Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach
The IoT system has become a significant component of next generation networks, and drawn a lot of research interest in academia and industry. As the sensor nodes in the IoT system are always battery-limited devices, the power control problem is a serious problem in the IoT system which needs to be s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-09-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/10/3173 |
| _version_ | 1828151880408104960 |
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| author | Jingtao Su Haitao Xu Ning Xin Guixing Cao Xianwei Zhou |
| author_facet | Jingtao Su Haitao Xu Ning Xin Guixing Cao Xianwei Zhou |
| author_sort | Jingtao Su |
| collection | DOAJ |
| description | The IoT system has become a significant component of next generation networks, and drawn a lot of research interest in academia and industry. As the sensor nodes in the IoT system are always battery-limited devices, the power control problem is a serious problem in the IoT system which needs to be solved. In this paper, we research the resource allocation in the wireless powered IoT system, which includes one hybrid access point (HAP) and many wireless sensor nodes, to obtain the optimal power level for information transmission and energy transfer simultaneously. The relationship between the HAP and the sensor nodes are formulated as the Stackelberg game, and the dynamic variations of the energy for both the HAP and IoT devices are formulated through the dynamic game with mean field control. Then the power control in the wireless powered IoT system is formulated as a mean field Stackelberg game model. We aim to minimize the transmission cost for each sensor node based on optimally power resource allocation. Meanwhile, we attempt to minimize the energy transfer cost based on power control. As a result, the optimal solutions based on the mean field control of the sensor nodes and the HAP are achieved through dynamic programming theory and the law of large numbers, and ε -Nash equilibriums can be obtained. The energy variations for both the sensor nodes and HAP after the control of resource allocation based on the proposed approach are verified based on the simulation results. |
| first_indexed | 2024-04-11T22:03:46Z |
| format | Article |
| id | doaj.art-0b07622d4958450e82a417873f055f6e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:03:46Z |
| publishDate | 2018-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0b07622d4958450e82a417873f055f6e2022-12-22T04:00:47ZengMDPI AGSensors1424-82202018-09-011810317310.3390/s18103173s18103173Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based ApproachJingtao Su0Haitao Xu1Ning Xin2Guixing Cao3Xianwei Zhou4Department of Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaDepartment of Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaInstitute of Telecommunication Satellite, China Academy of Space Technology, Beijing 100081, ChinaInstitute of Telecommunication Satellite, China Academy of Space Technology, Beijing 100081, ChinaDepartment of Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaThe IoT system has become a significant component of next generation networks, and drawn a lot of research interest in academia and industry. As the sensor nodes in the IoT system are always battery-limited devices, the power control problem is a serious problem in the IoT system which needs to be solved. In this paper, we research the resource allocation in the wireless powered IoT system, which includes one hybrid access point (HAP) and many wireless sensor nodes, to obtain the optimal power level for information transmission and energy transfer simultaneously. The relationship between the HAP and the sensor nodes are formulated as the Stackelberg game, and the dynamic variations of the energy for both the HAP and IoT devices are formulated through the dynamic game with mean field control. Then the power control in the wireless powered IoT system is formulated as a mean field Stackelberg game model. We aim to minimize the transmission cost for each sensor node based on optimally power resource allocation. Meanwhile, we attempt to minimize the energy transfer cost based on power control. As a result, the optimal solutions based on the mean field control of the sensor nodes and the HAP are achieved through dynamic programming theory and the law of large numbers, and ε -Nash equilibriums can be obtained. The energy variations for both the sensor nodes and HAP after the control of resource allocation based on the proposed approach are verified based on the simulation results.http://www.mdpi.com/1424-8220/18/10/3173power controlwireless energy transferIoT systemmean field Stackelberg game |
| spellingShingle | Jingtao Su Haitao Xu Ning Xin Guixing Cao Xianwei Zhou Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach Sensors power control wireless energy transfer IoT system mean field Stackelberg game |
| title | Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach |
| title_full | Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach |
| title_fullStr | Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach |
| title_full_unstemmed | Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach |
| title_short | Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach |
| title_sort | resource allocation in wireless powered iot system a mean field stackelberg game based approach |
| topic | power control wireless energy transfer IoT system mean field Stackelberg game |
| url | http://www.mdpi.com/1424-8220/18/10/3173 |
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