Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 5, Issue 1, Pages 310-321Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2017.2782367
Keywords
Energy trading; social welfare; Stackelberg game; sum throughput maximization (STM); wireless powered sensor networks (WPSNs)
Categories
Funding
- Natural Science Foundation of China [61701214]
- Young Natural Science Foundation of Jiangxi Province [20171BAB212002]
- China Post-Doctoral Science Foundation [2017M610400]
- Post-Doctoral Science Foundation of Jiangxi Province [2017KY04]
- National Nature Science Foundation of China [61571402, 61401402, 61711431, 61640005]
- National Science Foundation [ECCS-1308006, EARS-1547312]
- EPSRC [EP/R001588/1] Funding Source: UKRI
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This paper investigates a wireless powered sensor network, where multiple sensor nodes are deployed to monitor a certain external environment. A multiantenna power station (PS) provides the power to these sensor nodes during wireless energy transfer phase, and consequently the sensor nodes employ the harvested energy to transmit their own monitoring information to a fusion center during wireless information transfer (WIT) phase. The goal is to maximize the system sum throughput of the sensor network, where two different scenarios are considered, i.e., PS and the sensor nodes belong to the same or different service operator(s). For the first scenario, we propose a global optimal solution to jointly design the energy beamforming and time allocation. We further develop a closed-form solution for the proposed sum throughput maximization. For the second scenario in which the PS and the sensor nodes belong to different service operators, energy incentives are required for the PS to assist the sensor network. Specifically, the sensor network needs to pay in order to purchase the energy services released from the PS to support WIT. In this case, this paper exploits this hierarchical energy interaction, which is known as energy trading. We propose a quadratic energy trading-based Stackelberg game, linear energy trading-based Stackelberg game, and social welfare scheme, in which we derive the Stackelberg equilibrium for the formulated games, and the optimal solution for the social welfare scheme. Finally, numerical results are provided to validate the performance of our proposed schemes.
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