| 摘要: |
| 射频能量捕获是应对无线网络节点能量受限的有效方法之一.射频能量源（energy source，简称ES）的布置位置和发送功率决定了各个节点的能量捕获功率.现有的研究工作大部分考虑的是没有给定侯选位置的场景.然而，在实际应用场景中，网络区域往往存在很多不可布置能量源的区域，使得能量源只能在一些合理的候选位置中布置.目前仅有少量相关工作研究如何在ES的候选布置位置中选择合适布置位置.已知节点位置、节点的能量捕获功率需求值、ES的个数以及ES的候选布置位置.研究并设计了最小化ES总供能的ES布置与发送功率设置方案.首先将该问题建模为混合整数规划问题；然后分别提出了一种具有较低复杂度的启发式算法和一种能够达到更小总供能的基于遗传算法的算法.仿真结果表明，与布置位置随机挑选法相比，这两种算法的网络总功耗降低了约90%，而遗传算法可达到比启发式算法高约35%的节能效果.因此，基于遗传算法的布置算法可用于中小规模的ES布置场景，而启发式算法可用于大规模的ES布置场景. |
| 关键词: 射频能量捕获 能量源布置 网络功耗 |
| DOI： |
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| 基金项目:国家自然科学基金（61872322）；浙江省基础公益研究计划（LGG18F020005） |
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| Radio Frequency Energy Source Deployment and Transmit Power Setting to Minimize the Network Power Consumption |
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GE Hai-Jiang1,2, Xu Xing-Yuan1, LIU Si-Qi1, CHI Kai-Kai1, QIU Jie-Fan1
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1.School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China;2.Institute of Information Engineering, Hangzhou Vocational and Technical College, Hangzhou 310018, China
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| Abstract: |
| Radio frequency (RF) energy harvesting is one of the effective methods to deal with the energy limitation of wireless network nodes. The deployment positions and transmit power setting of RF energy sources (ESs) determine the energy harvesting rate of each node. Most of the existing research work considers scenarios where no candidate ES deployment positions are given. However, in the practical application scenario, there are often many areas inside the network region where the ESs cannot be placed. The ESs can only be arranged in some reasonable candidate locations. So far, almost no work has been done to study how to select appropriate deployment positions among candidate deployment positions of ESs. Given the nodes' locations, nodes' energy energy-harvesting-rate demand, the number of ESs and the candidate deployment positions of ESs, design the ES deployment schemes which minimize the total network power consumption. Firstly, the problem is modeled as a mixed integer programming problem. Then a low-complexity approximation heuristic scheme and a genetic algorithm based deployment scheme with lower total network power consumption are proposed, respectively. Simulation results show that the proposed two schemes reduce the total network power consumption by about 90% as compared to the scheme of randomly selecting the deployment locations and the total network power consumption of genetic scheme can be 35% lower than that of heuristic algorithm. Therefore, the deployment scheme based on genetic scheme can be used for the small and medium-sized ES deployment scenarios, while the heuristic scheme can be used for large-scale ES deployment scenarios. |
| Key words: Radio frequency energy harvesting energy source deployment network power consumption |
