Organizing Commitee

1. Prof. Nikumani Choudhury

2. Prof. Anakhi Hazarika

3. Prof. Moustafa Mazin Nasralla

4. Prof. Haleem Farman

Modern vehicular communication networks are evolving rapidly under the pressure of increased connectivity demand, the pervasive use of IoT, edge AI integration, and ambitious visions for 6G. Simultaneously, non-terrestrial networks (NTN), including LPWAN-based LEO/satellite links, are emerging as a promising solution for wide-area, low-power, resilient connectivity, especially in areas lacking terrestrial infrastructure. This workshop aims to bring together researchers and practitioners from the vehicular, IoT, satellite/NTN, edge-AI, and hardware communities to explore the convergence of these domains. Specifically, the workshop will focus on hardware platforms and testbeds that enable AI-driven vehicular networks integrated with terrestrial and non-terrestrial communication layers. It will highlight experimental deployments, proof-of-concept prototypes, challenges in mobility, latency, energy efficiency, cross-layer design, and discuss future research directions in 6G-era integrated vehicular-NTN systems. Through technical paper presentations, talks, demo presentations, and panel discussions, the workshop will foster cross-disciplinary collaboration, encourage novel contributions, and build momentum for research that realistically addresses the requirements and constraints of hybrid terrestrial-vehicular-satellite networks. The outcomes are expected to include design insights, research roadmaps, and potential collaborations for building next-generation vehicular networks leveraging both ground and space infrastructure.

Learning objectives 

By the end of this workshop, participants will:

1. Understand the state-of-the-art in terrestrial vehicular communication (V2X / IoV), LPWAN technologies, and non-terrestrial networks (e.g., LEO satellite / NTN) - including their advantages, limitations, and potential for integration.
   
   

2. Recognize challenges and opportunities in combining terrestrial vehicular networks with non-terrestrial links via LPWAN-based communication, particularly under mobility, power/energy constraints, latency, reliability, and coverage considerations.
   
   

3. Gain insight into hardware/testbed design: what it takes to build real-world platforms (on-vehicle units, edge gateways, satellite/LEO modules, LPWAN transceivers), and how AI-driven architectures (edge-AI, near-memory computing, FPGA or PIM-based accelerators) can be integrated to support real-time vehicular applications.
   
   

4. Explore research gaps and future directions: cross-layer protocol design, adaptive resource allocation, mobility-aware scheduling, energy-efficient communication, hybrid terrestrial–satellite handoff mechanisms, and standardization challenges for 6G vehicular–NTN systems.
   
   

5. Foster collaborations across communities by bringing together researchers from vehicular, IoT, satellite/NTN, hardware, and AI/ML communities, enabling interdisciplinary dialogues and identifying potential joint projects or proposals.
   
   

6. Provide concrete takeaways: hardware architecture guidelines, testbed reference models, evaluation metrics for hybrid networks, and draft roadmaps for future research and deployment.

Topics of Interest

Authors are invited to submit original, unpublished work in, but not limited to, the following areas:

• Hardware platforms and testbeds for vehicular–NTN integration (on-vehicle units, edge gateways, satellite/LEO modules, LPWAN transceivers)

• Edge-AI accelerators (FPGA/PIM/near-memory computing) for real-time vehicular/IoV data processing and decision making

• Protocol design, resource allocation, and cross-layer optimization for hybrid terrestrial–satellite vehicular networks (mobility support, handover, LPWAN + NTN coexistence)

• Performance evaluation, simulation, or real-world experiments for hybrid vehicular–NTN systems — latency, throughput, energy/ power, reliability metrics

• Use cases and applications: remote-area vehicular communication (rural roads, highways), disaster recovery, smart IoV, global-scale connectivity, autonomous vehicles with satellite fallback, IoT–vehicular hybrid scenarios

• Machine learning / AI techniques for adaptive scheduling, routing, gateway selection, fault tolerance, network resilience, and predictive maintenance in hybrid systems

• Security, privacy, and reliability challenges in terrestrial–non-terrestrial vehicular networks

• Standardization challenges, regulatory considerations, and deployment models for 6G-era integrated vehicular–NTN systems