Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC–WPT System
Abstract
:1. Introduction
- (1)
- We adopting the case of multiple MDs, leveraging multiple antennas on the AP to perform energy transmission and charging multiple MDs at the same time so that each MD completes the corresponding computing task based on the harvested energy. Using partial offloading, each MD arbitrarily divides the computing task into two independent parts, which are used for local computing and task offloading. In addition, our proposed scheme uses Time Division Multiple Access (TDMA) protocol to coordinate computation offloading, where different users offload their respective tasks to the AP over orthogonal time slots.
- (2)
- In the integrated system, we focus on the joint optimization of the communication and computing resources of the partial computation offloading system. Taking the multi-period execution tasks of MDs into account, we achieve the goal of maximizing the remaining energy under multiple MDs and multi-slots, which requires maximizing energy harvesting and minimizing energy consumption. Therefore, we jointly optimize the task offloading size, task offloading time, energy harvesting time and CPU frequency of the MDs.
- (3)
- We established the system model and formalized the residual energy maximum problem. The best solution was obtained by proposing effective convex optimization methods and the augmented Lagrangian method. The simulation results show that the proposed joint optimizing scheme outperforms the previous ones in the partial computation offloading system, and the proposed algorithm is proven to be more efficient.
2. Related Work
3. System Model
3.1. Local Computing Model
3.2. Task-Offloading Model
3.3. Energy Harvesting Model
4. Formalization of the Problem
4.1. Problem Formulation
4.2. Problem Analysis
5. Problem Solving
5.1. Through the Lagrange Method and KKT Conditions to Find the Optimal Solution Properties
5.2. Use the Optimal Solution Properties to Rearrange the Problem
5.3. Solving the Simplified Problem through the ALM Method
Algorithm 1: Augmented Lagrange Method |
Input: initial candidate point; penalty factor; termination limits; growth coefficient of penalty factor; iteration times |
1: repeat |
2: solving the unconstrained problem (18) by ALM method, get ; |
3: ; |
4: ; |
5: until |
Output: optimal |
5.4. Obtaining Other Optimization Variables
6. Simulation Results
- Local computation only: Each user completes its computation task only through local computations, and the entire time slice is used to harvest energy for each user, referred to as LOC.
- Only computation offload: Each user completes his computing task only by completely offloading the computing task to the AP, and the energy harvested by the user is used to support computing offload, referred to as OFF.
- Fixed energy harvesting time: The energy harvesting time of each user is fixed [28], and the task-offloading size and task-offloading time are optimized, referred to as FEH.
- Fixed task offloading size: The size of each user’s task-offloading is fixed [28], which can optimize energy harvesting time and offloading time, referred to as FTO.
6.1. Performance of Solution
6.2. Failure Ratio of Solution
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Parameters | Notation |
---|---|
The input bit of the task | |
The offload bit | |
The number of cycles for one bie | |
The effective capacitance coefficient | |
The frequency of the CPU | |
The communication bandwidth | B |
The channel gain from MD to AP | |
The transmission power from MD to AP | |
The distance between device and AP | |
The constant circuit power | |
Energy conversion efficiency | |
The channel gain of downlink channel | |
The AP transmission power | |
The energy harvesting time |
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Li, L.; Xu, G.; Liu, P.; Li, Y.; Ge, J. Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC–WPT System. Future Internet 2020, 12, 233. https://doi.org/10.3390/fi12120233
Li L, Xu G, Liu P, Li Y, Ge J. Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC–WPT System. Future Internet. 2020; 12(12):233. https://doi.org/10.3390/fi12120233
Chicago/Turabian StyleLi, Long, Gaochao Xu, Peng Liu, Yang Li, and Jiaqi Ge. 2020. "Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC–WPT System" Future Internet 12, no. 12: 233. https://doi.org/10.3390/fi12120233