Workshop on Resilience in Next Generation Networks (NGResNet)
In conjunction with
IEEE International Conference on Computer Communications (INFOCOM)
London, United Kingdom, May 19, 2025
Embedded Files
The workshop on resilience in next-generation networks (NGResNet) invites high-quality papers that address the urgent need for resilient next-generation networks. NGResNet seeks to address these challenges by gathering experts from academia and industry to discuss innovative approaches for enhancing network resilience. The workshop will cover topics ranging from machine learning-driven resilience mechanisms and predictive network management to adaptive resource allocation and edge-cloud collaboration for robust NGN operations.
Important Dates
Important Dates
Submission Deadline: December 19, 2024 Submission Link: https://edas.info/N33125
Notification of Acceptance: February 5, 2025
Camera Ready: February 26, 2025
Workshop: May 19, 2025
Scope of the Workshop
Scope of the Workshop
The NGResNet workshop focuses on advancing resilience strategies in Next-Generation Networks (NGNs), including 5G, 6G, and beyond. As the deployment of NGNs accelerates, they increasingly support critical applications such as autonomous vehicles, tele-robotics, and industrial automation, which demand ultra-reliable, low-latency performance. However, these networks face unprecedented challenges, including traffic bursts, hardware/software failures, and security threats, all of which can disrupt essential services. NGResNet seeks to address these challenges by gathering experts from academia and industry to discuss innovative approaches for enhancing network resilience. Topics will range from machine learning-driven resilience mechanisms and predictive network management to adaptive resource allocation and edge-cloud collaboration for robust NGN operations. By promoting research and development in these areas, NGResNet aims to contribute to the design of more resilient, adaptable networks capable of sustaining high performance under diverse, often unpredictable conditions
Topics of Interest
Topics of Interest
• Machine learning and AI-driven resilience for NGNs
• Proactive and predictive network resilience models
• Dynamic resource allocation and traffic management in response to network disruptions
• Security and privacy enhancements for resilient NGNs
• Edge and cloud network orchestration for robustness
• Adaptive network protocols for handling high variability and disruptions
• Reliable network slicing and resource management
• Resilience metrics and evaluation frameworks
• Distributed, decentralized, and self-healing architectures for NGNs
• Resilience for low-latency industrial applications
• Interference and congestion management in dense network deployments
• Testbeds and experimental frameworks for evaluating resilient NGNs
Submission Instructions
Submission Instructions
Papers must be formatted in the standard IEEE two-column format that is used by the INFOCOM 2025 main conference and must not exceed six pages in length (including references). All submitted papers will go through a peer review process, and all accepted papers, which are presented by one of the authors at the workshop, will be published in the IEEE INFOCOM 2025 proceedings and will appear on IEEE Xplore.
Organizing Team
Organizing Team
Steering Committee
Raouf Boutaba (University of Waterloo, ON Canada)
Ekram Hossain (University of Manitoba, MB Canada)
Harpreet Dhillon (Virginia Tech, USA)
Nirwan Ansari (New Jersey Institute of Technology, USA)
Ahmed Eltawil (King Abdullah University of Science & Technology, Saudi Arabia)
General Co-Chairs:
Junaid Farooq (University of Michigan-Dearborn, MI USA)
Juntao Chen (Fordahm University, NY USA)
Quanyan Zhu (New York University, NY USA)
Technical Program Co-Chairs:
Hongxin Hu (University of Buffalo, NY USA)
Ehab Al-Shaer (Carnegie Mellon University, USA)
Kemal Akkaya (Florida International University, USA)
Publicity Chairs:
Tao Li (New York University, NY USA)
Speakers
Speakers
Ness B. Shroff
Title: AI-EDGE: Designing future XG Networks and Distributed intelligence
Abstract: Networking and AI are two of the most transformative information technologies. These technologies have helped improve the quality of the human condition, contributed to national economic competitiveness, national security, and national defense. The AI-EDGE Institute is aimed at leveraging the synergie between both networking and AI to design the next generation of edge network. A new distributed intelligence plane will be developed to ensure that these networks are self-healing, adaptive, and self-optimized. The future of AI is distributed AI and these intelligent and adaptive networks will in turn unleash the power of collaboration to solve long-standing distributed AI challenges, making AI more efficient, interactive, and privacy preserving. The Institute plans to develop the key underlying technologies for distributed and networked intelligence to enable a host of future transformative applications such as intelligent transportation, remote healthcare, distributed robotics, and smart aerospace. Going beyond research, the Institute recognizes that it is a national priority to educate students, professionals, and practitioners in AI and networks, and substantially grow and diversify the workforce. The Institute will develop novel, efficient, and modular ways of creating and delivering education content and curricula at scale, and to spearhead a program that helps build a large diverse workforce in AI and networks spanning primary and secondary education to university students and faculty. In this talk, the speaker will first give an overview of the key research components of the Institute, identifying a set of research directions and open problems that may be of interest to the broader audience. The speaker will then describe through a case study involving edge-caching, why the edge is so different from the core of the network, and how Machine Learning (ML) tools and techniques can be developed to improve the performance in Edge Networks.
Biography: Ness B. Shroff received his Ph.D. degree from Columbia University, NY in 1994 and joined Purdue university immediately thereafter. At Purdue, he became Professor of the school of Electrical and Computer Engineering and director of CWSA in 2004, a university-wide center on wireless systems and applications. In July 2007, he joined the ECE and CSE departments at The Ohio State University, where he holds the Ohio Eminent Scholar Chaired Professorship of Networking and Communications. From 2009-2012, he also served as a Guest Chaired professor of Wireless Communications at Tsinghua University, Beijing, China, and an Honorary Guest Professor at Shanghai Jiatong University. He is a visiting professor at the Indian Institute of Technology, Bombay. He currently serves as the Principal Investigator and Institute Director of the NSF AI Institute on Future Edge Networks and Distributed Intelligence (ai-edge.osu.edu). Dr. Shroff’s research focuses on fundamental problems in machine learning, network optimization, stochastic control, and algorithmic design. Dr. Shroff is a Fellow of the IEEE, and a National Science Foundation CAREER awardee. He has received numerous best paper awards and has been on the list of highly cited researchers from Thomson Reuters ISI (previously ISI web of Science) in 2014 and 2015, and in Thomson Reuters Book on The World’s Most Influential Scientific Minds in 2014. He received the IEEE INFOCOM achievement award for seminal contributions to scheduling and resource allocation in wireless networks, in 2014.
Workshop Program
Workshop Program
08:30 – 08:45
Welcome and Opening Remarks
08:45 – 10:00
Keynote Speech
10:00 – 10:15
Coffee Break
10:30 – 12:00
Session 1: AI-Driven Network Optimization and Resource Management
A Graph Convolutional Network-Based Approach for Dynamic Connectivity Prediction in 5G Networks
Raul F. D. Barbosa (Universidade de Aveiro, Portugal & Capgemini, Portugal), Marco Araujo (Capgemini Engineering, Portugal), Petia Georgieva (University of Aveiro, DETI/IEETA, Portugal), Susana Sargento (Universidade de Aveiro, Portugal), Pedro Rito (University of Aveiro, Portugal & Instituto de Telecomunicações, Portugal)
DYNAPARC: AI-Driven Predictive Path Failure Management for Industrial IoT-Fog Networks
Rehab Alawadh (University of York, UK), Poonam Yadav (University of York, UK), Hamed Ahmadi (University of York, UK)
RAPID: Proactive Rate Control for Enabling High Data Rate and Low Latency in 5G Edge Networks
Hamid Hassani (Eindhoven University of Technology, The Netherlands), Sonia Heemstra de Groot (Eindhoven Technical University, The Netherlands), Ignas Niemegeers (Eindhoven University of Technology, The Netherlands), Kishor Chandra Joshi (TU Eindhoven, The Netherlands), George Exarchakos (Eindhoven University of Technology, The Netherlands)
12:00 – 13:30
Lunch Break
13:30 – 15:00
Session 2: Security and Threat Mitigation in Next-Gen Networks
Validating Evil Twin Attacks in 5G/6G Networks Based on MITRE FiGHT
Aris Cahyadi Cahyadi Risdianto, Purnima Murali Mohan, Yiyang Pei and Pedro Henrique Amorim Rezende (Singapore Institute of Technology, Singapore); Kartono Wihardja (PT. ITSEC Asia, Singapore)
A Hardware-Accelerated Intrusion Prevention System for Attack Mitigation Using DPUs
Rana Abu Bakar and Piero Castoldi (Scuola Superiore Sant'Anna, Italy); Filippo Cugini and Francesco Paolucci (CNIT, Italy)
A Resilient Distributed SDN and Underlay Routing Architecture for Operationally Constrained Networks
Benjamin A Montgomery (Raytheon BBN, USA); Dylan J Cirimelli-Low (Raytheon Technologies, USA); Brian Basnight (Raytheon BBN Technologies, USA); Bishal Thapa (RTX BBN Technologies, USA)
15:00 – 15:30
Coffee Break
15:30 – 17:00
Session 3: Resilient Network Architectures and Multi-Layer Optimization
Collaborative Resilience for Multi-Layer Heterogeneous Robotic Networks Under Adversarial Environments
Gabrielle Ebbrecht, Jason Hughes and Juntao Chen (Fordham University, USA); Junaid Farooq (University of Michigan-Dearborn, USA)
Multi-Techno-Band Cellular Network Resilience to Shocks and Aging: A Stochastic Geometry Approach
Ludmila Courtillat-Piazza (LTCI, Télécom Paris, Institut Polytechnique de Paris, France), Marceau Coupechoux (LTCI, Télécom Paris, Institut Polytechnique de Paris, France), Sophie Quinton (INRIA Grenoble, France)
A Contract-Based Incentive Mechanism for Optimal Pricing and Allocation in Shared AVPC
Gordon Owusu Boateng (UESTC, China); Xiansheng Guo (UESTC, China & NJIT, USA); Haonan Si (UESTC, China); Nirwan Ansari (NJIT, USA); Kehan Chen (UESTC, China)
LLM-Driven Agentic AI Approach to Enhanced O-RAN Resilience in Next-Generation Networks
Xingqi Wu (University of Michigan, USA); Yuhui Wang (University of Michigan - Dearborn, USA); Junaid Farooq (University of Michigan-Dearborn, USA); Juntao Chen (Fordham University, USA)
17:00
Closing Remarks
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