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Applied Sciences
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Machine Learning and Computational Intelligence in Sensors, Signals and Networks
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Machine Learning and Computational Intelligence in Sensors, Signals and Networks

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 1371

Special Issue Editor

Dr. Li Xie

E-Mail Website
Guest Editor
College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
Interests: information fusion; embedded computing; machine learning; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning and computational intelligence have revolutionized the way we process and analyze data. In recent years, these techniques have been widely applied in the fields of sensors, signals, and networks, leading to significant advancements in various domains, including smart cities, intelligent transportation, smart healthcare, next-generation communication networks and communication, etc.

To showcase the latest research in this area, we are pleased to announce a Special Issue titled "Machine Learning and Computational Intelligence in Sensors, Signals and Networks". This Special Issue will aim to collate the work of researchers and practitioners and encourage them to share their innovative ideas, methodologies, and applications related to machine learning and computational intelligence.

The significance of this Special Issue lies in its potential to enhance the accuracy, efficiency, and reliability of information processing and analysis.  It can also enable the development of intelligent systems that can adapt to changing environments and make informed decisions.

We welcome submissions from researchers working in this exciting field.  Topics of interest include but are not limited to the following: deep learning, neural networks, fuzzy logic, evolutionary computing, and swarm intelligence and their applications in sensors, signals, and networks.

We look forward to receiving your contributions, whether in theory or practice, and publishing high-quality papers that will advance research in this field.

  • Smart cities;
  • Intelligent surveillance;
  • Smart building；
  • Intelligent manufacturing；
  • Next-generation communication;
  • Smart health.

Dr. Li Xie
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 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. Applied Sciences 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 2400 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

  • machine learning
  • computational intelligence
  • artificial intelligence
  • deep learning
  • applications in sensors, signals, and networks

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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

23 pages, 5703 KiB  
Article
Localization of Sensor Nodes in 3D Wireless Sensor Networks with a Single Anchor by an Improved Adaptive Artificial Bee Colony (iaABC) Algorithm
by Dursun Ekmekci and Hüseyin Altınkaya
Appl. Sci. 2025, 15(7), 3548; https://doi.org/10.3390/app15073548 - 24 Mar 2025
Viewed by 194
Abstract
In terms of optimization, one of the core challenges in Wireless Sensor Networks is determining the locations of nodes. While simulating this problem in a 3D environment instead of the traditional 2D increases problem complexity, it is crucial for accurately representing real-world scenarios. [...] Read more.
In terms of optimization, one of the core challenges in Wireless Sensor Networks is determining the locations of nodes. While simulating this problem in a 3D environment instead of the traditional 2D increases problem complexity, it is crucial for accurately representing real-world scenarios. Furthermore, the success of locating moving nodes in a 3D space is closely linked to the overall efficiency of the network. This study proposes a solution that can detect the locations of target nodes at various levels using a single anchor node. The method employs the Improved Adaptive Artificial Bee Colony (iaABC) algorithm, a model of the classical ABC algorithm. This improvement updates the control parameter values during the scanning, allowing the algorithm to focus its search direction on better exploitation. The performance of the search and convergence of this method was tested on CEC 2022 test suits. The CEC 2022 benchmark functions have more up-to-date content and are fairer because they utilize the same initial solutions for each competing algorithm. Subsequently, the approach was used to determine node locations. The results demonstrated that iaABC can locate 100 target nodes with a single anchor in a 3D environment. Full article
(This article belongs to the Special Issue Machine Learning and Computational Intelligence in Sensors, Signals and Networks)
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16 pages, 6260 KiB  
Article
Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
by Xiaoyong Liu, Zhiyong Yang and Bowen Shi
Appl. Sci. 2025, 15(2), 614; https://doi.org/10.3390/app15020614 - 10 Jan 2025
Viewed by 598
Abstract
Weigh-in-motion systems can measure the number of axles to obtain a vehicle’s type and upper limit of weight, which, combined with the weight measured by the system, can be used for highway toll collection and overload management. This paper proposes a new modular [...] Read more.
Weigh-in-motion systems can measure the number of axles to obtain a vehicle’s type and upper limit of weight, which, combined with the weight measured by the system, can be used for highway toll collection and overload management. This paper proposes a new modular system based on multi-sensor fusion and neural network axle recognition to address issues concerning the high failure rate of axle recognition devices and low weighing accuracy. We use a modular system consisting of multiple weighing platforms, enabling whole-vehicle-load weighing with multiple vehicles traveling through platforms. In addition, we propose a sequential generation model based on a Transformer and Gated Recurrent Unit to calculate the weighing signal generated by the weighing sensors, and then obtain the number of axles and the gross vehicle weight. Finally, the axle recognition algorithm and modular systems are tested in multiple scenarios. The accuracy of the axle recognition is 99.51% and 99.84% in the test set and the toll station, respectively. The weighing error of the modular system in the test field is less than 0.5%, and 99.18% of vehicles had an error of less than 5% at the toll station. The modular system has the advantages of high accuracy, consistent performance, and high traffic efficiency. Full article
(This article belongs to the Special Issue Machine Learning and Computational Intelligence in Sensors, Signals and Networks)
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