Applied Sciences

Research

Jump to: Other

22 pages, 7436 KiB

Open AccessArticle

Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm

by Yuhua Li and Yuanhua Liu

Appl. Sci. 2025, 15(8), 4522; https://doi.org/10.3390/app15084522 - 19 Apr 2025

Viewed by 220

Abstract

With the development of artificial intelligence technology, automatic guided vehicle (AGV) path planning is widely used in many fields. Aiming at the problems of low convergence efficiency and easy to fall into local optimization of the traditional ant colony algorithm, this paper proposes [...] Read more.

With the development of artificial intelligence technology, automatic guided vehicle (AGV) path planning is widely used in many fields. Aiming at the problems of low convergence efficiency and easy to fall into local optimization of the traditional ant colony algorithm, this paper proposes an AGV path-planning method based on the artificial potential field-ant colony algorithm. The performance of the algorithm is improved by incorporating the artificial potential field attraction to construct the potential field heuristic function, dynamically adjusting the pheromone volatility coefficient, introducing multiple parameters to dynamically adjust the pheromone increment, and optimizing the path by using the pruning method and other improvement measures. The simulation experiments in 20 × 20 and 30 × 30 grid environments show that the improved algorithm already has significant advantages over the traditional algorithm and other improved ACO algorithms in terms of path length, convergence speed and the number of path inflection points, verifying its high efficiency and stability, and providing a better solution for AGV path planning. Full article

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

► Show Figures

Figure 1

18 pages, 2170 KiB

Open AccessArticle

Multiuser Access Control for 360° VR Video Service Systems Exploiting Proactive Caching and Mobile Edge Computing

by Qiyan Weng, Yijing Tang and Hangguan Shan

Appl. Sci. 2025, 15(8), 4201; https://doi.org/10.3390/app15084201 - 10 Apr 2025

Viewed by 274

Abstract

Mobile virtual reality (VR) is considered a killer application for future mobile broadband networks. However, for cloud VR, the long content delivery path and time-varying transmission rate from the content provider’s cloud VR server to the users make the quality-of-service (QoS) provisioning for [...] Read more.

Mobile virtual reality (VR) is considered a killer application for future mobile broadband networks. However, for cloud VR, the long content delivery path and time-varying transmission rate from the content provider’s cloud VR server to the users make the quality-of-service (QoS) provisioning for VR users very challenging. To this end, in this paper, we design a 360° VR video service system that leverages proactive caching and mobile edge computing (MEC) technologies. Furthermore, we propose a multiuser access control algorithm tailored to the system, based on analytical results of the delay violation probability, which is derived considering the impact of both the multi-hop wired network from the cloud VR server to the MEC server and the wireless network from the MEC server-connected base station (BS) to the users. The proposed access control algorithm aims to maximize the number of served users by exploiting real-time and dynamic network resources, while ensuring that the end-to-end delay violation probability for each accessed user remains within an acceptable limit. Simulation results are presented to analyze the impact of diverse system parameters on both the user access probability and the delay violation probability of the accessed users, demonstrating the effectiveness of the proposed multiuser access control algorithm. It is observed in the simulation that increasing the computing capacity of the MEC server or the communication bandwidth of the BS is one of the most effective methods to accommodate more users for the system. In the tested scenarios, when the MEC server’s computing capacity (the BS’s bandwidth) increases from 0.8 Tbps (50 MHz) to 3.2 Tbps (150 MHz), the user access probability improves on average by 92.53% (85.49%). Full article

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

► Show Figures

Figure 1

29 pages, 6572 KiB

Open AccessArticle

Robust Parking Space Recognition Approach Based on Tightly Coupled Polarized Lidar and Pre-Integration IMU

by Jialiang Chen, Fei Li, Xiaohui Liu and Yuelin Yuan

Appl. Sci. 2024, 14(20), 9181; https://doi.org/10.3390/app14209181 - 10 Oct 2024

Viewed by 1260

Abstract

Improving the accuracy of parking space recognition is crucial in the fields for Automated Valet Parking (AVP) of autonomous driving. In AVP, accurate free space recognition significantly impacts the safety and comfort of both the vehicles and drivers. To enhance parking space recognition [...] Read more.

Improving the accuracy of parking space recognition is crucial in the fields for Automated Valet Parking (AVP) of autonomous driving. In AVP, accurate free space recognition significantly impacts the safety and comfort of both the vehicles and drivers. To enhance parking space recognition and annotation in unknown environments, this paper proposes an automatic parking space annotation approach with tight coupling of Lidar and Inertial Measurement Unit (IMU). First, the pose of the Lidar frame was tightly coupled with high-frequency IMU data to compensate for vehicle motion, reducing its impact on the pose transformation of the Lidar point cloud. Next, simultaneous localization and mapping (SLAM) were performed using the compensated Lidar frame. By extracting two-dimensional polarized edge features and planar features from the three-dimensional Lidar point cloud, a polarized Lidar odometry was constructed. The polarized Lidar odometry factor and loop closure factor were jointly optimized in the iSAM2. Finally, the pitch angle of the constructed local map was evaluated to filter out ground points, and the regions of interest (ROI) were projected onto a grid map. The free space between adjacent vehicle point clouds was assessed on the grid map using convex hull detection and straight-line fitting. The experiments were conducted on both local and open datasets. The proposed method achieved an average precision and recall of 98.89% and 98.79% on the local dataset, respectively; it also achieved 97.08% and 99.40% on the nuScenes dataset. And it reduced storage usage by 48.38% while ensuring running time. Comparative experiments on open datasets show that the proposed method can adapt to various scenarios and exhibits strong robustness. Full article

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

► Show Figures

Figure 1

22 pages, 2585 KiB

Open AccessArticle

Ransomware Detection Model Based on Adaptive Graph Neural Network Learning

by Jun Li, Gengyu Yang and Yanhua Shao

Appl. Sci. 2024, 14(11), 4579; https://doi.org/10.3390/app14114579 - 27 May 2024

Cited by 5 | Viewed by 2233

Abstract

Ransomware is a type of malicious software that encrypts or locks user files and demands a high ransom. It has become a major threat to cyberspace security, especially as it continues to be developed and updated at exponential rates. Ransomware detection technology has [...] Read more.

Ransomware is a type of malicious software that encrypts or locks user files and demands a high ransom. It has become a major threat to cyberspace security, especially as it continues to be developed and updated at exponential rates. Ransomware detection technology has become a focus of research on information security risk detection methods. However, current ransomware detection techniques have high false positive and false negative rates, and traditional methods ignore global word co-occurrence and correlation information between key node steps in the entire process. This poses a significant challenge for accurately identifying and detecting ransomware. We propose a ransomware detection model based on co-occurrence information adaptive diffusion learning using a Text Graph Convolutional Network (ADC-TextGCN). Specifically, ADC-TextGCN first assign self-weights to word nodes based on sensitive API call functions and preserve co-occurrence information using Point Mutual Information Theory (COIR-PMI); then our model automatically learn the optimal neighborhood through an Adaptive Diffusion Convolution (ADC) strategy, thereby improving the ability to aggregate long-distance node information across layers and enhancing the network’s ability to represent ransomware behavior. Experimental results show that our method achieves an accuracy of over 96.6% in ransomware detection, proving its effectiveness and superiority compared to traditional methods based on CNN and RNN in ransomware detection. Full article

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

► Show Figures

Figure 1

14 pages, 579 KiB

Open AccessArticle

Performance Prediction of Power Beacon-Aided Wireless Sensor-Powered Non-Orthogonal Multiple-Access Internet-of-Things Networks under Imperfect Channel State Information

by Ngoc-Long Nguyen, Anh-Tu Le, Phuong-Loan T. Nguyen, Bui Vu Minh, Lubos Rejfek and Yong-Hwa Kim

Appl. Sci. 2024, 14(11), 4498; https://doi.org/10.3390/app14114498 - 24 May 2024

Viewed by 949

Abstract

In this paper, we investigate a novel power beacon (PB)-aided wireless sensor-powered non-orthogonal multiple-access (NOMA) Internet-of-Things (IoT) network under imperfect channel state information (CSI). Furthermore, the exact expression outage probability (OP) of two IoT users is derived to analyze the performance of the [...] Read more.

In this paper, we investigate a novel power beacon (PB)-aided wireless sensor-powered non-orthogonal multiple-access (NOMA) Internet-of-Things (IoT) network under imperfect channel state information (CSI). Furthermore, the exact expression outage probability (OP) of two IoT users is derived to analyze the performance of the considered network. To give further insight, the expression asymptotic OP and diversity order are also expressed when the transmit power at the PB goes to infinity. Furthermore, a deep neural network (DNN) framework is proposed to concurrently forecast IoT users’ OP in relation to real-time setups for IoT users. Additionally, when compared to the traditional analysis, our created DNN shows the shortest run-time prediction, and the outcomes predicted by the DNN model almost match those of the simulation. In addition, numerical results validate our analysis, simulation, and prediction through a Monte Carlo Simulation. Furthermore, the results show the impact of the main parameter on our proposed system. Finally, these findings show that NOMA performs better than the conventional orthogonal multiple-access (OMA) techniques. Full article

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

► Show Figures

Figure 1

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

Cited by 8 | Viewed by 1718

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 [...] Read more.

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))

► Show Figures

Figure 1

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 - 4 Mar 2024

Cited by 2 | Viewed by 1487

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 [...] Read more.

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))

► Show Figures

Figure 1

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 2732

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 [...] Read more.

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))

► Show Figures

Figure 1

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

Cited by 3 | Viewed by 1600

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 [...] Read more.

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))

► Show Figures

Figure 1

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 6 | Viewed by 2722

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 [...] Read more.

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))

► Show Figures

Figure 1

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

Cited by 6 | Viewed by 1491

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 [...] Read more.

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))

► Show Figures

Figure 1

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 9 | Viewed by 4765

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 [...] Read more.

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))

► Show Figures

Figure 1

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

Cited by 3 | Viewed by 1467

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 [...] Read more.

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))

► Show Figures

Figure 1

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 1646

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 [...] Read more.

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))

► Show Figures

Figure 1

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 8 | Viewed by 5138

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 [...] Read more.

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))

► Show Figures

Figure 1

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 5 | Viewed by 2303

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 [...] Read more.

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))

► Show Figures

Figure 1

Other

Jump to: Research

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 12 | Viewed by 3612

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 [...] Read more.

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))

► Show Figures

Figure 1

Journal Menu

Journal Browser

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

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (17 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI