Software-defined networking （SDN） is a generic term and one of the major interests of the telecoms industry （and beyond） over the past two years. However, defining SDN is a somewhat controversial exercise. The cla...Software-defined networking （SDN） is a generic term and one of the major interests of the telecoms industry （and beyond） over the past two years. However, defining SDN is a somewhat controversial exercise. The claimed flexibility, as well as other presumed assets of SDN, should be carefully investigated. In particular, the use of SDN to dynamically provision network services suggests the introduction of a certain level of automation in the overall network service delivery process, from service parameter negotiation to delivery and operation. This paper aims to clarify the SDN landscape and focuses on two main aspects of the SDN framework： net- work abstraction, and dynamic parameter exposure and negotiation.展开更多
Machine Learning concepts have raised executions in all knowledge domains,including the Internet of Thing(IoT)and several business domains.Quality of Service(QoS)has become an important problem in IoT surrounding sinc...Machine Learning concepts have raised executions in all knowledge domains,including the Internet of Thing(IoT)and several business domains.Quality of Service(QoS)has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors,information and usage.Sen-sor data gathering is an efficient solution to collect information from spatially dis-seminated IoT nodes.Reinforcement Learning Mechanism to improve the QoS(RLMQ)and use a Mobile Sink(MS)to minimize the delay in the wireless IoT s proposed in this paper.Here,we use machine learning concepts like Rein-forcement Learning(RL)to improve the QoS and energy efficiency in the Wire-less Sensor Network(WSN).The MS collects the data from the Cluster Head(CH),and the RL incentive values select CH.The incentives value is computed by the QoS parameters such as minimum energy utilization,minimum bandwidth utilization,minimum hop count,and minimum time delay.The MS is used to col-lect the data from CH,thus minimizing the network delay.The sleep and awake scheduling is used for minimizing the CH dead in the WSN.This work is simu-lated,and the results show that the RLMQ scheme performs better than the base-line protocol.Results prove that RLMQ increased the residual energy,throughput and minimized the network delay in the WSN.展开更多
We elaborate an error budget for the long-term accuracy of IGS（International Global Navigation Satellite System Service） polar motion estimates, concluding that it is probably about 25-30 μas（1-sigma）overall, alt...We elaborate an error budget for the long-term accuracy of IGS（International Global Navigation Satellite System Service） polar motion estimates, concluding that it is probably about 25-30 μas（1-sigma）overall, although it is not possible to quantify possible contributions（mainly annual） that might transfer directly from aliases of subdaily rotational tide errors. The leading sources are biases arising from the need to align daily, observed terrestrial frames, within which the pole coordinates are expressed and which are continuously deforming, to the secular, linear international reference frame. Such biases are largest over spans longer than about a year. Thanks to the very large number of IGS tracking stations, the formal covariance errors are much smaller,around 5 to 10 μas. Large networks also permit the systematic frame-related errors to be more effectively minimized but not eliminated. A number of periodic errors probably also influence polar motion results, mainly at annual, GPS（Global Positioning System） draconitic, and fortnightly periods, but their impact on the overall error budget is unlikely to be significant except possibly for annual tidal aliases. Nevertheless, caution should be exercised in interpreting geophysical excitations near any of the suspect periods.展开更多
文摘Software-defined networking （SDN） is a generic term and one of the major interests of the telecoms industry （and beyond） over the past two years. However, defining SDN is a somewhat controversial exercise. The claimed flexibility, as well as other presumed assets of SDN, should be carefully investigated. In particular, the use of SDN to dynamically provision network services suggests the introduction of a certain level of automation in the overall network service delivery process, from service parameter negotiation to delivery and operation. This paper aims to clarify the SDN landscape and focuses on two main aspects of the SDN framework： net- work abstraction, and dynamic parameter exposure and negotiation.
基金support by the Deanship of Scientific Research at King Khalid University under research grant number(RGP.2/241/43)。
文摘Machine Learning concepts have raised executions in all knowledge domains,including the Internet of Thing(IoT)and several business domains.Quality of Service(QoS)has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors,information and usage.Sen-sor data gathering is an efficient solution to collect information from spatially dis-seminated IoT nodes.Reinforcement Learning Mechanism to improve the QoS(RLMQ)and use a Mobile Sink(MS)to minimize the delay in the wireless IoT s proposed in this paper.Here,we use machine learning concepts like Rein-forcement Learning(RL)to improve the QoS and energy efficiency in the Wire-less Sensor Network(WSN).The MS collects the data from the Cluster Head(CH),and the RL incentive values select CH.The incentives value is computed by the QoS parameters such as minimum energy utilization,minimum bandwidth utilization,minimum hop count,and minimum time delay.The MS is used to col-lect the data from CH,thus minimizing the network delay.The sleep and awake scheduling is used for minimizing the CH dead in the WSN.This work is simu-lated,and the results show that the RLMQ scheme performs better than the base-line protocol.Results prove that RLMQ increased the residual energy,throughput and minimized the network delay in the WSN.
文摘We elaborate an error budget for the long-term accuracy of IGS（International Global Navigation Satellite System Service） polar motion estimates, concluding that it is probably about 25-30 μas（1-sigma）overall, although it is not possible to quantify possible contributions（mainly annual） that might transfer directly from aliases of subdaily rotational tide errors. The leading sources are biases arising from the need to align daily, observed terrestrial frames, within which the pole coordinates are expressed and which are continuously deforming, to the secular, linear international reference frame. Such biases are largest over spans longer than about a year. Thanks to the very large number of IGS tracking stations, the formal covariance errors are much smaller,around 5 to 10 μas. Large networks also permit the systematic frame-related errors to be more effectively minimized but not eliminated. A number of periodic errors probably also influence polar motion results, mainly at annual, GPS（Global Positioning System） draconitic, and fortnightly periods, but their impact on the overall error budget is unlikely to be significant except possibly for annual tidal aliases. Nevertheless, caution should be exercised in interpreting geophysical excitations near any of the suspect periods.
