智慧家庭聯網之 QoS 資源調配的匯聚策略

dc.contributor黃文吉zh_TW
dc.contributorHwang, Wen-Jyien_US
dc.contributor.author董一志zh_TW
dc.contributor.authorTung, Yi-Chihen_US
dc.date.accessioned2019-09-05T11:15:55Z
dc.date.available2022-08-07
dc.date.available2019-09-05T11:15:55Z
dc.date.issued2017
dc.description.abstract無中文摘要。zh_TW
dc.description.abstractThis dissertation recommends a novel rendezvous strategy for QoS pro- visioning with minimum computing cost over intelligent home networks. Although the usual QoS algorithms such as ow control can be adapted for simplifying the bandwidth allocation, the methods are limited only for local services. The proposed algorithm, termed GRNN QoS (Generalized Regression Neural Network for Quality of Service), is able to provide global home network services while requiring minimum computing complexity. The GRNN QoS is a hybrid combination of ow control and path selection. The ow control is adopted for the adjustment and/or allocation of local bandwidth; whereas, the path selection is used for the collection and/or delivery of local network information to the home networks. With GRNN computation, services provided by the home networks are then global optimality. This algorithm is well-suited for intelligent home networks where the fast QoS provisioning and low computing efforts of the small scale home networks are desired. GRNN QoS establishing profiles of user’s various responses to the communication links to different services is the first step for GRNN QoS algorithm. Based on the profiles GRNN QoS can estimate the future feedback to the services from a user. Upon the bandwidth allocations for receiving positive feedback, the algorithm finds a way to minimize the bandwidth usage of the networks. The main advantages of this algorithm are that it quickly adapts to user response and it does not require offline training. This dissertation provides both analytical and numerical analyses to demonstrate the efficacy of said algorithm.en_US
dc.description.sponsorship資訊工程學系zh_TW
dc.identifierG0899470018
dc.identifier.urihttp://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22G0899470018%22.&%22.id.&
dc.identifier.urihttp://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/106528
dc.language英文
dc.subjectQoSzh_TW
dc.subjectGRNNzh_TW
dc.subjectHome Networkzh_TW
dc.subjectQuality of Servicezh_TW
dc.subjectChannel allocationzh_TW
dc.subjectPrediction algorithmszh_TW
dc.subjectBandwidthzh_TW
dc.subjectHome automationzh_TW
dc.subjectAlgorithm design and analysiszh_TW
dc.subjectTrainingzh_TW
dc.subjectNetwork Access Devicezh_TW
dc.subjectQoSen_US
dc.subjectGRNNen_US
dc.subjectHome Networken_US
dc.subjectQuality of Serviceen_US
dc.subjectChannel allocationen_US
dc.subjectPrediction algorithmsen_US
dc.subjectBandwidthen_US
dc.subjectHome automationen_US
dc.subjectAlgorithm design and analysisen_US
dc.subjectTrainingen_US
dc.subjectNetwork Access Deviceen_US
dc.title智慧家庭聯網之 QoS 資源調配的匯聚策略zh_TW
dc.titleRendezvous Strategy of QoS Provisioning over Intelligent Home Networksen_US

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