Dissertation/Thesis Abstract

Energy-Efficient Mobile Sensing
by Mo, Tianli, Ph.D., University of Hawai'i at Manoa, 2020, 140; 27994747
Abstract (Summary)

Mobile sensing has emerged rapidly in the past few years as a promising avenue for collecting, leveraging and querying various information around users. One reason is that mobile devices have increasingly become central computing devices in people's daily lives. The other reason is that more affordable sensors have been embedded in smartphones. Since the mobile devices are battery-powered devices, we have to face the critical problem of how to reduce the energy consumption of mobile sensing, especially the processing of continuous queries on the sensed data, in order to extend the operational lifetime of mobile devices.

In this dissertation, we first propose the ACQUA framework to reduce the energy overhead of sensor data acquisition and processing for individual mobile devices. ACQUA reduces the energy consumption of individual mobile devices by modifying both the ordering and the segments of data streams that are retrieved by the continuous query evaluation. Then we propose CloQue, a framework that exploits correlation among the different sensing data streams. It can 1) reorder the order of predicate processing to preferentially select predicates, and 2) intelligently propagate the query evaluation results to dynamically update the confidence values of other correlated context predicates, in order to maximally reduce the energy consumption on individual mobile devices.

For energy savings on multiple mobile devices, we propose a collaborative query processing framework called CQP. CQP exploits the overlap (in both the sensor sources and the query predicates) across multiple executing queries, and then reduces the energy consumption of repetitive execution and data transmissions by having a set of `leader' mobile device nodes execute and disseminate these shareable partial results. CQP also utilizes lower-energy short-range wireless links to disseminate such results directly among proximate mobile devices. We also propose an improved framework called C2QP, that can exploit not only the correlation among multiple mobile devices and their sensed data streams, but can also process the continuous queries collaboratively by sharing partial results and shareable sensed data.

Indexing (document details)
Advisor: Lim, Lipyeow
Commitee: Casanova, Henri, Biagioni, Edoardo, Leigh, Jason, Dong, Yingfei
School: University of Hawai'i at Manoa
Department: Computer Science
School Location: United States -- Hawaii
Source: DAI-B 82/5(E), Dissertation Abstracts International
Subjects: Computer science
Keywords: Energy efficient, Mobile sensing
Publication Number: 27994747
ISBN: 9798698531876
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