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Enabling smart parking for smart cities using Internet of Things (IoT) and machine learning

PeerJ computer Science 2025年 11卷 e2544-e2544页

作者： Alymani, Mofadal Almoqhem, Lenah Abdulaziz Alabdulwahab, Dhuha Ahmed Alghamdi, Abdulrahman Abdullah Alshahrani, Hussain Raza, Khalid Department of Computer and Network Engineering College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Department of Computer Science Jamia Millia Islamia Delhi New Delhi India

With the escalating number of vehicles and the lack of parking spaces, the issue of parking has become a significant problem in major cities as it is a daily occurrence for educational institutions, companies, and government facilities, resulting in fuel wastage and time inefficiencies. In their work lives, employees often face problems when parking their cars in the work parking area. Finding a space for their vehicle can take a lot of time and effort, leading to late arrival for work. On the other hand, security guards have difficulty entering their employees’ cars. In this context, our proposed system attempts to address this pressing issue, which consists of two parts: one is a camera at the parking gate that recognizes the license plate using the Automatic Number Plate Recognition (ANPR) algorithm, where the camera captures the license plate and outputs the plate number using the optical character recognition (OCR) technique. After that, the resulting data is cross-referenced with database records for seamless entry authentication. This eliminates the need for security personnel to verify vehicle identities or stickers manually, streamlining access procedures. The second part is a camera in the car parks that distinguishes between vacant and available parking spaces and stores the data collected by the camera in the centralized database, enabling the real-time display of the nearest available parking spots on digital screens at entrance gates, significantly reducing the time and effort spent in locating parking spaces. Through this innovative solution, we aim to enhance urban mobility and alleviate the challenges associated with urban parking congestion, thereby resolving the problem of intelligent parking for smart cities with the help of machine learning. Copyright 2025 Alymani et al. Distributed under Creative Commons CC-BY 4.0

关键词： Optical character recognition

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Optimal Multi-Bitrate Video Caching and Processing in Edge Computing: A Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年

作者： Guo, Min Zhang, Di Xing, Weiwei Shao, Xun Liu, Zhi Zhang, Yaoxue Beijing Jiaotong University School of Software Engineering Beijing100044 China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Aichi441-8580 Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan Tsinghua University Department of Computer Science and Technology Beijing100084 China

By caching and transcoding video files on edge servers, video edge caching (VEC) can alleviate network congestion and improve user experience. To achieve this, VEC needs to address resource allocation and pricing problems and develop caching and transcoding strategies. However, existing works have not effectively coordinated and resolved these issues. Towards this end, we propose a game-theoretic multi-bitrate video caching and processing method for VEC. Specifically, to address these interdependent issues concurrently, we first incorporate multidimensional strategies into the system model and formalize the profit maximization problems of the network service provider and multiple video providers (VPs) as a constrained multi-objective optimization problem. Second, we transform the problem into a Stackelberg game and identify a Nash equilibrium as the solution. To solve the game problem, we decompose the game process into two stages focusing on resource competition and price determination, respectively. Then, we address the problems of transcoding and caching strategies development, and resource allocation in parallel across varying resource price conditions. The transcoding strategy is determined based on the constraints imposed by delay, value, and the source video. For the caching strategy development and resource allocation, we transform them into two grouped knapsack problems and apply the "dominant strategy» from game theory to identify the "feasible strategy» of VPs. Finally, we determine the resource prices in a traversal manner. Theoretical analysis shows that our proposed method achieves the optimal solution. Experimental results show that our algorithm achieve a 6.04% increase in overall profit compared to the greedy strategy algorithm. © 2025 IEEE.

关键词： Costs

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Task Offloading in Internet of Vehicles: A DRL-based Approach with Representation Learning for DAG Scheduling

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5045-5060页

作者： Deng, Xiaoheng Yang, Haoyu Zhang, Jingjing Gui, Jinsong Lin, Siyu Wang, Xin Min, Geyong Central South University School of Electronic Information Changsha410075 China Central South University Shenzhen Research Institute China Beijing Jiaotong University School of Electronic and Information Engineering The State Key Laboratory of Rail Traffic Control and Safety Beijing100044 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

The rapid evolution of the Internet-of-Vehicles (IoV) has amplified the need for mobile computing resources, driving the shift toward offloading tasks to edge servers or vehicles with idle resources to optimize computational efficiency. To this end, an approach based on Deep Reinforcement Learning (DRL) is presented in this paper, termed DVTP, which integrates Variational Graph Attention networks (VGAT) and Transformer models to optimize Directed Acyclic Graph (DAG) task scheduling in vehicular networks. DVTP effectively captures both the spatiotemporal information and task dependencies, enabling more accurate and efficient task offloading decisions. Extensive simulation experiments demonstrate that DVTP outperforms traditional methods in reducing task completion times across various multi-vehicle and multi-edge server scenarios, showcasing its potential for real-world IoV applications. © 2025 IEEE.

关键词： Deep reinforcement learning

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A network Path Method for Connecting Cross-Seam Using Ground Relay in LEO Satellite network

A Network Path Method for Connecting Cross-Seam Using Ground...

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Consumer Communications and networking Conference, CCNC IEEE

作者： Heon-Woo Chu Tae-Han Moon Won-Jae Lee Jae-Hyun Kim Department of Artificial Intelligence Convergence Network Ajou University Suwon South Korea Department of Electrical and Computer Engineering Ajou University Suwon South Korea

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

This paper proposes a method to overcome the cross-seam in a low Earth orbit (LEO) satellite network. In cross-seam, satellite links cannot be established due to differences in satellite movement directions. The proposed method involves utilizing ground relay to resolve this problem. The Walker-Star constellation has polar regions and cross-seams, where inter-satellite link connection is impossible due to relative velocity. In this paper, we compare the performance results in a satellite network when ground relay is utilized in a cross-seam. We analyze the propagation delay and the number of satellite hops. It analyzes satellite communication performance with and without a ground relay to major cities from Seoul. Also, the maximum performance improvement provided by the ground relay is analyzed. As a result of the analysis, the ground relay reduces propagation delay by up to 76% and reduces satellite hop count by 89%.

关键词： Satellites Urban areas Low earth orbit satellites Delays Relays Satellite communications Propagation delay

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Off-the-Shelf OFDM-ISAC System with Clustering Algorithm for Multipath Environments

Off-the-Shelf OFDM-ISAC System with Clustering Algorithm for...

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IEEE Conference on Wireless Communications and networking

作者： Ha-Eun Lee Young-Seok Lee Bang Chul Jung Department of Artificial Intelligence Covergence Network Ajou University Suwon South Korea Department of Electrical and Computer Engineering Ajou University Suwon South Korea

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Recently, integrated sensing and communication (ISAC) techniques have gained significant attention due to their ability to simultaneously perform sensing, localization, and communication using shared wireless resources and signal waveforms, thereby enhancing spectral and cost efficiency. Orthogonal frequency division multiplexing (OFDM) is also expected to remain a key technology in next-generation mobile communication systems due to its robustness against multipath fading and its strong support for high data rates. Building on these points, we propose a novel orthogonal sequence (OS)- based OFDM-ISAC technique fully compatible with conventional OFDM-based communication systems. This technique intelligently exploits the autocorrelation properties of OSs and the spectral efficiency of index modulation while incorporating a clustering algorithm to address challenges in multipath environments. Extensive computer simulations verify that the proposed OFDM-ISAC system delivers reliable communication and localization performance in realistic multipath environments. even in the absence of a line-of-sight (LOS) link.

关键词： Location awareness Wireless communication Wireless sensor networks OFDM computer simulation Modulation Line-of-sight propagation Clustering algorithms Integrated sensing and communication Indexes

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Satellite-UAV networks for 6G Control: A Sensing-Communication-Computing-Control Closed Loop Perspective

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IEEE network 2025年

作者： Lei, Chengleyang Fang, Xinran Feng, Wei Chen, Yunfei Xiao, Ming Ge, Ning Mao, Shiwen The Department of Electronic Engineering State Key Laboratory of Space Network and Communications Tsinghua University Beijing100084 China The Department of Engineering University of Durham DurhamDH1 3LE United Kingdom The Department of Information Science and Engineering School of Electrical Engineering and Computer Science Royal Institute of Technology Sweden The Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

The future sixth-generation (6G) networks are envisioned to support machines and robots besides human beings. In order to efficiently support machines and robots working in remote and post-disaster areas, satellites and unmanned aerial vehicles (UAVs) can be utilized. In this article, we investigate how satellite-UAV networks can serve robots for their control tasks. We introduce the typical use cases of the satellite-UAV networks for control and outline the relevant challenges. The control process usually involves sensing, communication, computing, and control components, which forms a sensing-communication-computing-control (SC3) closed loop. In analogy with the reflex arc structure of humans, we reveal the indivisibility of the SC3 closed loops and propose a closed-loop-oriented system design framework. Within this framework, we take the closed-loop control performance as the design target, and highlight the holistic consideration of sensing, communication, computing, and control components in the SC3 closed loop. Furthermore, we discuss the extended SC3 closed loop with multiple sensors and robots to decompose complex control systems. A case study is provided to show the performance gain of the proposed closed-loop-oriented system design. Finally, we outline open issues in designing more efficient satellite-UAV networks for control. ©1986-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Integration of STAR-RIS With Index Modulation: Novel Attraction Solutions for IoT Applications

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IEEE Internet of Things Journal 2025年

作者： Yue, Ming Peng, Yuyang Ye, Runlong AL-Hazemi, Fawaz Mirza, Mohammad Meraj Lee, Juho Macau University of Science and Technology School of Computer Science and Engineering China University of Jeddah Department of Computer and Network Engineering Jeddah21959 Saudi Arabia Taif University College of Computers and Information Technology Department of Computer Science Taif21944 Saudi Arabia Samsung Electronics Samsung Research Seoul06765 Korea Republic of

The integration of the Sixth-Generation (6G) communication technology with the Internet of Things (IoT) is one of the directions in achieving a digitalized world, aiming to provide ultra-high-speed communication services that exhibit higher energy-efficiency, lower latency, and heightened reliability and availability. In this paper, we investigate the use of the Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS), one of the candidate technologies for 6G communication, along with the Index Modulation (IM) in multi-antennas Ambient Backscatter Communication System (ABCS) to form the STAR-RIS-assisted ABCS with Spatial Modulation (STAR-RIS-ABCS-SM). In particular, through the utilization of STAR-RIS and backscatter communication, we meet the demand for low-power wireless communication devices in the green communication, which is also a crucial characteristic of the IoT. Specifically, we introduce STAR-RIS into ABCS, which not only significantly boost the signal strength but also achieve full communication coverage. Meanwhile, we apply the SM technology in the multi-antennas Backscatter Tags (BTs), located on both sides of the STAR-RIS, and use joint detection at the reader to enhance the system’s Spectral Efficiency (SE) as well as its Bit Error Rate (BER) performance. Furthermore, we divide the STAR-RIS into various subsurfaces to form patterns that further enhance SE, which is termed STAR-RIS-assisted ABCS with Pattern Index and SM (STAR-RIS-ABCS-PISM) scheme. Finally, theoretical BER analytical expressions for both schemes are derived and presented. Simulation and theoretical results demonstrate the enhanced performance of the proposed schemes as well as the validity of the system model. © 2014 IEEE.

关键词： Spectrum efficiency

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Lossless LiDAR Point Cloud Reflectance Compression with a Deep Hierarchical KNN Context Model

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IEEE Transactions on Multimedia 2025年

作者： Hou, Lizhi Fan, Tingyu Xu, Yiling Li, Zhu Shanghai Jiao Tong University Cooperative Media Network Innovation Center Shanghai200240 China New York University Tandon School of Engineering Electrical and Computer Engineering Department BrooklynNY11201 United States University of Missouri-Kansas City Department of Computer Science and Electrical Engineering KansasMI64110 United States

Recently, numerous learning-based point cloud compression methods with outstanding performance have been developed. The majority of them concentrate on point cloud geometry compression, and several works have demonstrated advances in the color attribute compression for dense point clouds. However, compression of the reflectance attribute attached to the point captured by the light detection and ranging (LiDAR) sensors remains a major challenge. In this paper, we present a lossless reflectance compression method for LiDAR point clouds (LPCs) that learns reflectance probability distributions with a deep hierarchical k-nearest-neighbors (KNN) context model, namely, the HK-PCRC. We first represent the original LPC with a series of hierarchical layers. Relying on the hierarchical structure, points in the same layer are coded in parallel by referencing the points in the previously coded layers. The approach balances the coding efficiency and time complexity while also supporting the progressive coding functionality. By introducing the KNN context, the context size is significantly reduced, which eases the computational burden while maintaining the coding performance. To enrich the context information, we further search for enhanced neighbors for each point in the context window. For each enhanced neighbor, in addition to its reflectance value, the relative distance, elevation angle, and local density are further collected. Then, a transformer-style sequential model is applied to construct an accurate deep context model. Furthermore, to efficiently fuse context features from different sources, a cross-feature fusion attention mechanism is designed for the transformer network. The comprehensive experimental results on SemanticKITTI, a large scale LiDAR benchmark, and Ford, an MPEG-specified dataset, demonstrate that our proposed framework achieves a state-of-the-art reflectance lossless compression performance, with average bit savings of 11.3% and 9.6% when compared to the

关键词： Nearest neighbor search

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Maximizing Harvested Energy in Natural Energy Powered RF WPT with Nonlinear EH Model

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IEEE Transactions on Wireless Communications 2025年

作者： Zhang, Xiang Xiong, Ke Chen, Wei Fan, Pingyi Gao, Bo Letaief, Khaled Ben Collaborative Innovation Center of Railway Traffic Safety School of Computer Science and Technology Engineering Research Center of Network Management Technology for High Speed Railway Ministry of Education China Beijing Jiaotong University National Engineering Research Center of Advanced Network Technologies Beijing100044 China Department of Electronic Engineering China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China Department of Electrical and Computer Engineering Hong Kong Hong Kong

In the typical radio frequency (RF)-based wireless power transfer (WPT) system, the wireless power station (WPS) connected to the grid transmits energy to charge low-power sensors via radio signals. Such a system may not be green and also difficult to deploy in some special areas including deserts and mountainous areas, because it depends on the grid. To achieve a green RF WPT system design, this paper considers that the WPS is powered by natural energy sources rather than the grid. To explore the maximal total amount of the energy that can be harvested by the sensors, we focus on the offline setting, so similar to many existing works on offline optimization, we assume that the WPS knows prior knowledge about energy arrivals and channel changes, and then formulate an optimization problem to maximize the total harvested energy via optimizing the WPS's time-domain transmit power subject to multiple constraints, including the finite battery capacity at the WPS, the causal relationship between the natural energy harvesting and the WPT, and the transmit power budget of the WPS, where for practicality, the nonlinear energy harvesting (EH) model is also taken into account. To solve this non-convex problem, we first equivalently transform it by using the epigraph reformulation and the variable substitution, and then use the first-order Taylor expansion to get an approximate convex version. Then, we present a successive convex approximation (SCA)-based algorithm to improve the accuracy of the obtained solution for approaching the optimal one. For the special case with a single sensor, we further propose a branch and bound (BB)-based algorithm that is able to get a more accurate solution with lower complexity than the SCA-based one. Numerical results demonstrate that the proposed algorithms are able to achieve the near-global optimal solution. As the average recharge rate increases, compared with the other two baselines, i.e., the greedy power (GP) policy and the constant pow

关键词： Budget control

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Online Flow Scheduling in Virtualized Time-Sensitive networks: A Joint Admission Control and VNF Embedding Approach

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4651-4667页

作者： Zhang, Yajing Chen, Cailian Li, Mingyan You, Chaoqun Guan, Xinping Quek, Tony Q. S. Shanghai Satellite Network Research Institute Co. Ltd. Shanghai200120 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Chongqing University College of Computer Science Chongqing400044 China Fudan University School of Computer Science Shanghai200433 China Singapore University of Technology and Design 487372 Singapore

Time-sensitive networking (TSN) is proposed to satisfy the increasingly stringent demands of Industrial 4.0 for deterministic transmission. This is achieved by generating a series of centrally configured gate control lists to strictly restrict the forwarding time of arriving flows. However, such a centralized scheme requires prior information of all flows, severely impeding TSN from providing an online response to dynamic industrial applications. To solve this problem, we innovatively propose to use admission control (AC) to realize deterministic transmission in the virtualized TSN network. In this approach, AC is distributively executed on each node and link, whereby flows of applications are served by passing through a series of virtual network functions (VNFs). This distributed AC execution is regarded as a VNF embedding (VNE) process. Specifically, we propose a two-stage online framework, Smart Admission Control (SmartAC), to cater to dynamic applications. The first stage, referred to as the static stage, obtains a deterministic VNE solution by synthesizing AC decisions of individual TSN nodes and links. The second stage, referred to as the dynamic stage, fine-tunes VNE solutions obtained from the static stage to adapt to the harsh environment with insufficient resources or limited VNF migration budgets. Simulation results demonstrate the effectiveness of SmartAC in improving response rate and resource utilization ratio. Notably, SmartAC reduces runtime by 90% compared to existing algorithms and exhibits robustness across different network topologies. © 2002-2012 IEEE.

关键词： Budget control

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