{"title":"一种节能且qos有效的wban资源分配方案","authors":"Zhiqiang Liu, B. Liu, C. Chen, C. Chen","doi":"10.1109/BSN.2016.7516285","DOIUrl":null,"url":null,"abstract":"Wireless Body Area Networks (WBANs) represent one of the most promising networks to provide health applications for improving the quality of life, such as ubiquitous e-Health services and real-time health monitoring. The resource allocation of an energy-constrained, heterogeneous WBAN is a critical issue that should consider both energy efficiency and Quality of Service (QoS) requirements with the dynamic link characteristics, especially when the limited resource cannot satisfy the expected QoS requirements. In this paper, we propose an Energy-efficient and QoS-effective resource allocation that considers a mix-cost parameter characterizing both energy cost and QoS cost between attainable QoS support and QoS requirements. Based on the mix-cost parameter, we first formulate the resource allocation problem as a mixed integer nonlinear programming (MINP) for optimizing the transmission power, the transmission rate and allocated time slots for each sensor to minimize total mix-cost of the system. Then we propose a sub-optimal greedy resource allocation algorithm, which has a much lower complexity compared to exhaustive search. Simulation results demonstrate the advantage of the mix-cost parameter to evaluate energy efficiency and attainable QoS support, as well as verifying the effectiveness of the proposed resource allocation algorithm.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"An energy-efficient and QoS-effective resource allocation scheme in WBANs\",\"authors\":\"Zhiqiang Liu, B. Liu, C. Chen, C. Chen\",\"doi\":\"10.1109/BSN.2016.7516285\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Wireless Body Area Networks (WBANs) represent one of the most promising networks to provide health applications for improving the quality of life, such as ubiquitous e-Health services and real-time health monitoring. The resource allocation of an energy-constrained, heterogeneous WBAN is a critical issue that should consider both energy efficiency and Quality of Service (QoS) requirements with the dynamic link characteristics, especially when the limited resource cannot satisfy the expected QoS requirements. In this paper, we propose an Energy-efficient and QoS-effective resource allocation that considers a mix-cost parameter characterizing both energy cost and QoS cost between attainable QoS support and QoS requirements. Based on the mix-cost parameter, we first formulate the resource allocation problem as a mixed integer nonlinear programming (MINP) for optimizing the transmission power, the transmission rate and allocated time slots for each sensor to minimize total mix-cost of the system. Then we propose a sub-optimal greedy resource allocation algorithm, which has a much lower complexity compared to exhaustive search. Simulation results demonstrate the advantage of the mix-cost parameter to evaluate energy efficiency and attainable QoS support, as well as verifying the effectiveness of the proposed resource allocation algorithm.\",\"PeriodicalId\":205735,\"journal\":{\"name\":\"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BSN.2016.7516285\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BSN.2016.7516285","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An energy-efficient and QoS-effective resource allocation scheme in WBANs
Wireless Body Area Networks (WBANs) represent one of the most promising networks to provide health applications for improving the quality of life, such as ubiquitous e-Health services and real-time health monitoring. The resource allocation of an energy-constrained, heterogeneous WBAN is a critical issue that should consider both energy efficiency and Quality of Service (QoS) requirements with the dynamic link characteristics, especially when the limited resource cannot satisfy the expected QoS requirements. In this paper, we propose an Energy-efficient and QoS-effective resource allocation that considers a mix-cost parameter characterizing both energy cost and QoS cost between attainable QoS support and QoS requirements. Based on the mix-cost parameter, we first formulate the resource allocation problem as a mixed integer nonlinear programming (MINP) for optimizing the transmission power, the transmission rate and allocated time slots for each sensor to minimize total mix-cost of the system. Then we propose a sub-optimal greedy resource allocation algorithm, which has a much lower complexity compared to exhaustive search. Simulation results demonstrate the advantage of the mix-cost parameter to evaluate energy efficiency and attainable QoS support, as well as verifying the effectiveness of the proposed resource allocation algorithm.