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Smart Internet of Things (IoT)
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Smart Internet of Things (IoT)

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5564

Special Issue Editors

Prof. Dr. Tie Qiu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Tianjin University, No.135 Yaguan Road, Haihe Education Park, Tianjin 300050, China
Interests: internet of things; big data; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wenbing Zhao

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, USA
Interests: computational intelligence; Internet of Things; big data
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chen Chen

E-Mail Website
Guest Editor
School of Telecommunication Engineering, Xidian University, Xi’an 710071, China
Interests: Internet of Things; intelligent transportation systems; satellite communications; space-air-ground cooperation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) plays an important role in the current and future generation of information, network, and communication development and applications. Smart IoT is an exciting emerging research field that has great potential to transform both our understanding of fundamental computer science principles and our standard of living. IoT is being employed in more and more areas, making “Everything Smart”, such as smart homes, smart cities, intelligent transportation, environment monitoring, security systems, and advanced manufacturing. It is crucial to be able to quickly analyze and learn from the massive amount of generated data. Current approaches for big data analytics require full data transfer to a platform with large computational power, such as the cloud. Given the projected explosion in the number of devices and the resulting data generation rate, this is not feasible. In addition, many other open problems and challenges still exist, such as AI-built security issues, cloud attacks, and botnet problems. The International Conference on Smart Internet of Things (SmartIoT) and the International Conference on Information Science and Technology (ICIST) focus on these challenges.

This Special Issue is dedicated to publishing original research papers which propose new methodologies, new research directions and discuss approaches or schemes for current existing issues. The key innovation is to use devices and computing power within the Internet of Things network itself to perform data analysis in a scalable, reliable fashion.

Papers from the conferences SmartIoT2022 and WASA2022 are especially welcomed.

Topics of interests include, but are not limited to:

  • IoT Sensing, monitoring, networking and routing
  • Big data analysis and cloud computing
  • Edge computing/fog computing
  • Smart cities, intelligent transportation and internet of vehicles
  • Artificial intelligence, machine learning and evolutionary computing
  • Social networks, multimedia and mobile computing
  • Blockchain and emerging research or technologies
  • Industrial 4.0 and industrial IoT
  • Security and privacy for smart IoT or CPS
  • Control and decision making for smart IoT or CPS

Prof. Dr. Tie Qiu
Prof. Dr. Wenbing Zhao
Prof. Dr. Chen Chen
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.

Published Papers (4 papers)

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Research

23 pages, 1213 KiB  
Article
Optimizing Internet of Things Fog Computing: Through Lyapunov-Based Long Short-Term Memory Particle Swarm Optimization Algorithm for Energy Consumption Optimization
by Sheng Pan, Chenbin Huang, Jiajia Fan, Zheyan Shi, Junjie Tong and Hui Wang
Sensors 2024, 24(4), 1165; https://doi.org/10.3390/s24041165 - 10 Feb 2024
Viewed by 563
Abstract
In the era of continuous development in Internet of Things (IoT) technology, smart services are penetrating various facets of societal life, leading to a growing demand for interconnected devices. Many contemporary devices are no longer mere data producers but also consumers of data. [...] Read more.
In the era of continuous development in Internet of Things (IoT) technology, smart services are penetrating various facets of societal life, leading to a growing demand for interconnected devices. Many contemporary devices are no longer mere data producers but also consumers of data. As a result, massive amounts of data are transmitted to the cloud, but the latency generated in edge-to-cloud communication is unacceptable for many tasks. In response to this, this paper introduces a novel contribution—a layered computing network built on the principles of fog computing, accompanied by a newly devised algorithm designed to optimize user tasks and allocate computing resources within rechargeable networks. The proposed algorithm, a synergy of Lyapunov-based, dynamic Long Short-Term Memory (LSTM) networks, and Particle Swarm Optimization (PSO), allows for predictive task allocation. The fog servers dynamically train LSTM networks to effectively forecast the data features of user tasks, facilitating proper unload decisions based on task priorities. In response to the challenge of slower hardware upgrades in edge devices compared to user demands, the algorithm optimizes the utilization of low-power devices and addresses performance limitations. Additionally, this paper considers the unique characteristics of rechargeable networks, where computing nodes acquire energy through charging. Utilizing Lyapunov functions for dynamic resource control enables nodes with abundant resources to maximize their potential, significantly reducing energy consumption and enhancing overall performance. The simulation results demonstrate that our algorithm surpasses traditional methods in terms of energy efficiency and resource allocation optimization. Despite the limitations of prediction accuracy in Fog Servers (FS), the proposed results significantly promote overall performance. The proposed approach improves the efficiency and the user experience of Internet of Things systems in terms of latency and energy consumption. Full article
(This article belongs to the Special Issue Smart Internet of Things (IoT))
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15 pages, 2011 KiB  
Article
Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0
by Lawrence He, Mark Eastburn, James Smirk and Hong Zhao
Sensors 2023, 23(12), 5754; https://doi.org/10.3390/s23125754 - 20 Jun 2023
Cited by 3 | Viewed by 1796
Abstract
Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by connecting patients, [...] Read more.
Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by connecting patients, medical devices, hospitals, clinics, medical suppliers, and other healthcare-related components. Body chemical sensor and biosensor networks (BSNs) provide the necessary platform for Healthcare 4.0 to collect various medical data from patients. BSN is the foundation of Healthcare 4.0 in raw data detection and information collecting. This paper proposes a BSN architecture with chemical sensors and biosensors to detect and communicate physiological measurements of human bodies. These measurement data help healthcare professionals to monitor patient vital signs and other medical conditions. The collected data facilitates disease diagnosis and injury detection at an early stage. Our work further formulates the problem of sensor deployment in BSNs as a mathematical model. This model includes parameter and constraint sets to describe patient body characteristics, BSN sensor features, as well as biomedical readout requirements. The proposed model’s performance is evaluated by multiple sets of simulations on different parts of the human body. Simulations are designed to represent typical BSN applications in Healthcare 4.0. Simulation results demonstrate the impact of various biofactors and measurement time on sensor selections and readout performance. Full article
(This article belongs to the Special Issue Smart Internet of Things (IoT))
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21 pages, 3627 KiB  
Article
A Practice-Distributed Thunder-Localization System with Crowd-Sourced Smart IoT Devices
by Bingxian Lu, Ruochen Wang, Zhenquan Qin and Lei Wang
Sensors 2023, 23(9), 4186; https://doi.org/10.3390/s23094186 - 22 Apr 2023
Viewed by 1195
Abstract
Lightning localization is of great significance to weather forecasting, forest fire prevention, aviation, military, and other aspects. Traditional lightning localization requires the deployment of base stations and expensive measurement equipment. With the development of IoT technology and the continuous expansion of application scenarios, [...] Read more.
Lightning localization is of great significance to weather forecasting, forest fire prevention, aviation, military, and other aspects. Traditional lightning localization requires the deployment of base stations and expensive measurement equipment. With the development of IoT technology and the continuous expansion of application scenarios, IoT devices can be interconnected through sensors and other technical means to ultimately achieve the goal of automatic intelligent computing. Therefore, this paper proposes a low-cost distributed thunder-localization system based on IoT smart devices, namely ThunderLoc. The main idea of ThunderLoc is to collect dual-microphone data from IoT smart devices, such as smartphones or smart speakers, through crowdsourcing, turning the localization problem into a search problem in Hamming space. We studied the dual microphones integrated with smartphones and used the sign of Time Difference Of Arrival (TDOA) as measurement information. Through a simple generalized cross-correlation method, the TDOA of thunderclaps on the same smartphone can be estimated. After quantifying the TDOA measurement from the smartphone node, thunder localization was performed by minimizing the Hamming distance between the binary sequence and the binary vector measured in a database. The ThunderLoc system was evaluated through extensive simulations and experiments (a testbed with 30 smartphone nodes). The extensive experimental results demonstrate that ThunderLoc outperforms the main existing schemes in terms of effectively locating position and good robustness. Full article
(This article belongs to the Special Issue Smart Internet of Things (IoT))
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17 pages, 27408 KiB  
Communication
Generated Image Editing Method Based on Global-Local Jacobi Disentanglement for Machine Learning
by Jianlong Zhang, Xincheng Yu, Bin Wang and Chen Chen
Sensors 2023, 23(4), 1815; https://doi.org/10.3390/s23041815 - 06 Feb 2023
Viewed by 1190
Abstract
Accurate semantic editing of the generated images is extremely important for machine learning and sample enhancement of big data. Aiming at the problem of semantic entanglement in generated image latent space of the StyleGAN2 network, we proposed a generated image editing method based [...] Read more.
Accurate semantic editing of the generated images is extremely important for machine learning and sample enhancement of big data. Aiming at the problem of semantic entanglement in generated image latent space of the StyleGAN2 network, we proposed a generated image editing method based on global-local Jacobi disentanglement. In terms of global disentanglement, we extract the weight matrix of the style layer in the pre-trained StyleGAN2 network; obtain the semantic attribute direction vector by using the weight matrix eigen decomposition method; finally, utilize this direction vector as the initialization vector for the Jacobi orthogonal regularization search algorithm. Our method improves the speed of the Jacobi orthogonal regularization search algorithm with the proportion of effective semantic attribute editing directions. In terms of local disentanglement, we design a local contrast regularized loss function to relax the semantic association local area and non-local area and utilize the Jacobi orthogonal regularization search algorithm to obtain a more accurate semantic attribute editing direction based on the local area prior MASK. The experimental results show that the proposed method achieves SOTA in semantic attribute disentangled metrics and can discover more accurate editing directions compared with the mainstream unsupervised generated image editing methods. Full article
(This article belongs to the Special Issue Smart Internet of Things (IoT))
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