• 디지털산업정보학회논문지
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• 제14권4호
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• pp.27-33
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• 2018

The recent efforts in academia and industry represent a paradigm shift that will extend the IoT from the home environment so that it is interoperable with the Internet of Vehicles (IoV). IoV is a special kind of IoT. It allows to connect between vehicle and things located in infrastructure. Furthermore, IoV enable to create new intelligent services through collaboration with existing various services such as smart city and connected home. In this paper, we develop a service in order to realize IoV. To this end, we design a novel vehicle service platform which could automatical controlling the IoT device according to drivers' voice. To show practical usability of our proposed platform, we develop a prototype service could be call car-to-thing (C2T). We expect that our proposed platform could eventually contribute to realizing IoV.

• 디지털산업정보학회논문지
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• 제13권4호
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• pp.33-40
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• 2017

Internet of Vehicles (IoV) is a subset of Internet of Things (IoT) and it is an infrastructure for vehicles. Therefore, IoV consists of three main network including inter-vehicle network, intra-vehicle network, and vehicular mobile internet. IoV mainly used in urban traffic environment to provide network access for drivers, passengers and traffic management. Accordingly, many research works have focused on network technology. But, recent concerted efforts in academia and industry point to paradigm shift in IoV system. In this paper, we proposed a knowledge base for intelligence service in IoV. A detailed design and implementation of the proposed knowledged base is illustrated. We hope this work will show power of IoV as a disruptive technology.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.389-401
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• 2022

The business of Internet of Vehicles (IoV) is growing rapidly, and the large amount of data exchange has caused problems of large mobile network communication delay and large energy loss. A strategy for resource allocation of IoV communication based on mobile edge computing (MEC) is thus proposed. First, a model of the cloud-side collaborative cache and resource allocation system for the IoV is designed. Vehicles can offload tasks to MEC servers or neighboring vehicles for communication. Then, the communication model and the calculation model of IoV system are comprehensively analyzed. The optimization objective of minimizing delay and energy consumption is constructed. Finally, the on-board computing task is coded, and the optimization problem is transformed into a knapsack problem. The optimal resource allocation strategy is obtained through genetic algorithm. The simulation results based on the MATLAB platform show that: The proposed strategy offloads tasks to the MEC server or neighboring vehicles, making full use of system resources. In different situations, the energy consumption does not exceed 300 J and 180 J, with an average delay of 210 ms, effectively reducing system overhead and improving response speed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권11호
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• pp.4573-4594
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• 2020

Announcement protocol in Internet of Vehicles (IoV) is an intelligent application to enhance public safety, alleviate traffic jams and improve transportation quality. It requires communication between vehicles, roadside units and pedestrian to disseminate safety-related messages. However, as vehicles connected to internet, it makes them accessible globally to a potential adversary. Safety-related application requires a message to be reliable, however it may intrude the privacy of a vehicle. Contrarily, if some misbehaviour emerges, the malicious vehicles must be able to traceable and revoke from the network. This is a contradiction between privacy and accountability since the privacy of a user should be preserved. For a secure communication among intelligent entities, we propose a novel announcement protocol in IoV using group signature. To the best of our knowledge, our work is the first comprehensive construction of an announcement protocol in IoV that deploys group signature. We show that our protocol efficiently solves these conflicting security requirements of message reliability, privacy and accountability using 5G communication channel. The performance analysis and simulation results signify our work achieves performance efficiency in IoV communication.

• 정보보호학회논문지
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• 제31권5호
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• pp.889-899
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• 2021

본 논문은 블록체인 기술에서 탈중앙화된 시스템 접근 방식을 활용하여 차량 인터넷(IoV)에서 신뢰할 수 있는 데이터 공유 체계를 제안한다. 스마트 계약에 기초한 인센티브 메커니즘을 채택하여, 차량은 올바른 교통 정보 메시지를 정직하게 공유함으로써 시스템으로부터 특정한 보상을 받게 된다. 이후 차량은 평판 등급을 생성하여 수신되는 모든 정보 메세지를 검증함으로서 메시지에 대한 신뢰성을 유지한다. 한편 네트워크 성능을 분석하기 위해 이산 이벤트 시뮬레이터를 사용하여 IoT 네트워크를 시뮬레이션하였고, 인센티브 모델은 분산형 접근 방식의 이더리움 스마트 계약을 활용하여 설계하였다.

• Journal of Information Processing Systems
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• 제18권3호
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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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• 제18권6호
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• pp.1462-1477
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• 2024

With the evolving complexity of connected vehicle features, the volume and diversity of data generated during driving continue to escalate. Enabling data sharing among interconnected vehicles holds promise for improving users' driving experiences and alleviating traffic congestion. Yet, the unintentional disclosure of users' private information through data sharing poses a risk, potentially compromising the interests of vehicle users and, in certain cases, endangering driving safety. Federated learning (FL) is a newly emerged distributed machine learning paradigm, which is expected to play a prominent role for privacy-preserving learning in autonomous vehicles. While FL holds significant potential to enhance the architecture of the Internet of Vehicles (IoV), the dynamic mobility of vehicles poses a considerable challenge to integrating FL with vehicular networks. In this paper, a novel clustered FL framework is proposed which is efficient for reducing communication and protecting data privacy. By assessing the similarity among feature vectors, vehicles are categorized into distinct clusters. An optimal vehicle is elected as the cluster head, which enhances the efficiency of personalized data processing and model training while reducing communication overhead. Simultaneously, the Local Differential Privacy (LDP) mechanism is incorporated during local training to safeguard vehicle privacy. The simulation results obtained from the 20newsgroups dataset and the MNIST dataset validate the effectiveness of the proposed scheme, indicating that the proposed scheme can ensure data privacy effectively while reducing communication overhead.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.327-347
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• 2024

Edge computing is frequently employed in the Internet of Vehicles, although the computation and communication capabilities of roadside units with edge servers are limited. As a result, to perform distributed machine learning on resource-limited MEC systems, resources have to be allocated sensibly. This paper presents an Improved MADDPG algorithm to overcome the current IoV concerns of high delay and limited offloading utility. Firstly, we employ the MADDPG algorithm for task offloading. Secondly, the edge server aggregates the updated model and modifies the aggregation model parameters to achieve optimal policy learning. Finally, the new approach is contrasted with current reinforcement learning techniques. The simulation results show that compared with MADDPG and MAA2C algorithms, our algorithm improves offloading utility by 2% and 9%, and reduces delay by 29.6%.

• 전자통신동향분석
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• 제30권5호
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• pp.11-21
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• 2015

최근 IT기술과 산업 간 융합이 활발한 가운데 자동차에도 각종 첨단 IT기술이 접목되면서 운전자의 안전과 편의성이 향상된 스마트카(smart car)가 속속 개발되고 있다. 가까운 미래에 스마트카의 도움으로 운전자가 전방주시 의무에서 자유롭게 될 수 있게 되면, 운행 중에 언제 어디서나 모바일 인터넷을 통한 정보접근이 가능하도록 지원하는 컴퓨팅 환경인 자동차 사물인터넷(Internet of Vehicles, Automotive IoT)이 중요하게 대두될 것으로 전망된다. 자동차 사물인터넷의 개념이 아직은 명확히 잡혀있지 않지만, 대체로 모바일 연결성(mobile connectivity)을 중심으로, 교통안전 혼잡해소뿐만 아니라 다양한 사용자 맞춤형 서비스 산업을 창출할 수 있는 컴퓨팅 환경을 의미한다. 즉, 운전자와 자동차, 자동차와 주변환경 및 교통인프라, 그리고 일상생활의 모든 요소가 자동차를 매개로 해서 유기적으로 연결되는 컴퓨팅 환경을 의미하며, 가까운 미래에 이런 컴퓨팅 환경을 지원하는 자동차가 상용화될 것으로 전망된다. 본고에서는 이러한 전망을 반영하여 자동차 사물인터넷 환경의 스마트카에 적용될 주요 기술과 서비스를 분석하고, 스마트카와 자율주행의 핵심기술인 인포테인먼트 플랫폼의 주요 동항 및 이슈를 살펴보고자 한다.

• 전자공학회논문지
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• 제52권11호
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• pp.87-104
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• 2015

최근 지능화된 사물들이 연결되는 네트워크를 통해 사람과 사물, 사물과 사물 간에 상호 소통하고 상황인식 기반의 지식이 결합되어 인공지능 서비스를 제공하는 사물인터넷 (IoT : Internet of Things) 환경이 급속도로 발전하고 있다. 이러한 사물인터넷의 발전과 더불어 C-ITS (Cooperative Intelligent Transport System) 환경에서 고속으로 이동하는 차량이 기존의 노변 인프라 외에 주행 중인 다른 차량까지 교통 인프라에 포함하여 차선 및 번호판 인식, 전방 사고 및 도로 공사 감지 등 쌍방향 정보 공유를 통해 효율적인 도로 주행을 함으로써 운전자에게 편리성과 안전성을 높여주고 나아가 교통 효율성을 높이고자 하는 연구가 활발히 진행되고 있다. 본 논문에서는 C-ITS 환경에서 고속도로 주행 시 버스전용 차선 인식 후 교통 인프라와 연계하여 버스전용 차선 내 주행차량의 주행 가능 여부를 판단하고 이에 따른 후속 조치에 관한 연구를 진행하였다. 버스전용차선 인식을 통해 버스전용 차로의 위치를 파악한 후 후속 차량의 정면 전방 및 측면 전방 차량의 번호판 인식을 진행하고 향후 교통 인프라로 하여금 인지하게 하는 방법에 관한 학습과 해당 실험결과를 제시하였다.