IoT–IoV Convergence, Smart Mobility, Connected Infrastructure, and Autonomous Systems

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 2580

Special Issue Editors


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Guest Editor
Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Interests: wireless communications; signal processing; optical–wireless communications; machine learning; IoT; tracking and localization; integrated sensing and localization; VANETs; aerial–terrestrial networks
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Guest Editor
Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Interests: software engineering; quantum machine learning; artificial intelligence and application to ad hoc networks; Internet of Things; heterogeneous networks; software-defined networks

Special Issue Information

Dear Colleagues,

The exponential growth of the Internet of Things (IoT) and the Internet of Vehicles (IoV) is transforming the future of the intelligent transportation ecosystem, enabling seamless connectivity among vehicles, infrastructure, devices, pedestrians, and cloud/edge systems. As cities move toward smart mobility, autonomous driving, and intelligent infrastructure, the convergence of IoT and IoV technologies has become essential for achieving safe, efficient, resilient, and sustainable transportation ecosystems.

This Special Issue invites high-quality original research, visionary perspectives, and comprehensive surveys that advance the state of the art in IoT–IoV integration, connected vehicular intelligence, cooperative sensing, and next-generation mobility services. Contributions addressing theoretical foundations, algorithmic innovations, practical deployments, and real-world datasets are strongly encouraged.

Topics of Interest (include but are not limited to):

  • IoT–IoV interoperability frameworks and cross-layer architectures;
  • VLC, DSRC, and 5G/6G-enabled V2X communications and edge-assisted vehicular intelligence;
  • Autonomous driving, cooperative perception, and multi-modal sensor fusion;
  • Intelligent transportation systems and mobility-as-a-service (MaaS) models
  • Digital Twins and Federated DT  for transportation, connected infrastructure, and smart cities;
  • Edge/cloud computing, federated learning, and distributed AI for IoT/IoV;
  • Cybersecurity, certificate management, CRLs/OCSP, and intrusion detection systems (IDSs);
  • Energy-efficient and sustainable connected mobility strategies;
  • Traffic flow optimization, routing, and real-time decision-making;
  • Large-scale datasets, simulation platforms, and real-world IoT/IoV deployments.

Prof. Dr. Xavier Fernando
Dr. Noha Hassan
Guest Editors

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Keywords

  • Internet of Things (IoT)
  • vehicle-to-everything (V2X) communication
  • edge computing
  • 5G/6G networks
  • connected infrastructure
  • intelligent transportation systems (ITSs)
  • vehicle cybersecurity
  • traffic management
  • sensor networks
  • artificial intelligence (AI) for autonomous systems

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Published Papers (1 paper)

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Research

18 pages, 3270 KB  
Article
SLEVA-AV: An Edge-Centric IoT Security Architecture Using Multi-Stage Lightweight Encryption for Autonomous Vehicle Applications
by Lordwin Cecil Prabhaker Micheal, Xavier Fernando, Mathan Kumar Arumugasamy, Neelamegam Devarasu and Daisy Merina Rathinarajan
Future Internet 2026, 18(5), 245; https://doi.org/10.3390/fi18050245 - 5 May 2026
Viewed by 407
Abstract
Autonomous vehicle (AV) networks require secure and efficient data processing under strict latency and resource constraints. This paper proposes a secure, lightweight edge-centric framework, SLEVA-AV, for Internet of Things (IoT)-enabled autonomous vehicle communication. The framework integrates multi-modal sensor data processing, lightweight key management, [...] Read more.
Autonomous vehicle (AV) networks require secure and efficient data processing under strict latency and resource constraints. This paper proposes a secure, lightweight edge-centric framework, SLEVA-AV, for Internet of Things (IoT)-enabled autonomous vehicle communication. The framework integrates multi-modal sensor data processing, lightweight key management, multi-stage encryption, and integrity verification within a unified pipeline. A key derivation function (KDF) is employed to generate session keys using contextual parameters, enabling efficient re-keying during vehicular mobility without repeated handshake overhead. The encryption process combines PRESENT, SPECK, and lightweight encryption algorithm (LEA) ciphers to enhance cryptographic strength, while SHA-256 ensures data integrity. The proposed system is implemented using a CARLA-based simulation environment and validated through CrypTool 2-based cryptographic analysis. Performance evaluation over 10,000 samples demonstrates low latency (0.039–0.794 s), reduced energy consumption (0.0196–0.0589 J), and negligible key management overhead. Comparative analysis with recent state-of-the-art approaches shows improved scalability and efficiency. Security validation through attack simulations demonstrates resistance against brute-force (2336 key space), differential (2185), replay, and tampering attacks, achieving 100% detection accuracy. The results indicate that the proposed framework strikes a balanced trade-off among security strength, computational efficiency, and real-time performance, and it is suitable for deployment in IoT environments with high mobility and dynamic edge connectivity. Full article
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