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Advanced Wireless Sensor Network Deployment in Smart Cities, Industry 4.0, and Agriculture 4.0 (2nd Edition)

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 6047

Special Issue Editor

Prof. Dr. Rafael Asorey-Cacheda

E-Mail Website
Guest Editor
Department of Information and Communication Technologies, Universidad Politécnica de Cartagena (UPCT), Campus Muralla del Mar, E-30202 Cartagena, Spain
Interests: wireless networks; internet of things; nanocommunications; streaming services
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless sensor networks (WSNs) are a well-established and sound ICT technology consisting of an arbitrarily large number of low-cost, small, portable, low-consumption, and connected devices that work collaboratively to offer a wide range of pervasive and ubiquitous smart services and innovative applications. Under this technological umbrella, advances in WSNs have become the cornerstone of recent paradigms such as the Internet of Things (IoT). The deployment of WSNs has already created new opportunities for the development of smart cities, Industry 4.0, and Agriculture 4.0, among other relevant sectors. This has enabled groundbreaking applications that represent a step forward to significantly improving our quality of life and promoting the progress of industry and agriculture. In fact, WSN deployment in these environments still has open issues that require attention from the community. The topics of interest include, but are not limited to, the following:

  • Internet of Things breakthroughs in smart cities, Industry 4.0, and Agriculture 4.0;
  • Low-power wide-area network solutions for smart cities, Industry 4.0, and Agriculture 4.0 applications;
  • Mobility management, roaming, and advanced routing techniques for WSNs and low-power wide-area networks;
  • Crowdsourcing/crowdsensing in IoT networks;
  • Artificial intelligence and machine learning applied to IoT networks;
  • Security and privacy issues in IoT networks for smart cities, Industry 4.0, and Agriculture 4.0.

Prof. Dr. Rafael Asorey-Cacheda
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

  • internet of things (IoT)
  • wireless sensor networks
  • WSN
  • artificial intelligence

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Related Special Issue

Published Papers (3 papers)

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Research

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22 pages, 12886 KB  
Article
Digital Twin Prospects in IoT-Based Human Movement Monitoring Model
by Gulfeshan Parween, Adnan Al-Anbuky, Grant Mawston and Andrew Lowe
Sensors 2025, 25(21), 6674; https://doi.org/10.3390/s25216674 - 1 Nov 2025
Viewed by 657
Abstract
Prehabilitation programs for abdominal pre-operative patients are increasingly recognized for improving surgical outcomes, reducing post-operative complications, and enhancing recovery. Internet of Things (IoT)-enabled human movement monitoring systems offer promising support in mixed-mode settings that combine clinical supervision with home-based independence. These systems enhance [...] Read more.
Prehabilitation programs for abdominal pre-operative patients are increasingly recognized for improving surgical outcomes, reducing post-operative complications, and enhancing recovery. Internet of Things (IoT)-enabled human movement monitoring systems offer promising support in mixed-mode settings that combine clinical supervision with home-based independence. These systems enhance accessibility, reduce pressure on healthcare infrastructure, and address geographical isolation. However, current implementations often lack personalized movement analysis, adaptive intervention mechanisms, and real-time clinical integration, frequently requiring manual oversight and limiting functional outcomes. This review-based paper proposes a conceptual framework informed by the existing literature, integrating Digital Twin (DT) technology, and machine learning/Artificial Intelligence (ML/AI) to enhance IoT-based mixed-mode prehabilitation programs. The framework employs inertial sensors embedded in wearable devices and smartphones to continuously collect movement data during prehabilitation exercises for pre-operative patients. These data are processed at the edge or in the cloud. Advanced ML/AI algorithms classify activity types and intensities with high precision, overcoming limitations of traditional Fast Fourier Transform (FFT)-based recognition methods, such as frequency overlap and amplitude distortion. The Digital Twin continuously monitors IoT behavior and provides timely interventions to fine-tune personalized patient monitoring. It simulates patient-specific movement profiles and supports dynamic, automated adjustments based on real-time analysis. This facilitates adaptive interventions and fosters bidirectional communication between patients and clinicians, enabling dynamic and remote supervision. By combining IoT, Digital Twin, and ML/AI technologies, the proposed framework offers a novel, scalable approach to personalized pre-operative care, addressing current limitations and enhancing outcomes. Full article
(This article belongs to the Special Issue Advanced Wireless Sensor Network Deployment in Smart Cities, Industry 4.0, and Agriculture 4.0 (2nd Edition))
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37 pages, 4178 KB  
Article
An AI-Based Integrated Multi-Sensor System with Edge Computing for the Adaptive Management of Human–Wildlife Conflict
by Mirosław Hajder, Janusz Kolbusz and Mateusz Liput
Sensors 2025, 25(20), 6415; https://doi.org/10.3390/s25206415 - 17 Oct 2025
Viewed by 1047
Abstract
Escalating Human–Wildlife Conflict (HWC), particularly involving protected large carnivores such as the wolf, poses a significant challenge in Europe. This problem, exacerbated by ecological pressure, necessitates the development of innovative, non-lethal, and effective prevention methods that overcome the limitations of current passive solutions, [...] Read more.
Escalating Human–Wildlife Conflict (HWC), particularly involving protected large carnivores such as the wolf, poses a significant challenge in Europe. This problem, exacerbated by ecological pressure, necessitates the development of innovative, non-lethal, and effective prevention methods that overcome the limitations of current passive solutions, such as habituation. This article presents the design and implementation of a prototype for an autonomous, multi-sensory preventive system. Its three-layer architecture is based on a decentralized network of sensory-deterrent nodes that utilize Edge AI for real-time species detection and adaptive selection of deterrent stimuli. During field validation, the prototype’s biological efficacy as a proof-of-concept was confirmed in a crop protection scenario against the European roe deer (Capreolus capreolus). The system’s deployment led to a near-total elimination of damages. The paper also presents key technical performance metrics (e.g., response time, energy consumption) and the accuracy of the implemented AI detection model, verified using both field and historical data. The positive test results demonstrate that the developed platform provides an effective and flexible foundation for preventive systems. Its successful validation on a common herbivore species represents a crucial, measurable step toward the target implementation and further research on the system’s effectiveness in providing protection against large carnivores. Full article
(This article belongs to the Special Issue Advanced Wireless Sensor Network Deployment in Smart Cities, Industry 4.0, and Agriculture 4.0 (2nd Edition))
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25 pages, 737 KB  
Systematic Review
A Systematic Literature Review on the Implementation and Challenges of Zero Trust Architecture Across Domains
by Sadaf Mushtaq, Muhammad Mohsin and Muhammad Mujahid Mushtaq
Sensors 2025, 25(19), 6118; https://doi.org/10.3390/s25196118 - 3 Oct 2025
Cited by 1 | Viewed by 3937
Abstract
The Zero Trust Architecture (ZTA) model has emerged as a foundational cybersecurity paradigm that eliminates implicit trust and enforces continuous verification across users, devices, and networks. This study presents a systematic literature review of 74 peer-reviewed articles published between 2016 and 2025, spanning [...] Read more.
The Zero Trust Architecture (ZTA) model has emerged as a foundational cybersecurity paradigm that eliminates implicit trust and enforces continuous verification across users, devices, and networks. This study presents a systematic literature review of 74 peer-reviewed articles published between 2016 and 2025, spanning domains such as cloud computing (24 studies), Internet of Things (11), healthcare (7), enterprise and remote work systems (6), industrial and supply chain networks (5), mobile networks (5), artificial intelligence and machine learning (5), blockchain (4), big data and edge computing (3), and other emerging contexts (4). The analysis shows that authentication, authorization, and access control are the most consistently implemented ZTA components, whereas auditing, orchestration, and environmental perception remain underexplored. Across domains, the main challenges include scalability limitations, insufficient lightweight cryptographic solutions for resource-constrained systems, weak orchestration mechanisms, and limited alignment with regulatory frameworks such as GDPR and HIPAA. Cross-domain comparisons reveal that cloud and enterprise systems demonstrate relatively mature implementations, while IoT, blockchain, and big data deployments face persistent performance and compliance barriers. Overall, the findings highlight both the progress and the gaps in ZTA adoption, underscoring the need for lightweight cryptography, context-aware trust engines, automated orchestration, and regulatory integration. This review provides a roadmap for advancing ZTA research and practice, offering implications for researchers, industry practitioners, and policymakers seeking to enhance cybersecurity resilience. Full article
(This article belongs to the Special Issue Advanced Wireless Sensor Network Deployment in Smart Cities, Industry 4.0, and Agriculture 4.0 (2nd Edition))
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