IEEE INFOCOM 2024

Session Break-3-1: Coffee Break Session Break-3-2: Coffee Break
Session A-8: A-8: Mobile Networks and Applications Session A-9: A-9: Crowdsourcing and crowdsensing Session A-10: A-10: RF and Physical Layer Session A-11: A-11: Topics in Wireless and Edge Networks
Session B-8: B-8: Streaming Systems Session B-9: B-9: Localization and Tracking Session B-10: B-10: Network Verification and Tomography Session B-11: B-11: Topics in Secure and Reliable Networks
Session C-8: C-8: Staleness and Age of Information (AoI) Session C-9: C-9: Internet of Things (IoT) Networks Session C-10: C-10: Network Security and Privacy Session C-11: C-11: User experience, Orchestration, and Telemetry
Session D-8: D-8: Backscatter Networking Session D-9: D-9: RFID and Wireless Charging Session D-10: D-10: High Speed Networking Session D-11: D-11: Network Computing and Offloading
Session E-8: E-8: Machine Learning 2 Session E-9: E-9: Machine Learning 3 Session E-10: E-10: Machine Learning 4 Session E-11: E-11: Machine Learning 5
Session F-8: F-8: Internet Architectures and Protocols Session F-9: F-9: Hashing, Clustering, and Optimization Session F-10: F-10: Spectrum Access and Sensing
Session Lunch-3: Conference Lunch (for Registered Attendees)
Session G-8: G-8: Ethereum Networks and Smart Contracts Session G-9: G-9: Modeling and Optimization Session G-10: G-10: Edge Networks

Session A-8

A-8: Mobile Networks and Applications

Conference
8:30 AM — 10:00 AM PDT
Local
May 23 Thu, 11:30 AM — 1:00 PM EDT
Location
Regency A

AIChronoLens: Advancing Explainability for Time Series AI Forecasting in Mobile Networks

Claudio Fiandrino, Eloy Pérez Gómez, Pablo Fernández Pérez, Hossein Mohammadalizadeh, Marco Fiore and Joerg Widmer (IMDEA Networks Institute, Spain)

0
Next-generation mobile networks will increasingly rely on the ability to forecast traffic patterns for resource management. Usually, this translates into forecasting diverse objectives like traffic load, bandwidth, or channel spectrum utilization, measured over time. Among the other techniques, Long-Short Term Memory (LSTM) proved very successful for this task. Unfortunately, the inherent complexity of these models makes them hard to interpret and, thus, hampers their deployment in production networks. To make the problem worsen, EXplainable Artificial Intelligence (XAI) techniques, which are primarily conceived for computer vision and natural language processing, fail to provide useful insights: they are blind to the temporal characteristics of the input and only work well with highly rich semantic data like images or text. In this paper, we take the research on XAI for time series forecasting one step further proposing AIChronoLens, a new tool that links legacy XAI explanations with the temporal properties of the input. In such a way, AIChronoLens makes it possible to dive deep into the model behavior and spot, among other aspects, the hidden cause of errors. Extensive evaluations with real-world mobile traffic traces pinpoint model behaviors that would not be possible to spot otherwise and model performance can increase by 32%.
Speaker
Speaker biography is not available.

Characterizing 5G Adoption and its Impact on Network Traffic and Mobile Service Consumption

Sachit Mishra and André Felipe Zanella (IMDEA Networks Institute, Spain); Orlando E. Martínez-Durive (IMDEA Networks Institute & Universidad Carlos III de Madrid, Spain); Diego Madariaga (IMDEA Networks Institute, Spain); Cezary Ziemlicki (Orange labs, France); Marco Fiore (IMDEA Networks Institute, Spain)

0
The roll out of 5G, coupled with the traffic monitoring capabilities of modern industry-grade networks, offers an unprecedented opportunity to closely observe the impact that the introduction of a new major wireless technology has on the end users. In this paper, we seize such a unique chance, and carry out a first-of-its-kind in-depth analysis of 5G adoption along spatial, temporal and service dimensions. Leveraging massive measurement data about application-level demands collected in a nationwide 4G/5G network, we characterize the impact of the new technology on when, where and how mobile subscribers consume 5G traffic both in aggregate and for individual types of services. This lets us unveil the overall incidence of 5G in the total mobile network traffic, its spatial and temporal fluctuations, its effect on the way 5G services are consumed, the way individual services and geographical locations contribute to fluctuations in the 5G demand, as well as surprising connections between socioeconomic status of local populations and the way the 5G technology is presently consumed.
Speaker
Speaker biography is not available.

Exploiting Multiple Similarity Spaces for Efficient and Flexible Incremental Update of Mobile Applications

Lewei Jin (ZheJiang University, China); Wei Dong, Jiang BoWen, Tong Sun and Yi Gao (Zhejiang University, China)

0
Mobile application updates occur frequently, and they continue to add considerable traffic over the Internet. Differencing algorithms, which compute a small delta between the new version and old version, are often employed to reduce the update overhead. Transforming the old and new files into the decoded similarity spaces can drastically reduce the delta size. However, this transformation is often hindered by two practical reasons: (1) insufficient decoding. (2) long recompression time. To address this challenge, we have proposed two general approaches to transforming the compressed files into the full decoded similarity space and partial decoded similarity space, with low recompression time. The first approach uses recompression-aware searching mechanism, based on a general full decoding tool to transform deflate stream to the full decoded similarity space with a configurable searching complexity. The second approach uses a novel solution to transform a deflate stream into the partial decoded similarity space with differencing-friendly LZ77 token reencoding. We have also proposed an algorithm called MDiffPatch to exploit the full and partial decoded similarity spaces. Extensive evaluation results show that MDiffPatch achieves lower compression ratio than state-of-the-art algorithms and its tunable parameter allows us to achieve a good tradeoff between compression ratio and recompression time.
Speaker Lewei Jin (Zhejiang University)

Lewei Jin graduated with a bachelor's degree from Hangzhou University of Electronic Science and Technology. I am currently pursuing a PhD in Software Engineering at Zhejiang University, with a research interest in mobile application security.

LoPrint: Mobile Authentication of RFID-Tagged Items Using COTS Orthogonal Antennas

Yinan Zhu (The Hong Kong University of Science and Technology (HKUST), Hong Kong); Qian Zhang (Hong Kong University of Science and Technology, Hong Kong)

0
Authenticating RFID-tagged items during mobile inventory is a critical task for anti-counterfeiting. However, past authentication solutions using commercial off-the-shelf (COTS) devices cannot be applied in mobile scenarios, due to either high latency or non-robustness to tag movement. This paper introduces LoPrint, the first system to effectively authenticate mobile tagged items using the COTS orthogonal antennas existing in most infrastructures. The key insight of LoPrint is to randomly attach multiple tags on each item as a tag group and leverage the stable layout relationships of this tag group as novel fingerprints, including the relative distance matrix (RDM) and relative orientation matrix (ROM). Additionally, a new hardware fingerprint called cross-polarization ratio (CPR) is proposed to help distinguish the tag category. Furthermore, a lightweight approach is designed to robustly extract RDM, ROM, and CPR from RSSI and phase sequences. LoPrint is prototyped and deployed on a conveyor in a lab environment and a tunnel in a real-world RFID warehouse, where 726 tagged items with random layouts are used for evaluation. Experimental results show that LoPrint can achieve a high authentication accuracy of 82.92% on the fixed conveyor and 79.48% on the random warehouse trolley, outperforming the transferred state-of-the-art solution by over 10x.
Speaker Yinan Zhu (Hong Kong University of Science and Technology)

Yinan Zhu is currently a PhD candidate at the Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST).

Session Chair

Ruozhou Yu (North Carolina State University, USA)

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Session A-9

A-9: Crowdsourcing and crowdsensing

Conference
10:30 AM — 12:00 PM PDT
Local
May 23 Thu, 1:30 PM — 3:00 PM EDT
Location
Regency A

Seer: Proactive Revenue-Aware Scheduling for Live Streaming Services in Crowdsourced Cloud-Edge Platforms

Shaoyuan Huang, Zheng Wang, Zhongtian Zhang and Heng Zhang (Tianjin University, China); Xiaofei Wang (Tianjin Key Laboratory of Advanced Networking, Tianjin University, China); Wenyu Wang (Shanghai Zhuichu Networking Technologies Co., Ltd., China)

0
As live streaming services skyrocket, Crowdsourced Cloud-edge service Platforms (CCPs) have surfaced as pivotal intermediaries catering to the mounting demand. Despite the role of stream scheduling to CCP's Quality of Service (QoS) and revenue, conventional optimization strategies struggle to enhancing CCP's revenue, primarily due to the intricate relationship between server utilization and revenue. Additionally, the substantial scale of CCPs magnifies the difficulties of time-intensive scheduling. To tackle these challenges, we propose Seer, a proactive revenue-aware scheduling system for live streaming services in CCPs. The design of Seer is motivated by meticulous measurements of real-world CCP environments, which allows us to achieve accurate revenue modeling and overcome three key obstacles that hinder the integration of prediction and optimal scheduling. Utilizing an innovative Pre-schedule-Execute-Re-schedule paradigm and flexible scheduling modes, Seer achieves efficient revenue-optimized scheduling in CCPs. Extensive evaluations demonstrate Seer's superiority over competitors in terms of revenue, utilization, and anomaly penalty mitigation, boosting CCP revenue by 147% and expediting scheduling 3.4 times faster.
Speaker Damien Saucez
Damien Saucez is a researcher at Inria Sophia Antipolis since 2011. He's current research interest is Software Defined Networking (SDN) with a particular focus on resiliency and robustness for very large networks. He's actively working to promote reproducibility in research by leading the ACM SIGCOMM 2017 Reproducibility Workshop and by chairing the ACM SIGCOMM and ACM CoNEXT Artifacts Evaluation Committee.

QUEST: Quality-informed Multi-agent Dispatching System for Optimal Mobile Crowdsensing

Zuxin Li, Fanhang Man and Xuecheng Chen (Tsinghua University, China); Susu Xu (Stony Brook University, USA); Fan Dang (Tsinghua University, China); Xiao-Ping (Steven) Zhang (Tsinghua Shenzhen Internation Graduate School, China); Xinlei Chen (Tsinghua University, China)

0
In this work, we address the challenges in achieving optimal Quality of Information (QoI) for non-dedicated vehicular Mobile Crowdsensing (MCS) systems, by utilizing vehicles not originally designed for sensing purposes to provide real-time data while moving around the city. These challenges include the coupled sensing coverage and sensing reliability, as well as the uncertainty and time-varying vehicle status. To tackle these issues, we propose QUEST, a QUality-informed multi-agEnt diSpaTching system, that ensures high sensing coverage and sensing reliability in non-dedicated vehicular MCS. QUEST optimizes QoI by introducing a novel metric called ASQ (aggregated sensing quality), which considers both sensing coverage and sensing reliability jointly. Additionally, we design a mutual-aided truth discovery dispatching method to estimate sensing reliability and improve ASQ under uncertain vehicle statuses. Real-world data from our deployed MCS system in a metropolis is used for evaluation, demonstrating that QUEST achieves up to 26% higher ASQ improvement, leading to reduction of reconstruction map errors by 32-65% for different reconstruction algorithms.
Speaker Ahmed Imteaj
Ahmed Imteaj is an Assistant Professor of the School of Computing at Southern Illinois University, Carbondale. He is the director of the Security, Privacy and Edge intElligence for Distributed networks Laboratory (SPEED Lab). He received his Ph.D. in Computer Science from Florida International University in 2022, earning the distinction of being an FIU Real Triumph Graduate, where he received his M.Sc. with the Outstanding Master's Degree Graduate Award. Prior to that, Ahmed received a B.Sc. degree in Computer Science and Engineering from Chittagong University of Engineering and Technology. His research interests encompass a wide range of fields, including Federated Learning, Generative AI, Interdependent Networks, Cybersecurity, and IoT. Ahmed has made significant contributions to the domains of privacy-preserving distributed machine learning and IoT, with his research published in prestigious conferences and peer-reviewed journals. He has received several accolades, such as the 2022 Outstanding Student Life: Graduate Scholar of the Year Award, 2021 Best Graduate Student in Research Award from FIU's Knight Foundation School of Computing and Information Sciences.

Combinatorial Incentive Mechanism for Bundling Spatial Crowdsourcing with Unknown Utilities

Hengzhi Wang, Laizhong Cui and Lei Zhang (Shenzhen University, China); Linfeng Shen and Long Chen (Simon Fraser University, Canada)

0
Incentive mechanisms in Spatial Crowdsourcing (SC) have been widely studied as they provide an effective way to motivate mobile workers to perform spatial tasks. Yet, most existing mechanisms only involve single tasks, neglecting the presence of complementarity and substitutability among tasks. This limits their effectiveness in practice cases. Motivated by this, we consider task bundles for incentive mechanism design and closely analyze the mutual exclusion effect that arises with task bundles. We then develop a combinatorial incentive mechanism, including three key policies: In the offline case, we propose a combinatorial assignment policy to address the conflict between mutual exclusion and assignment efficiency. We next study the conflict between mutual exclusion and truthfulness, and build a combinatorial pricing policy to pay winners that yields both incentive compatibility and individual rationality. In the online case with unknown workers' utilities, we present an online combinatorial assignment policy that balances the exploration-exploitation trade-off under the mutual exclusion constraints. Through theoretical analysis and numerical simulations using real-world mobile networking datasets, we demonstrate the effectiveness of the proposed mechanism.
Speaker
Speaker biography is not available.

Few-Shot Data Completion for New Tasks in Sparse CrowdSensing

En Wang, Mijia Zhang and Bo Yang (Jilin University, China); Yang Xu (Hunan University, China); Zixuan Song and Yongjian Yang (Jilin University, China)

0
Mobile Crowdsensing is a type of technology that utilizes mobile devices and volunteers to gather data about specific topics at large scales in real-time. However, in practice, limited participation leads to missing data, i.e., the collected data may be sparse, which makes it difficult to perform accurate analysis. A possible technique called sparse crowdsensing incorporates the sparse case with data completion, where unsensed data could be estimated through inference. However, sparse crowdsensing typically suffers from poor performance during the data completion stage due to various challenges: the sparsity of the sensed data, reliance on numerous timeslots, and uncertain spatiotemporal connections. To resolve such few-shot issues, the proposed solution uses the Correlated Data Fusion for Matrix Completion (CDFMC) approach, which leverages a small amount of objective data to retrain an auxiliary dataset-based pre-trained model that can estimate unsensed data efficiently. CDFMC is trained using a combination of the traditional Deep Matrix Factorization and the Kalman Filtering, which not only enables the efficient representation and comparison of data samples but also fuses the objective data and auxiliary data effectively. Evaluation results show that the proposed CDFMC outperforms baseline techniques, achieving high accuracy in completing unsensed data with minimal training data.
Speaker Mijia Zhang (Jilin University)

Mijia Zhang received his Ph.D. degree in computer system architecture from Jilin University, Changchun, China, in 2023. His current work focuses on sparse Mobile CrowdSensing, Neural Networks, spatiotemporal data inference and matrix completion.

Session Chair

Srinivas Shakkottai (Texas A&M University, USA)

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Session A-10

A-10: RF and Physical Layer

Conference
1:30 PM — 3:00 PM PDT
Local
May 23 Thu, 4:30 PM — 6:00 PM EDT
Location
Regency A

Cross-Shaped Separated Spatial-Temporal UNet Transformer for Accurate Channel Prediction

Hua Kang (Noah's Ark Lab, Huawei, Hong Kong); Qingyong Hu (Hong Kong University of Science and Technology, Hong Kong); Huangxun Chen (Hong Kong University of Science and Technology (Guangzhou), China); Qianyi Huang (Sun Yat-Sen University, China & Peng Cheng Laboratory, China); Qian Zhang (Hong Kong University of Science and Technology, Hong Kong); Min Cheng (Noah's Ark Lab, Huawei, Hong Kong)

0
Accurate channel estimation is crucial for the performance gains of massive multiple-input multiple-output (mMIMO) technologies. However, it is bandwidth-unfriendly to estimate large channel matrix frequently to combat the time-varying wireless channel. Deep learning-based channel prediction has emerged to exploit the temporal relationships between historical and future channels to address the bandwidth-accuracy trade-off. Existing methods with convolutional or recurrent neural networks suffer from their intrinsic limitations, including restricted receptive fields and propagation errors. Therefore, we propose a Transformer-based model, CS3T-UNet tailored for mMIMO channel prediction. Specifically, we combine the cross-shaped spatial attention with a group-wise temporal attention scheme to capture the dependencies across spatial and temporal domains, respectively, and introduce the shortcut paths to well-aggregate multi-resolution representations.
Thus, CS3T-UNet can globally capture the complex spatial-temporal relationship and predict multiple steps in parallel, which can meet the requirement of channel coherence time. Extensive experiments demonstrate that the prediction performance of CS3T-UNet surpasses the best baseline by at most 6.86 dB with a smaller computation cost on two channel conditions.
Speaker Hua KANG (Noah's Ark Lab, Huawei)

I graduated from HKUST in August, 2023 and am currently a researcher at Noah's Ark Lab, Huawei in Hong Kong. 

I'm actively working on topics at the intersection of IoT sensing, wireless communication and deep learning, with a focus on building ubiquitous, privacy-friendly and efficient machine learning systems for IoT applications. 

Diff-ADF: Differential Adjacent-dual-frame Radio Frequency Fingerprinting for LoRa Devices

Wei He, Wenjia Wu, Xiaolin Gu and Zichao Chen (Southeast University, China)

0
Nowadays, LoRa radio frequency fingerprinting has gained widespread attention due to its lightweight nature and difficulty in being forged. The existing fingerprint extraction methods are mainly divided into two categories: deep learning-based methods and feature engineering-based methods. Deep learning-based methods have poor robustness and require significant resource costs for model training. Although feature engineering-based methods can overcome these drawbacks, the feature it commonly uses, such as carrier frequency offset (CFO) and phase noise, lack sufficient discriminative power. Therefore, it is very challenging to design a radio frequency fingerprinting solution with high-accuracy and stable identification performance. Fortunately, we find that the differential phase noise between adjacent dual frames possesses excellent discriminative power and stability. Then, we design the corresponding radio frequency fingerprinting solution called Diff-ADF, which utilizes a classifier with differential phase noise as the primary feature, complemented by the use of CFO as an auxiliary feature. Finally, we implement the Diff-ADF and conduct experiments in real environments. Experimental results demonstrate that our proposed solution achieves an accuracy of over 90% on training and test data collected from different days, which is significantly superior to deep learning-based methods. Even in non-line-of-sight environments, our identification accuracy can still reach close to 85%.
Speaker Wei He (Southeast University)

Graduate student, School Of Cyber Science and Engineering, Southeast University

Cross-domain, Scalable, and Interpretable RF Device Fingerprinting

Tianya Zhao and Xuyu Wang (Florida International University, USA); Shiwen Mao (Auburn University, USA)

0
In this paper, we propose a cross-domain, scalable, and interpretable radio frequency (RF) fingerprinting system using a modified prototypical network (PTN) and an explanation-guided data augmentation across various domains and datasets with only a few samples. Specifically, a convolutional neural network is employed as the feature extractor of the PTN to extract RF fingerprint features. The predictions are made by comparing the similarity between prototypes and feature embedding vectors. To further improve the system performance, we design a customized loss function and deploy an eXplainable Artificial Intelligence (XAI) method to guide data augmentation during fine-tuning. To evaluate the effectiveness of our system in addressing domain shift and scalability problems, we conducted extensive experiments in both cross-domain and novel-device scenarios. Our study shows that our approach achieves exceptional performance in the cross-domain case, exhibiting an accuracy improvement of approximately 80\% compared to convolutional neural networks in the best case. Furthermore, our approach demonstrates promising results in the novel-device case across different datasets. Our customized loss function and XAI-guided data augmentation can further improve authentication accuracy to a certain degree.
Speaker Tianya Zhao (Florida International University)

Tianya Zhao is a second-year Ph.D. student studying computer science at FIU, supervised by Dr. Xuyu Wang. Prior to this, he received his Master's degree from Carnegie Mellon University and Bachelor's degree from Hunan University. In his current Ph.D. program, he is focusing on AIoT, AI Security, Wireless Sensing, and Smart Health.

PRISM: Pre-training RF Signals in Sparsity-aware Masked Autoencoders

Liang Fang, Ruiyuan Song, Zhi Lu, Dongheng Zhang, Yang Hu, Qibin Sun and Yan Chen (University of Science and Technology of China, China)

0
This paper introduces a novel paradigm for learning-based RF sensing, termed Pre-training RF signals In Sparsity-aware Masked autoencoders (PRISM), which shifts the RF sensing paradigm from supervised training on limited annotated datasets to unsupervised pre-training on large-scale unannotated datasets, followed by fine-tuning with a small annotated dataset. PRISM leverages a carefully designed sparsity-aware masking strategy to predict missing contents by masking a portion of RF signals, resulting in an efficient pre-training framework that significantly reduces computation and memory resources. This addresses the major challenges posed by large-scale and high-dimensional RF datasets, where memory consumption and computation speed are critical factors. We demonstrate PRISM's excellent generalization performance across diverse RF sensing tasks by evaluating it on three typical scenarios: human silhouette segmentation, 3D pose estimation, and gesture recognition, involving two general RF devices, radar and WiFi. The experimental results provide strong evidence for the effectiveness of PRISM as a robust learning-based solution for large-scale RF sensing applications.
Speaker
Speaker biography is not available.
Session Chair

Shiwen Mao (Auburn University, USA)

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Session A-11

A-11: Topics in Wireless and Edge Networks

Conference
3:30 PM — 5:00 PM PDT
Local
May 23 Thu, 6:30 PM — 8:00 PM EDT
Location
Regency A

Talk2Radar: Talking to mmWave Radars via Smartphone Speaker

Kaiyan Cui (Nanjing University of Posts and Telecommunications & The Hong Kong Polytechnic University, China); Leming Shen and Yuanqing Zheng (The Hong Kong Polytechnic University, Hong Kong); Fu Xiao (Nanjing University of Posts and Telecommunications, China); Jinsong Han (Zhejiang University & School of Cyber Science and Technology, China)

0
Integrated Sensing and Communication (ISAC) is gaining a tremendous amount of attention from both academia and industry. Recent work has brought communication capability to sensing-oriented mmWave radars, enabling more innovative applications. These solutions, however, either require hardware modifications or suffer from limited data rates. This paper presents Talk2Radar, which builds a faster communication channel between smartphone speakers and mmWave radars, without any hardware modification to either commodity smartphones or off-the-shelf radars. In Talk2Radar, a smartphone speaker sends messages by playing carefully designed sounds. A mmWave radar acting as a data receiver captures the emitted sounds by detecting the sound-induced smartphone vibrations, and then decodes the messages. Talk2Radar characterizes smartphone speakers for speaker-to-mmWave radar communication and addresses a series of technical challenges, including modulation and demodulation of extremely weak sound-induced vibrations, multi-speaker concurrent communication and human motion suppression. We implement and evaluate Talk2Radar in various practical settings. Experimental results show that Talk2Radar can achieve a data rate of up to 400bps with an average BER of less than 5%, outperforming the state-of-the-art by approximately 33x.
Speaker Kaiyan Cui (Nanjing University of Posts and Telecommunications & The Hong Kong Polytechnic University, China)

Kaiyan Cui, an Assistant Professor in the School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China. She received the Joint Ph.D. degree from Hong Kong Polytechnic University, Hong Kong, China and Xi'an Jiaotong University, Xi’an, China, in 2023. Her research interests include smart sensing, mobile computing, and IoT.

Distributed Experimental Design Networks

Yuanyuan Li and Lili Su (Northeastern University, USA); Carlee Joe-Wong (Carnegie Mellon University, USA); Edmund Yeh and Stratis Ioannidis (Northeastern University, USA)

0
As edge computing capabilities increase, model learning deployments at a diverse edge environment have emerged. In experimental design networks, introduced recently, network routing and rate allocation is designed to aid the transfer of data from sensors to heterogeneous learners. We design efficient experimental design network algorithms that are (a) distributed and (b) use multicast transmissions. This poses significant challenges as classic decentralization approaches often operate on (strictly) concave objectives under differentiable constraints. In contrast, the problem we study here has a non-convex, continuous DR-submodular objective while multicast transmissions naturally result in non-differentiable constraints From a technical standpoint, we propose a distributed Frank-Wolfe and a distributed projected gradient ascent algorithm that, coupled with a relaxation of non-differentiable constraints, yield allocations within a $1-1/e$ factor from the optimal. Numerical evaluations show that our proposed algorithms outperforms competitors w.r.t. model learning quality.
Speaker
Speaker biography is not available.

Roaming across the European Union in the 5G Era: Performance, Challenges, and Opportunities

Rostand A. K. Fezeu (University of Minnesota, USA); Claudio Fiandrino (IMDEA Networks Institute, Spain); Eman Ramadan, Jason Carpenter, Daqing Chen and Yiling Tan (University of Minnesota - Twin Cities, USA); Feng Qian (University of Minnesota, Twin Cities, USA); Joerg Widmer (IMDEA Networks Institute, Spain); Zhi-Li Zhang (University of Minnesota, USA)

0
Roaming provides users with voice and data connectivity when traveling abroad. This is particularly the case in Europe where the ``Roam like Home'' policy established by the European Union in 2017 has made roaming affordable. Nonetheless, due to various policies employed by operators, roaming can incur considerable performance penalty as shown in past studies of 3G/4G networks. As 5G provides significantly higher bandwidth, how does roaming affect user-perceived performance? We present, to the best of our knowledge, the first comprehensive and comparative measurement study of commercial 5G in four European countries.

Our measurement study is unique in the way it makes it possible to link key 5G mid-band channels and configuration parameters (``policies'') used by various operators in these countries with their effect on the observed 5G performance from the network (in particular, the physical and MAC layer) and applications perspectives. Our measurement study not only portrays the observed quality of experience of users when roaming, but also provides guidance to optimize the network configuration as well as to users and application developers in choosing mobile operators. Moreover, our contribution provides the research community with, to our knowledge, the largest cross-country roaming 5G dataset to stimulate further research.
Speaker
Speaker biography is not available.

Two-Stage Distributionally Robust Edge Node Placement Under Endogenous Demand Uncertainty

Jiaming Cheng (University of British Columbia, Canada); Duong Thuy Anh Nguyen and Duong Tung Nguyen (Arizona State University, USA)

0
Edge computing (EC) promises to deliver low-latency and ubiquitous computation to numerous devices at the network edge. This paper aims to jointly optimize edge node (EN) placement and resource allocation for an EC platform, considering demand uncertainty. Diverging from existing approaches treating uncertainties as exogenous, we propose a novel two-stage decision-dependent distributionally robust optimization (DRO) framework to effectively capture the interdependence between EN placement decisions and uncertain demands. The first stage involves making EN placement decisions, while the second stage optimizes resource allocation after uncertainty revelation. We present an exact mixed-integer linear program reformulation for solving the underlying ``min-max-min" two-stage model. We further introduce a valid inequality method to enhance computational efficiency, especially for large-scale networks. Extensive numerical experiments demonstrate the benefits of considering endogenous uncertainties and the advantages of the proposed model and approach.
Speaker
Speaker biography is not available.
Session Chair

Duong Tung Nguyen (Arizona State University, USA)

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