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AIoT-Enabled Intelligent Sensing Systems for Smart Cities and Connected Transportation
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AIoT-Enabled Intelligent Sensing Systems for Smart Cities and Connected Transportation

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 5 July 2026 | Viewed by 2751

Special Issue Editor

Dr. Weiwei Jiang

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: Internet of Things; artificial intelligence; machine learning; big data; wireless communication; edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The convergence of artificial intelligence (AI) and the Internet of Things (IoT) is redefining how cities sense, reason with, and respond to dynamic urban environments. By embedding machine learning pipelines directly into distributed sensor networks, AIoT upgrades traditional IoT “data pipes” into adaptive, city-wide nervous systems that optimize energy, traffic, safety, and public health in real time; this is particularly important for connected transportation systems such as autonomous driving, vehicle-to-everything (V2X) communications, and intelligent traffic infrastructure. This Special Issue invites original studies that advance research on the sensing layer that makes these intelligent services possible for both smart city and intelligent transportation applications.

We welcome contributions on the following topics:

  1. Resource-aware AI architectures for edge and fog nodes;
  2. Novel MEMS and nano-, bio-, and hyperspectral sensors that deliver high-resolution, multi-modal data streams for AI models;
  3. On-device learning, federated analytics, and privacy-preserving inference tailored to constrained urban sensor hardware;
  4. Energy harvesting, ultra-low-power wireless, and self-calibrating systems that extend deployment lifetimes;
  5. Open datasets, benchmarks, and reproducible field trials in living labs;
  6. The security, trust, and explainability of AI decisions at extreme edges.

Review articles that critically map sensing challenges in emerging AIoT applications, such as adaptive lighting, predictive maintenance of critical infrastructure, and real-time air-quality micro-clusters, are also encouraged.

This Special Issue welcomes original research on the convergence of AI and the IoT (AIoT) in transforming urban environments. We particularly encourage studies addressing innovations in sensor hardware, resource-aware AI architectures, and intelligent transportation infrastructure (such as V2X and autonomous driving), with a focus on real-time optimization and edge computing.

Dr. Weiwei Jiang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial Internet of Things
  • edge artificial intelligence
  • federated learning
  • smart city
  • wireless sensor network
  • internet of vehicles
  • real-time urban analytics
  • intelligent transportation systems
  • V2X communication

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Published Papers (2 papers)

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Research

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25 pages, 852 KB  
Article
Hardware Implementation-Based Lightweight Privacy- Preserving Authentication Scheme for Internet of Drones Using Physically Unclonable Function
by Razan Alsulieman, Eduardo Hernandez Escobar, Richard Swilley, Ahmed Sherif, Kasem Khalil, Mohamed Elsersy and Rabab Abdelfattah
Sensors 2026, 26(7), 2224; https://doi.org/10.3390/s26072224 - 3 Apr 2026
Viewed by 629
Abstract
The Internet of Drones (IoD) has emerged as a critical extension of the Internet of Things, enabling unmanned aerial vehicles to support diverse applications, including precision agriculture, logistics, disaster monitoring, and security surveillance. Despite its rapid growth, securing IoD communications remains a significant [...] Read more.
The Internet of Drones (IoD) has emerged as a critical extension of the Internet of Things, enabling unmanned aerial vehicles to support diverse applications, including precision agriculture, logistics, disaster monitoring, and security surveillance. Despite its rapid growth, securing IoD communications remains a significant challenge due to the open wireless environment, high drone mobility, and strict computational and energy constraints. Existing authentication mechanisms either rely on computationally expensive cryptographic operations or remain validated only at the protocol or simulation level, leaving a critical gap in practical, hardware-validated solutions suitable for resource-constrained drone platforms. This gap motivates the need for a lightweight, privacy-preserving authentication scheme that is both theoretically sound and experimentally deployable on real hardware. To address this, we propose a Physically Unclonable Functions (PUF)-assisted lightweight authentication scheme for IoD environments that binds cryptographic keys to each drone’s intrinsic hardware characteristics via PUFs. The scheme employs dynamically generated pseudo-identities to conceal permanent drone identities and prevent tracking, while authentication and key agreement are achieved using efficient symmetric cryptographic primitives, including SHA-256 for key derivation and updates, AES-256 for secure communication, and lightweight XOR operations to minimize overhead. Forward secrecy is ensured through rolling key updates, and periodic renewal of PUF challenges enhances resistance to replay and modeling attacks. To validate practicality, both software-based and hardware-based implementations were developed and evaluated. The software evaluation demonstrates a low communication overhead of 708.5 bytes and an average computation time of 18.87 ms. The hardware implementation on a Nexys A7-100T FPGA operates at 100 MHz with only 12.49% LUT utilization and low dynamic power consumption of approximately 182.5 mW. These results confirm that the proposed framework achieves an effective balance between security, privacy, and efficiency. The significance of this work lies in providing a fully hardware-validated, PUF-based authentication framework specifically tailored to the real-world constraints of IoD environments, offering a practical foundation for securing next-generation drone networks. Full article
(This article belongs to the Special Issue AIoT-Enabled Intelligent Sensing Systems for Smart Cities and Connected Transportation)
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Review

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42 pages, 1024 KB  
Review
From Concrete to Code: A Survey of AI-Driven Transportation Infrastructure, Security, and Human Interaction
by Nuri Alperen Kose, Kubra Kose and Fan Liang
Sensors 2026, 26(7), 2219; https://doi.org/10.3390/s26072219 - 3 Apr 2026
Cited by 1 | Viewed by 873
Abstract
The transition to AI-driven Cyber–Physical Systems has fundamentally reshaped transportation, introducing systemic risks that transcend traditional physical boundaries. Unlike prior reviews focused on isolated technological domains, this survey proposes a novel “End-to-End” analytical framework that models the causal propagation of vulnerabilities from physical [...] Read more.
The transition to AI-driven Cyber–Physical Systems has fundamentally reshaped transportation, introducing systemic risks that transcend traditional physical boundaries. Unlike prior reviews focused on isolated technological domains, this survey proposes a novel “End-to-End” analytical framework that models the causal propagation of vulnerabilities from physical sensing hardware to human cognitive responses. Synthesizing 140 research contributions (2017–2025), we evaluate the paradigm shift from deterministic control to Generative AI and Large Language Models (Transportation 5.0). To substantiate our framework, we introduce a structured cross-layer threat matrix and mathematically formalize the technology–cognition cascade, explicitly mapping how physical layer perturbations, such as optical jamming, bypass digital edge security to trigger hazardous behavioral reactions in human drivers. We conclude that ensuring the resilience of next-generation infrastructure requires a unified analytical architecture that formally bounds hardware constraints, algorithmic safety, and human trust. Full article
(This article belongs to the Special Issue AIoT-Enabled Intelligent Sensing Systems for Smart Cities and Connected Transportation)
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