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Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach
Jingtao Su1, Haitao Xu2, Ning Xin3
1Department of Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China. 18518788115@163.com.
Sensors (Basel, Switzerland)
|September 23, 2018
Summary
This study optimizes power control in wireless-powered Internet of Things (IoT) systems. It minimizes transmission and energy transfer costs for sensor nodes and hybrid access points using a mean-field Stackelberg game model.
Area of Science:
- Computer Science
- Electrical Engineering
- Network Engineering
Background:
- Internet of Things (IoT) systems rely on battery-limited sensor nodes, making power control a critical challenge.
- Wireless power transfer is essential for sustaining IoT device operation, necessitating efficient resource allocation.
- Optimizing simultaneous information transmission and energy transfer is key for next-generation networks.
Purpose of the Study:
- To develop an optimal power control strategy for wireless-powered IoT systems.
- To minimize transmission costs for sensor nodes and energy transfer costs for the hybrid access point (HAP).
- To formulate a mean-field Stackelberg game model for resource allocation.
Main Methods:
- Formulating the HAP-sensor node relationship as a Stackelberg game.
- Modeling dynamic energy variations using mean-field control for a dynamic game.
- Developing a mean-field Stackelberg game model for power control.
- Applying dynamic programming theory and the law of large numbers to find optimal solutions.
Main Results:
- Achieved optimal power resource allocation for information transmission and energy transfer.
- Minimized transmission costs for sensor nodes and energy transfer costs for the HAP.
- Obtained ε-Nash equilibriums through the proposed mean-field control approach.
- Verified energy variations in sensor nodes and HAP via simulation.
Conclusions:
- The proposed mean-field Stackelberg game model effectively optimizes power control in wireless-powered IoT systems.
- The approach balances transmission and energy transfer efficiency, addressing battery limitations.
- Dynamic programming and mean-field control provide a robust framework for resource allocation in complex IoT networks.
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