STiNCA 2026

STiNCA 2026 – 1st Workshop on Spatio-Temporal Intelligence for Network Computing and Applications

Co-located with: IEEE NCA 2026 (Nov 10–13, 2026, Ortigia–Syracuse, Italy)

Website (NCA): https://www.nca-ieee.org/2026/
Website STiNCA: TBA

Workshop Organizers

Workshop Description

The confluence of modern, complex network infrastructures and the proliferation of distributed sensing and data collection points within networked systems has created a rich yet challenging research landscape. Contemporary networked environments, encompassing massive Internet of Things (IoT) deployments, next-generation 5G and nascent 6G wireless systems, the decentralized architecture of the cloud-edge continuum, and sophisticated cyber-physical systems, are increasingly evolving into intelligent, data-driven systems. These environments are characterized by the constant generation of vast streams of data, which are inherently spatio-temporal, meaning each data point is associated with a specific location and time of origin. In this context, spatio-temporal information becomes a key enabler for understanding, predicting, and optimizing network behaviour, supporting more efficient, adaptive, and autonomous networked systems.

Efficiently operating and managing these networked environments in the presence of massive spatio-temporal data streams presents a multi-faceted set of challenges. Beyond data processing, the core challenge lies in understanding and optimizing network behaviour, where spatio-temporal data serves as a key signal for decision-making. These challenges span critical areas, including the design and optimization of network architectures, intelligent resource management, distributed processing strategies, and the integration of advanced machine learning techniques under strict communication, latency, reliability, and deployment constraints. In this context, spatio-temporal data and learning methods become key enablers for intelligent network management, supporting tasks such as traffic prediction, anomaly detection, adaptive routing, resource allocation, and orchestration across distributed edge and cloud infrastructures.

The STiNCA 2026 workshop is established as a premier, dedicated forum specifically designed to bring together leading researchers, innovative practitioners, and industry experts. The primary objective is to facilitate the presentation and intensive discussion of novel methods, innovative systems, and practical applications that sit precisely at this critical intersection of spatio-temporal data and advanced networking. The workshop further emphasizes the role of spatio-temporal intelligence in the design of adaptive, self-optimizing, and scalable networked systems, aligning closely with core challenges in network computing and applications.

Topics of Interest

Submissions are welcome on (but not limited to) the following topics:

❖ Routing and forwarding for geo-located and time-sensitive flows

❖ SDN/NFV orchestration of spatio-temporal pipelines

❖ In-network computing and programmable data planes

❖ Network slicing and QoS for location-aware applications

❖ Edge, fog, and cloud-edge continuum architectures

❖ Named Data Networking (NDN) for spatio-temporal data

❖ Time-sensitive networking (TSN) for cyber-physical systems

❖ Cross-layer design in heterogeneous networks

❖ Spatio-temporal resource management in 5G/6G

❖ Predictive mobility management and handover

❖ Reconfigurable intelligent surfaces (RIS)

❖ Non-terrestrial networks: LEO, HAPS, UAV-assisted collection

❖ V2X communications and cooperative ST awareness

❖ Joint communication and sensing (ISAC)

❖ Delay-tolerant and opportunistic networking

❖ Deep learning on spatio-temporal graphs

❖ GNNs for topology modelling and link-state forecasting

❖ Time-series forecasting for capacity planning and SLA

❖ Reinforcement learning for resource allocation and scheduling

❖ Foundation models and pre-training for network intelligence

❖ Edge AI and on-device inference for low-latency decisions

❖ Continual and transfer learning for non-stationary networks

❖ Real-time analytics of geo-located IoT data streams

❖ Network-aware data placement, replication, and edge caching

❖ Trajectory and mobility data analysis for network planning

❖ Semantic and task-oriented communication

❖ Multi-modal and heterogeneous sensor fusion

❖ Benchmarking and open datasets for ST network research

❖ Federated learning across distributed network nodes

❖ Privacy-preserving ST analytics: differential privacy, secure aggregation

❖ Intrusion and anomaly detection via spatio-temporal correlation

❖ Location privacy and trajectory anonymisation

❖ Sybil attack detection in vehicular and cooperative ITS environments

❖ Spoofing, replay, and falsification attacks on location- and time-based data

❖ Attack detection using vehicle trajectory patterns and mobility traces

❖ Anomaly detection using spatio-temporal graph learning in traffic and vehicle networks

❖ Secure cooperative perception in autonomous driving systems

❖ Adversarial robustness of spatio-temporal network models

❖ Trusted execution environments and confidential edge computing

❖ Decentralized P2P overlays for spatio-temporal data

❖ Blockchain-based coordination and timestamping of ST streams

❖ Smart contracts for location-aware resource allocation

❖ Decentralized storage and CDN for geo-distributed ST data

❖ Tokenisation and incentives for participatory data collection

❖ Web3/traditional network interoperability (5G, edge, IoT)

❖ Sybil, eclipse, and consensus attacks on ST data integrity

❖ Smart contract security for location-aware applications

❖ Zero-knowledge proofs for spatio-temporal claims

❖ Cross-chain security in multi-network ST architectures

❖ Forensics and auditability on distributed ledgers

❖ Digital twins for network infrastructure with ST modelling

❖ Visualisation and situational awareness for NOCs

❖ Self-healing and self-optimising networks via ST analytics

❖ Intent-based networking and closed-loop automation

❖ Energy-aware ST scheduling and workload placement

❖ Carbon footprint modelling for distributed network systems

Important Dates:

• Full paper submission: July 24, 2026
• Notification: September 11, 2026
• Camera-ready: TBD (per NCA schedule)

Submission Instructions

Authors are invited to submit original research works (not published or under review elsewhere). Papers should be limited to 6 pages, with the possibility of one additional page at extra cost.

Submissions must follow the IEEE conference proceedings format. Papers must be submitted electronically in PDF format via EasyChair (link TBD). At least one author of each accepted paper must register for NCA 2026 and present the work at the workshop.

Accepted workshop papers will be submitted for inclusion in the IEEE Xplore Digital Library, as part of the NCA 2026 proceedings, subject to IEEE’s publication requirements.Review Process: Submissions will be reviewed under a single-blind peer review process. Each submitted paper will receive at least 3 peer reviews, evaluated on originality, technical soundness, significance of contributions, relevance to the workshop topics, and clarity of presentation.