IEEE INFOCOM 2020

Program at a Glance

IEEE INFOCOM 2020

Session Keynote-1: Opening and Keynote Session Keynote-2: Industry Keynote Session N2Women: N2Women Lunch Meeting

Session Coffee-Break-2-AM: Virtual Coffee Break Session Lunch-Break-2: Virtual Lunch Break Session Coffee-Break-2-PM: Virtual Coffee Break

Session 1-A: IoT and Health Session 2-A: RFID and Backscatter Systems I

Session 1-B: Scheduling I Session 2-B: Network Optimization I

Session 1-C: Privacy I Session 2-C: Security I

Session 1-D: Network Intelligence I Session 2-D: Network Intelligence II

Session 1-E: Multi-Armed Bandits Session 2-E: Age of Information

Session 1-F: UAV I Session 2-F: Wireless Networks

Session 1-G: Edge Computing I Session 2-G: Caching I

Session Demo-Session-1: Demo Session 1 Session Demo-Session-2: Demo Session 2

Session Poster-Session-1: Poster Session 1 Session Poster-Session-2: Poster Session 2

Session Demo-Session-1

Demo Session 1

Conference

8:00 PM — 10:00 PM EDT

Local

Jul 7 Tue, 5:00 PM — 7:00 PM PDT

High Voltage Discharge Exhibits Severe Effect on ZigBee-based Device in Solar Insecticidal Lamp Internet of Things

Kai Huang, Kailiang Li, Lei Shu and Xing Yang (Nanjing Agricultural University, China)

With Solar Insecticidal Lamp (SIL) releasing high-voltage pulse discharge while migratory insects with phototaxis feature contact with the metal mesh, the interference from the generated strong electromagnetic pulse (EMP) to ZigBee-based device in the new agricultural Internet of Things, i.e., Solar insecticidal Lamp Internet of Things (SIL-IoTs), remains elusive. Aiming to qualitatively explore whether the interference is existing or not during the process of discharge, a discharge simulation module and a wireless communication device are designed, and the SIL is modified separately in this demo to acquire the key parameter, i.e., the number of microprocessors' Falling Edge Trigger (FET). The experiment results demonstrate that the interference exists in the form of the changing number of FET.

FingerLite: Finger Gesture Recognition Using Ambient Light

Miao Huang and Haihan Duan (Sichuan University, China); Yanru Chen (Sichuan University, USA); Yanbing Yang (Sichuan University, China); Jie Hao (Nanjing University of Aeronautics and Astronautics, China); Liangyin Chen (Sichuan University & University of Minnesota, China)

Free hand interaction with devices is a promising trend with the advent of Internet of Things (IoT). The unmodulated ambient light, which can be an exciting modality for interaction, is still deficient in research and practice when most of the efforts in the field of visible light sensing are put into solutions based on modulated light. In this paper, we propose a low-cost ambient light-based system which performs finger gesture recognition in real-time. The system relies on a recurrent neural network (RNN) architecture without complicated pre-processing algorithms for the gesture classification task. The results of experimental evaluation proves that the solution that we put forward achieves a rather high recognition accuracy across different users while being lightweight and efficient.

An SDR-in-the-loop Carla Simulator for C-V2X-Based Autonomous Driving

Wei Zhang (Communication and Information Engineering of Shanghai University, China); Siyu Fu, Zixu Cao, Zhiyuan Jiang, Shunqing Zhang and Shugong Xu (Shanghai University, China)

In this demo, we showcase an integrated hardware-software evaluation platform for collaborative autonomous driving. The vehicle control and motion dynamics are simulated by the SUMO simulator [1], and the communications among vehicles are realized by software defined radios, which are programmed to run the standardized Cellular Vehicle-to-Everything (C-V2X) Mode 4 protocol. We implement our parallel communication scheme [2] and demonstrate a platooning autonomous driving system. The platform can be extended to run more advanced collaborative autonomous driving applications in the future.

INFOCOM 2020 Best Demo: Cross-Technology Communication between LTE-U/LAA and WiFi

Piotr Gawłowicz and Anatolij Zubow (Technische Universität Berlin, Germany); Suzan Bayhan (University of Twente, The Netherlands)

Although modern wireless technologies like LTE and 802.11 WiFi provide very high peak data rates they suffer from performance degradation in dense heterogeneous deployments as they rely on rather primitive coexistence schemes. Hence, for efficient usage of the shared unlicensed spectrum a cross-technology communication (CTC) between co-located LTE unlicensed and WiFi devices is beneficial as it enables direct coordination between the co-located heterogeneous technologies. We present OfdmFi, the first system that enables to set-up a bi-directional CTC channel between co-located LTE unlicensed and WiFi networks for the purpose of cross-technology collaboration. We demonstrate a running prototype of OfdmFi. First, we present the performance of a bi-directional CTC channel between LTE unlicensed and WiFi. Second, we show that partial channel state information of the CTC channel can be obtained. Third, we demonstrate the possibility to transmit a cross-technology broadcast packet which is received simultaneously by the two heterogeneous technologies, WiFi and LTE. During the demo, we display all the relevant performance metrics in real-time.

Increasing the Data Rate for Reflected Optical Camera Communication Using Uniform LED Light

Zequn Chen, Runji Lin and Haihan Duan (Sichuan University, China); Yanru Chen (Sichuan University, USA); Yanbing Yang (Sichuan University, China); Rengmao Wu (Zhejiang University, China); Liangyin Chen (Sichuan University & University of Minnesota, China)

Optical Camera Communication (OCC) systems relying on commercial-off-the-shelf (COTS) devices have attracted substantial attention recently, thanks to the pervasive deployment of indoor LED lighting infrastructure and the popularity of smartphones. However, the achievable throughput by such practical systems is still very low due to its availability for only low order modulation schemes and transmission frequency. In this demo, we propose a novel reflected OCC system, UniLight, which takes advantage of uniform light emission of an LED luminaire with lens to increase both region of interest (RoI) and signal-to-noise ratio (SNR) so as to improve data rate. UniLight employs a COTS LED spotlight with lens as the transmitter to uniformly illuminate a reflector so that avoids a gradual reduction of brightness from the center to both sides in the frame captured by a camera receiver. By adopting a hybrid modulation scheme for generating multi-level pulse amplitude modulation (M-PAM) symbols on the transmitter and a machine learning based demodulator on the smartphone receiver, UniLight can achieve much higher data rate than existing works with a single small-size LED spotlight.

Elastic Deployment of Robust Distributed Control Planes with Performance Guarantees

Daniel F. Perez-Ramirez (RISE Research Institutes of Sweden, Sweden); Rebecca Steinert (RISE SICS AB, Sweden); Dejan Kostic (KTH Royal Institute of Technology, Sweden); Natalia V. Vesselinova (RISE, Sweden)

Recent control plane solutions in a software-defined network (SDN) setting assume physically distributed but logically centralized control instances: a distributed control plane (DCP). As networks become more heterogeneous with increasing amount and diversity of network resources, DCP deployment strategies must be both fast and flexible to cope with varying network conditions whilst fulfilling constraints. However, many approaches are too slow for practical applications and often address only bandwidth or delay constraints, while control-plane reliability is overlooked and control-traffic routability is not guaranteed. We demonstrate the capabilities of our optimization framework [1]-[3] for fast deployment of DCPs, guaranteeing routability in line with control service reliability, bandwidth and latency requirements. We show that our approach produces robust deployment plans under changing network conditions. Compared to state of the art solvers, our approach is magnitudes faster, enabling deployment of DCPs within minutes and seconds, rather than days and hours.

Sensing and Communication Integrated System for Autonomous Driving Vehicles

Qixun Zhang, Huan Sun, Zhiqing Wei and Zhiyong Feng (Beijing University of Posts and Telecommunications, China)

Facing fatal collisions due to the sensor's failure, the raw sensor information sharing among autonomous driving vehicles is critical important to guarantee the driving safety with the enhanced see-through ability. This paper proposes a novel sensing and communication integrated system based on the 5G New Radio frame structure using the millimeter wave (mmWave) communication technology to guarantee the low-latency and high data rate information sharing among vehicles. And the smart weighted grid searching based fast beam alignment and beam tracking algorithms are proposed and evaluated by the developed hardware testbed. Field test results prove that our proposed algorithms can achieve a stable data rate of 2.8 Gbps within 500 ms latency in a mobile vehicle communication scenario.

Session Chair

Xiaonan Zhang (Florida State University)

Session Demo-Session-2

Demo Session 2

Conference

8:00 PM — 10:00 PM EDT

Local

Jul 7 Tue, 5:00 PM — 7:00 PM PDT

Seamless Mobile Video Streaming in Multicast Multi-RAT Communications

Pavlos Basaras (Trinity College, Ireland); Stepan Kucera (Bell Labs, Alcatel-Lucent Ltd., Ireland); Kariem Fahmi (Trinity College Dublin, Ireland); Holger Claussen (Nokia Bell Labs, Ireland); George Iosifidis (Trinity College Dublin, Ireland)

In this demo, we propose a software defined transport-layer proxy architecture as a video streaming solution, that combines multicast and unicast transmissions to provide a seamless video experience. The proposed model employs zero-touch deployment to the handsets and the operator, and allows the combination of different wireless links, e.g., 4G, 5G, WiFi, in a simple and backwards compatible manner. We showcase in a real setup and emulated networks a mobile scenario, where a user migrates from a home DSL network to multicast 5G, and experiences a continuous decline in channel conditions as he moves from the cell centre towards the edge. The multicast service is supplemented by different radio technologies (e.g., 4G, WiFi) through unicast and multicast transmissions, by traffic splitting and by provisioning application layer forward error correction (FEC). The proposed Augmented Multicast mUltipath ServicE (AMUSE) is compared against the state-of-the-art, i.e., single radio access multicast service. As the user channel conditions gradually deteriorate, we demonstrate a seamless video experience for AMUSE clients, whereas the typical client suffers from frequent re-buffer events, and eventually a service breakdown.

Leveraging AI players for QoE estimation in cloud gaming

German Sviridov (Politecnico di Torino, Italy); Cedric Beliard (Huawei Technologies, Co. Ltd., France); Gwendal Simon (Huawei, France); Andrea Bianco and Paolo Giaccone (Politecnico di Torino, Italy); Dario Rossi (Telecom ParisTech, France)

Quality of Experience (QoE) assessment in video games is notorious for its burdensomeness. Employing human subjects to understand network impact on the perceived gaming QoE presents major drawbacks in terms of resources requirement, results interpretability and poor transferability across different games.

To overcome these shortcomings, we propose to substitute human players with artificial agents trained with state-of-the-art Deep Reinforcement Learning techniques. Equivalently to traditional QoE assessment, we measure the in-game score achieved by an artificial agent for the game of Doom for varying network parameters. Our results show that the proposed methodology can be applied to understand fine-grained impact of network conditions on gaming experience while opening a lot of new opportunities for network operators and game developers.

Loop Avoidance in Computer Networks Using a Meshed Tree Protocol

Peter Willis, Nirmala Shenoy and Hrishikesh B Acharya (Rochester Institute of Technology, USA)

Loop-avoidance is essential in Ethernet networks to avoid indefinite looping of broadcast traffic. Traditionally, spanning trees are constructed on network topologies to overcome this problem. However, during topology changes, data forwarding latency is introduced when rebuilding and identifying new spanning tree paths. Meshed Tree Bridging through the Meshed Tree Protocol provides a novel loop-avoidance system that decreases downtime latency and efficiently reconverges networks. This is proven via testing and demonstrations on the Global Environment for Network Innovations testbed.

AutoPCT: An Agile Protocol Conformance Automatic Test Platform Based on Editable EFSM

Zhu Tang (National University of Defense Technology, China); Li Sudan (National University of Defanse Technology, China); Peng Xun (National University of Defense Technology, China); Wenping Deng (National University of Defense Technology & ETH Zurich, China); Baosheng Wang (National University of Defense Technology, China)

Currently, the biggest barrier to adopt the model-based test (MBT) is modeling itself. To simplify the protocol modeling process, an agile protocol conformance automatic test platform (AutoPTC) is proposed in this paper. With our platform, the protocol test state machine can be easily designed and modified in graphical mode, and the conformance test scripts can be automatically generated and executed through integrating enhanced formal modeling tool EFM and TTCN-3 test tool Titan. Meanwhile, editable EFSM (Enhanced Finite State Machine) user interface and flexible input/output packet structure design tool are introduced in our platform to improve the development efficiency of protocol conformance test. Finally, the effectiveness of our proposed platform is analyzed through practical protocol test cases.

CLoRa-A Covert Channel over LoRa PHY

Ningning Hou and Yuanqing Zheng (The Hong Kong Polytechnic University, Hong Kong)

LoRa adopts a unique modulation scheme (chirp spread spectrum (CSS)) to enable long range communication at low power consumption. CSS uses the initial frequencies of LoRa chirps to differentiate LoRa symbols, while simply ignoring other RF parameters (e.g., amplitude and phase). Driven by this observation, we build a covert channel (named CLoRa) by embedding covert information with a modulation scheme orthogonal to CSS. We implement CLoRa with a COTS LoRa node (Tx) and a low-cost receive-only SDR dongle (Rx). The experiment results show that CLoRa can send covert information over 250 m. This demo reveals that the LoRa physical layer leaves sufficient room to build a covert channel by embedding covert information with a modulation scheme orthogonal to CSS.

Real-time Edge Analytics and Concept Drift Computation for Efficient Deep Learning From Spectrum Data

Zaheer Khan and Janne Lehtomäki (University of Oulu, Finland); Adnan Shahid (Gent University - imec, Belgium); Ingrid Moerman (Ghent University - imec, Belgium)

Cloud managed wireless network resource configuration platforms are being developed for efficient network utilization. These platforms can improve their performance by utilizing real-time edge analytics of key wireless metrics, such as wireless channel utilization (CU). This paper demonstrates a real-time spectrum edge analytics system which utilizes field-programmable gate array (FPGA) to process in real-time hundreds of millions of streaming inphase and quadrature (IQ) samples per second. It computes not only mean and maximum values of CU but also computes histograms to obtain probability distribution of CU values. It sends in real-time these descriptive statistics to an entity which collects these statistics and utilises them to train a deep learning model for prediction of future CU values. Even though utilization in a wireless channel can often exhibit stable seasonal patterns, they can be affected by uncertain usage events, such as sudden increase/decrease in channel usage within a certain time period. Such changes can unpredictably drift concept of CU data (underlying distribution of incoming CU data) over time. In general, concept drift can deteriorate the prediction performance of deep learning models which in turn can impact the performance of cloud managed resource allocation solution. This paper also demonstrates a real-time concept drift computation method which measures the changes in the probability distribution of CU data. Our implemented demonstration includes: 1) spectrum analytics and concept drift computation device which is realized in practical implementation by prototyping it on a low-cost ZedBoard with AD9361 RF transceiver attached to it. ZedBoard is equipped with a Xilinx Zynq-7000 system on chip; 2) a laptop which is connected to the Zedboard and it provides graphical real-time displays of computed CU values, CU histograms, and concept drift computation values. A laptop is also used to develop a deep learning based model for prediction of future CU values. For the INFOCOM we will show a live demonstration of the complete prototyped system in which the device performs real-time computations in an unlicensed frequency channel following the implemented algorithms on the FPGA of a Zedboard.

Opening the Deep Pandora Box:Explainable Traffic Classification

Cedric Beliard (Huawei Technologies, Co. Ltd., France); Alessandro Finamore (HUAWEI France, France); Dario Rossi (Telecom ParisTech, France)

Fostered by the tremendous success in the image recognition field, recently there has been a strong push for the adoption of Convolutional Neural Networks (CNN) in networks, especially at the edge, assisted by low-power hardware equipment (known as ”tensor processing units”) for the acceleration of CNN-related computations. The availability of such hardware has re-ignited the interest for traffic classification approaches that are based on Deep Learning. However, unlike tree-based approaches that are easy to interpret, CNNs are in essence represented by a large number of weights, whose interpretation is particularly obscure for the human operators. Since human operators will need to deal, troubleshoot, and maintain these automatically learned models, that will replace the more easily human-readable heuristic rules of DPI classification engine, there is a clear need to open the ”deep pandora box”, and make it easily accessible for network domain experts. In this demonstration, we shed light in the inference process of a commercial-grade classification engine dealing with hundreds of classes, enriching the classification workflow with tools to enable better understanding of the inner mechanics of both the traffic and the models.

Session Chair

Biao Han (National University of Defense Technology, China)

Program at a Glance