Evaluating time-sensitive networking features on open testbeds

Time-Sensitive Networking (TSN) is vital to enable time-critical deterministic communication, especially for applications with industrial-grade requirements. IEEE TSN standards are key enablers to provide deterministic and reliable operation on top of Ethernet networks. Much of the research is still done in simulated environments or using commercial TSN switches lacking flexibility in terms of hardware and software support. In this demonstration, we use an open Cloud testbed for TSN experimentation, leveraging the hardware features that support precise time synchronization, and fine-grained scheduling according to TSN standards. We demonstrate the setup and operation of a Linux-based TSN network in the testbed using our modular Centralized Network Configuration (CNC) controller prototype. With our CNC we are able to quickly initialize the TSN bridges and end nodes, as well as manage their configurations, modify schedules, and visualize overall network operation in real-time. The results show how the TSN features can be effectively used for traffic management and resource isolation

Time-sensitive networking experimentation on open testbeds

Time-Sensitive Networking (TSN) is vital to enable time-critical deterministic communication, especially for applications with industrial-grade requirements. IEEE TSN standards are key enablers to provide deterministic and reliable operation of Ethernet networks. However, much of the research is still done in simulated environments or using commercial TSN switches lacking flexibility in terms of hardware and software support. In this work, we evaluate two different Cloud testbeds for TSN experimentation, analyzing their hardware features, the influence of the testbed management infrastructure, and the data plane performance. Furthermore, we present a prototype of a modular Software-Defined Networking (SDN) controller that facilitates the deployment of Linux-based TSN networks. We identify and discuss the controller modules and evaluate its feasibility by using it to deploy TSN networks on different testbeds. Finally, we provide insights for researchers interested in experimenting with TSN features on open Cloud testbeds and discuss the features and limitations that we found during our experiments

SD-RAN Interactive Management Using In-band Network Telemetry in IEEE 802.11 Networks

Future digital factories are becoming more and more softwarized. This introduces flexibility, but the industry also demands robustness and Quality of Service (QoS) support. While 5G envisions to enable real-time applications with strict performance requirements, IEEE 802.11 networks continue to be a viable option for indoor scenarios. However, IEEE 802.11 networks currently cannot be programmed fine-grained enough to fully support and ensure QoS. Besides, due to the unknown or coarse-grained monitoring information over the wireless links, detecting performance degradation is still challenging. In this paper, we propose a Software-Defined Radio Access Network (SD-RAN) interactive management approach assisted by In-band Network Telemetry (INT) in IEEE 802.11 networks. We design a system-level architecture that includes the network administrator in the management loop through monitoring, visualization, and configuration activities. To demonstrate the feasibility of our approach, we developed a prototype and evaluated its flexibility in a real-world testbed. We argue that, with the fine-grained network information from INT and the benefits of the Software-Defined Networking (SDN) paradigm, an SDN-tailored system can assist in troubleshooting and assess enhanced QoS delivery

Capacity of a LoRaWAN Cell

International audienceIn this paper, we consider the problem of evaluating the capacity of a LoRaWAN cell. Previous analytical studies investigated LoRaWAN performance in terms of the Packet Delivery Ratio (PDR) given a number of devices around a gateway and its range. We improve the model for PDR by taking into consideration that the following two events are dependent: successful capture during a collision and successful frame decoding despite ambient noise. We consider a realistic traffic model in which all devices generate packets with the same inter-transmission times corresponding to the duty cycle limitation at the highest SF, regardless of the distance to the gateway. Based on the developed model, we optimize the Spreading Factor (SF) boundaries to even out PDR throughout the cell. We validate the analytical results with simulations, compare our model with previous work, and experimentally validate the hypothesis of Rayleigh fading for the LoRa channel. The important conclusion from our results is that a LoRa cell can handle a relatively large number of devices. We also show that there is practically no inter-SF interference (cross interference between transmissions with different SFs): interference from higher SFs comes from nodes located farther away, so they face greater attenuation and thus, they do not interfere with lower SF nodes

Demo abstract:identification of LPWAN technologies using convolutional neural networks

Abstract This paper demonstrates a Convolutional Neural Network (CNN) based mechanism for identification of low power wide area network (LPWAN) technologies such as LoRA, Sigfox, and IEEE 802.15.4g. Since the technologies operate in unlicensed bands and can interfere with each other, it becomes essential to identify technologies (or interference in general) so that the impact of interference can be minimized by better managing the spectrum. Contrary to the traditional rule-based identification mechanisms, we use Convolutional Neural Networks (CNNs) for identification, which do not require any domain expertise. We demonstrate two types of CNN based classifiers: (i) CNN based on raw IQ samples, and (ii) CNN based on Fast Fourier Transform (FFT), which give classification accuracies close to 95% and 98%, respectively. In addition, an online video is created for demonstrating the process [1]