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Advances in Wireless Sensor and Mobile Networks
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Advances in Wireless Sensor and Mobile Networks

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1392

Special Issue Editors

Dr. Princy Johnson

E-Mail Website
Guest Editor
School of Engineering, Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: wireless sensor; mobile networks; sensors; intelligent algorithms; Internet of Things
Prof. Dr. Mitra Mirhassani

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada
Interests: hardware realization of neural networks; hardware security; analog and mixed-signal integrated circuits
Prof. Dr. Daniel Gutiérrez Reina

E-Mail Website
Guest Editor
Department of Electronic Engineering, Camino de Los Descubrimientos, s/n, University of Seville, 41009 Sevilla, Spain
Interests: evolutionary algorithms; wireless communications; ad hoc networks; UAVs; vehicular ad hoc networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent years have seen unprecedented advances in the area of wireless sensors and mobile networks due to technological advancements both in the realms of hardware and software. This has opened up huge opportunities for new areas of application, and has brought its own challenges to the field. For example, wireless robotics networks, including aerial, aquatic, and terrestrial vehicle networks, will impose new challenges in the requirements of operational ability of mobile networks. Classical approaches developed for static networks are no longer efficient for the high mobility requirement of the networks. Therefore, new approaches that work across network layers should be developed In addition, growth in areas such as Artificial Intelligence, including machine learning and deep learning techniques, has opened up possibilities that have not been imagined before. AI can leverage a massive amount and diversity of data generated by actual sensors, allowing for new approaches in the application layer. For this to happen, however, sensor networks need to manage data and compute data-driven algorithms with scarce resources, as well as efficient AI algorithms that are suitable for those networks to be developed. Moreover, Internet of Things architectures can alleviate the burdens of computational power by relying on the central servers that run data-based algorithms. In this way, edge computing and federated learning are paradigms to be applied in the new generation of wireless sensor and mobile networks.

This Special Issue, therefore, aims to collect original research and review articles on recent advances, technologies, solutions, applications, and challenges in the field of wireless sensors and networks.

Potential topics of interest include, but are not limited to:

  • Network architectures.
  • Internet of Things.
  • Smart Cities.
  • Edge computing.
  • Cross-layer optimization.
  • Performance optimization.
  • Artificial intelligence.
  • Federated and distributed learning.
  • Quality of Service.
  • Security and Privacy.
  • Emerging Technologies.

Dr. Princy Johnson
Prof. Dr. Mitra Mirhassani
Prof. Dr. Daniel Gutiérrez Reina
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • wireless sensors
  • network architectures
  • internet of things
  • smart cities
  • security and privacy

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

32 pages, 2840 KiB  
Article
Multi-Feature Unsupervised Domain Adaptation (M-FUDA) Applied to Cross Unaligned Domain-Specific Distributions in Device-Free Human Activity Classification
by Muhammad Hassan and Tom Kelsey
Sensors 2025, 25(6), 1876; https://doi.org/10.3390/s25061876 - 18 Mar 2025
Viewed by 315
Abstract
Human–computer interaction (HCI) drives innovation by bridging humans and technology, with human activity recognition (HAR) playing a key role. Traditional HAR systems require user cooperation and infrastructure, raising privacy concerns. In recent years, Wi-Fi devices have leveraged channel state information (CSI) to decode [...] Read more.
Human–computer interaction (HCI) drives innovation by bridging humans and technology, with human activity recognition (HAR) playing a key role. Traditional HAR systems require user cooperation and infrastructure, raising privacy concerns. In recent years, Wi-Fi devices have leveraged channel state information (CSI) to decode human movements without additional infrastructure, preserving privacy. However, these systems struggle with unseen users, new environments, and scalability, thereby limiting real-world applications. Recent research has also demonstrated that the impact of surroundings causes dissimilar variations in the channel state information at different times of the day. In this paper, we propose an unsupervised multi-source domain adaptation technique that addresses these challenges. By aligning diverse data distributions with target domain variations (e.g., new users, environments, or atmospheric conditions), the method enhances system adaptability by leveraging public datasets with varying domain samples. Experiments on three public CSI datasets using a preprocessing module to convert CSI into image-like formats demonstrate significant improvements to baseline methods with an average micro-F1 score of 81% for cross-user, 76% for cross-user and cross-environment, and 73% for cross-atmospheric tasks. The approach proves effective for scalable, device-free sensing in realistic cross-domain HAR scenarios. Full article
(This article belongs to the Special Issue Advances in Wireless Sensor and Mobile Networks)
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29 pages, 1608 KiB  
Article
Performance Analysis and Design Principles of Wireless Mutual Broadcast Using Heterogeneous Transmit Power for Proximity-Aware Services
by Taesoo Kwon and HyeonWoo Lee
Sensors 2024, 24(24), 8045; https://doi.org/10.3390/s24248045 - 17 Dec 2024
Viewed by 694
Abstract
As proximity-aware services among devices such as sensors, IoT devices, and user equipment are expected to facilitate a wide range of new applications in the beyond 5G and 6G era, managing heterogeneous environments with diverse node capabilities becomes essential. This paper analytically models [...] Read more.
As proximity-aware services among devices such as sensors, IoT devices, and user equipment are expected to facilitate a wide range of new applications in the beyond 5G and 6G era, managing heterogeneous environments with diverse node capabilities becomes essential. This paper analytically models and characterizes the performance of heterogeneous random access-based wireless mutual broadcast (RA-WMB) with distinct transmit (Tx) power levels, leveraging a marked Poisson point process to account for nodes’ various Tx power. In particular, this study enables the performance of RA-WMB with heterogeneous Tx power to be represented in terms of the performance of RA-WMB with a common Tx power by deriving an equivalent Tx power based on the probability distribution of heterogeneous Tx power and the path loss exponent. This approach allows for an analytical and quantitative comparison of heterogeneous RA-WMB performance with the common Tx power configuration. Further, the study derives performance ratios among node groups with distinct Tx power levels and formulates an optimization problem to design a heterogeneous Tx power configuration that balances individual node group performance improvements with overall network performance, yielding the optimal Tx power configuration. A closed-form suboptimal transmission probability (TxPr) is also proposed to improve heterogeneous RA-WMB performance, providing an efficient alternative to iterative methods for the optimal TxPr. Numerical results demonstrate the accuracy of performance analysis and highlight the effectiveness of the proposed designs. Full article
(This article belongs to the Special Issue Advances in Wireless Sensor and Mobile Networks)
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