LAPSE:2018.0537
Published Article
LAPSE:2018.0537
Energy-Efficient Clusters for Object Tracking Networks
September 21, 2018
Smart cities have hundreds of thousands of devices for tracking data on crime, the environment, and traffic (such as data collected at crossroads and on streets). This results in higher energy usage, as they are recording information persistently and simultaneously. Moreover, a single object tracking device, on a corner at an intersection for example has a limited scope of view, so more object tracking devices are added to broaden the view. As an increasing number of object tracking devices are constructed on streets, their efficient energy consumption becomes a significant issue. This work is concerned with decreasing the energy required to power these systems, and proposes energy-efficient clusters (EECs) of object tracking systems to achieve energy savings. First, we analyze a current object tracking system to establish an equivalent model. Second, we arrange the object tracking system in a cluster structure, which facilitates the evaluation of energy costs. Third, the energy consumption is assessed as either dynamic or static, which is a more accurate system for determining energy consumption. Fourth, we analyze all possible scenarios of the object’s location and the resulting energy consumption, and derive a number of formulas for the fast computation of energy consumption. Finally, the simulation results are reported. These results show the proposed EEC is an effective way to save energy, compared with the energy consumption benchmarks of current technology.
Record ID
Keywords
embedded system, energy saving, object tracking networks
Subject
Suggested Citation
Fan YH. Energy-Efficient Clusters for Object Tracking Networks. (2018). LAPSE:2018.0537
Author Affiliations
Fan YH: Department of Computer Science and Information Engineering, National Taitung University, Taitung 95092, Taiwan
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Journal Name
Energies
Volume
11
Issue
8
Article Number
E2015
Year
2018
Publication Date
2018-08-02
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en11082015, Publication Type: Journal Article
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Published Article
LAPSE:2018.0537
This Record
External Link
doi:10.3390/en11082015
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652
Version History
[v1] (Original Submission)
Sep 21, 2018
Verified by curator on
Sep 21, 2018
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v1
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URL Here
https://psecommunity.org/LAPSE:2018.0537
Original Submitter
Calvin Tsay
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