K-DUMBs IoRT: Knowledge Driven Unified Model Block Sharing in the Internet of Robotic Things DOI
Muhammad Waqas Nawaz, Olaoluwa Popoola, Muhammad Ali Imran

et al.

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), Journal Year: 2023, Volume and Issue: unknown, P. 1 - 6

Published: June 1, 2023

6G is expected to revolutionize the Internet of things (IoT) applications toward a future completely intelligent and autonomous systems. Conventional machine-learning approaches involve centralizing training data in center, where algorithms can be used for analysis inference. To promote green computing IoT applications, Machine-2-Machine (M2M) technologies are largely focused on lowering energy consumption creating effective IT infrastructure. In this paper, we introduce an AI-enabled One-Shot Interference(O-SI) Knowledge-Driven unified model block sharing (K-Dumbs) framework which actionable knowledge aggregated from perception robots facilitate others at Edge vicinity. demonstrate practicality proposed concept, explore K-Dumb Fed-Average (FedAvg) algorithm meet massively distributed unbalanced pattern privacy requirement Robotic Things(IoRT). Simulation results show that, when compared traditional Federated Learning (FL) systems, FedAvg architecture delivers higher information-sharing learning quality. addition, validate our method using MNIST handwritten digits image processing with accuracy that close centralized solution up 80% reduction amount exchange O-SI method. Furthermore, suggested reduces IoRT by 10 times protects privacy.

Language: Английский

Edge Intelligence in Intelligent Transportation Systems: A Survey DOI
Taiyuan Gong, Li Zhu, F. Richard Yu

et al.

IEEE Transactions on Intelligent Transportation Systems, Journal Year: 2023, Volume and Issue: 24(9), P. 8919 - 8944

Published: May 24, 2023

Edge intelligence (EI) is becoming one of the research hotspots among researchers, which believed to help empower intelligent transportation systems (ITS). ITS generates a large amount data at network edge by millions devices and sensors. Data-driven artificial (AI) core development. By pushing AI frontier edge, EI enables applications have lower latency, higher security, less pressure on backbone better use big data. This paper surveys Intelligence in Intelligent Transportation Systems. We first introduce challenges faces explain motivation using ITS. then explore framework ITS, including EI-based architecture, gathering communication methods, processing service delivery, performance indexes. The enabling technologies, such as models, Internet Things, Computing technologies used are reviewed intensively. discuss fields depth. Typical application scenarios, autonomous driving, vehicular computing, system, unmanned aerial vehicle (UAV) environment, rail control management, explored. general platforms EI, training inference well benchmark datasets, introduced. Finally, we some future directions

Language: Английский

Citations

52
Federated Learning for Computational Offloading and Resource Management of Vehicular Edge Computing in 6G-V2X Network DOI
Mohammad Kamrul Hasan, Nusrat Jahan, Mohd Zakree Ahmad Nazri

et al.

IEEE Transactions on Consumer Electronics, Journal Year: 2024, Volume and Issue: 70(1), P. 3827 - 3847

Published: Jan. 26, 2024

The Sixth Generation network (6G) can support autonomous driving along with various vehicular applications like Vehicular Edge Computing (VEC), a distributed computing architecture for connected vehicles. Computational offloading and resource management of help sort out some issues, such as high communication costs, privacy protection, an excessively long training process, etc., by proposing efficient model the Federated Learning computational in environment. Two research issues are highlighted this paper. One problem is related to current system: smart structure operating system. Consistent access cloud services, regardless installed system or used hardware, still challenging. Another issue security privacy. Security two important features that should be maintained data centers transmission during management. In survey paper, going proposed which will give partial solution these issues. solution, found while conducting review, offers train update edge devices' information. entire provide updated information devices solve difficulties getting key necessary model-related optimization. This also enhance effectiveness frameworks 6G-V2X communication.

Language: Английский

Citations

21
A mathematical model for the development of distributed energy storage devices in the V2V charging process systems based on fuzzy graph theory DOI
Rayid Ghani, Ebrahim Farjah, Mohammad Reza Oboudi

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 110, P. 115269 - 115269

Published: Jan. 6, 2025

Language: Английский

Citations

2
Digital twin-assisted resource allocation framework based on edge collaboration for vehicular edge computing DOI
Sekione Reward Jeremiah, Laurence T. Yang, Jong Hyuk Park

et al.

Future Generation Computer Systems, Journal Year: 2023, Volume and Issue: 150, P. 243 - 254

Published: Sept. 7, 2023

Language: Английский

Citations

31
Intelligent Vehicle Computation Offloading in Vehicular Ad Hoc Networks: A Multi-Agent LSTM Approach with Deep Reinforcement Learning DOI Creative Commons

Dingmi Sun,

Yimin Chen, Hao Li

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(3), P. 424 - 424

Published: Jan. 28, 2024

As distributed computing evolves, edge has become increasingly important. It decentralizes resources like computation, storage, and bandwidth, making them more accessible to users, particularly in dynamic Telematics environments. However, these environments are marked by high levels of uncertainty due frequent changes vehicle location, network status, server workload. This complexity poses substantial challenges rapidly accurately handling computation offloading, resource allocation, delivering low-latency services such a variable environment. To address challenges, this paper introduces “Cloud–Edge–End” collaborative model for computing. Building upon model, we develop novel service offloading method, LSTM Muti-Agent Deep Reinforcement Learning (L-MADRL), which integrates deep learning with reinforcement learning. method includes predictive capable forecasting the future demands on intelligent vehicles servers. Furthermore, conceptualize computational problem as Markov decision process employ Multi-Agent Deterministic Policy Gradient (MADDPG) approach autonomous, decision-making. Our empirical results demonstrate that L-MADRL algorithm substantially reduces latency energy consumption 5–20%, compared existing algorithms, while also maintaining balanced load across servers diverse scenarios.

Language: Английский

Citations

9
DRL-assisted delay optimized task offloading in automotive-industry 5.0 based VECNs DOI Creative Commons
Muhammad Ayzed Mirza, Junsheng Yu, Salman Raza

et al.

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2023, Volume and Issue: 35(6), P. 101512 - 101512

Published: Feb. 24, 2023

The rapid growth of Automotive-Industry 5.0 and its emergence with beyond fifth-generation (B5G) communications, is making vehicular edge computing networks (VECNs) increasingly complex. latency constraints modern automotive applications make it difficult to run complex on vehicle on-board units (OBUs). While multi-access (MEC) can facilitate task offloading execute these applications, still a challenge access them promptly optimally. Traditional algorithms struggle guarantee accuracy in such dynamic environment, but deep reinforcement learning (DRL) methods offer improved accuracy, robustness, real-time decision-making capabilities. In this paper, we propose DRL-based mobility, contact, load aware cooperative (DCTO) scheme. DCTO designed for both cellular mmWave radio technologies (RATs), binary partial mechanisms. targets delay minimization by opportunistically switching RATs We consider relative efficacy neutrality factors as key performance indicators use derive the DRL agent's reward function. Extensive evaluations demonstrate that scheme exhibits substantial enhancement success rate, an increase from 2.61% 21.34%. It also improves factor 1.38 3.52 reduces 4.99 0.76. Furthermore, average processing time reduced range 3.77% 24.15%. Additionally, outperforms other evaluated schemes terms TFPS ratio.

Language: Английский

Citations

21
A survey on privacy-preserving authentication protocols for secure vehicular communication DOI
Kartick Sutradhar,

Beena G Pillai,

Ruhul Amin

et al.

Computer Communications, Journal Year: 2024, Volume and Issue: 219, P. 1 - 18

Published: March 1, 2024

Language: Английский

Citations

6
A comprehensive review on internet of things task offloading in multi-access edge computing DOI Creative Commons

Wang Dayong,

Kamalrulnizam Bin Abu Bakar,

Babangida Isyaku

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(9), P. e29916 - e29916

Published: April 22, 2024

With the rapid development of Internet Things (IoT) technology, Terminal Devices (TDs) are more inclined to offload computing tasks higher-performance servers, thereby solving problems insufficient capacity and battery consumption TD. The emergence Multi-access Edge Computing (MEC) technology provides new opportunities for IoT task offloading. It allows TDs access networks through multiple communication technologies supports mobility terminal devices. Review studies on offloading MEC have been extensive, but none them focus in MEC. To fill this gap, paper a comprehensive in-depth understanding algorithms mechanisms network. For each paper, main solved by mechanism, technical classification, evaluation methods, supported parameters extracted analyzed. Furthermore, shortcomings current research future trends discussed. This review will help potential researchers quickly understand panorama approaches find appropriate paths.

Language: Английский

Citations

6
A survey on reconfigurable intelligent surfaces assisted multi-access edge computing networks: State of the art and future challenges DOI
Manzoor Ahmed, Salman Raza, Aized Amin Soofi

et al.

Computer Science Review, Journal Year: 2024, Volume and Issue: 54, P. 100668 - 100668

Published: Aug. 26, 2024

Language: Английский

Citations

6
Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review DOI Creative Commons
Fahmida Islam, M M Nabi, John Ball

et al.

Sensors, Journal Year: 2022, Volume and Issue: 22(21), P. 8463 - 8463

Published: Nov. 3, 2022

When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one them. In order safely navigate through any known or unknown environment, AGV must be able detect important elements on the path. Detection applicable both on-road and off-road, but they are much different in each environment. The key environment that identify drivable pathway whether there obstacles around it. Many works have been published focusing components various ways. this paper, a survey most recent advancements methods intended specifically for off-road has presented. For this, we divided literature into three major groups: positive negative obstacles. Each portion further multiple categories based technology used, example, single sensor-based, how data analyzed. Furthermore, added critical findings technology, challenges associated with possible future directions. Authors believe work will help reader finding who doing similar works.

Language: Английский

Citations

28