A Proposal for VLC-Assisting IEEE802.11p Communication for Vehicular Environment Using a Prediction-based Handover

International audienceDespite years of development and deployment, the standardized IEEE802.11p communication for vehicular networks can be pushed toward insatiable performance demands for wireless network data access, with a remarkable increase of both latency and channel congestion levels when subjected to scenarios with a very high vehicle density. In some hard safety applications such as convoys, IEEE802.11p could seriously fail to meet the fundamental vehicular safety requirements. On the other hand, the advent of LED technologies has opened up the possibility of leveraging the more robust Visible Light Communication (VLC) technology to assist IEEE802.11p and provide seamless connectivity in dense vehicular scenarios. In this paper, we propose and validate a prediction-based vertical handover (PVHO) between VLC and IEEE802.11p meant to afford seamless switching and ensure the autonomous driving safety requirements. Algorithm validation and platoon system performance were evaluated using a specially implemented 802.11p-VLC module in the NS3 Network Simulator. The simulation results showed a speed-based dynamic redundancy before and after VLC interruptions with seamless switching. Moreover, the deployment of VLC for platoon intra-communication can achieve a 10-25% PDR gain in high-density vehicular scenarios

Enabling P4 Network Telemetry in Edge Micro Data Centers With Kubernetes Orchestration

Integrating computation resources with networking technologies is an hot research topic targeting the optimization of containers deployment on a set of host machines interconnected by a network infrastructure. Particularly, next generation edge nodes will offer significant advantages leveraging on integrated computation resources and networking awareness, enabling configurable, granular and monitorable quality of service to different micro-services, applications and tenants, especially in terms of bounded end-to-end latency. In this regard, SDN is a key technology enabling network telemetry and traffic switching with the granularity of the single traffic flow. However, currently available solutions are based on legacy SDN techniques, not enabling the matching of tunneled traffic, and thus require a tricky integration inside the hosts where containers are deployed. This work considers Kubernetes clusters deployed on next generation edge micro data center platforms and proposes an innovative SDN solution exploiting the P4 technology to gain visibility inside tunnelled traffic exchanged among pods. This way, the integration is achieved at the control plane level through the communication between Kubernetes and the SDN controller. The proposed solution is experimentally validated including a comprehensive framework enabling effective traffic switching and in-band telemetry at pod level. The major paper contributions consist in the design and the development of: (i) the networking applications at SDN control plane level; (ii) the P4 switch pipeline at the data plane level; (iii) the monitoring system used to collect, aggregate and elaborate the telemetry data

Realistische Modellierung der physikalischen Schicht zur akkuraten Simulation drahtloser Netzwerke

Die Bedeutung von Simulationen im Bereich drahtloser Netzwerke hat trotz ihrer Vorteile während der Entwicklung und Evaluation neuer Protokolle abgenommen. Zu den Vorteilen zählen unter anderem die Reproduzierbarkeit und Skalierbarkeit von Experimenten. Der Grund hierfür liegt darin, dass die Gültigkeit von Ergebnissen auf Basis simulierter, drahtloser Netzwerke innerhalb der Forschungsgemeinschaft sehr kritisch betrachtet und nicht selten hinterfragt wird. Gleichzeitig haben Evaluationen und Ergebnisse aus realen Testbeds an Bedeutung gewonnen. Allerdings ist dieser Ansatz im Vergleich zu Simulationen sehr zeitintensiv und stellt im Allgemeinen eine größere Herausforderung dar. Diese Dissertation verbessert die Konfidenz von Ergebnissen auf Basis von Simulationen von IEEE 802.11 basierenden Netzwerken. Zu diesem Zweck entwickeln wir ein ortsspezifisches Modell der IEEE 802.11 physikalischen Schicht. Die Eingangsparameter unseres Modells basieren dabei auf Messergebnissen realer Testbeds. Dadurch verbessern wir die Fähigkeiten von Simulationswerkzeugen, realistische und wiederholbare Experimente durchzuführen. Der wissenschaftliche Beitrag dieser Arbeit umfasst verschiedene Modellierungen: empirisch ermittelte Wellenausbreitung, Übertragungsfehler, Frameerkennung und Interferenz. Der Entwicklung dieser Modelle geht unsere detaillierte Analyse voraus, in der die Signifikanz verschiedener Parameter bezüglich der Simulationsgenauigkeit untersucht werden. Dabei evaluierten wir deren Einfluss in verschiedenen Umgebungen (in Gebäuden sowie im Außenbereich) unter Berücksichtigung verschiedener Szenarien (z.B. einzelne oder mehrere parallele Verkehrsströme, 1-hop oder multi-hop Verbindungen) und bezüglich der physikalischen Netzwerkschicht und der Transportschicht. Unsere Ergebnisse zeigen einen bedeutenden Einfluss von Umgebungsbedingungen sowie Gerätespezifikationen auf die drahtlose Kommunikation auf. Im Vergleich zu herkömmlichen Simulationsmodellen verbessert unsere ortsspezifische Modellierung die Simulationsergebnisse von IEEE 802.11 basierenden Netzwerken signifikant.The importance of simulation has diminished in wireless networking despite the advantages of simulation in developing and evaluating new protocols, such as repeatability and scalability. This is due to constant questioning of the validity of the wireless simulation results by the wireless networking community. As an alternative, testbed evaluations, have become popular since they provide more confident results. However, adopting this approach is typically more time consuming and challenging. This thesis takes a step forward in improving the confidence of simulating IEEE 802.11 wireless networks. To this end, we introduce a site-specific IEEE 802.11 Physical (PHY) layer model for wireless networks, which is representative of an actual testbed. It uses testbed measurements as input to the model. As the main advantage of this model, we gain the ability to run realistic and repeatable experiments. The main contributions of this thesis are empirical propagation, frame error, frame detection and interference models. These contributions are based on a thorough analysis of which wireless parameters impact the accuracy of the simulation results. Then, we evaluate their impact both at PHY and transport layers, in different environments (indoor vs. outdoor) and set-ups (e.g., single flow, multiple concurrent flows, multi-hop scenarios). Our results show that environmental conditions and device specifications have a major impact on the wireless communication. Moreover, the site-specific approach for modeling wireless networks significantly improves the simulation results in comparison to the typical simulation models

Quality of perception prediction in 5G slices for e-Health services using user-perceived QoS

In order to compete for a prominent market share, network operators and service providers should retain and increase the verticals’ subscription, catering to their needs in order to differentiate themselves from competitors. In this scenario, verticals’ satisfaction arises of paramount importance. As such, user experience is becoming a reliable indicator for service providers and telecommunication operators to convey overall end-to-end system functioning. To properly estimate end user satisfaction, operators and service providers require efficient means for quality monitoring and estimation at all layers, in conjunction with mechanisms able to maintain said quality at optimum levels. Given these factors, this paper proposes a mechanism for Quality of Perception (QoP) estimation in e-Health services, enabling the QoP-aware management of network slices fulfilling the requirements of supported services. To this end, the paper proposes a cognitive-based architecture which allows for the collection and monitoring of verticals’ data to estimate QoP and provides mechanisms to re-configure the underlying network slices according to the monitored quality levels. A machine learning (ML) model is introduced that aims to forecast any future degradation in the quality perceived by vertical users. In case of a predicted degradation, the proposed architecture reacts and triggers the necessary remedial actions, referred as actuations. In order to evaluate the developed ML model and to showcase the interaction between the different components of the proposed architecture, an experimental study is presented with real data extracted from a roaming ambulance. In addition, a Proof of Concept of the actuation mechanism is demonstrated through an experimental testbed emulating e-Health services.This work has been funded in part through the European Union’s H2020 program, under grant agreement No 761913: project SliceNet. The authors would like to thank all SliceNet partners for their support in this work.Peer ReviewedPostprint (author's final draft