{"title":"优化射频充电多跳物联网网络中的样本传输","authors":"Muchen Jiang;Kwan-Wu Chin","doi":"10.1109/TGCN.2023.3311590","DOIUrl":null,"url":null,"abstract":"This paper studies sample delivery in a multi-hop network where a power beacon charges devices via radio frequency (RF) signals. Devices forward samples with a deadline from a source to a sink. The goal is to minimize the power beacon’s transmit power and guarantee that samples arrive at the sink with probability \n<inline-formula> <tex-math>$(1-\\epsilon)$ </tex-math></inline-formula>\n by their deadline, where \n<inline-formula> <tex-math>$\\epsilon $ </tex-math></inline-formula>\n is a given probability of failure. A key challenge is that the power beacon does not have instantaneous channel gains information to devices and also between devices; i.e., it does not know the energy level of devices. To this end, we formulate a chance-constrained stochastic program for the problem at hand, and employ the sample-average approximation (SAA) method to compute a solution. We also outline two novel approximation methods: Sampling based Probabilistic Optimal Power Allocation (S-POPA) and Bayesian Optimization based Probabilistic Optimal Power Allocation (BO-POPA). Briefly, S-POPA generates a set of candidate solutions and iteratively learns the solution that returns a high probability of success. On the other hand, BO-POPA applies the Bayesian optimization framework to construct a surrogate model to predict the reward value of transmit power allocations. Numerical results show that the performance of S-POPA and BO-POPA achieves on average 86.91% and 79.25% of the transmit power computed by SAA.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing Sample Delivery in RF-Charging Multi-Hop IoT Networks\",\"authors\":\"Muchen Jiang;Kwan-Wu Chin\",\"doi\":\"10.1109/TGCN.2023.3311590\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper studies sample delivery in a multi-hop network where a power beacon charges devices via radio frequency (RF) signals. Devices forward samples with a deadline from a source to a sink. The goal is to minimize the power beacon’s transmit power and guarantee that samples arrive at the sink with probability \\n<inline-formula> <tex-math>$(1-\\\\epsilon)$ </tex-math></inline-formula>\\n by their deadline, where \\n<inline-formula> <tex-math>$\\\\epsilon $ </tex-math></inline-formula>\\n is a given probability of failure. A key challenge is that the power beacon does not have instantaneous channel gains information to devices and also between devices; i.e., it does not know the energy level of devices. To this end, we formulate a chance-constrained stochastic program for the problem at hand, and employ the sample-average approximation (SAA) method to compute a solution. We also outline two novel approximation methods: Sampling based Probabilistic Optimal Power Allocation (S-POPA) and Bayesian Optimization based Probabilistic Optimal Power Allocation (BO-POPA). Briefly, S-POPA generates a set of candidate solutions and iteratively learns the solution that returns a high probability of success. On the other hand, BO-POPA applies the Bayesian optimization framework to construct a surrogate model to predict the reward value of transmit power allocations. Numerical results show that the performance of S-POPA and BO-POPA achieves on average 86.91% and 79.25% of the transmit power computed by SAA.\",\"PeriodicalId\":13052,\"journal\":{\"name\":\"IEEE Transactions on Green Communications and Networking\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2023-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Green Communications and Networking\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10238843/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Green Communications and Networking","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10238843/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
Optimizing Sample Delivery in RF-Charging Multi-Hop IoT Networks
This paper studies sample delivery in a multi-hop network where a power beacon charges devices via radio frequency (RF) signals. Devices forward samples with a deadline from a source to a sink. The goal is to minimize the power beacon’s transmit power and guarantee that samples arrive at the sink with probability
$(1-\epsilon)$
by their deadline, where
$\epsilon $
is a given probability of failure. A key challenge is that the power beacon does not have instantaneous channel gains information to devices and also between devices; i.e., it does not know the energy level of devices. To this end, we formulate a chance-constrained stochastic program for the problem at hand, and employ the sample-average approximation (SAA) method to compute a solution. We also outline two novel approximation methods: Sampling based Probabilistic Optimal Power Allocation (S-POPA) and Bayesian Optimization based Probabilistic Optimal Power Allocation (BO-POPA). Briefly, S-POPA generates a set of candidate solutions and iteratively learns the solution that returns a high probability of success. On the other hand, BO-POPA applies the Bayesian optimization framework to construct a surrogate model to predict the reward value of transmit power allocations. Numerical results show that the performance of S-POPA and BO-POPA achieves on average 86.91% and 79.25% of the transmit power computed by SAA.