sensors-logo

Journal Browser

Journal Browser

New Trends in Artificial Intelligence of Things

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

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 3165

Special Issue Editors


E-Mail Website
Guest Editor
Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Interests: AIoT; artificial intelligence pervasive computing; cyber physical system; robotics; urban sensing brain computer interface; human computer interface
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Computer Science, Southeast University, Nanjing, China
Interests: data analytics; internet of things
Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology, Beijing 100081, China
Interests: reinforcement learning; intelligent estimation and control of CPSs; neural network learning system; distributed adaptive control

Special Issue Information

Dear Colleagues,

With the development and integration of artificial intelligence and Internet of Things technology, the new era of Artificial Intelligence of Things has made tremendous progress. Artificial Intelligence of Things is being deployed in many applications, such as indoor positioning and navigation, anonymous environmental monitoring, human–machine interaction sensing, and even fine-grained activity and gesture recognition, providing intelligent advanced services to improve quality of life. To achieve the Artificial Intelligence of Things, various intelligent sensors, multi-process information fusion technologies, and other advanced artificial intelligence and collaborative methods are required. Intelligent sensors, as the foundation of Artificial Intelligence of Things, are developing towards high precision, high sensitivity, miniaturization, micro-miniaturization, and intelligence. Many researchers are designing intelligent sensors based on the concept of integrating sensing, storage, and computing using new materials. Information fusion, as a promising technology in the field of Artificial Intelligence of Things, is constantly developing and providing better data support and a decision-making basis for the application of artificial intelligence. In addition, information fusion is also promoting the development of other research fields, including intelligent unmanned systems, mobile computing, etc. Recently, many researchers have tried to obtain better information and fuse sensing data based on signal processing and estimation theory, statistical information theory, decision theory, etc. With this progress, the application of intelligent information fusion technology in intelligent sensing and intelligent environments has been extended, such as in human–machine interaction sensing, intrusion detection, and autonomous environmental monitoring, to improve human quality of life.

This Special Issue aims to solicit original research articles from researchers in the academic and industrial communities to discuss their contributions to intelligent sensors, information fusion, and their applications in intelligent sensing. In addition, this issue encourages authors to discuss new trends in Artificial Intelligence of Things. This Special Issue will allow readers to identify the latest developments and applications in intelligent sensors and information fusion. Topics of interests include (but are not limited to) the following categories:

  • Smart/multi-functional sensor design and testing
  • Novel information fusion methods for smart sensing
  • The security and privacy for sensing network
  • Fairness, equity, and transparency issues in IoT and CPS
  • Machine learning and deep learning on sensor data
  • Computer vision for resource-constrained and mobile platforms
  • Modeling of big data from multi-sensor systems
  • Artificial intelligence technology in multi-sensor information fusion
  • Data fusion based on artificial intelligence
  • Integration of fuzzy logic and neural network interfaces in distributed sensors
  • Protocols and standards for smart sensing
  • Data acquisition and storage in collaborative sensors
  • Advanced Intelligent sensing principles for multi-sensor coupling
  • Resource-efficient machine learning and AI for mobile devices
  • Systems for location and context sensing and awareness
  • Mobile computing support for pervasive computing
  • Novel applications of sensing technologies in cyber-physical systems
  • Data-drive-based machine learning for sensor modeling and sensor data processing 

Dr. Xinlei Chen
Prof. Dr. Shuai Wang
Dr. Yuezu Lv
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 (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

49 pages, 1345 KiB  
Article
A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning
by Tesfahunegn Minwuyelet Mengistu, Taewoon Kim and Jenn-Wei Lin
Sensors 2024, 24(3), 968; https://doi.org/10.3390/s24030968 - 01 Feb 2024
Cited by 1 | Viewed by 948
Abstract
Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play [...] Read more.
Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements. Full article
(This article belongs to the Special Issue New Trends in Artificial Intelligence of Things)
Show Figures

Figure 1

12 pages, 317 KiB  
Communication
Distributed NN-Based Formation Control of Multi-Agent Systems: A Reduced-Order Appointed-Time Observer Approach
by Yuting Feng, Shuai Sun, Yuezu Lv and Changhao Sun
Sensors 2024, 24(2), 589; https://doi.org/10.3390/s24020589 - 17 Jan 2024
Viewed by 495
Abstract
Although the formation control of multi-agent systems has been widely investigated from various aspects, the problem is still not well resolved, especially for the case of distributed output-feedback formation controller design without input information exchange among neighboring agents. Using relative output information, this [...] Read more.
Although the formation control of multi-agent systems has been widely investigated from various aspects, the problem is still not well resolved, especially for the case of distributed output-feedback formation controller design without input information exchange among neighboring agents. Using relative output information, this paper presents a novel distributed reduced-order estimation of the formation error at a predefined time. Based on the proposed distributed observer, a neural-network-based formation controller is then designed for multi-agent systems with connected graphs. The results are verified by both theoretical demonstration and simulation example. Full article
(This article belongs to the Special Issue New Trends in Artificial Intelligence of Things)
Show Figures

Figure 1

22 pages, 12069 KiB  
Article
Smart Public Transportation Sensing: Enhancing Perception and Data Management for Efficient and Safety Operations
by Tianyu Zhang, Xin Jin, Song Bai, Yuxin Peng, Ye Li and Jun Zhang
Sensors 2023, 23(22), 9228; https://doi.org/10.3390/s23229228 - 16 Nov 2023
Viewed by 1144
Abstract
The use of cloud computing, big data, IoT, and mobile applications in the public transportation industry has resulted in the generation of vast and complex data, of which the large data volume and data variety have posed several obstacles to effective data sensing [...] Read more.
The use of cloud computing, big data, IoT, and mobile applications in the public transportation industry has resulted in the generation of vast and complex data, of which the large data volume and data variety have posed several obstacles to effective data sensing and processing with high efficiency in a real-time data-driven public transportation management system. To overcome the above-mentioned challenges and to guarantee optimal data availability for data sensing and processing in public transportation perception, a public transportation sensing platform is proposed to collect, integrate, and organize diverse data from different data sources. The proposed data perception platform connects multiple data systems and some edge intelligent perception devices to enable the collection of various types of data, including traveling information of passengers and transaction data of smart cards. To enable the efficient extraction of precise and detailed traveling behavior, an efficient field-level data lineage exploration method is proposed during logical plan generation and is integrated into the FlinkSQL system seamlessly. Furthermore, a row-level fine-grained permission control mechanism is adopted to support flexible data management. With these two techniques, the proposed data management system can support efficient data processing on large amounts of data and conducts comprehensive analysis and application of business data from numerous different sources to realize the value of the data with high data safety. Through operational testing in real environments, the proposed platform has proven highly efficient and effective in managing organizational operations, data assets, data life cycle, offline development, and backend administration over a large amount of various types of public transportation traffic data. Full article
(This article belongs to the Special Issue New Trends in Artificial Intelligence of Things)
Show Figures

Figure 1

Back to TopTop