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Advances and Challenges in the Next-Generation Internet of Things (IoT)

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 14556

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Special Issue Editors

Dr. Chaojie Gu

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Guest Editor

College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Interests: LoRaWAN; LoRa; Internet of Things; IIoT; edge computing

Dr. Zhiguo Shi

E-Mail Website
Guest Editor

College of Information and Electronic Engineering, Zhejiang University, Hangzhou 310058, China
Interests: array signal processing; positioning and target tracking; mobile crowd-sensing; data acquisition in IoT; secure and privacy in IoT; signal processing for communications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Rongxing Lu

E-Mail Website
Guest Editor

Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3A 0A1, Canada
Interests: blockchain; distributed concensus; distributed ledger; smart contract; security; privacy, trust, applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last decade, significant and exciting advances have been achieved in the Internet of Things (IoT) research community. The next-generation IoT will empower a broad spectrum of applications and redefine the challenges and frontiers of IoT research. Therefore, this Special Issue will be dedicated to recent advances in the next-generation IoT.

This Special Issue is an interdisciplinary forum that brings together academic, industrial, and government researchers and practitioners to discuss issues, innovations, and new directions in developing a next-generation IoT. We would like to draw special attention to the following growth areas that have recently reshaped our field, including, but not limited to:

Machine learning and edge AI;
AR/VR and metaverse;
digital twins for networked systems;
IoT security and privacy;
network protocols for IoT;
wearable or embedded networked frontends;
signal processing for IoT;
Industrial IoT (IIoT) systems;
IIoT network protocols;
localization and tracking;
and deployment experiences.

Dr. Chaojie Gu
Dr. Zhiguo Shi
Dr. Rongxing Lu
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. 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

Internet of Things
networked systems
IoT protocols
industrial IoT
AIoT

Published Papers (12 papers)

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Research

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17 pages, 621 KiB

Open AccessArticle

Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT

by Shunfang Hu, Shaoping Jiang, Qing Miao, Fan Yang, Weihong Zhou and Peng Duan

Appl. Sci. 2024, 14(8), 3187; https://doi.org/10.3390/app14083187 - 10 Apr 2024

Viewed by 394

Abstract

With the rise of the Internet of Things (IoT), maintaining data confidentiality and protecting user privacy have become increasingly challenging. End devices in the IoT are often deployed in unattended environments and connected to open networks, making them vulnerable to physical tampering and other security attacks. Different authentication key agreement (AKA) schemes have been used in practice; several of them do not cover the necessary security features or are incompatible with resource-constrained end devices. Their security proofs have been performed under the Random-Oracle model. We present an AKA protocol for end devices and servers. The proposal leverages the ECC-based key exchange mechanism and one-way hash function-based message authentication method to achieve mutual authentication, user anonymity, and forward security. A formal security proof of the proposed scheme is performed under the standard model and the eCK model with the elliptic curve encryption computational assumptions, and formal verification is performed with ProVerif. According to the performance comparison, it is revealed that the proposed scheme offers user anonymity, perfect forward security, and mutual authentication, and resists typical attacks such as ephemeral secret leakage attacks, impersonation attacks, man-in-the-middle attacks, and key compromise impersonation attacks. Moreover, the proposed scheme has the lowest computational and communication overhead compared to existing schemes. Full article

(This article belongs to the Special Issue Advances and Challenges in the Next-Generation Internet of Things (IoT))

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27 pages, 8523 KiB

Open AccessArticle

A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs

by Jin Li, Tao Han, Wenyang Guan and Xiaoqin Lian

Appl. Sci. 2024, 14(5), 2151; https://doi.org/10.3390/app14052151 - 04 Mar 2024

Viewed by 627

Abstract

With the development and popularization of Intelligent Transportation Systems (ITS), Vehicle Ad-Hoc Networks (VANETs) have attracted extensive attention as a key technology. In order to achieve real-time monitoring, VANET technology enables vehicles to collect real-time traffic updates through information collection devices and transmit this information to Roadside Units (RSUs), which are processed and integrated by an information processing center. However, high vehicle density leads to a conflict between minimizing the interval for vehicles to send Basic Safety Messages (BSMs) to RSUs and the limited communication resources of VANETs. To address this issue, we propose a MAC protocol based on the 802.11 CSMA/CA mechanism with the Preemptive-Resume Priority scheme. The arbitration device provides preemptive service to data packets with higher priority levels, thereby reducing data transmission delay. Moreover, queuing theory is employed to calculate the total delay for vehicles to send BSMs to a drone receiver, minimizing the BSM transmission interval and achieving minimal delay to meet safety driving requirements. The effectiveness and superiority of this mechanism and algorithm are demonstrated through simulation experiments. Full article

(This article belongs to the Special Issue Advances and Challenges in the Next-Generation Internet of Things (IoT))

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24 pages, 5198 KiB

Open AccessArticle

Privacy Protection Method for Blockchain Transactions Based on the Stealth Address and the Note Mechanism

by Zeming Wei, Jiawen Fang, Zhicheng Hong, Yu Zhou, Shansi Ma, Junlang Zhang, Chufeng Liang, Gansen Zhao and Hua Tang

Appl. Sci. 2024, 14(4), 1642; https://doi.org/10.3390/app14041642 - 18 Feb 2024

Viewed by 712

Abstract

Blockchain is a distributed ledger technology that possesses characteristics such as decentralization, tamper resistance, and programmability. However, while blockchain ensures transaction openness and transparency, transaction privacy is also at risk of being exposed. Therefore, this paper proposes the blockchain transaction privacy protection method based on the stealth address and the note mechanism to address the privacy leakage risk in blockchain public environments. Firstly, the proposed method generates a random seed known only to the parties involved based on the Diffie–Hellman key exchange protocol, ensuring the privacy of transactions. Then, it utilizes the Note Commitments table to maintain the binding relationship between the stealth address and the corresponding note, enabling efficient transfer and verification of note ownership. The uniqueness of the stealth address is utilized as an invalidation identifier for notes in the Nullifier table, ensuring efficient verification of the correctness of note invalidation identifiers. Additionally, this method employs Pedersen commitment and Bulletproofs range proof to generate proof of the legality of transaction amounts, enabling the concealment of transaction amounts and facilitating private transactions between the parties involved. Finally, this paper presents a detailed performance analysis, implementation, and testing of the method. From the results, it can be concluded that the method proposed can effectively prevent fraudulent behavior by various transaction participants and ensure the security, privacy, and integrity of the transaction. Critical processes consume only milliseconds, and the related commitments and proofs are also minimal, which is crucial for controlling transaction costs. At the same time, this method achieves a completely decentralized privacy transaction solution. Full article

(This article belongs to the Special Issue Advances and Challenges in the Next-Generation Internet of Things (IoT))

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18 pages, 7618 KiB

Open AccessArticle

Adaptive Orthogonal Basis Function Detection Method for Unknown Magnetic Target Motion State

by Zitong Wang, Enrang Zheng, Jianguo Liu and Tuo Guo

Appl. Sci. 2024, 14(2), 902; https://doi.org/10.3390/app14020902 - 20 Jan 2024

Viewed by 734

Abstract

Traditional methods of orthogonal basis function decomposition have been extensively used to detect magnetic anomaly signals. However, the determination of the relative velocity between the detection platform and the magnetic target remains elusive in practical detection. And, the non-ideal uniform motion of the magnetic target further complicates the process and adversely impacts the detector’s performance. To address this challenge, this paper introduces an adaptive scale factor method based on orthogonal basis function decomposition. This new method can be used to adjust the relative velocity between the detection platform and the magnetic target and to refine the characteristic time in the basis functions. In this paper, a mathematical relationship between the scale factor and the relative velocity is established, which is subsequently fitted into a Gauss function curve. The optimal scale factor, denoted as β, is adaptively chosen from the fitting curve when the magnetic target moves at a non-ideal uniform velocity with an unknown motion state. The results of simulations indicate that the scale factor improves the signal-to-noise ratio of the magnetic anomaly signals in a non-ideal state. And, this method can improve the energy value of OBF decomposition by 17.7%. Simultaneously, this method ensures that the moment the magnetic target passes the CPA coincides with the energy peak of the orthogonal basis detection, which improves the accuracy by 54.1% compared with the traditional method. The effectiveness and precision of the proposed method are verified using simulations and practical experiments. Full article

(This article belongs to the Special Issue Advances and Challenges in the Next-Generation Internet of Things (IoT))

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16 pages, 2333 KiB

Open AccessArticle

A LiDAR Multi-Object Detection Algorithm for Autonomous Driving

by Shuqi Wang and Meng Chen

Appl. Sci. 2023, 13(23), 12747; https://doi.org/10.3390/app132312747 - 28 Nov 2023

Cited by 1 | Viewed by 1015

Abstract

Three-dimensional object detection is the core of an autonomous driving perception system, which detects and analyzes targets around the vehicle to obtain their sizes, shapes, and categories to provide reliable operational decisions for achieving autonomous driving. To improve the detection and localization accuracy of multi-object targets such as surrounding vehicles and pedestrians in autonomous driving scenarios, based on PointPillars fast object detection network, a three-dimensional object detection algorithm based on the channel attention mechanism, ECA Modules-PointPillars, is proposed. Firstly, the improved algorithm uses point cloud columnarization features to convert a three-dimensional point cloud into a two-dimensional pseudo-image. Then, combining the 2D backbone network for feature extraction with the Efficient Channel Attention (ECA) modules to achieve the enhancement of the positional feature information in the pseudo-image and the weakening of the irrelevant feature information such as background noise. Finally, the single-shot multibox detector (SSD) algorithm was used to complete the 3D object detection task. The experimental results show that the improved algorithm improves the mAP by 3.84% and 4.04% in BEV mode and 3D mode, respectively, compared to PointPillars, which improves the mAP by 4.64% and 5.89% in BEV mode and 3D mode, respectively, compared to F-PointNet, improves the mAP by 11.78% and 14.19% in BEV mode and 3D mode, respectively, compared to VoxelNet, and improves the mAP by 9.47% and 6.55% in BEV mode and 3D mode, respectively, compared to SECOND, demonstrating the effectiveness and reliability of the improved algorithms in autonomous driving scenarios. Full article

(This article belongs to the Special Issue Advances and Challenges in the Next-Generation Internet of Things (IoT))

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18 pages, 2774 KiB

Open AccessArticle

An Intrusion Detection Method Based on Hybrid Machine Learning and Neural Network in the Industrial Control Field

by Duo Sun, Lei Zhang, Kai Jin, Jiasheng Ling and Xiaoyuan Zheng

Appl. Sci. 2023, 13(18), 10455; https://doi.org/10.3390/app131810455 - 19 Sep 2023

Viewed by 812

Abstract

Aiming at the imbalance of industrial control system data and the poor detection effect of industrial control intrusion detection systems on network attack traffic problems, we propose an ETM-TBD model based on hybrid machine learning and neural network models. Aiming at the problem of high dimensionality and imbalance in the amount of sample data in the massive data of industrial control systems, this paper proposes an IG-based feature selection method and an oversampling method for SMOTE. In the ETM-TBD model, we propose a hyperparameter optimization method based on Bayesian optimization used to optimize the parameters of the four basic machine learners in the model. By introducing a multi-head-attention mechanism, the Transformer module increases the attention between local features and global features, enabling the discovery of the internal relationship between features. Additionally, the BiGRU is used to preserve the temporal features of the dataset, while the DNN is used to extract deeper features. Finally, the SoftMax classifier is used to classify the output. By analyzing the results of the comparison and ablation experiments, it can be concluded that the F1-score of the ETM-TBD model on a robotic arm dataset is 0.9665 and the model has very low FNR and FPR scores of 0.0263 and 0.0081, respectively. It can be seen that the model in this paper is better than the traditional single machine learning algorithm as well as the algorithm lacking any of the modules. Full article

(This article belongs to the Special Issue Advances and Challenges in the Next-Generation Internet of Things (IoT))

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23 pages, 8164 KiB

Open AccessArticle

LoRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh

by Bin Li, Yihao Xu, Ying Liu and Zhiguo Shi

Appl. Sci. 2023, 13(17), 9501; https://doi.org/10.3390/app13179501 - 22 Aug 2023

Cited by 3 | Viewed by 1718

Abstract

The positioning task of the Internet of Things (IoT) for outdoor environments requires that the node devices meet the requirements of low power consumption, long endurance, and low cost and that the positioning system can achieve high-precision positioning and wide-area coverage. Considering that traditional IoT positioning technology cannot balance the cost, energy consumption, and positioning performance well, a Wide-Area Positioning System Based on Long Range Mesh (LoRaWAPS), which is a low-cost and low-power outdoor positioning system with multi-anchor wireless mesh networking and multi-dimensional data fusion, is designed in this paper. To meet the need for a positioning system, a low-power consumption and high-reliability LoRa Mesh protocol is proposed. Aiming at the problem that the accuracy of LoRa ranging is easily affected by the non-line-of-sight (NLOS) path propagation of signals, a distance estimation algorithm based on the fusion of time of flight (TOF) and received signal strength indicator (RSSI) multi-sampling data is proposed. Furthermore, a position estimation algorithm is designed to minimize the posteriori RSSI error for multi-anchor cooperative estimation scenarios. Furthermore, the prototype of LoRaWAPS is built and tested in the campus environment. The experimental results show that the proposed system can provide reliable location service with low power and low cost. Full article

(This article belongs to the Special Issue Advances and Challenges in the Next-Generation Internet of Things (IoT))

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18 pages, 3999 KiB

Open AccessArticle

Mobile Charging Scheduling Approach for Wireless Rechargeable Sensor Networks Based on Multiple Discrete-Action Space Deep Q-Network

by Chengpeng Jiang, Shuai Chen, Jinglin Li, Haoran Wang, Jing Wang, Taian Xu and Wendong Xiao

Appl. Sci. 2023, 13(14), 8513; https://doi.org/10.3390/app13148513 - 23 Jul 2023

Viewed by 768

Abstract

Wireless energy transfer technology (WET)-enabled mobile charging provides an innovative strategy for energy replenishment in wireless rechargeable sensor networks (WRSNs), where the mobile charger (MC) can charge the sensors sequentially by WET according to the mobile charging scheduling scheme. Although there have been fruitful studies, they usually assume that all sensors will be charged fully once scheduled or charged to a fixed percentage determined by a charging upper threshold, resulting in low charging performance as they cannot adjust the charging operation on each sensor adaptively according to the real-time charging demands. To tackle this challenge, we first formulate the mobile charging scheduling as a joint mobile charging sequence scheduling and charging upper threshold control problem (JSSTC), where the charging upper threshold of each sensor can adjust adaptively. Then, we propose a novel multi-discrete action space deep Q-network approach for JSSTC (MDDRL-JSSTC), where MC is regarded as an agent exploring the environment. The state information observed by MC at each time step is encoded to construct a high-dimensional vector. Furthermore, a two-dimensional action is mapped to the charging destination of MC and the corresponding charging upper threshold at the next time step, using bidirectional gated recurrent units (Bi-GRU). Finally, we conduct a series of experiments to verify the superior performance of the proposed approach in prolonging the lifetime compared with the state-of-the-art approaches. Full article

(This article belongs to the Special Issue Advances and Challenges in the Next-Generation Internet of Things (IoT))

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20 pages, 3332 KiB

Open AccessArticle

Offloading Strategy of Multi-Service and Multi-User Edge Computing in Internet of Vehicles

by Hongwei Zhao, Jingyue You, Yangyang Wang and Xike Zhao

Appl. Sci. 2023, 13(10), 6079; https://doi.org/10.3390/app13106079 - 15 May 2023

Cited by 2 | Viewed by 995

Abstract

An edge computing offloading strategy was proposed with the goal of addressing the issue of low edge computing efficiency and service quality in the multi-service and multi-user intersections of networked vehicles. This strategy took into account all relevant factors, including the matching of users and service nodes, offloading ratio, bandwidth and computing power resource allocation, and system energy consumption. It is mainly divided into 2 tasks: (1) Service node selection: A fuzzy logic-based service node selection algorithm (SNFLC) is proposed. The linear equation for node performance value is determined through fuzzy reasoning by specifying three performance indexes as input. Gradient descent method is used to find the optimal value of the objective function, and the Lyapunov criterion coefficient is introduced to improve the stability of the algorithm. (2) Offload ratio and resource allocation are solved: The coupling between offload ratio and bandwidth resource allocation is confirmed by relaxing integer variables because the optimization goal problem is a NP problem, and the issue is divided into two sub-problems. At the same time, a low-complexity alternate iteration resource allocation algorithm (LC-IRA) is proposed to solve the bandwidth resource and computational power resource allocation. According to simulation results, the performance of genetic ant colony algorithm (G_ACA), non orthogonal multiple access technology (NOMA) and LC-IRA are improved by 26.5%, 31.37%, and 45.52%, respectively, compared with the random unloading allocation (RUA) and average distribution (AD). Full article

(This article belongs to the Special Issue Advances and Challenges in the Next-Generation Internet of Things (IoT))

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19 pages, 8016 KiB

Open AccessArticle

Non-Contact and Non-Intrusive Add-on IoT Device for Wireless Remote Elevator Control

by Elena Rubies, Ricard Bitriá, Eduard Clotet and Jordi Palacín

Appl. Sci. 2023, 13(6), 3971; https://doi.org/10.3390/app13063971 - 21 Mar 2023

Cited by 3 | Viewed by 2351

Abstract

This work proposes an Internet-of-Things (IoT) device for remote elevator control. The new contribution of this proposal to the state-of-the-art is that it can convert a manually operated elevator into a remote controlled elevator without requiring any intrusive manipulation or wiring connection in the elevator. This IoT device has been designed as an add-on non-contact tool which is placed over the original elevator button panel, using servomotors to press the original buttons. This design allows its fast deployment as a remote control tool that increases elevator accessibility through the use of messages, a webpage or a QR code. Some application examples of this proposal are non-contact use of elevators in pandemic conditions, and the unsupervised use of elevators by autonomous cleaning or delivery mobile robots. The experimental evaluation of the IoT device in real operational conditions has validated its non-contact control features. Full article

(This article belongs to the Special Issue Advances and Challenges in the Next-Generation Internet of Things (IoT))

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26 pages, 3276 KiB

Open AccessArticle

Honesty-Based Social Technique to Enhance Cooperation in Social Internet of Things

by Shad Muhammad, Muhammad Muneer Umar, Shafiullah Khan, Nabil A. Alrajeh and Emad A. Mohammed

Appl. Sci. 2023, 13(5), 2778; https://doi.org/10.3390/app13052778 - 21 Feb 2023

Cited by 2 | Viewed by 1337

Abstract

The Social Internet of Things (SIoT) can be seen as integrating the social networking concept into the Internet of Things (IoT). Such networks enable different devices to form social relationships among themselves depending on pre-programmed rules and the preferences of their owners. When SIoT devices encounter one another on the spur of the moment, they seek out each other’s assistance. The connectivity of such smart objects reveals new horizons for innovative applications empowering objects with cognizance. This enables smart objects to socialize with each other based on mutual interests and social aspects. Trust building in social networks has provided a new perspective for providing services to providers based on relationships like human ones. However, the connected IoT nodes in the community may show a lack of interest in forwarding packets in the network communication to save their resources, such as battery, energy, bandwidth, and memory. This act of selfishness can highly degrade the performance of the network. To enhance the cooperation among nodes in the network a novel technique is needed to improve the performance of the network. In this article, we address the issue of the selfishness of the nodes through the formation of a credible community based on honesty. A social process is used to form communities and select heads in these communities. The selected community heads having social attributes prove effective in determining the social behavior of the nodes as honest or selfish. Unlike other schemes, the dishonest nodes are isolated in a separate domain, and they are given several chances to rejoin the community after increasing their honesty levels. The proposed social technique was simulated using MATLAB and compared with existing schemes to show its effectiveness. Our proposed technique outperforms the existing techniques in terms of throughput, overhead, packet delivery ratio (PDR), and packet-delivery latency. Full article

(This article belongs to the Special Issue Advances and Challenges in the Next-Generation Internet of Things (IoT))

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Other

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25 pages, 3950 KiB

Open AccessSystematic Review

Feature Engineering and Model Optimization Based Classification Method for Network Intrusion Detection

by Yujie Zhang and Zebin Wang

Appl. Sci. 2023, 13(16), 9363; https://doi.org/10.3390/app13169363 - 18 Aug 2023

Cited by 2 | Viewed by 1760

Abstract

In light of the escalating ubiquity of the Internet, the proliferation of cyber-attacks, coupled with their intricate and surreptitious nature, has significantly imperiled network security. Traditional machine learning methodologies inherently exhibit constraints in effectively detecting and classifying multifarious cyber threats. Specifically, the surge in high-dimensional network traffic data and the imbalanced distribution of classes exacerbate the predicament of ideal classification performance. Notably, the presence of redundant information within network traffic data undermines the accuracy of classifiers. To address these challenges, this study introduces a novel approach for intrusion detection classification which integrates advanced techniques of feature engineering and model optimization. The method employs a feature engineering approach that leverages mutual information maximum correlation minimum redundancy (mRMR) feature selection and synthetic minority class oversampling technique (SMOTE) to process network data. This transformation of raw data into more meaningful features effectively addresses the complexity and diversity inherent in network data, enhancing classifier accuracy by reducing feature redundancy and mitigating issues related to class imbalance and the detection of rare attacks. Furthermore, to optimize classifier performance, the paper applies the Optuna method to fine-tune the hyperparameters of the Catboost classifier, thereby determining the optimal model configuration. The study conducts binary and multi-classification experiments using publicly available datasets, including NSL_KDD, UNSW-NB15, and CICIDS-2017. Experimental results demonstrate that the proposed method outperforms traditional approaches regarding accuracy, recall, precision, and F-value. These findings highlight the method’s potential and performance in network intrusion detection. Full article

(This article belongs to the Special Issue Advances and Challenges in the Next-Generation Internet of Things (IoT))

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