity or context, via sensors embedded or associated with personal mobile devices (e.g., smartphones). Reducing the energy overheads of sensor data acquisition and processing is essential to ensure the successful continuous operation of such applications, especially on battery-limited mobile devices. To achieve this goal, this paper presents a framework, called ACQUA, for ‘acquisition-cost’ aware continuous query processing. ACQUA replaces the current paradigm, where the data is typically streamed (pushed) from the sensors to the one or more smartphones, with a pull-based asynchronous model, where a smartphone retrieves appropriate blocks of relevant sensor data from individual sensors, as an integral part of the query evaluation process. 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 query evaluation component, based on a combination of (a) the communication cost & selectivity properties of individual sensor streams, and (b) the occurrence of the stream predicates in multiple concurrently executing queries. We also show how a transformation of a group of stream queries into a disjunctive normal form provides us with significantly greater degrees of freedom in choosing this sequence, in which individual sensor streams are retrieved and evaluated. While the algorithms can apply to a broad category of sensor-based applications, we specifically demonstrate their application to a scenario where multiple stream processing queries execute on a single smartphone, with the sensors transferring their data over an appropriate PAN technology, such as Bluetooth or IEEE 802.11. Extensive simulation experiments indicate that ACQUA’s intelligent batch-oriented data acquisition process can result in as much as 80 % reduction in the energy overhead of continuous query processing, without any loss in the fidelity of the processing logic. 2012-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1493 info:doi/10.1007/s10619-012-7093-3 https://ink.library.smu.edu.sg/context/sis_research/article/2492/viewcontent/dapd12_AdaptiveDataAcqSmartphone.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 Mobile data management Streams Complex event processing Energy efficiency Activity recognition Mobile Sensing Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Mobile data management
Streams
Complex event processing
Energy efficiency
Activity recognition
Mobile Sensing
Software Engineering
spellingShingle Mobile data management
Streams
Complex event processing
Energy efficiency
Activity recognition
Mobile Sensing
Software Engineering
LIM, Lipyeow
MISRA, Archan
MO, Tianli
Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams
description There is a growing interest in applications that utilize continuous sensing of individual activity or context, via sensors embedded or associated with personal mobile devices (e.g., smartphones). Reducing the energy overheads of sensor data acquisition and processing is essential to ensure the successful continuous operation of such applications, especially on battery-limited mobile devices. To achieve this goal, this paper presents a framework, called ACQUA, for ‘acquisition-cost’ aware continuous query processing. ACQUA replaces the current paradigm, where the data is typically streamed (pushed) from the sensors to the one or more smartphones, with a pull-based asynchronous model, where a smartphone retrieves appropriate blocks of relevant sensor data from individual sensors, as an integral part of the query evaluation process. 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 query evaluation component, based on a combination of (a) the communication cost & selectivity properties of individual sensor streams, and (b) the occurrence of the stream predicates in multiple concurrently executing queries. We also show how a transformation of a group of stream queries into a disjunctive normal form provides us with significantly greater degrees of freedom in choosing this sequence, in which individual sensor streams are retrieved and evaluated. While the algorithms can apply to a broad category of sensor-based applications, we specifically demonstrate their application to a scenario where multiple stream processing queries execute on a single smartphone, with the sensors transferring their data over an appropriate PAN technology, such as Bluetooth or IEEE 802.11. Extensive simulation experiments indicate that ACQUA’s intelligent batch-oriented data acquisition process can result in as much as 80 % reduction in the energy overhead of continuous query processing, without any loss in the fidelity of the processing logic.
format text
author LIM, Lipyeow
MISRA, Archan
MO, Tianli
author_facet LIM, Lipyeow
MISRA, Archan
MO, Tianli
author_sort LIM, Lipyeow
title Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams
title_short Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams
title_full Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams
title_fullStr Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams
title_full_unstemmed Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams
title_sort adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url https://ink.library.smu.edu.sg/sis_research/1493
https://ink.library.smu.edu.sg/context/sis_research/article/2492/viewcontent/dapd12_AdaptiveDataAcqSmartphone.pdf
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