In conjunction with
22nd International Conference on Network and Service Management
Alcalá de Henares (Madrid), Spain // 26 - 30 October, 2026
Int. Workshop on Emerging Trade-offs in Networking: Sovereignty, Security, reSilience, and Sustainability (4S-Net)
Future communication networks must balance multiple, often conflicting requirements, including sovereignty, security, resilience, and sustainability, alongside traditional Quality of Service and important user-centric metrics. These dimensions are deeply interdependent: for example, security can raise energy costs, resilience demands redundancy, security may conflict with latency or availability. Addressing such trade-offs is critical for next-generation systems like 6G, non-terrestrial networks, and AI-driven infrastructures, where data protection, explainability, and efficiency are central challenges. Consequently, the 4S-Net workshop explores methods, architectures, and operational strategies to design networks that align technical performance with societal, industrial, and environmental priorities. A key focus is the development of measurable metrics and indicators to guide decisions across diverse network types and scales.
Papers should be prepared using the IEEE 2-column conference style and are limited to 6 pages including references. Papers must be submitted electronically in PDF format through the EDAS system. EDAS link is following shortly.
Paper Submission Deadline: 14 August 2026
Acceptance Notification: 11 September 2026
Camera Ready: 18 September 2026
Workshop date: 26th or 30th October
Metrics and frameworks for assessing sovereignty: fundamentals on measuring sovereignty and different facets of digital sovereignty, like network sovereignty, data sovereignty, technological or operational sovereignty
Control of infrastructure, data, and standards: ensuring sovereignty through national/regional ownership of backbone, cloud, and 6G RAN vs. limiting vendor diversity, slowing innovation, and raising costs.
Open vs. proprietary ecosystems: use of open source and access software stacks in networking (e.g., ORAN, Kubernetes, Open Source MANO) to strengthen sovereignty vs. interoperability, security patching, and integration complexity.
AI-driven network management: sovereign AI models for routing, orchestration, and anomaly detection including data and infrastructure sovereignty, model transparency and explainability, usage of open standards and open-source frameworks, avoiding monopoly control, strategic autonomy for independent decision making vs. dependency on foreign hardware accelerators and proprietary AI frameworks.
Cross-border interoperability vs. data sovereignty: seamless roaming, cloud federation, and satellite–terrestrial integration vs. regulatory conflicts, lawful interception, and compliance overhead.
Chipsets and hardware sovereignty: local semiconductor and equipment production vs. cost-efficiency and performance of globally integrated supply chains.
Strong cryptographic safeguards: protecting user data, IoT device integrity, and network control planes vs. increased computational load, latency, and power demand (e.g., in constrained IoT or LPWANs).
Inline prevention and anomaly detection: real-time intrusion detection in optical backbones, RAN, or edge clouds vs. high energy consumption and possible QoS degradation.
AI/ML-based security: adaptive defense against evolving threats vs. risk of adversarial ML, lack of explainability, and computational overhead for training/inference.
End-to-end encryption vs. lawful access: protecting privacy and confidentiality vs. regulatory, forensic, and operational challenges.
Virtualized and multi-tenant environments: securing SDN/NFV, MEC, and cloud-native networks vs. trade-offs in performance, scalability, and sustainability.
Redundancy and fault tolerance: ensuring service continuity in 5G/6G RANs, optical backbones, and data centers vs. higher energy use, hardware demand, and operational complexity.
Multi-path and multi-technology access (ATSS, satellite-terrestrial integration): resilient connectivity for critical services vs. additional signaling overhead, cost, and spectrum usage.
AI-driven fault prediction and real-time anomaly detection: improving service continuity in industrial automation, smart grids, and transport networks vs. high computational demand and risk of false positives.
Decentralized control and edge/cloud balance: distributing functions to edge for resilience vs. increased complexity in orchestration, synchronization, and consistency.
Delay-tolerant networking and opportunistic caching: maintaining connectivity in challenged environments (NTNs, disaster recovery) vs. potential latency increase and reduced QoE.
Interdependent network resilience: protecting against cascading failures between communication and energy infrastructures vs. sovereignty challenges in cross-border grid and network dependencies, amplified cyber–physical security risks, and sustainability costs of modeling and coordinating complex infrastructures.
AI/ML for performance and sustainability improvement: smarter scheduling, traffic aggregation, and RAN orchestration vs. the significant training and inference overhead of AI/LLM models.
Impact of overhead on sustainability: assessment of overhead for increased security or resilience and cost for sovereignty and impact on sustainability and resource demand
Integration of new technologies: identification and integration to increase sustainability and system performance without impairing security or resilience, increasing resource or energy demand, increased management overhead.
End-to-end sustainability: comprehensive assessment of sustainability and system quality from user, application, and network or system view including the full way from data generation to processing and reply back to the user.
Energy and carbon aware next-gen networks: network monitoring, measurements, and management for carbon and energy aware networks for the next generation including impact of AI, data centers, data processing, and data transmission.
Lifecycle considerations: sustainability considerations for complete lifecycle of networks, systems, or specific hardware parts.
Authors are invited to submit original contributions (written in English) in PDF format. Only original papers not published or submitted for publication elsewhere will be considered for the workshop.
Frank Loh, University of Würzburg, Germany (frank.loh@uni-wuerzburg.de)
Jacopo Talpini, University of Applied Sciences and Arts of Southern Switzerland, Switzerland (jacopo.talpini@supsi.ch)
Christoph Lipps, German Research Center for Artificial Intelligence, Germany (christoph.lipps@dfki.de)
Carlos Natalino Da Silva, Chalmers University of Technology, Sweden (carlos.natalino@chalmers.se)
Benedict Herzog, Ruhr University Bochum, Germany (benedict.herzog@rub.de)