Opera is a scalable local simulation network for experimental researches on distributed systems. To the best of our knowledge, it is the first Java-based event-driven simulator for distributed systems with a modular network, induced churn and latency traces from real-world systems, full life cycle management of the nodes, and a production-grade simulation monitoring. In this demo paper, we present the key features of Opera, its software architecture, as well as a sample demo scenario.

Next generation mobile networks require flexibility and dynamicity to satisfy the needs of vertical industries. This may entail the deployment of slices instantiated in the form of composite network services (NSs) spanning multiple administrative domains through network service federation (NSF). In this way, different nested NSs of the composite service can be deployed by different service providers. But fulfilling the needs of verticals is not only needed during instantiation time but also during NS operation to honour the required service level agreements (SLAs) under changing network conditions. In this demonstration, we present the capabilities of the 5Growth platform to handle the scaling of federated NSs. In particular, we show the scale out/in of a nested NS deployed in a federated domain, which is part of a composite NS. These scaling operations, triggered to maintain the NS SLAs, imply a set of coordinated operations between involved administrative domains.

This demo abstract presents the visualization of deep reinforcement learning (DRL)-based autonomous aerial mobility simulations. In order to implement the software, Unity-RL is used and additional buildings are introduced for urban environment. On top of the implementation, DRL algorithms are used and we confirm it works well in terms of trajectory and 3D visualization.

Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We demonstrate a novel machine learning approach to solve resource orchestration problems in energy-constrained vRANs. Specifically, we demonstrate two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient-converge an order of magnitude faster than other machine learning methods-and have provably performance, which is paramount for carrier-grade vRANs. We demonstrate the advantages of our approach in a testbed comprised of fully-fledged LTE stacks and a power meter, and implementing our approach into O-RAN's non-real-time RAN Intelligent Controller (RIC).

Timely efficient sensor information sharing among different autonomous driving vehicles (ADVs) is crucial to guarantee the safety of ADVs. The radar and communication integrated system (RCIS) can overcome the time consuming problems of data format transfer and complex data fusion across multiple sensors in ADVs. This paper designs a 5G New Radio frame structure based RCIS by sharing the same hardware equipments to realize both radar and communication functions. An integrated waveform enabled smart time and frequency resource filling (STFRF) algorithm is proposed to realize a flexible time and frequency resources sharing and utilization. Field test results verify that the proposed STFRF algorithm for RCIS can achieve an acceptable target detection performance of the average position error of 0.2 m, as well as a stable data rate of 2.86 Gbps for communication system in the millimeter wave frequency band enabled ADV scenario.

Today's Wireless Local Area Networks (WLANs) rely on a centralized Access Controller (AC) entity for managing a fleet of Access Points (APs). Real-time analytics enable the AC to optimize the radio resource allocation (i.e. channels) on-line in response to sudden traffic shifts. Deep Reinforcement Learning (DRL) relieves the pressure of finding good optimization heuristics by learning a policy through interactions with the environment. However, it is not granted that DRL will behave well in unseen conditions. Tools such as the WiFi Dynoscope introduced here are necessary to gain this trust. In a nutshell, this demo dissects the dynamics of WLAN networks, both simulated and from real large-scale deployments, by (i) comparatively analyzing the performance of different algorithms on the same deployment at high level and (ii) getting low-level details and insights into algorithmic behaviour.

This demo proposes a novel virtual content registration system (DeepMix) for MR applications, which integrates state-of-the-art computer vision technology and allows real-time interaction between virtual contents and arbitrary real objects in the physical environment for MR devices. DeepMix effectively utilizes different sensors on MR devices to measure the dimension and spatial location of real objects in the physical environment and improves the quality of experience (QoE) of users adaptively under various situations. Compared with state-of-the-art virtual content registration methods, DeepMix is a light-weight registration system with more flexibility, higher intelligence, and stronger adaptability.

This paper designs and implements a deep learning based hybrid kitchen safety guarding system, called DeepSafe, using embedded devices and onboard sensors to detect abnormal events and block gas sources in time through the Internet of Things (IoT). In the sensing mode, the DeepSafe system can prevent the kitchen from fire/explosion disasters by detecting gas concentration, recognizing fire intensity, and estimating vibration levels. In the control mode, the DeepSafe system can automatically block the gas source as detecting an abnormal event, remotely monitor the kitchen status via real-time streaming videos, and manually turn off the gas source using a smartphone as necessary. To accurately recognize the intensity of stove fire and detect abnormal fire intensity, deep learning based fire recognition methods using conventional and densely connected convolutional neural networks are developed to further improve the recognition accuracy of DeepSafe. In particular, the prototype consisting of an Android based APP and a Raspberry Pi based IoT device with the gas detector, image sensor, and 3-axis accelermeter are implemented to verify the feasibility and correctness of our DeepSafe system.

In DC microgrids (DCmGs), distributed control is becoming a promising framework due to prominent scalability and efficiency. To transmit essential data and information for system control, various communication network topologies and protocols have been employed in modern DCmGs. However, such communication also exposes the DCmG to unexpected cyber attacks. In this demo, a scalable cyber security testbed is established for conducting hardware-in-the-loop (HIL) experiments and comprehensively investigating the security impact on DCmGs. Specifically, the testbed employs a Typhoon HIL 602+ emulator, which is professional in power electronics system emulation, to demonstrate four (12 at most) distributed energy resources (DERs). The communication network is implemented through the self-loop RS-232 interface. Based on the testbed, we systematically investigate the impact of two kinds of typical cyber attacks (i.e., false data injection and replay attacks). Experimental results show that both attacks will deteriorate the point-of-common coupling (PCC) voltages of the DERs and jeopardize the stability of the whole DCmG.

This demo showcased how application-aware G-SRv6 network provides fine-grained traffic steering with more economical IPv6 source routing encapsulation, effectively supporting 5G eMBB, mMTC and uRLLC services. G-SRv6, a new IPv6 source routing paradigm, introduces much less overhead than SRv6 and is fully compatible with SRv6. Up to 75 percent overhead of an SRv6 SID List can be reduced by using 32-bit compressed SID with G-SRv6, allowing most merchant chipsets to support up to 10 SIDs processing without introducing packet recirculation, significantly mitigating the challenges of SRv6 hardware processing overhead and facilitating large-scale SRv6 deployments. Furthermore, for the first time, by integrating with Application-aware IPv6 networking (APN6), the G-SRv6 network ingress node is able to steer a particular application flow into an appropriate G-SRv6 TE policy to guarantee its SLA requirements and save the transmission overhead in the meanwhile.

The radio wave is the carrier that transmits information in wireless communications. It propagates across space through a complex and dynamic mechanism. However, the radio waves are invisible, which makes it hard to design wireless communication systems. This demonstration presents a system architecture and implementation via virtual reality (VR) that can make people open the "eyes" to see the radio waves, and provide a novel method of using the radio waves designing and optimizing wireless communication systems. Users can see how the radio waves propagate in a 3D view. Furthermore, channel impulse responses (CIRs) and channel fading properties, e.g., path loss and delay spread, can be derived and visualized through the virtual interface. Also, this demo enables users to investigate the performance of base station (BS) deployment and hybrid beamforming (HBF) algorithms via VR. In a nutshell, this demo is helpful to feel, understand, and use the radio waves to improve the efficiency of wireless communication technologies and systems.

Recently, there has been significant enthusiasms in exploiting physical (PHY-) layer characteristics for secure wireless communication. However, most existing PHY-layer security paradigms are information theoretical methodologies, which are infeasible to real and practical systems. In this paper, we propose a weighted fractional Fourier transform (WFRFT) pre-coding scheme to enhance the security of wireless transmissions against eavesdropping. By leveraging the concept of WFRFT, the proposed scheme can easily change the characteristics of the underlying radio signals to complement and secure upper-layer cryptographic protocols. We demonstrate a running prototype based on the LTE-framework. First, the compatibility between the WFRFT pre-coding scheme and the conversational LTE architecture is presented. Then, the security mechanism of the WFRFT pre-coding scheme is demonstrated. Experimental results validate the practicability and security performance superiority of the proposed scheme.

We demonstrate a wireless local area network (WLAN) standard-based free space optical (FSO) transmission in indoor and outdoor environments at a 100meter distance. In our demonstration, we employ USRP for baseband signal processing, and the signal is modulated by a laser diode operating at the wavelength of 1550nm. We measure the error vector magnitude (EVM) of the received signals and compare it to the WLAN standard requirements. In both indoor and outdoor cases, it is shown that the bit error rate (BER) below 10e-5 can be achieved. From our demonstration, we confirm that the FSO communications can be applied to the feasible wireless backhaul link.

This demonstration presents an AI/ML platform that is offered as a service (AIMLaaS) and integrated in the management and orchestration (MANO) workflow defined in the project 5Growth following the recommendations of various standardization organizations. In such a system, SLA management decisions (scaling, in this demo) are taken at runtime by AI/ML models that are requested and downloaded by the MANO stack from the AI/ML platform at instantiation time, according to the service definition. Relevant metrics to be injected into the model are also automatically configured so that they are collected, ingested, and consumed along the deployed data engineering pipeline. The use case to which it is applied is a digital twin service, whose control and motion planning function has stringent latency constraints (directly linked to its CPU consumption), eventually determining the need for scaling out/in to fulfill the SLA.

Novel Internet applications often require low latency and high throughput at the same time, posing challenges to access aggregation networks (AAN). Low-Latency Low-Loss Scalable-Throughput (L4S) Internet service and related schedulers have been proposed to meet these requirements and also allow the coexistence of Classic and L4S flows in the same system. AANs generally apply Hierarchical QoS (HQoS) to enforce fairness among their subscribers. It allows subscribers to utilize their fair share as they desire, and it also protects traffic of various subscribers from each other. The traffic management engines of available P4-programmable hardware switches do not support complex HQoS and L4S scheduling. In this demo paper, we show how a recent core-stateless L4S AQM proposal called VDQ-CSAQM can be implemented in P4, and executed in high-speed programmable hardware switches. We also show how a cloud-rendered gaming service benefits from the low latency and HQoS provided by our VDQ-CSAQM.

The Border Gateway Protocol (BGP) is the inter-domain routing protocol that glues the Internet. BGP does not incorporate security and instead, it relies on careful configuration and manual filtering to offer some protection. As a consequence, the current inter-domain routing infrastructure is partially vulnerable to prefix and path hijacks as well as in misconfigurations that results in route leaks. There are many instances of these vulnerabilities being exploited by malicious actors on the Internet, resulting in disruption of services. To address this issue the IETF has designed RPKI, a centralised trust architecture that relies on Public Key Infrastructure. RPKI has slow adoption and its centralised nature is problematic: network administrators are required to trust CAs and do not have the ultimate control of their own critical Internet resources (e.g,. IP blocks, AS Numbers). In this context, we have built the Decentralised Internet Infrastructure (DII), a distributed ledger to securely store inter-domain routing information. The main advantages of DII are (i) it offers flexible trust models where the Internet community can define the rules of a consensus algorithm that properly reflects the power balance of its members and, (ii) offers protection against vulnerabilities (path hijack and route leaks) that goes well beyond what RPKI offers. We have deployed the prototype on the wild in a worldwide testbed including 7 ASes, we will use the testbed to demonstrate in a realistic scenario how allocation and delegation of Internet resources in DII work, and how this protects ASes against artificially produced path and prefix hijack as well as a route leak.