{"title":"Throughput Optimization in Energy Harvesting based Cognitive IoT with Cooperative Sensing","authors":"Yan Long, Ye Li, Honghao Ju, Rong He, X. Fang","doi":"10.1109/VTC2021-Spring51267.2021.9448917","DOIUrl":null,"url":null,"abstract":"In this paper, we study the throughput optimization problem in energy harvesting based cognitive Internet of Things (IoT), under cooperative spectrum sensing mode. Considering the user diversity in energy harvesting efficiency, spectrum sensing performance and data quality-of-service requirement, we optimize the harvesting-sensing-transmission tradeoff. To achieve this, we formulate it as a network-level throughput optimization problem by jointly optimizing time splitting and sensor selection. With the proposed throughput-based greedy algorithm, we first fix the sensor selection variable, and then transform the problem into an equivalent convex optimization problem. Simulation results show that our proposed scheme has great advantages in terms of secondary network throughput.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448917","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, we study the throughput optimization problem in energy harvesting based cognitive Internet of Things (IoT), under cooperative spectrum sensing mode. Considering the user diversity in energy harvesting efficiency, spectrum sensing performance and data quality-of-service requirement, we optimize the harvesting-sensing-transmission tradeoff. To achieve this, we formulate it as a network-level throughput optimization problem by jointly optimizing time splitting and sensor selection. With the proposed throughput-based greedy algorithm, we first fix the sensor selection variable, and then transform the problem into an equivalent convex optimization problem. Simulation results show that our proposed scheme has great advantages in terms of secondary network throughput.