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Smart Sensors and Devices: Recent Advances and Applications Volume II
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Smart Sensors and Devices: Recent Advances and Applications Volume II

A special issue of Applied System Innovation (ISSN 2571-5577).

Deadline for manuscript submissions: 30 May 2025 | Viewed by 1068

Special Issue Editor

Prof. Dr. Subhas Mukhopadhyay

E-Mail Website
Guest Editor
School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: drones; robots; swarm drones; swarm robotics; IoT; smart sensors; mechatronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensors play an important role and are the brains and hearts of intelligent systems, such as unmanned aerial vehicles, autonomous guided vehicles, drones, and mobile robots. With the development of nanomaterials, advanced signal processing techniques, smart interfacing electronics, low-power embedded processors, long-range communication technologies, and multidisciplinary interactions, increasingly more smart sensors and devices are being proposed and fabricated under increasing demands from homes, the industry, the environment, and military fields. This Special Issue will report on the development of the above technologies and different applications including, but not limited to, the following:

  • Smart sensors;
  • Sensing technology;
  • Wireless sensors;
  • Smart devices;
  • IoT sensors and devices;
  • Intelligent processing;
  • Machine learning;
  • Artificial intelligence;
  • Smart homes;
  • Smart environments;
  • Healthcare;
  • Environmental monitoring;
  • Industrial applications.

You may choose our Joint Special Issue in Applied Sciences.

Prof. Dr. Subhas Mukhopadhyay
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 System Innovation 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 1400 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

  • smart sensors
  • sensing technology
  • wireless sensors
  • smart devices
  • iot sensors and devices
  • intelligent processing
  • machine learning
  • artificial intelligence
  • smart homes
  • smart environments
  • healthcare
  • environmental monitoring
  • industrial applications

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

Published Papers (2 papers)

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27 pages, 10754 KiB  
Article
Efficient and Explainable Human Activity Recognition Using Deep Residual Network with Squeeze-and-Excitation Mechanism
by Sakorn Mekruksavanich and Anuchit Jitpattanakul
Appl. Syst. Innov. 2025, 8(3), 57; https://doi.org/10.3390/asi8030057 - 24 Apr 2025
Viewed by 171
Abstract
Wearable sensors for human activity recognition (HAR) have gained significant attention across multiple domains, such as personal health monitoring and intelligent home systems. Despite notable advancements in deep learning for HAR, understanding the decision-making process of complex models remains challenging. This study introduces [...] Read more.
Wearable sensors for human activity recognition (HAR) have gained significant attention across multiple domains, such as personal health monitoring and intelligent home systems. Despite notable advancements in deep learning for HAR, understanding the decision-making process of complex models remains challenging. This study introduces an advanced deep residual network integrated with a squeeze-and-excitation (SE) mechanism to improve recognition accuracy and model interpretability. The proposed model, ConvResBiGRU-SE, was tested using the UCI-HAR and WISDM datasets. It achieved remarkable accuracies of 99.18% and 98.78%, respectively, surpassing existing state-of-the-art methods. The SE mechanism enhanced the model’s ability to focus on essential features, while gradient-weighted class activation mapping (Grad-CAM) increased interpretability by highlighting essential sensory data influencing predictions. Additionally, ablation experiments validated the contribution of each component to the model’s overall performance. This research advances HAR technology by offering a more transparent and efficient recognition system. The enhanced transparency and predictive accuracy may increase user trust and facilitate smoother integration into real-world applications. Full article
(This article belongs to the Special Issue Smart Sensors and Devices: Recent Advances and Applications Volume II)
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18 pages, 1562 KiB  
Article
Enhanced Grey Wolf Optimization for Efficient Transmission Power Optimization in Wireless Sensor Network
by Mohamad Nurkamal Fauzan, Rendy Munadi, Sony Sumaryo and Hilal Hudan Nuha
Appl. Syst. Innov. 2025, 8(2), 36; https://doi.org/10.3390/asi8020036 - 14 Mar 2025
Viewed by 477
Abstract
The Internet of Things (IoT) and Wireless Sensor Networks (WSNs) heavily rely on the lifetime of sensor nodes, which is inversely proportional to transmission power. Nodes with greater separation demand higher transmission power, while those closer together require less power. In practice, node [...] Read more.
The Internet of Things (IoT) and Wireless Sensor Networks (WSNs) heavily rely on the lifetime of sensor nodes, which is inversely proportional to transmission power. Nodes with greater separation demand higher transmission power, while those closer together require less power. In practice, node placement varies significantly due to diverse terrain and contours, making power transmission configuration a critical and challenging issue in WSNs. This paper introduces an Enhanced Grey Wolf Optimization (EGWO) algorithm designed to optimize power transmission in WSN environments. Traditional Grey Wolf Optimization (GWO) employs a parameter that decreases linearly with iterations to regulate exploitation. In contrast, the proposed EGWO adopts a concave decline in the exploitation rate, allowing for more precise optimization in areas under exploration. The enhancement utilizes a cosine function that gradually decreases from 1 to 0, providing a smoother and more controlled transition. The experimental results demonstrate that EGWO outperforms other optimization algorithms. The proposed method achieves the lowest fitness value of −4.21, compared to 1.22 for standard GWO, −2.81 for PSO, and 2.86 for BESO, indicating its superiority in optimizing power transmission in WSNs. Full article
(This article belongs to the Special Issue Smart Sensors and Devices: Recent Advances and Applications Volume II)
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