• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1752-1767
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• 2015

As a special type of mobile ad-hoc network, Vehicular Ad-hoc Network (VANET) is considered as an attractive topic by many researchers. In VANETs, vehicles act as routers and clients, which are connected with each other through unreliable wireless links. Due to the dynamic nature of vehicles, developing communication protocols for VANETs is a challenging task. In this paper, we tackle the problem of real-world VANET operations and propose a new dual-level communication scheme through the combination of power and rate control algorithms. Based on the game theoretic approach, the proposed scheme effectively formulates the interactive situation among several vehicles. With a simulation study, it is confirmed that the proposed scheme can achieve better performance than other existing schemes under widely diverse VANET environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.135-158
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• 2018

High-Speed vehicles can be considered as multiple mobile nodes that move together in a large-scale mobile network. High-speed makes the time allowed for a mobile node to complete a handover procedure shorter and more frequently. Hence, several protocols are used to manage the mobility of mobile nodes such as Network Mobility (NEMO). However, there are still some problems such as high handover latency and packet loss. So efficient handover management is needed to meet Quality of Service (QoS) requirements for real-time applications. This paper utilizes the cross-layer seamless handover technique for network mobility presented in cellular networks. It extends this technique to propose QoS-aware NEMO protocol which considers QoS requirements for real-time applications. A novel analytical framework is developed to compare the performance of the proposed protocol with basic NEMO using cost functions for realistic city mobility model. The numerical results show that QoS-aware NEMO protocol improves the performance in terms of handover latency, packet delivery cost, location update cost, and total cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.443-456
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• 2022

An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.8
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• pp.1388-1403
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• 2011

A vehicular ad-hoc network (VANET) consists of vehicles that form a network without any additional infrastructure, thus allowing the vehicles to communicate with each other. VANETs have unique characteristics, including high node mobility and rapidly changing network topology. Because of these characteristics, routing algorithms based on greedy forwarding such as greedy perimeter stateless routing (GPSR) are known to be very suitable for a VANET. However, greedy forwarding just selects the node nearest to the destination node as a relay node within its transmission range. This increases the possibility of a local maximum and link loss because of the high mobility of vehicles and the road characteristics in urban areas. Therefore, this paper proposes a reliability-improving position-based routing (RIPR) algorithm to solve those problems. The RIPR algorithm predicts the positions, velocities, and moving directions of vehicles after receiving beacon messages, and estimates information about road characteristics to select the relay node. Thus, it can reduce the possibility of getting a local maximum and link breakage. Simulation results using ns-2 revealed that the proposed routing protocol performs much better than the existing routing protocols based on greedy forwarding.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3523-3543
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• 2022

Autonomous vehicles are gradually being regarded as the mainstream trend of future development of the automobile industry. Autonomous driving networks generate many intensive and delay-sensitive computing tasks. The storage space, computing power, and battery capacity of autonomous vehicle terminals cannot meet the resource requirements of the tasks. In this paper, we focus on the task scheduling problem of autonomous driving in software-defined edge networks. By analyzing the intensive and delay-sensitive computing tasks of autonomous vehicles, we propose an emotion model that is related to task urgency and changes with execution time and propose an optimal base station (BS) task scheduling (OBSTS) algorithm. Task sentiment is an important factor that changes with the length of time that computing tasks with different urgency levels remain in the queue. The algorithm uses task sentiment as a performance indicator to measure task scheduling. Experimental results show that the OBSTS algorithm can more effectively meet the intensive and delay-sensitive requirements of vehicle terminals for network resources and improve user service experience.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.7-12
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• 2022

Safety and security are the topmost priority in every environment. With the aid of Artificial Intelligence (AI), many objects are becoming more intelligent, conscious, and curious of their surroundings. The recent scientific breakthroughs in autonomous vehicular designs and development; powered by AI, network of sensors and the rapid increase of Internet of Things (IoTs) could be utilized in maintaining safety and security in our environments. AI based on deep learning architectures and models, such as Deep Neural Networks (DNNs), is being applied worldwide in the automotive design fields like computer vision, natural language processing, sensor fusion, object recognition and autonomous driving projects. These features are well known for their identification, detective and tracking abilities. With the embedment of sensors, cameras, GPS, RADAR, LIDAR, and on-board computers in many of these autonomous vehicles being developed, these vehicles can properly map their positions and proximity to everything around them. In this paper, we explored in detail several ways in which these enormous features embedded in these autonomous vehicles, such as the network of sensors fusion, computer vision and natural image processing, natural language processing, and activity aware capabilities of these automobiles, could be tapped and utilized in safeguarding our lives and environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2399-2416
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• 2024

The current automotive industry is transitioning from Internal Combustion Engine (ICE) vehicles to Electric Vehicles (EVs), adopting a mixed assembly production approach to respond to fluctuating demand. While mixed assembly production offers the advantages of lower investment costs and flexibility in responding to changing demands, the supply of EV components requires more extensive provisioning compared to ICE vehicle components, potentially leading to unexpected issues such as congestion of transport vehicles. This study proposes a digital twin system architecture that uses Discrete Event Simulation (DES) and Business Intelligence (BI) tools to specifically address logistics challenges. The proposed architecture facilitates real-time, data-driven decision making across three layers; Data source, Simulation, and BI. It was implemented in factories engaged in the mixed assembly production of ICE and EV vehicles. The simulation challenges involve a tier 1 vendor supplying parts to Korean automobile manufacturers that produce both ICE and EV parts. A total of 240 scenarios were created to run the simulations. The deployment of the proposed architecture demonstrates its capability to quickly respond to diverse experimental situations and promptly identify potential issues.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.5
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• pp.889-899
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• 2021

This paper proposes a reliable data sharing scheme on the internet of vehicles (IoV) by utilizing blockchain technology for constructing a decentralized system approach. In our model, to maintain the credibility of the information messages sent by the vehicles to the system, we propose a reputation rating mechanism, in which neighboring vehicles validate every received information message. Furthermore, we incorporate an incentive mechanism based on smart contracts, so that vehicles will get certain rewards from the system when they share correct traffic information messages. We simulated the IoV network using a discrete event simulator to analyze network performance, whereas the incentive model is designed by leveraging the smart contract available in the Ethereum platform.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.865-882
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• 2017

To improve passenger convenience and safety, today's vehicle is evolving into a "connected vehicle," which mounts various sensors, electronic control devices, and wired/wireless communication devices. However, as the number of connections to external networks via the various electronic devices of connected vehicles increases and the internal structures of vehicles become more complex, there is an increasing chance of encountering issues such as malfunctions due to various functional defects and hacking. Recalls and indemnifications due to such hacking or defects, which may occur as vehicles evolve into connected vehicles, are becoming a new risk for automakers, causing devastating financial losses. Therefore, automakers need to make voluntary efforts to comply with security ethics and strengthen their responsibilities. In this study, we investigated potential security issues that may occur under a connected vehicle environment (vehicle-to-vehicle, vehicle-to-infrastructure, and internal communication). Furthermore, we analyzed several case studies related to automaker's legal risks and responsibilities and identified the security requirements and necessary roles to be played by each player in the automobile development process (design, manufacturing, sales, and post-sales management) to enhance their responsibility, along with measures to manage their legal risks.