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Energies 2018, 11(8), 2015;

Energy-Efficient Clusters for Object Tracking Networks

Department of Computer Science and Information Engineering, National Taitung University, Taitung 95092, Taiwan
Received: 31 May 2018 / Revised: 29 July 2018 / Accepted: 31 July 2018 / Published: 2 August 2018
(This article belongs to the Special Issue Selected Papers from the IEEE ICASI 2018)
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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. View Full-Text
Keywords: object tracking networks; energy saving; embedded system object tracking networks; energy saving; embedded system

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Fan, Y.-H. Energy-Efficient Clusters for Object Tracking Networks. Energies 2018, 11, 2015.

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