Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 617 - 625
Опубликована: Янв. 1, 2024
Язык: Английский
IEEE Access, Год журнала: 2024, Номер 12, С. 3437 - 3463
Опубликована: Янв. 1, 2024
The Internet of Vehicles (IoV) represents a paradigm shift in vehicular communication, aiming to enhance traffic efficiency, safety, and the driving experience by leveraging interconnected vehicles. Despite its promise, IoV faces challenges such as efficient task offloading, energy management, data security. Mobile Edge Computing (MEC) emerges solution some these bringing computational resources closer network's edge, yet it raises critical concerns regarding resource service continuity, scalability dynamic environments. Addressing both MEC necessitates robust optimization mechanisms. In response challenges, our study introduces multi-objective approach using Double Deep Q-Networks (DDQN), cutting-edge application Reinforcement Learning (DRL). This algorithm combines strengths Neural Networks (DNNs) (DL) techniques, enabling decision-making that can adapt changing conditions. By considering multiple objectives, DDQN allows for sophisticated trade-off analysis, efficiently balancing between different objectives optimize overall system performance. Through use Blockchain technology, known secure, decentralized structure, model enhances integrity data, providing reliable IoV-MEC systems. We conducted comparative analysis against standard Q-Network (DQN) Deterministic Policy Gradient (DDPG) algorithms, which are prevalent this field. Our demonstrated significant improvements over traditional methods: consumption was reduced 26.4%, latency decreased 6.87%, cost minimized 7.41%.
Язык: Английский
Процитировано
28
Computer Science Review, Год журнала: 2024, Номер 53, С. 100651 - 100651
Опубликована: Июль 5, 2024
Язык: Английский
Процитировано
28
IEEE Transactions on Vehicular Technology, Год журнала: 2023, Номер 73(4), С. 5647 - 5658
Опубликована: Ноя. 9, 2023
In the context of unmanned aerial vehicle (UAV)-assisted vehicular networking system, more network factors need to be considered ensure safe operation connected vehicles. A large volume delay-sensitive and computationally demanding tasks necessitate offloading UAVs or roadside units for processing. And efficient allocation various resources vehicles, UAVs, under constrained conditions determines efficiency task offloading. Deep reinforcement learning (DRL) has demonstrated its efficacy as an experienced approach solving such problems. this article, we delve into utilization deep design UAV-assisted edge computing strategy. Under constraints limited bandwidth UAV power, trajectory strategy are jointly optimized. The primary objective our proposed is achieve a notable reduction in system delay network. Given dynamic variability arrival, employ long short-term memory (LSTM) with attention mechanism deterministic policy gradient (DDPG) algorithm effectively model optimization problem Markov decision process. This can obtain optimal through interactive from environment. experiment results illustrate that outperforms other baseline strategies terms convergence speed, delay, ratio.
Язык: Английский
Процитировано
32
Energy Science & Engineering, Год журнала: 2024, Номер 12(3), С. 1242 - 1264
Опубликована: Фев. 14, 2024
Abstract The increasing trend of energy generation and management systems towards decentralized structures such as using renewable resources makes it necessary to use digital smart platforms for exchanging information even conducting financial transactions in a manner, known the peer‐to‐peer model. transaction verification cryptocurrencies possible these encrypted currencies blockchain networks carry out related carbon trading. Carbon other greenhouse gas (GHG) emission trading reduce competitiveness fossil fuel projects market accelerate investment low‐carbon sources wind photovoltaic power units. This mechanism allows large entities countries companies that emit GHGs into atmosphere buy sell gases. paper reviews solutions developed markets. Studies design contracts platform are investigated. Special used field green introduced. In addition, application artificial intelligence game theory is stated. study different frameworks shows can have significant impact on measures achieving goals Kyoto Treaty, value volume transactions. These technologies offer promising avenue creating more decentralized, efficient, environmentally conscious ecosystem.
Язык: Английский
Процитировано
13
Heliyon, Год журнала: 2024, Номер 10(19), С. e38917 - e38917
Опубликована: Окт. 1, 2024
Язык: Английский
Процитировано
9
Internet of Things, Год журнала: 2024, Номер 25, С. 101115 - 101115
Опубликована: Фев. 15, 2024
Язык: Английский
Процитировано
6
Security and Privacy, Год журнала: 2023, Номер 6(6)
Опубликована: Апрель 20, 2023
Abstract The Internet of vehicles (IoV) has appeared as an effective method obtaining intelligent transportation system able to deliver various innovative solutions and enable several applications a replacement for vehicular ad‐hoc networks (VANETs). To help IoV contexts, enormous quantities information are created transmitted between communication components wirelessly across multiple channels, which may entice attackers put the network at risk. Security is one vital concerns critical issues in VANETs networks. Blockchain employed create distributed secure overcome some centralized enhance architecture. This article presents systematic detailed review by selecting 28 articles from 2018 2022 on blockchain‐based (BIoV). We investigate latest survey regarding their aspects, contributions, findings, limitations, strong points address unsolved problems BIoV. In this article, review/survey systematically reviewed resolve taxonomy aspects intended provide new discuss security concerns, open issues, future directions so that researchers field can quickly easily access recent content do not need read numerous articles.
Язык: Английский
Процитировано
16
Computers & Electrical Engineering, Год журнала: 2025, Номер 123, С. 110069 - 110069
Опубликована: Янв. 15, 2025
Язык: Английский
Процитировано
0
ACM Transactions on Multimedia Computing Communications and Applications, Год журнала: 2025, Номер unknown
Опубликована: Янв. 28, 2025
The current landscape of data-centric Internet Vehicles (IoVs) encompasses a fusion Human-driven (HVs), Autonomous (AVs), Road-Side Units (RSUs), and edge-based devices engaged in periodic communication. Given the stringent latency requirements inherent vehicular communications, emergence Digital Twins (DTs) plays pivotal role problem-solving, ensuring rapid response, regulatory compliance, seamless availability. While these communications serve as backbone IoV, they also create an opportune environment for cybercriminals to exploit. Vulnerabilities at network layer facilitate intrusions, resulting surge data falsification attacks recent years. Addressing this challenge demands resilient intelligent threat detection schemes capable adapting dynamic nature IoV. This study conducts comprehensive examination vulnerabilities Vehicle-to-Digital twin (V2DT) communication through lens attacker utilizing False Data Injection Attack (FDIA). It utilizes cutting-edge Blockchain-based decentralized storage buffering mechanisms vehicle dynamics en route DTs. Further, deep learning-powered sensor analysis serves additional security. Evaluation proposed mitigation model demonstrates 100% tamper V2DT communication, coupled with 96% accurate classification anomalous driving behaviors, including aggressive or FDIAs.
Язык: Английский
Процитировано
0
Transactions on Emerging Telecommunications Technologies, Год журнала: 2025, Номер 36(4)
Опубликована: Апрель 1, 2025
ABSTRACT The Internet of Vehicles (IoV) is a critical component the smart city. Various nodes exchange sensitive data for urban mobility, such as identification, position, messages, speed, and traffic statistics. Along with developing cities come threats to privacy security through networks. Security highest priority, considering various security‐privacy risks from wellness, safety, confidentiality men women inside vehicle. This survey presents detailed analysis state‐of‐the‐art evolving challenges IoV systems. It handles challenges, integrity privacy. also includes review literature identify gaps in current mechanisms. uses complete mathematical modeling case studies show practical effectiveness proposed solutions. aims guide future development implementation more secure, efficient, resilient systems, particularly city environments. introduces novel Intrusion Detection System (IDS) Artificial Intelligence (AI), contracts, blockchain technology. These contracts ensure instant utmost level vulnerability In addition, we hybrid multi‐layered framework using Fog conserve resources at vehicle level. We used proof assess this framework. Merging blockchain, AI into IoVs could increase human by removing significant vulnerabilities.
Язык: Английский
Процитировано
0