Link and Network-wide Study of Incoherent GN/EGN Models

An unprecedented comparison of closed-form incoherent GN (InGN) models is presented with heterogeneous spans and partially loaded links in elastic optical networks. Results reveal that with accumulated dispersion correction and modulation format terms, the InGN shows higher accuracy

End-to-end survivable connections in multi-domain GMPLS networks

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Context-aware QoS provisioning for an M-health service platform

Inevitably, healthcare goes mobile. Recently developed mobile healthcare (i.e. m-health) services allow healthcare professionals to monitor a mobile patient’s vital signs and provide feedback to this patient anywhere and any time. Due to the nature of current supporting mobile services platforms, mhealth services are delivered with a best-effort, i.e., there are no guarantees on the delivered quality of service (QoS). In this paper, we argue that the use of contextual information in an mhealth services platform improves the delivered QoS. We give a first attempt to merge contextual information with a QoS-aware mobile services platform in the m-health services domain. We illustrate this with an epilepsy tele-monitoring scenario

Enabling Remote Human-to-Machine Applications With AI-Enhanced Servers Over Access Networks

The recent research trends for achieving ultra-reliable and low-latency communication networks are largely driven by smart manufacturing and industrial Internet-of-Things applications. Such applications are being realized through Tactile Internet that allows users to control remote things and involve the bidirectional transmission of video, audio, and haptic data. However, the end-to-end propagation latency presents a stubborn bottleneck, which can be alleviated by using various artificial intelligence-based application layer and network layer prediction algorithms, e.g., forecasting and preempting haptic feedback transmission. In this paper, we study the experimental data on traffic characteristics of control signals and haptic feedback samples obtained through virtual reality-based human-to-machine teleoperation. Moreover, we propose the installation of edge-intelligence servers between master and slave devices to implement the preemption of haptic feedback from control signals. Harnessing virtual reality-based teleoperation experiments, we further propose a two-stage artificial intelligence-based module for forecasting haptic feedback samples. The first-stage unit is a supervised binary classifier that detects if haptic sample forecasting is necessary and the second-stage unit is a reinforcement learning unit that ensures haptic feedback samples are forecasted accurately when different types of material are present. Furthermore, by evaluating analytical expressions, we show the feasibility of deploying remote human-to-machine teleoperation over fiber backhaul by using our proposed artificial intelligence-based module, even under heavy traffic intensity

CASTBA: Internet Traffic Measurements over the Spanish R&D ATM Network

The increasing adoption of ATM technology in the main Internet backbones is introducing new factors of complexity in the network dimensioning, management, and operation fields. On the one hand, the behavior of the IP/ATM protocol stack is still not well understood. On the other hand, the aggregated traffic supported by high-speed Internet backbones responds to complex patterns which are highly variable and unpredictable. Most of the works on Internet traffic characterization over ATM networks rely on theoretical studies. They are however slightly underwritten by measured results, which often explains why the real behavior differs from the expected one. This paper provides a summary of the traffic measurements collected during approximately on year over the Spanish R&D IP/ATM Internet Backbone (RedIris). This data collection has served us to evaluate the performance of the IP/ATM protocol architecture under a real network scenario. We have also performed traffic characterization study identifying the most frequent Internet services and applications. The results have shown that some of them (e.g. the WWW) are not efficiently transported over ATM. In addition, we have obtained several other interesting data and conclusions that characterize the Spanish R&D Internet backbone. Some of them could be easily extrapolated to other Internet ATM backbones

Context-aware QoS provisioning for an M-health service platform

Inevitably, healthcare goes mobile. Recently developed mobile healthcare (i.e., m-health) services allow healthcare professionals to monitor mobile patient’s vital signs and provide feedback to this patient anywhere at any time. Due to the nature of current supporting mobile service platforms, m-health services are delivered with a best-effort, i.e., there are no guarantees on the delivered Quality of Service (QoS). In this paper, we argue that the use of context information in an m-health service platform improves the delivered QoS. We give a first attempt to merge context information with a QoS-aware mobile service platform in the m-health services domain. We illustrate this with an epilepsy tele-monitoring scenario

SDN-controlled energy-efficient mobile fronthaul: An experimental evaluation in federated testbeds

When evolved NodeB (eNB) flexible functional split is implemented in Cloud-Radio Access Network (Cloud-RAN) 5G systems, fronthaul connectivity between the virtualized functions must be always guaranteed. This study proposes the utilization of Software Defined Networking (SDN) to control mobile fronthaul. In particular, this study investigates the ability of the SDN-based control of reconfiguring the fronthaul to maintain virtualized network function connectivity when cell and optical access turn into sleep mode (off mode) for energy efficiency purposes. The experiments in two federated testbeds show that, upon cell and optical access turning on and off, the fronthaul reconfiguration time is limited to few tens of milliseconds