IEEE INFOCOM 2023

Program at a Glance

IEEE INFOCOM 2023

Session Break-1-Day2: Coffee Break Session Lunch-Day2: Conference Lunch Session Break-2-Day2: Coffee Break Session Dinner-Day2: Conference Dinner

Session A-4: Blockchain Session A-6: Quantum Computing/Networking

Session B-4: Federated Learning 4 Session B-6: Federated Learning 5

Session C-4: UAV Session C-5: Optical and Mobile Session C-6: Programmable Switches

Session D-4: Fingerprinting and Classification Session D-5: Internet Measurement/Monitoring Session D-6: Age of Information

Session E-4: Optimization Session E-5: Wireless/Mobile Security 1 Session E-6: Wireless/Mobile Security 2

Session F-4: Activity Sensing

Session G-4: Traffic Shaping and Inspection

Session Demo-2: Demo Session 2

Session Panel: Panel - Networking: An Agenda for the Next Decade

Session Award: A Reflection with INFOCOM Achievement Award Winner

Session Poster-1: Poster Session 1

Session Demo-2

Demo Session 2

Conference

10:00 AM — 12:00 PM EDT

Local

May 18 Thu, 10:00 AM — 12:00 PM EDT

Location

Babbio Lobby

Demo Abstract: Predictive Radio Environment for Digital Twin Communication Platform via Enhanced Sensing

Yinghe Miao (Beijing University of Posts and Telecommunications, China); Yuxiang Zhang (Beijing University of Posts & Telecommunications, China); Jianhua Zhang, Yutong Sun, Yixuan Tian and Li Yu (Beijing University of Posts and Telecommunications, China); Guangyi Liu (Research Institute of China Mobile, China)

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.

Speaker Yinghe Miao; Yuxiang Zhang; Yutong Sun

Speaker biography is not available.

Demo Abstract: Demonstrating Resource-Efficient SLAM in Virtual Spacecraft Environments

Ying Chen (Duke University, USA); John Sarik (Columbia University, USA); Hazer Inaltekin (Macquarie University, Australia); Maria Gorlatova (Duke University, USA)

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].

Speaker Ying Chen

Speaker biography is not available.

A Scalable Byzantine Consensus Parallelism and its Practical Implementation

Xiao Chen (University of Edinburgh, United Kingdom (Great Britain))

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.

Speaker

Speaker biography is not available.

AR-Span: A Multi-Screen Adaptive Displaying System Using Smart Handheld Devices Based on Mobile Networks

Lien-Wu Chen, Ai-Ni Li and Yu-An Shi (Feng Chia University, Taiwan)

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.

Speaker Yu-An Shi

Speaker biography is not available.

Demonstrating Flow-Level In-Switch Inference

Michele Gucciardo (IMDEA Networks Institute, Spain); Aristide Tanyi-Jong Akem (IMDEA Networks Institute, Spain & Universidad Carlos III de Madrid, Spain); Beyza Butun (Universidad Carlos III de Madrid & IMDEA Networks, Spain); Marco Fiore (IMDEA Networks Institute, Spain)

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.

Speaker Michele Gucciardo (IMDEA Networks Institute)

Powering Inaccessible IoT Devices Through a WPT-enabled Sustainable UAV Network

Prodromos-Vasileios Mekikis, Pavlos Bouzinis, Nikos Mitsiou, Sotiris A. Tegos and Vasilis K. Papanikolaou (Aristotle University of Thessaloniki, Greece); Dimitrios Tyrovolas (Aristotle University of Thessaloniki & Technical University of Chania, Greece); Panagiotis D. Diamantoulakis and George K. Karagiannidis (Aristotle University of Thessaloniki, Greece)

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.

Speaker Prodromos-Vasileios Mekikis

Speaker biography is not available.

Demo: Collaborative Mixed-Reality-Based Firefighter Training

Zhanchen Dong, Jiangong Chen and Bin Li (The Pennsylvania State University, USA)

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).

Speaker

Speaker biography is not available.

Demo: Missed An Exit? Confusion Zone Detection

Seyhan Ucar (Toyota Motor North America R&D, InfoTech Labs, USA); Takamasa Higuchi (Toyota Motor North America R&D, USA); Onur Altintas (Toyota Motor North America R&D, InfoTech Labs, USA)

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.

Speaker Seyhan Ucar

Dr. Uçar is currently working as a Principal Researcher in Intelligent Mobility Systems at InfoTech Labs, Toyota Motor North America USA. He received his B.Sc. degree in Computer Engineering from İzmir Institute of Technology in 2011. He received his M.Sc. and a Ph.D. degree in Computer Science and Engineering from Koç University in 2013 and 2017, respectively. Throughout his M.Sc. studies, he worked on developing multi-hop clustering algorithms and Long-Term Evaluation (LTE) based heterogeneous architectures for vehicular ad hoc networks. During his Ph.D., he focuses on Visible Light Communication (VLC) and automated car following (or platooning) where a group of vehicles travels within close proximity through communication. He is now working on intelligent transportation systems and applications and analyzing the impact of connected vehicles on transportation safety and management.

A Runtime Anomaly Detector via Service Communication Proxy for 5G Mobile Networks

Yin-Chi Li, Ping-Tsan Liu, Yi-An Tai, Che-Hung Liu, Man-Hsin Chen and Chi-Yu Li (National Yang Ming Chiao Tung University, Taiwan); Guan-Hua Tu (Michigan State Unversity, USA)

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.

Speaker Yin-Chi Li

Speaker biography is not available.

AI/ML Data-driven Control Loop for Managing O-RAN SDR-based RANs

Jaswanth S. R. Mallu (Virginia Tech Commonwealth Cyber Initiative, USA); Joao F. Santos (Virginia Tech & Commonwealth Cyber Initiative, USA); Aloizio Pereira Da Silva (Virginia Tech, USA & Commonwealth Cyber Initiative, USA); Prateek Sethi (Virginia Tech Commonwealth Cyber Initiative, USA); Vikas Krishnan Radhakrishnan (Virginia Tech, USA); Luiz DaSilva (Virginia Tech, USA & Trinity College Dublin, Ireland)

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.

Speaker

Speaker biography is not available.

Program at a Glance