With the accelerated development of digitalization and intelligentization of communications, sixth generation (6G) network is envisioned to support digital twin (DW) for intelligent network autonomy. However, higher carrier frequencies, more complex technologies, and diverse scenarios lead to dynamic radio environment, which makes 6G network intractable to design and optimize. In this demonstration, we propose a platform architecture and implementation via enhanced sensing to achieve radio environment prediction for 6G digital twin communication. The platform consists of three modules. First, the environmental information is captured by environment sensing module, and then the radio propagation path can be calculated by radio propagation prediction module. At last, the channel impulse response (CIR) is calculated and visualized by data processing and visualization module. In a nutshell, this platform is promising to realize the prediction of radio environment to improve the adaptability of the 6G network in environmental changes from bottom to top.

The performance of simultaneous localization and mapping (SLAM) systems is impacted by the constrained computation capabilities of mobile devices. Given that the advancement of these systems relies on accurate evaluation of SLAM performance, this issue is exacerbated by the difficulty in evaluating SLAM performance in practice, due to the unavailability of ground truth data. In this demo, we present SpacecraftWalk, a resource-efficient SLAM framework that constructs maps (of the environments) with minimal uncertainty under resource budgets. SpacecraftWalk is evaluated within virtual spacecraft environments (meeting NASA lighting standards) in game engine-based emulators that generate ground truth automatically. Demo participants will navigate in virtual environments while creating their own moving trajectories for evaluating SLAM. They will develop an intuition for how uncertainty-based map construction improves resource efficiency. This demonstration accompanies [1].

Lack of scalability is one of the major obstacles to the broader adoption of Byzantine fault tolerance (BFT)-based blockchain consensus for large-scale networks. Recent blockchains use sharding to improve scalability at the cost of safety and responsiveness. To address this issue, we have proposed SharBFT which is a novel sharding-based BFT consensus parallelism with improved safety and responsiveness while retaining scalability. This demo presents a practical implementation of SharBFT, i.e., SharBFT-SMaRt which is a scalable Byzantine fault-tolerant state machine replication library developed in Java, which prioritises simplicity and robustness. Our main objective is to offer a code base that can be expanded to develop new protocols and utilised to construct blockchain systems.

In this paper, we design and implement a multi-screen adaptive displaying system, called AR-Span, which can enable the assembling of heterogeneous mobile devices into one multi-screen displaying equipment. The AR-Span system consists of multiple mobile devices with various screen sizes and a dedicated server communicating and coordinating among these mobile devices. AR-Span can cooperatively display a complete image on multiple mobile devices. In addition, AR-Span can precisely partition a video and synchronically play the video on multiple mobile devices. Furthermore, AR-Span can arbitrarily customize an electronic marquee on multiple mobile devices. In particular, AR-Span can immediately adjust and control the displayed content in an augmented reality based manipulation manner. Experimental results show that AR-Span manipulates the desired content much faster than existing methods/systems and can significantly reduce the total operating time. Moreover, AR-Span can improve the displaying time inconsistency and unsynchronized content gap on heterogeneous mobile devices.

Existing approaches for in-switch inference with Random Forest (RF) models that can run on production-level hardware do not support flow-level features and have limited scalability to the task size. This leads to performance barriers when tackling complex inference problems with sizable decision spaces. Flowrest is a complete RF model framework that fills existing gaps in the existing literature and enables practical flow-level inference in commercial programmable switches. In this demonstration, we exhibit how Flowrest can classify individual traffic flows at line rate in an experimental platform based on Intel Tofino switches. To this end, we run experiments with real-world measurement data, and show how Flowrest yields improvements in accuracy with respect to solutions that are limited to packet-level inference in programmable hardware.

Powering Internet of Things (IoT) devices in hard to reach or hazardous locations could be prohibitive in terms of cost and safety. In this work, we demonstrate a thorough solution that tackles this problem using a network of unmanned aerial vehicles (UAVs) with wireless power transfer (WPT) capabilities. Given the large-scale IoT deployments of the future and the energy needs of the UAVs, we consider an infrastructure of charging stations for the UAVs that allow an uninterrupted and flexible UAV deployment based on the current energy needs of the IoT devices. Then, we exhibit an orchestrator that communicates with the entire infrastructure and handles the UAV traffic and energy decisions, as well as the digital representation and interaction of the network with the user.

Wireless collaborative mixed reality (WCMR) has many fascinating applications in education, training, manufacturing, and gaming. In this demo, we develop a WCMR-based firefighter training system that provides firefighters with experiences in extreme and diverse fire accidents without any safety concerns. In such a system, it is important to ensure that all firefighters can see almost the same status of the fire accident to facilitate collaborative training. This is challenging due to the heterogeneous communication delays of different network users. We propose the latency compensation algorithm that determines when the edge server should transmit the message to users based on the estimated latency of each user. Our experiment demonstrates around 55% synchronization performance improvement while guaranteeing at least 60 frames per second (FPS).

Drivers may get confused for any number of reasons. For example, due to confusion, they miss their intended exits in some regions. In such cases, confused drivers should loop back around. However, some drivers exhibit risky driving (e.g., back up on the highway or perform abrupt lane changes). Inferring such zones where most drivers get confused could be helpful. Drivers can use such prior knowledge and plan their exits or turns accordingly. In this paper, we focus on this use case. Connected vehicles share their location data with the remote server, and the remote server examines data to identify turn loops in real time. The region becomes a confusion zone when a turn loop is executed by several vehicles. We demonstrate the feasibility of confusion zone detection through a simulation-based demonstration.

With the growing popularity of the 5G mobile network, its security is becoming important. Although the newly introduced 5G security mechanisms have addressed many legacy security issues, there may be still vulnerabilities in the 5G network due to newly deployed components and used technologies. To detect security threats, we develop a runtime anomaly detector (RAD) platform, designated as 5G-RAD, to cooperate with the operational 5G core network via the service communication proxy (SCP). It validates the core network operation in terms of state machine and message content by analyzing control-plane messages. We demonstrate its effectiveness by building a 5G mobile network architecture with SCP based on the open-source free5GC and UERANSIM. The 5G-RAD is tested with three attacks, including DoS, authentication bypass, and invalid message injection; it can successfully detect them at run time.

Open Radio Access Network (O-RAN) introduced a common control and management overlay, allowing mobile network operators to embed networking intelligence using different types of third-party applications: xApps for real-time control loops, and rApps for Artificial Intelligence (AI)/Machine Learning (ML)-based classification and decision-making. However, the development of reference implementations for rApps lags behind the progress in other O-RAN-related standardization efforts. In this demonstration, we showcase a proof-of- concept rApp capable of generating policies to steer the behavior of xApps, and detail how we extended a RAN slicing xApp to react to such policies, creating the first experimental ML-based RAN slicing platform based on O-RAN.