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Challenges and Future Trends of Artificial Internet of Things

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 6098

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Special Issue Editors

Prof. Dr. Dapeng Wu

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Guest Editor

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: wireless body area network; wireless networks; privacy and security
Special Issues, Collections and Topics in MDPI journals

Dr. Zhidu Li

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Guest Editor

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: vehicular edge computing; internet of vehicle
Special Issues, Collections and Topics in MDPI journals

Dr. Boran Yang

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Guest Editor

School of Artificial Intelligence, Chongqing University of Technology, Chongqing 400054, China
Interests: edge computing; edge resource sharing; and network security

Special Issue Information

Dear Colleagues,

In recent years, scholars and industry experts have proposed integrating AI and cloud–edge–terminal collaborations into a highly functional underpinning infrastructure for intelligent IoT applications. Artificial intelligence of things (AIoT) has become a hot research area to realize real-time information acquisition through IoT sensors and perform intelligent data analysis tasks anywhere along the terminal–edge–cloud continuum, to form a smart and enabling ecosystem that brings not only extensive economic benefits but also social welfare.

Although the de facto AIoT solutions have been deployed in many application fields (smart homes, smart cities, eHealth services, auto-piloting vehicles, industrial control, etc.), there are some scientific and technical issues regarding the unique features of AIoT, including communication protocols, resource management, task assignments, data analytics, network security, etc. To address the challenges in these directions, effective and efficient technologies are urgently needed for AIoT. As a cutting-edge concept, AIoT is expected to develop and evolve even more rapidly and fruitfully, with its future trends thoroughly investigated and inspiringly identified.

This Special Issue aims to discuss future trends and address the challenges in AIoT. Suggested topics include, but are not limited to, the following.

Framework designs for AIoT;
Sensing technologies for AIoT;
Knowledge inference technologies for AIoT;
Key technologies and core applications for AIoT;
Trusted, secure, and privacy computing system designs for AIoT;
Big data analytics for AIoT;
Prototypes and case studies for AIoT;
Performance benchmarks for AIoT;
Algorithm design for AIoT;
Prospects for next-generation IoT.

Prof. Dr. Dapeng Wu
Dr. Zhidu Li
Dr. Boran Yang
Guest Editors

Manuscript Submission Information

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Keywords

artificial intelligence of things
internet of things
connected things
edge computing
smart cities
eHealth
big data

Published Papers (6 papers)

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Research

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17 pages, 1844 KiB

Open AccessArticle

Tamper Detection in Industrial Sensors: An Approach Based on Anomaly Detection

by William Villegas-Ch, Jaime Govea and Angel Jaramillo-Alcazar

Sensors 2023, 23(21), 8908; https://doi.org/10.3390/s23218908 - 02 Nov 2023

Viewed by 1026

Abstract

The Industrial Revolution 4.0 has catapulted the integration of advanced technologies in industrial operations, where interconnected systems rely heavily on sensor information. However, this dependency has revealed an essential vulnerability: Sabotaging these sensors can lead to costly and dangerous interruptions in the production chain. To address this threat, we introduce an innovative methodological approach focused on developing an anomaly detection algorithm specifically designed to track manipulations in industrial sensors. Through a series of meticulous tests in an industrial environment, we validate the robustness and accuracy of our proposal. What distinguishes this study is its unique adaptability to various sensor conditions, achieving high detection accuracy and prompt response. Our algorithm demonstrates superiority in accuracy and sensitivity compared to previously established methodologies. Beyond detection, we incorporate a proactive alert and response system, guaranteeing timely action against detected anomalies. This work offers a tangible solution to a growing challenge. It lays the foundation for strengthening security in industrial systems of the digital age, harmonizing efficiency with protection in the Industry 4.0 landscape. Full article

(This article belongs to the Special Issue Challenges and Future Trends of Artificial Internet of Things)

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22 pages, 8705 KiB

Open AccessArticle

Gearbox Compound Fault Diagnosis in Edge-IoT Based on Legendre Multiwavelet Transform and Convolutional Neural Network

by Xiaoyang Zheng, Lei Chen, Chengbo Yu, Zijian Lei, Zhixia Feng and Zhengyuan Wei

Sensors 2023, 23(21), 8669; https://doi.org/10.3390/s23218669 - 24 Oct 2023

Cited by 1 | Viewed by 728

Abstract

The application of edge computing combined with the Internet of Things (edge-IoT) has been rapidly developed. It is of great significance to develop a lightweight network for gearbox compound fault diagnosis in the edge-IoT context. The goal of this paper is to devise a novel and high-accuracy lightweight neural network based on Legendre multiwavelet transform and multi-channel convolutional neural network (LMWT-MCNN) to fast recognize various compound fault categories of gearbox. The contributions of this paper mainly lie in three aspects: The feature images are designed based on the LMWT frequency domain and they are easily implemented in the MCNN model to effectively avoid noise interference. The proposed lightweight model only consists of three convolutional layers and three pooling layers to further extract the most valuable fault features without any artificial feature extraction. In a fully connected layer, the specific fault type of rotating machinery is identified by the multi-label method. This paper provides a promising technique for rotating machinery fault diagnosis in real applications based on edge-IoT, which can largely reduce labor costs. Finally, the PHM 2009 gearbox and Paderborn University bearing compound fault datasets are used to verify the effectiveness and robustness of the proposed method. The experimental results demonstrate that the proposed lightweight network is able to reliably identify the compound fault categories with the highest accuracy under the strong noise environment compared with the existing methods. Full article

(This article belongs to the Special Issue Challenges and Future Trends of Artificial Internet of Things)

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18 pages, 1490 KiB

Open AccessArticle

Channel Prediction-Based Security Authentication for Artificial Intelligence of Things

by Xiaoying Qiu, Jinwei Yu, Wenying Zhuang, Guangda Li and Xuan Sun

Sensors 2023, 23(15), 6711; https://doi.org/10.3390/s23156711 - 27 Jul 2023

Cited by 1 | Viewed by 837

Abstract

The emerging physical-layer unclonable attribute-aided authentication (PLUA) schemes are capable of outperforming traditional isolated approaches, with the advantage of having reliable fingerprints. However, conventional PLUA methods face new challenges in artificial intelligence of things (AIoT) applications owing to their limited flexibility. These challenges arise from the distributed nature of AIoT devices and the involved information, as well as the requirement for short end-to-end latency. To address these challenges, we propose a security authentication scheme that utilizes intelligent prediction mechanisms to detect spoofing attack. Our approach is based on a dynamic authentication method using long short term memory (LSTM), where the edge computing node observes and predicts the time-varying channel information of access devices to detect clone nodes. Additionally, we introduce a Savitzky–Golay filter-assisted high order cumulant feature extraction model (SGF-HOCM) for preprocessing channel information. By utilizing future channel attributes instead of relying solely on previous channel information, our proposed approach enables authentication decisions. We have conducted extensive experiments in actual industrial environments to validate our prediction-based security strategy, which has achieved an accuracy of 97%. Full article

(This article belongs to the Special Issue Challenges and Future Trends of Artificial Internet of Things)

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21 pages, 1367 KiB

Open AccessArticle

Delay-Informed Intelligent Formation Control for UAV-Assisted IoT Application

by Lihan Liu, Mengjiao Xu, Zhuwei Wang, Chao Fang, Zhensong Li, Meng Li, Yang Sun and Huamin Chen

Sensors 2023, 23(13), 6190; https://doi.org/10.3390/s23136190 - 06 Jul 2023

Cited by 1 | Viewed by 1061

Abstract

Multiple unmanned aerial vehicles (UAVs) have a greater potential to be widely used in UAV-assisted IoT applications. UAV formation, as an effective way to improve surveillance and security, has been extensively of concern. The leader–follower approach is efficient for UAV formation, as the whole formation system needs to find only the leader’s trajectory. This paper studies the leader–follower surveillance system. Owing to different scenarios and assignments, the leading velocity is dynamic. The inevitable communication time delays resulting from information sending, communicating and receiving process bring challenges in the design of real-time UAV formation control. In this paper, the design of UAV formation tracking based on deep reinforcement learning (DRL) is investigated for high mobility scenarios in the presence of communication delay. To be more specific, the optimization UAV formation problem is firstly formulated to be a state error minimization problem by using the quadratic cost function when the communication delay is considered. Then, the delay-informed Markov decision process (DIMDP) is developed by including the previous actions in order to compensate the performance degradation induced by the time delay. Subsequently, an extended-delay informed deep deterministic policy gradient (DIDDPG) algorithm is proposed. Finally, some issues, such as computational complexity analysis and the effect of the time delay are discussed, and then the proposed intelligent algorithm is further extended to the arbitrary communication delay case. Numerical experiments demonstrate that the proposed DIDDPG algorithm can significantly alleviate the performance degradation caused by time delays. Full article

(This article belongs to the Special Issue Challenges and Future Trends of Artificial Internet of Things)

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17 pages, 802 KiB

Open AccessArticle

Preference-Aware User Access Control Policy in Internet of Things

by Songnong Li, Yao Yan, Yongliang Ji, Wenxin Peng, Lingyun Wan and Puning Zhang

Sensors 2023, 23(13), 5989; https://doi.org/10.3390/s23135989 - 28 Jun 2023

Viewed by 799

Abstract

There are multiple types of services in the Internet of Things, and existing access control methods do not consider situations wherein the same types of services have multiple access options. In order to ensure the QoS quality of user access and realize the reasonable utilization of Internet of Things network resources, it is necessary to consider the characteristics of different services to design applicable access control strategies. In this paper, a preference-aware user access control strategy in slices is proposed, which can increase the number of users in the system while balancing slice resource utilization. First, we establish the user QoS model and slice QoS index range according to the delay, rate and reliability requirements, and we select users with multiple access options. Secondly, a user preference matrix is established according to the user QoS requirements and the slice QoS index range. Finally, a preference matrix of the slice is built according to the optimization objective, and access control decisions are made for users through the resource utilization state of the slice and the preference matrix. The verification results show that the proposed strategy not only balances slice resource utilization but also increases the number of users who can access the system. Full article

(This article belongs to the Special Issue Challenges and Future Trends of Artificial Internet of Things)

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Review

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26 pages, 22141 KiB

Open AccessReview

The Role of Artificial Intelligence of Things in Achieving Sustainable Development Goals: State of the Art

by Georgios Lampropoulos, Juan Garzón, Sanjay Misra and Kerstin Siakas

Sensors 2024, 24(4), 1091; https://doi.org/10.3390/s24041091 - 07 Feb 2024

Viewed by 868

Abstract

With the environmental and societal changes, the achievement of sustainable development goals (SDGs) and the realization of sustainability in general is now more important than ever. Through a bibliometric analysis and scientific mapping analysis, this study aims to explore and provide a review regarding the role of artificial intelligence (AI), the Internet of Things (IoT), and artificial intelligence of things (AIoT) in realizing sustainable development and achieving SDGs. AIoT can be defined as the combination of AI with IoT to create more efficient and data-driven interconnected, intelligent, and autonomous IoT systems and infrastructure that use AI methods and algorithms. The analysis involved 9182 documents from Scopus and Web of Science (WoS) from 1989 to 2022. Descriptive statistics of the related documents and the annual scientific production were explored. The most relevant and impactful authors, articles, outlets, affiliations, countries, and keywords were identified. The most popular topics and research directions throughout the years and the advancement of the field and the research focus were also examined. The study examines the results, discusses the main findings, presents open issues, and suggests new research directions. Based on the results of this study, AIoT emerged as an important contributor in ensuring sustainability and in achieving SDGs. Full article

(This article belongs to the Special Issue Challenges and Future Trends of Artificial Internet of Things)

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