IEEE INFOCOM 2023

Program at a Glance

IEEE INFOCOM 2023

Session Keynote: Opening, Awards, and Keynote

Session Break-1-Day1: Coffee Break Session Lunch-Day1: Conference Lunch Session Dinner-Day1: Chartered Cruise Dinner (for attendees with Full Conference Registrations)

Session A-1: Cloud/Edge Computing 1 Session A-2: Wireless/Mobile Learning Session A-3: Security and Privacy

Session B-1: Federated Learning 1 Session B-2: Federated Learning 2 Session B-3: Federated Learning 3

Session C-1: LoRa and LPWAN Session C-2: Satellite/Space Networking Session C-3: Internet Routing

Session D-1: mmWave 1 Session D-2: mmWave 2 Session D-3: mmWave 3

Session E-1: Video Streaming 1 Session E-2: Video Streaming 2 Session E-3: Video Streaming 3

Session F-1: Datacenter and Switches Session F-2: Memory/Cache Management 1 Session F-3: Internet Measurement

Session G-1: Theory 1 Session G-2: Theory 2 Session G-3: 5G

Session Demo-1: Demo Session 1

ChirpKey: A Chirp-level Information-based Key Generation Scheme for LoRa Networks via Perturbed Compressed Sensing

Huanqi Yang and Zehua Sun (City University of Hong Kong, Hong Kong); Hongbo Liu (Electronic Science and Technology of China, China); Xianjin Xia (The Hong Kong Polytechnic University, Hong Kong); Yu Zhang and Tao Gu (Macquarie University, Australia); Gerhard Hancke and Weitao Xu (City University of Hong Kong, Hong Kong)

Physical-layer key generation is promising in establishing a pair of cryptographic keys for emerging LoRa networks. However, existing key generation systems may perform poorly since the channel reciprocity is critically impaired due to low data rate and long range in LoRa networks. To bridge this gap, this paper proposes a novel key generation system for LoRa networks, named ChirpKey. We reveal that the underlying limitations are coarse-grained channel measurement and inefficient quantization process. To enable fine-grained channel information, we propose a novel LoRa-specific channel measurement method that essentially analyzes the chirp-level changes in LoRa packets. Additionally, we propose a LoRa channel state estimation algorithm to eliminate the effect of asynchronous channel sampling. Instead of using quantization process, we propose a novel perturbed compressed sensing based key delivery method to achieve a high level of robustness and security. Evaluation in different real-world environments shows that ChirpKey improves the key matching rate by 11.03-26.58% and key generation rate by 27-49X compared with the state-of-the-arts. Security analysis demonstrates that ChirpKey is secure against several common attacks. Moreover, we implement a ChirpKey prototype and demonstrate that it can be executed in 0.2s.

Speaker Huanqi Yang (City University of Hong Kong)

Huanqi Yang is currently a second-year Ph.D. student at the Department of Computer Science, City University of Hong Kong. His research interests lay in IoT security, and wireless networks.

One Shot for All: Quick and Accurate Data Aggregation for LPWANs

Ningning Hou, Xianjin Xia, Yifeng Wang and Yuanqing Zheng (The Hong Kong Polytechnic University, Hong Kong)

This paper presents our design and implementation of a fast and accurate data aggregation strategy for LoRa networks named One-shot. To facilitate data aggregation, One-shot assigns distinctive chirps for different LoRa nodes to encode individual data. One-shot coordinates the nodes to concurrently transmit encoded packets. Receiving concurrent transmissions, One-shot gateway examines the frequencies of superimposed chirp signals and computes application-defined aggregate functions (e.g., sum, max, count, etc.), which give a quick overview of sensor data in a large monitoring area. One-shot develops techniques to handle a series of practical challenges involved in frequency and time synchronization of concurrent chirps. We evaluate the effectiveness of One-shot with extensive experiments. Results show that One-shot substantially outperforms state-of-the-art data aggregation methods in terms of aggregation accuracy as well as query efficiency.

Speaker Ningning Hou (The Hong Kong Ploytechnic University)

Dr. Ningning Hou is a postdoctoral fellow at The Hong Kong Polytechnic University. Her research interests include Internet-of-Things, wireless sensing and networking, LPWANs, and physical layer security. She is going to join Macquarie University as a lecturer.

Recovering Packet Collisions below the Noise Floor in Multi-gateway LoRa Networks

Wenliang Mao, Zhiwei Zhao and Kaiwen Zheng (University of Electronic Science and Technology of China, China); Geyong Min (University of Exeter, United Kingdom (Great Britain))

LoRa has been widely applied in various vertical areas such as smart grids, smart cities, etc. Packet collisions caused by concurrent transmissions have become one of the major limitations of LoRa networks due to the ALOHA MAC protocol and dense deployment. The existing studies on packet recovery usually assume that the collided packet signals are above the noise floor. However, considering the large-scale deployment and low-power nature of LoRa communications, many collided packets are below the noise floor. Consequently, the existing schemes will suffer from significant performance degradation in practical LoRa networks. To address this issue, we propose CPR, a Cooperative Packet Recovery mechanism aiming at recovering the collided packets below the noise floor. CPR firstly employs the incoherence of signals and noises at multiple gateways to detect and extract the frequency features of the collided packets hidden in the noise. Then, CPR adopts a novel gateway selection strategy to select the most appropriate gateways based on their packet power domain features extracted from collision detection, such that the interference can be eliminated and the original packets can be recovered. Extensive experimental results demonstrate that CPR can significantly increase the symbol recovery ratio in low-SNR scenarios.

Speaker Wenliang Mao (University of Electronic Science and Technology of China)

Wenliang Mao received the B.S. degree from the School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), in 2019, where he is currently pursuing the Ph.D. degree with the School of Computer Science and Engineering. His research interests include LoRa networks, data-driven performance modeling, and network protocols.

Push the Limit of LPWANs with Concurrent Transmissions

Pengjin Xie (Beijing University of Posts and Telecommunications, China); Yinghui Li, Zhenqiang Xu and Qian Chen (Tsinghua University, China); Yunhao Liu (Tsinghua University & The Hong Kong University of Science and Technology, China); Jiliang Wang (Tsinghua University, China)

Low Power Wide Area Networks (LPWANs) have been shown promising in connecting large-scale low-cost devices with low-power long-distance communication. However, existing LPWANs cannot work well for real deployments due to severe packet collisions. We propose OrthoRa, a new technology which significantly improves the concurrency for low-power long-distance LPWAN transmission. The key of OrthoRa is a novel design, Orthogonal Scatter Chirp Spreading Spectrum (OSCSS), which enables orthogonal packet transmissions while providing low SNR communication in LPWANs. Different nodes can send packets encoded with different orthogonal scatter chirps, and the receiver can decode collided packets from different nodes. We theoretically prove that OrthoRa provides very high concurrency for low SNR communication under different scenarios. For real networks, we address practical challenges of multiple-packet detection for collided packets, scatter chirp identification for decoding each packet and accurate packet synchronization with Carrier Frequency Offset. We implement OrthoRa on HackRF One and extensively evaluate its performance. The evaluation results show that OrthoRa improves the network throughput and concurrency by 60X compared with LoRa.

Speaker Pengjin Xie

Pengjin Xie is currently an associate Researcher with the School of Artificial Intelligence, in Beijing

University of Posts and Telecommunications. Her current research interests include AIOT and mobile

computing.

Session Chair

Yimin Chen

SaTCP: Link-Layer Informed TCP Adaptation for Highly Dynamic LEO Satellite Networks

Xuyang Cao and Xinyu Zhang (University of California San Diego, USA)

Low-Earth-orbit (LEO) satellite networking is a promising way of providing low-latency and high-throughput global Internet access. Unlike the static terrestrial network infrastructure, LEO satellites constantly revolve around the Earth and thus bring instability to their networks. Understanding the dynamics and properties of a LEO satellite network and developing mechanisms to address the dynamics become crucial. In this work, we first introduce a high-fidelity and highly configurable real-time emulator called LeoEM to capture detailed dynamics of LEO satellite networks. We then present SaTCP, a cross-layer solution that enables TCP to avoid unnecessary congestion controls and improve its performance under high LEO link dynamics. As an upgrade to CUBIC TCP, SaTCP forecasts the time of disruptive events (i.e., satellite handovers or route updates) by tactfully utilizing the predictability of satellite locations, considers prediction inaccuracy, and informs TCP to adopt its decision to prevent unnecessary throughput reduction. Experiments across various scenarios show SaTCP increases the goodput by multi-folds compared with state-of-the-art protocols while preserving fairness.

Speaker Xuyang Cao (University of California San Diego)

Xuyang Cao is currently a master student in computer science at UC San Diego, advised by Professor Xinyu Zhang. Before, he did his undergraduate study in computer engineering at UC San Diego too. Xuyang's interests mainly include systems & networking, network infrastructure, and wireless communications. 😊

Achieving Resilient and Performance-Guaranteed Routing in Space-Terrestrial Integrated Networks

Zeqi Lai, Hewu Li, Yikun Wang, Qian Wu, Yangtao Deng, Jun Liu, Yuanjie Li and Jianping Wu (Tsinghua University, China)

Satellite routers in emerging space-terrestrial integrated networks (STINs) are operated in a failure-prone, intermittent and resource-constrained space environment, making it very critical but challenging to cope with various network failures effectively. Existing resilient routing approaches either suffer from continuous re-convergences with low network reachability or involve prohibitive pre-computation and storage overhead due to the huge amount of possible failure scenarios in STINs.

This paper presents STARCURE, a novel resilient routing mechanism for futuristic STINs. STARCURE aims at achieving fast and efficient routing restoration while maintaining the low-latency, high-bandwidth service capabilities in failure-prone space environments. First, STARCURE incorporates a new network model, called the topology-stabilizing model (TSM) to eliminate topological uncertainty by converting the topology variations caused by various failures to traffic variations. Second, STARCURE adopts an adaptive hybrid routing scheme, collaboratively combining a constraint optimizer to efficiently handle predictable failures, together with a location-guided protection routing strategy to quickly deal with unexpected failures. Extensive evaluations driven by realistic constellation information show that STARCURE can protect routing against various failures, achieving close-to-100% reachability and better performance restoration with acceptable system overhead, as compared to other existing resilience solutions.

Speaker Zeqi Lai

Zeqi Lai is currently an assistant professor at the Institute for Network Sciences and Cyberspace at Tsinghua University. Before joining Tsinghua University, he was a senior researcher at Tencent Media Lab from 2018 to 2019 and developed the network protocols and congestion control algorithms for VooV, a large-scale commercial videoconferencing application. His research interests include next-generation Internet architecture and protocols, integrated space and terrestrial networks~(ISTN), wireless and mobile computing, and video streaming.

Network Characteristics of LEO Satellite Constellations: A Starlink-Based Measurement from End Users

Sami Ma, Yi Ching Chou, Haoyuan Zhao and Long Chen (Simon Fraser University, Canada); Xiaoqiang Ma (Douglas College, Canada); Jiangchuan Liu (Simon Fraser University, Canada)

Low Earth orbit Satellite Networks (LSNs) have been advocated as a key infrastructure for truly global coverage in the forthcoming 6G. This paper presents our initial measurement results and observations on the end-to-end network characteristics of Starlink, arguably the largest LSN constellation to date. Our findings confirm that LSNs are a promising solution towards ubiquitous Internet coverage over the Earth; yet, we also find that the users of Starlink experience much more dynamics in throughput and latency than terrestrial network users, and even frequent outages. Its user experiences are heavily affected by environmental factors such as terrain, solar storms, rain, clouds, and temperature, including the power consumption. We further analyze Starlink's current bent-pipe relay strategy and its limits, particularly for cross-ocean routes. We have also explored its mobility and portability potentials, and extended our experiments from urban cities to wild remote areas that are facing distinct practical and cultural challenges.

Speaker Sami Ma (Simon Fraser University)

Sami Ma received the B.Sc. degree with distinction in Computing Science at Simon Fraser University, BC, Canada in 2019. Currently, he is continuing doctoral studies in Computing Science at Simon Fraser University. His research interests include low earth orbit satellite networks, internet architecture and protocols, deep learning, and computer vision.

FALCON: Towards Fast and Scalable Data Delivery for Emerging Earth Observation Constellations

Mingyang Lyu, Qian Wu, Zeqi Lai, Hewu Li, Yuanjie Li and Jun Liu (Tsinghua University, China)

Exploiting a constellation of small satellites to realize continuous earth observations (EO) is gaining popularity. Large-volume EO data acquired from space needs to be transferred to the ground. However, existing EO delivery approaches are either: (a) efficiency-limited, suffering from long delivery completion time due to the intermittent ground-space communication, or (b) scalability-limited since they fail to support concurrent delivery for multiple satellites in an EO constellation.
To make big data delivery for emerging EO constellations fast and scalable, we propose FALCON, a multi-path EO delivery framework that wisely exploits diverse paths in broadband constellations to collaboratively deliver EO data effectively. Specifically, we formulate the constellation-wide EO data multipath download (CEOMP) problem, which aims at minimizing the delivery completion time of requested data for all EO sources. We prove the hardness of solving CEOMP, and further present a heuristic multipath routing and bandwidth allocation mechanism to tackle the technical challenges caused by time-varying satellite dynamics and flow contention, and solve the CEOMP problem efficiently. Evaluation results based on public orbital data of real EO constellations show that as compared to other state-of-the-art approaches, FALCON can reduce at least 51% delivery completion time for various data requests in large EO constellations.

Speaker Mingyang Lyu (Tsinghua University)

Mingyang Lv received the B.S. degree in Network Engineering from Sun Yat-Sen University in 2018. He is currently working toward the M.S. degree in the institute for Network Sciences and Cyberspace at Tsinghua university. His research interests mainly include big data distribution and routing in integrated space and terrestrial networks (ISTN).

Session Chair

Chunyi Peng

LARRI: Learning-based Adaptive Range Routing for Highly Dynamic Traffic in WANs

Minghao Ye (New York University, USA); Junjie Zhang (Fortinet, Inc., USA); Zehua Guo (Beijing Institute of Technology, China); H. Jonathan Chao (NYU Tandon School of Engineering, USA)

Traffic Engineering (TE) has been widely used by Internet service providers to improve network performance and provide better service quality to users. One major challenge for TE is how to generate good routing strategies adaptive to highly dynamic future traffic scenarios. Unfortunately, existing works could either experience severe performance degradation under unexpected traffic fluctuations or sacrifice performance optimality for guaranteeing the worst-case performance when traffic is relatively stable. In this paper, we propose LARRI, a learning-based TE to predict adaptive routing strategies for future unknown traffic scenarios. By learning and predicting a routing to handle an appropriate range of future possible traffic matrices, LARRI can effectively realize a trade-off between performance optimality and worst-case performance guarantee. This is done by integrating the prediction of future demand range and the imitation of optimal range routing into one step. Moreover, LARRI employs a scalable graph neural network architecture to greatly facilitate training and inference. Extensive simulation results on six real-world network topologies and traffic traces show that LARRI achieves near-optimal load balancing performance in future traffic scenarios with up to 43.3% worst-case performance improvement over state-of-the-art baselines, and also provides the lowest end-to-end delay under dynamic traffic fluctuations.

Speaker Minghao Ye (New York University)

Minghao Ye is a 4th-year Ph.D. Candidate at the Department of Electrical and Computer Engineering of New York University (NYU), working with Professor H. Jonathan Chao at the NYU High-Speed Networking Lab. His research mainly focuses on traffic engineering, network optimization, software-defined networks, and machine learning for networking.

A Learning Approach to Minimum Delay Routing in Stochastic Queueing Networks

Xinzhe Fu (Massachusetts Institute of Technology, USA); Eytan Modiano (MIT, USA)

We consider the minimum delay routing problem in stochastic queueing networks where the goal is to find the optimal static routing policy that minimizes the average delay in the network. Previous works on minimum delay routing rely on knowledge of the delay function that maps the routing policies to their corresponding average delay, which is typically unavailable in stochastic queueing networks due to the complex dependency of the delay function on the distributional characteristics of network links. In this paper, we propose a learning approach to the minimum delay routing problem, whereby instead of relying on aprior information on the delay function, we seek to learn the delay function through observations. We design an algorithm that leverages finite-time observations of network queue lengths to approximate the values of the delay function, uses the approximate values to estimate the gradient of the delay function, and performs gradient descent based on the estimated gradient to optimize the routing policy. We prove that our algorithm converges to the optimal static routing policy when the delay function is convex, which is a reasonable condition in practical settings.

Speaker Eytan Modiano

Eytan Modiano is a Professor in the Laboratory for Information and Decision Systems (LIDS) at MIT.

Resilient Routing Table Computation Based on Connectivity Preserving Graph Sequences

János Tapolcai and Péter Babarczi (Budapest University of Technology and Economics, Hungary); Pin-Han Ho (University of Waterloo, Canada); Lajos Rónyai (Budapest University of Technology and Economics (BME), Hungary)

Fast reroute (FRR) mechanisms that can instantly handle network failures in the data plane are gaining attention in packet-switched networks. In FRR no notification messages are required as the nodes adjacent to the failure are prepared with a routing table such that the packets are re-routed only based on local information. However, designing the routing algorithm for FRR is challenging because the number of possible sets of failed network links and nodes can be extremely high while the algorithm should keep track of which nodes are aware of the failure. In this paper, we propose a generic algorithmic framework that combines the benefits of Integer Linear Programming (ILP) and an effective approach from graph theory related to constructive graph characterization of k-connected graphs, i.e., edge splitting-off. We illustrate these benefits through arborescence design for FRR and show that (i) due to the ILP we have great flexibility in defining the routing problem, while (ii) the problem can still be solved very fast. We demonstrate through simulations that our framework outperforms state-of-the-art FRR mechanisms and provides better resilience with shorter paths in the arborescences.

Speaker János Tapolcai (Budapest University of Technology and Economics)

János Tapolcai received an MSc degree in technical informatics and a Ph.D. degree in computer science from the Budapest University of Technology and Economics (BME), Budapest, in 2000 and 2005, respectively, and a D.Sc. degree in engineering science from the Hungarian Academy of Sciences (MTA) in 2013. He is a Full Professor with the High-Speed Networks Laboratory, Department of Telecommunications and Media Informatics, BME. He has authored over 150 scientific publications.

He received several Best Paper Awards, including ICC'06, DRCN'11, HPSR'15, and NaNa'16. He won the MTA Lendület Program, the Google Faculty Award in 2012, and Microsoft Azure Research Award in 2018. He is a TPC member of leading conferences, e.g., IEEE INFOCOM 2012-, and the general chair of ACM SIGCOMM 2018.

Impact of International Submarine Cable on Internet Routing

Honglin Ye (Tsinghua University, China); Shuai Wang (Zhongguancun Laboratory, China); Dan Li (Tsinghua University, China & Zhongguancun Laboratory, China)

International submarine cables (ISCs) connect various countries/regions worldwide, and serve as the foundation of Internet routing. However, little attention has been paid to studying the impact of ISCs on Internet routing. This study addresses two questions to bridge the gap between ISCs and Internet routing: (1) For a given ISC, which Autonomous Systems (ASs) are using it, and (2) How dependent is Internet routing on ISCs. To tackle the first question, we propose Topology to Topology (or T2T), a framework for the large-scale measurement of static mapping between ASs and ISCs, and apply T2T to the Internet to reveal the status, trends, and preferences of ASs using ISCs. We find that ISCs used by Tier-1 ASs are more than 30\(\times\) of stub ASs. For the second question, we design an Internet routing simulator, and evaluate the behavior change of Internet routing when an ISC fails based on the mapping between ASs and ISCs. The results show that benefiting from the complex mesh of ISCs, the failures of most ISCs have limited impact on Internet routing, while a few ISCs can have a significant impact. Finally, we analyze severely affected ASs and recommend how to improve the resilience of the Internet.

Speaker Honglin Ye (Tsinghua University)

Honglin Ye is currently working toward the M.S. degree in the institute for Network Sciences and Cyberspace at Tsinghua university. Her research interests mainly include submarine cable measurement and inter-domain routing.

Session Chair

Sergio Palazzo

Program at a Glance