Phase-Aware Directional Energy Transmission Algorithm in Multiple Directional RF Energy Source Environments
- Authors
- Ko, Haneul; Pack, Sangheon
- Issue Date
- 1월-2019
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Internet of Things (IoT); Energy harvesting; radio frequency (RF) energy; directional energy transmission; constraint Markov decision process (CMDP)
- Citation
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.68, no.1, pp.359 - 367
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Volume
- 68
- Number
- 1
- Start Page
- 359
- End Page
- 367
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/68425
- DOI
- 10.1109/TVT.2018.2879723
- ISSN
- 0018-9545
- Abstract
- Radio frequency (RF) energy harvesting technique has received high attention as a promising technology for Internet of Things (IoT) based industrial systems due to its flexibility of energy supply. When multiple IoT gateways are used as directional RF energy sources, the direction and phase of RF energy transmissions influence the RF energy transmission efficiency. In this paper, we propose a phase-aware directional energy transmission algorithm (PA-DETA) where directional RF energy sources (e.g., IoT gateways) are connected to a controller and their operation parameters (i.e., RF energy transmission direction and initial phase) are decided by the controller to maximize the total harvested energy while maintaining the energy consumption in RF energy sources and the number of discarded packets due to energy depletion of harvesting nodes below certain levels. To optimize the performance of PA-DETA, we formulate a constraint Markov decision process and the optimal policy on the direction and initial phase of RF energy transmissions is obtained by a linear programming. Evaluation results demonstrate that PA-DETA with the optimal policy outperforms the comparison schemes in terms of the total harvested energy, the energy consumption in RF energy sources, and the packet discard rate.
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Collections - Graduate School > Department of Computer and Information Science > 1. Journal Articles
- College of Engineering > School of Electrical Engineering > 1. Journal Articles
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