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Zilong Jin,Chi Zhang,Lejun Zhang 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.5
Autonomous vehicles use onboard sensors to sense the surrounding environment. In complex autonomous driving scenarios, the detection and recognition capabilities are constrained, which may result in serious accidents. An efficient way to enhance the detection and recognition capabilities is establishing collaborations with the neighbor vehicles. However, the collaborations introduce additional challenges in terms of the data heterogeneity, communication cost, and data privacy. In this paper, a novel personalized federated learning framework is proposed for addressing the challenges and enabling efficient collaborations in autonomous driving environment. For obtaining a global model, vehicles perform local training and transmit logits to a central unit instead of the entire model, and thus the communication cost is minimized, and the data privacy is protected. Then, the inference similarity is derived for capturing the characteristics of data heterogeneity. The vehicles are divided into clusters based on the inference similarity and a weighted aggregation is performed within a cluster. Finally, the vehicles download the corresponding aggregated global model and train a personalized model which is personalized for the cluster that has similar data distribution, so that accuracy is not affected by heterogeneous data. Experimental results demonstrate significant advantages of our proposed method in improving the efficiency of collaborative perception and reducing communication cost.
An Analytic Model for the Optimal Number of Relay Stations in Two-Hop Relay Networks
Zilong Jin,Weidong Su,Jinsung Cho,Een-Kee Hong IEEE 2013 IEEE COMMUNICATIONS LETTERS Vol.17 No.2
<P>The RS(Relay Station) has become an important radio resource in next generation wireless communication systems. The optimal number of RSs is one of the crucial issues in configuring a cost-effective RS-based network architecture. In this letter, we present an analytic model to describe the impact of the number of RSs on the channel capacity of two-hop relay network. From the mathematical analysis, the optimal number of RSs is obtained to satisfy QoS requirements of users considering various levels of MCS(Modulation and Coding Scheme). In addition to the mathematical analysis to determine the feasibility of the analytic model, we also examine its performance through a set of simulations. The simulation results show the validity of the proposed analytic model.</P>
A Context-aware Task Offloading Scheme in Collaborative Vehicular Edge Computing Systems
( Zilong Jin ),( Chengbo Zhang ),( Guanzhe Zhao ),( Yuanfeng Jin ),( Lejun Zhang ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.2
With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.
Trajectory-prediction based relay scheme for time-sensitive data communication in VANETs
( Zilong Jin ),( Yuxin Xu ),( Xiaorui Zhang ),( Jin Wang ),( Lejun Zhang ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.8
In the Vehicular Ad-hoc Network (VANET), the data transmission of time-sensitive applications requires low latency, such as accident warnings, driving guidance, etc. However, frequent changes of topology in VANET will result in data transmission failures. In order to improve the efficiency of VANETs data transmission and increase the timeliness of data, this paper proposes a relay scheme based on Recurrent Neural Network (RNN) trajectory prediction, which can be used to select the optimal relay vehicle to transmit data. The proposed scheme learns vehicle trajectory in a distributed manner and calculates the predicted trajectory, and then the optimal vehicle can be selected to complete the data transmission, which ensures the timeliness of the data. Finally, we carry out a set of simulations to demonstrate the performance of the algorithm. Simulation results show that the proposed scheme enhances the timeliness of the data and the accuracy of the predicted driving trajectory.
An Analysis on Optimal Cluster Ratio in Cluster-Based Wireless Sensor Networks
Zilong Jin,Dae-Young Kim,Jinsung Cho,Ben Lee IEEE 2015 IEEE Sensors Journal Vol. No.
<P>In wireless sensor networks, clustering schemes have been adopted as an efficient solution to prolong the network lifetime. In these schemes, the performance of energy-efficient data transmission is affected the cluster ratio (CR). This paper analyzes the optimal CR from the perspective of network energy efficiency, and its impact on the network lifetime. In order to provide a generic analytic model, various data propagation cases are mathematically analyzed. In addition, the network lifetime is extended by jointly optimizing the network transmission count and link reliability. Our simulation results show that the optimal CR derived based on the proposed analytical model enhances the energy efficiency and effectively increases the network lifetime.</P>
계층적 센서네트워크에서 에너지 효율성을 위한 최적의 클러스터 비율 분석
김자룡(Zilong Jin),김대영(Dae-Young Kim),조진성(Jinsung Cho) 한국통신학회 2013 韓國通信學會論文誌 Vol.38 No.6B
무선 센서네트워크에서 클러스터링 기법은 네트워크 확장성과 네트워크 수명 연장에 효율적이라고 인정받고 있다. 본 논문에서는 클러스터 기반 센서 네트워크에서 multi-hop to one-hop 전송 환경을 고려하여 에너지 효율성에 최적인 클러스터 비율(cluster ratio, CR)을 분석하는데 초점을 둔다. 본 논문에서는 지정한 클러스터 비율을 통한 시스템 홉 수(hop-count) 최소화와 노드 간 패킷수신율(packet reception ratio, RPP) 최대화 사이의 이해득실(trade-off) 관계를 분석하고 이 두 요소를 종합적으로 고려하여 목표함수를 유도한다. 제안한 목표함수를 통하여 얻은 최적의 클러스터 비율은 네트워크에서 패킷 전송에 드는 비용뿐만 아니라 노드 간 재전송 오버헤드를 줄여줌으로써 에너지 효율성을 향상시킨다. 본 논문에서 제안한 기법은 최소 홉 수 클러스터링 방안과 비교되며 시뮬레이션을 통하여 향상된 에너지 효율성을 검증하였다. Clustering schemes have been adopted as an efficient solution to prolong network lifetime and improve network scalability. In such clustering schemes cluster ratio is represented by the rate of the number of cluster heads and the number of total nodes, and affects the performance of clustering schemes. In this paper, we mathematically analyze an optimal clustering ratio in wireless sensor networks. We consider a multi-hop to one-hop transmission case and aim to provide the optimal cluster ratio to minimize the system hop-count and maximize packet reception ratio between nodes. We examine its performance through a set of simulations. The simulation results show that the proposed optimal cluster ratio effectively reduce transmission count and enhance energy efficiency in wireless sensor networks.
A Sobel Operator Combined with Patch Statistics Algorithm for Fabric Defect Detection
( Jiein Jiang ),( Zilong Jin ),( Boheng Wang ),( Li Ma ),( Yan Cui ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.2
In the production of industrial fabric, it needs automatic real-time system to detect defects on the fabric for assuring the defect-free products flow to the market. At present, many visual-based methods are designed for detecting the fabric defects, but they usually lead to high false alarm. Base on this reason, we propose a Sobel operator combined with patch statistics (SOPS) algorithm for defects detection. First, we describe the defect detection model. mean filter is applied to preprocess the acquired image. Then, Sobel operator (SO) is applied to deal with the defect image, and we can get a coarse binary image. Finally, the binary image can be divided into many patches. For a given patch, a threshold is used to decide whether the patch is defect-free or not. Finally, a new image will be reconstructed, and we did a loop for the reconstructed image to suppress defects noise. Experiments show that the proposed SOPS algorithm is effective.