Ke Zhang

University of Electronic Science and Technology of China

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Supeng Leng

University of Electronic Science and Technology of China

Yan Zhang

University of Oslo

Sabita Maharjan

University of Oslo

Yuming Mao

Jiangnan University

Yueyue Dai

Huazhong University of Science and Technology

Fan Wu

University of Electronic Science and Technology of China

Yejun He

University of Electronic Science and Technology of China

Yunlong Lu

Beijing Jiaotong University

Fan Wu

University of Electronic Science and Technology of China

Xiaoyan Huang

University of Electronic Science and Technology of China

Cooperative Institutions

University of Electronic Science and Technology of China

109

Chinese Academy of Sciences

Tsinghua University

University of Chinese Academy of Sciences

Zhejiang University

Harbin Institute of Technology

Huazhong University of Science and Technology

Sun Yat-sen University

Shanghai Jiao Tong University

Beijing University of Posts and Telecommunications

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Deep Reinforcement Learning for Edge Computing and Resource Allocation in 5G Beyond

Yueyue Dai Du Xu Ke Zhang Yunlong Lu Sabita Maharjan

By extending computation capacity to the edge of wireless networks, edge computing has the potential to enable computation-intensive and delay-sensitive applications in 5G and beyond via computation offloading. However, in multi-user heterogeneous networks, it is challenging to capture complete network information, such as wireless channel state, available bandwidth or computation resources. The strong couplings among devices on application requirements or radio access mode make it more difficult to design an optimal computation offloading scheme. Deep Reinforcement Learning (DRL) is an emerging technique to address such an issue with limited and less accurate network information. In this paper, we utilize DRL to design an optimal computation offloading and resource allocation strategy for minimizing system energy consumption. We first present a multi-user edge computing framework in heterogeneous networks. Then, we formulate the joint computation offloading and resource allocation problem as a DRL form and propose a new DRL-inspired algorithm to minimize system energy consumption. Numerical results based on a realworld dataset demonstrate demonstrate the effectiveness of our proposed algorithm, compared to two benchmark solutions.

Edge device

10.1109/icct46805.2019.8947146

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Citations (15)

Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles

IEEE Transactions on Vehicular Technology (2020)

Yunlong Lu Xiaohong Huang Ke Zhang Sabita Maharjan Yan Zhang

In Internet of Vehicles (IoV), data sharing among vehicles for collaborative analysis can improve the driving experience and service quality. However, the bandwidth, security and privacy issues hinder data providers from participating in the data sharing process. In addition, due to the intermittent and unreliable communications in IoV, the reliability and efficiency of data sharing need to be further enhanced. In this paper, we propose a new architecture based on federated learning to relieve transmission load and address privacy concerns of providers. To enhance the security and reliability of model parameters, we develop a hybrid blockchain architecture which consists of the permissioned blockchain and the local Directed Acyclic Graph (DAG). Moreover, we propose an asynchronous federated learning scheme by adopting Deep Reinforcement Learning (DRL) for node selection to improve the efficiency. The reliability of shared data is also guaranteed by integrating learned models into blockchain and executing a two-stage verification. Numerical results show that the proposed data sharing scheme provides both higher learning accuracy and faster convergence.

Data Sharing

10.1109/tvt.2020.2973651

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Citations (557)

Geochemistry and Sr‐Nd‐Pb Isotopes of the Granites from the Hashitu Mo Deposit of Inner Mongolia, China: Constraints on Their Origin and Tectonic Setting

Acta Geologica Sinica - English Edition (2016)

Chengwu Ding Pan Dai Leon Bagas Nie Fengjun Sihong Jiang

Abstract The Hashitu molybdenum deposit is located in the southern part of the Great Hinggan Range, NE China. Molybdenum mineralization is hosted by and genetically associated with monzogranite and porphyritic syenogranite. Sr‐Nd‐Pb isotopes of the intrusions show that the porphyritic syenogranite has initial 87 Sr/ 86 Sr ratios of 0.70418–0.70952, ε Nd ( t ) values of 1.3 to 2.1 ( t =143 Ma), 206 Pb/ 204 Pb ratios of 19.191–19.573, 207 Pb/ 204 Pb ratios of 15.551–15.572, and 208 Pb/ 204 Pb ratios of 38.826–39.143. The monzogranite has initial 87 Sr/ 86 Sr ratios of 0.70293–0.71305, ε Nd ( t ) values of 1.1 to 2.0 ( t =147 Ma), 206 Pb/ 204 Pb ratios of 19.507–20.075, 207 Pb/ 204 Pb ratios of 15.564–15.596, and 208 Pb/ 204 Pb ratios of 39.012–39.599. The calculated Nd model ages ( T DM ) for monzogranite and porphyritic syenogranite range from 866 to 1121 Ma and 795 to 1020 Ma, respectively. The granitic rocks in the Hashitu area have the same isotope range as granites in the southern parts of the Great Hinggan Range. The isotope composition indicates that these granites are derived from the partial melting of a juvenile lower crust originating from a depleted mantle with minor contamination by ancient continental crust. The integrating our results with published data and the Late Mesozoic regional tectonic setting of the region suggest that the granites in the Hashitu area formed in an intra‐continent extensional setting, and they are related to the thinning of the thickened lithosphere and upwelling of the asthenosphere.

Porphyritic

Underplating

Geochronology

Petrogenesis

10.1111/1755-6724.12645

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Citations (15)

Model-Free Predictive Current Control Strategy Considering Noise Error Compensation

Lecture notes in electrical engineering (2023)

Yao Wang Tao Rui Wenping Cao Ke Zhang Cungang Hu

Robustness

10.1007/978-981-99-4334-0_106

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Automated gradation design of natural waste gravel soil stabilized by composite soil stabilizer based on a novel DNNSS-APDM-PFC model

Waste Management (2025)

Yulong Zhao Ke Zhang Dong Fei Yaofei Luo Song Liu

Gradation

Stabilizer (aeronautics)

10.1016/j.wasman.2024.12.046

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Citations (1)

Design of concurrent immune IDS system with three protection-layers based on IXP2350

Jisuanji yingyong yanjiu (2008)

Ke Zhang

This paper introduced a design of new architecture of intrusion detection system based on Intel IXP2350 network processor-concurrent immune intrusion detection system with three protection-layers.Combining the detection based on host and network,this IDS system provided integrative multilayer protection for computer system,like the artificial immune system.This system used the network processor as its analyses engine for the high speed powerful programmable multiprocessing characteristic of Intel IXP2350.This IDS system has more flexible and extensible characteristic.

Extensibility

Network processor

Source

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Pricing Strategy for a Virtual Power Plant Operator with Electric Vehicle Users Based on the Stackelberg Game

World Electric Vehicle Journal (2023)

Qiang Liu Jiale Tian Ke Zhang Qingxin Yan

With the popularity and promotion of electric vehicles (EVs), virtual power plants (VPPs) provide a new means for the orderly charging management of decentralized EVs. How to set the price of electricity sales for VPP operators to achieve a win–win situation with EV users is a hot topic of current research. Based on this, this paper first proposes a Stackelberg game model in which the VPP participates in the orderly charging management of EVs as a power sales operator, where the operator guides the EV users to charge in an orderly manner by setting a reasonable power sales price and coordinates various distributed resources to jointly participate in the power market. Furthermore, taking into account the impact of wind power output uncertainty on VPP operation, a robust optimization method is used to extend the deterministic Stackelberg game pricing model into a robust optimization model, and a robust adjustment factor is introduced to flexibly adjust the conservativeness of the VPP operator’s bidding scheme in the energy market. The model is then transformed into a robust mixed-integer linear programming (RMILP) problem solved by Karush–Kuhn–Tucker (KKT) conditions and strong dyadic theory. Finally, the effectiveness of the solution method is verified in the calculation example, which gives the optimal pricing strategy for the VPP operator, the optimal charging scheme for EV users, and the remaining internal resources’ contribution plan, providing an important idea for the VPP to centrally manage the charging behavior of EVs and improve its own operating revenue.

Stackelberg competition

Virtual power plant

Operator (biology)

Karush–Kuhn–Tucker conditions

Bilevel optimization

Demand Response

10.3390/wevj14030072

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Citations (4)

PAC-PLRU: A Cache Replacement Policy to Salvage Discarded Predictions from Hardware Prefetchers

Ke Zhang Zhensong Wang Yong Chen Huaiyu Zhu Xian–He Sun

Cache replacement policy plays an important role in guaranteeing the availability of cache blocks, reducing miss rates, and improving applications' overall performance. However, recent research efforts on improving replacement policies require either significant additional hardware or major modifications to the organization of the existing cache. In this study, we propose the PAC-PLRU cache replacement policy. PAC-PLRU not only utilizes but also judiciously salvages the prediction information discarded from a widely-adopted stride prefetcher. The main idea behind PAC-PLRU is utilizing the prediction results generated by the existing stride prefetcher and preventing these predicted cache blocks from being replaced in the near future. Experimental results show that leveraging the PAC-PLRU with a stride prefetcher reduces the average L2 cache miss rate by 91% over a baseline system with only PLRU policy, and by 22% over a system using PLRU with an unconnected stride prefetcher. Most importantly, PAC-PLRU only requires minor modifications to existing cache architecture to get these benefits. The proposed PAC-PLRU policy is promising in fostering the connection between prefetching and replacement policies, and have a lasting impact on improving the overall cache performance.

STRIDE

Cache invalidation

Smart Cache

Cache pollution

10.1109/ccgrid.2011.27

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Citations (9)

Privacy-aware smart card based biometric authentication scheme for e-health

Peer-to-Peer Networking and Applications (2020)

Li Chen Ke Zhang

Mutual authentication

Session key

Security Analysis

10.1007/s12083-020-01008-y

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Citations (10)

Degree of link dependence-based LTE-V2V clustering and alarm information forwarding

2022 IEEE/CIC International Conference on Communications in China (ICCC) (2016)

Qian Chen Fan Wu Supeng Leng Sibo Qi Ke Zhang

The LTE-based V2V communications is considered to be the key technology to improve the performance of the intelligent transportation system. In order to allocate LTE resources efficiently on the scenarios of large-scale LTE-V2V networks, a novel clustering algorithm based on the similarity of mobility is proposed in this paper. We present a new definition - degree of link dependence to measure the similarity of mobility and a novel maintenance scheme which can reduce the rebuilt cost. In the proposed algorithm, vehicles are grouped in one cluster only if they are with similarity mobility. The nodes selected as heads will not be reselected every cycle but keep their roles on the straight road, and only under some certain circumstance the heads list will be updated; but the ordinary nodes always continuously execute attaching-head process by themselves. In addition, under the topological structure of cluster, we devise an alarm information forwarding scheme which can distribute the alarm packets efficiently and reliably under half-duplex communication mode. The simulation results represent the performance of the algorithm in terms of the cluster head lifetime and the average reattaching-head times of ordinary nodes.

Link (geometry)

Similarity (geometry)

10.1109/iccchina.2016.7636893

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Citations (7)