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Blockchain-Based Reliable Fog-Cloud Service Solution for IIoT

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Chinese Journal of Electronics 2021年第2期30卷 359-366页

作者： DAI Meiling xu siya SHAO Sujie GUO Shaoyong QIU xuesong XIONG Ao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Industrial Internet of things(IIoT) deploys a large number of smart devices to obtain industrial data, which will be transmitted to cloud for analysis to improve industrial productivity. The management of large-scale devices is complicated, and it's also a challenge to choose a high-quality cloud service for data analysis as the number of service with similar functions increases. To address these issues, we propose a reliable fog-cloud service solution with blockchain-based fog-cloud architecture. In fog layer, we build a management blockchain between fog servers and design a management method for industrial devices; In cloud layer, we construct a service blockchain between cloud service providers to form an open "service market". Quality of service and reputation based matching algorithm and reputation-based consensus algorithm are designed. The simulation results show correctness and efficiency of algorithms, and validate effectiveness of our proposed solution.

关键词： Data security Information networks Internet software Blockchain Fog-cloud architecture Reputation Cloud service matching Industrial internet of things

Intelligent service function chain mapping framework for cloud-and-edge-collaborative IoT

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The Journal of China Universities of Posts and Telecommunications 2022年第3期29卷 54-68,104页

作者： Yang Chao Li Yimin Li Tong xu siya Qi Jun Zhang Yu Information and Communication Branch State Grid Liaoning Electric Power Co.Ltd.Shenyang 110004China State Key Laboratory of Networking and Switching Technology Beijing University of Postsand TelecommunicationsBeijing 100876China State Grid Liaoning Electric Power Research Institute Shenyang 110004China

With the rapid development of Internet of thing(IoT) technology, it has become a challenge to deal with the increasing number and diverse requirements of IoT services. By combining burgeoning network function virtualization(NFV) technology with cloud computing and mobile edge computing(MEC), an NFV-enabled cloud-and-edge-collaborative IoT(CECIoT) architecture can efficiently provide flexible service for IoT traffic in the form of a service function chain(SFC) by jointly utilizing edge and cloud resources. In this promising architecture, a difficult issue is how to balance the consumption of resource and energy in SFC mapping. To overcome this challenge, an intelligent energy-and-resource-balanced SFC mapping scheme is designed in this paper. It takes the comprehensive deployment consumption as the optimization goal, and applies a deep Q-learning(DQL)-based SFC mapping(DQLBM) algorithm as well as an energy-based topology adjustment(EBTA) strategy to make efficient use of the limited network resources, while satisfying the delay requirement of users. Simulation results show that the proposed scheme can decrease service delay, as well as energy and resource consumption.

关键词： Internet of thing(IoT) mobile edge computing network function virtualization(NFV) deep Q-learning(DQL)

Load-Balancing and QoS Based Dynamic Resource Allocation Method for Smart Gird Fiber-Wireless Networks

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Chinese Journal of Electronics 2019年第6期28卷 1234-1243页

作者： xu siya LI Peng QI Feng GUO Shaoyong ZHOU Guiping DENG Wei LI Yueyue State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Shenyang Electric Power Supply Company State Grid Liaoning Electric Power Company Co.LTd. Beijing Guodiantong Network Technical Co.LTd.

Real-time network resource allocation based on virtualization technology is an important method to solve the solidification problem of Fiber-wireless(Fi-Wi) access networks. To increase the resource utilization and meet the specific QoS requirements of smart gird communication services, a Load-balancing and QoS based dynamic resource allocation method(LbQ-DR) is proposed with three sub-mechanisms. A time-window based substrate network resource update mechanism is designed to describe the realtime resource consumption of substrate networks, which can balance the accuracy of resource status and the complexity of the allocation algorithm.A QoS-based Virtual network request(VNR) sorting mechanism is presented to precisely calculate the priority of VNRs and reasonably sort the incoming services.A load-balancing based resource allocation mechanism is designed to avoid unbalanced resource ***, the channel interference is considered in the cost of embedding and a collision domain mechanism is introduced to decrease interference. Simulation results demonstrate that the proposed method can provide heterogeneous smart grid businesses with differentiated service, improve the utilization and economic benefits of the network and make the network more balanced.

关键词： Load-balancing Quality of Service(QoS) Smart grid communication networks Fiber-wireless(Fi-Wi) networks Resource allocation

A QoS-Aware Packet Scheduling Mechanism in Cognitive Radio Networks for Smart Grid Applications

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China Communications 2016年第2期13卷 68-78页

作者： xu siya WEI Lei LIU Zhu GUO Shaoyong QIU xuesong MENG Luoming State Key Laboratory of Network and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Information&Telecommunication Branch of Nanjing Power Supply Company of State Grid Jiangsu Electric Power Company Nanjing 210008China State Grid Information&Telecommunication Co. Ltd.Beijing 100031China

Cognitive radio sensor network is applied to facilitate network monitoring and management, and achieves high spectrum efficiencies in smart grid. However, the conventional traffic scheduling mechanisms are hard to provide guaranteed quality of service for the secondary users. It is because that they ignore the influence of diverse transition requirements in heterogeneous traffi c. Therefore, a novel Qo S-aware packet scheduling mechanism is proposed to improve transmission quality for secondary users. In this mechanism, a Qo S-based prioritization model is established to address data classification firstly. And then, channel quality and the effect of channel switch are integrated into priority-based packet scheduling mechanism. At last, the simulation is implemented with MATLAB and OPNET. The results show that the proposed scheduling mechanism improves the transmission quality of high-priority secondary users and increase the whole system utilization by 10%.

关键词： smart grid cognitive radio sensornetworks (CRSN) quality of service (QoS) scheduling mechanism

Deep Reinforcement Learning Empowered Edge Collaborative Caching Scheme for Internet of Vehicles

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Computer Systems Science & Engineering 2022年第7期42卷 271-287页

作者： Xin Liu siya xu Chao Yang Zhili Wang Hao Zhang Jingye Chi Qinghan Li State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China Information and Communication Branch State Grid Liaoning Electric Power Co.Ltd.Shenyang110000China China Global Energy Interconnection Research Institute Nanjing210000China Syracuse University New York13244USA

With the development of internet of vehicles,the traditional centralized content caching mode transmits content through the core network,which causes a large delay and cannot meet the demands for delay-sensitive *** solve these problems,on basis of vehicle caching network,we propose an edge colla-borative caching *** side unit(RSU)and mobile edge computing(MEC)are used to collect vehicle information,predict and cache popular content,thereby provide low-latency content delivery ***,the storage capa-city of a single RSU severely limits the edge caching performance and cannot handle intensive content requests at the same *** content sharing,col-laborative caching can relieve the storage burden on caching ***,we integrate RSU and collaborative caching to build a MEC-assisted vehicle edge collaborative caching(MVECC)scheme,so as to realize the collaborative caching among cloud,edge and *** uses deep reinforcement learning to pre-dict what needs to be cached on RSU,which enables RSUs to cache more popular *** addition,MVECC also introduces a mobility-aware caching replace-ment scheme at the edge network to reduce redundant cache and improving cache efﬁciency,which allows RSU to dynamically replace the cached content in response to the mobility of *** simulation results show that the pro-posed MVECC scheme can improve cache performance in terms of energy cost and content hit rate.

关键词： Internet of vehicles vehicle caching network collaborative caching caching replacement deep reinforcement learning

Liveness detection of occluded face based on dual-modality convolutional neural network

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The Journal of China Universities of Posts and Telecommunications 2021年第4期28卷 1-12页

作者： Ming Yue Li Wenmin xu siya Gao Lifang Zhang Hua Shao Sujie Yang Huifeng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China State Grid Hebei Information and Telecommunication Branch Shijiazhuang 050013China

Facial recognition has become the most common identity authentication technologies. However, problems such as uneven light and occluded faces have increased the hardness of liveness detection. Nevertheless, there are a few pieces of research on face liveness detection under occlusion conditions. This paper designs a face recognition technique suitable for different degrees of facial occlusion, which employs the facial datasets of near-infrared(NIR) images and visible(VIS) light images to examine the single-modality detection accuracy rate(experimental control group) and the corresponding high-dimensional features through the residual network(ResNet). Based on the idea of data fusion, we propose two feature fusion methods. The two methods extract and fuse the data of one and two convolutional layers from two single-modality detectors respectively. The fusion of high-dimensional features apply a new ResNet to get the dual-modality detection accuracy. And then, a new ResNet is applied to test the accuracy of dual-modality detection. The experimental results show that the dual-modality face liveness detection model improves face live detection accuracy and robustness compared with the single-modality. The fusion of two-layer features from the single-modality detector can also improve face detection accuracy by utilizing the above-mentioned dual-modality detector, and it doesn’t increase the algorithm’s complexity.

关键词： face deception face occlusion near-infrared image residual network feature fusion

A Trusted Edge Resource Allocation Framework for Internet of Vehicles

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Computers, Materials & Continua 2023年第11期77卷 2629-2644页

作者： Yuxuan Zhong siya xu Boxian Liao Jizhao Lu Huiping Meng Zhili Wang Xingyu Chen Qinghan Li State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China State Grid Henan Information&Telecommunication Company Zhengzhou450018China School of Information Studies Syracuse UniversitySyracuse13244USA

With the continuous progress of information technique,assisted driving technology has become an effective technique to avoid traffic *** to the complex road conditions and the threat of vehicle information being attacked and tampered with,it is difficult to ensure information *** paper uses blockchain to ensure the safety of driving information and introduces mobile edge computing technology to monitor vehicle information and road condition information in real time,calculate the appropriate speed,and plan a reasonable driving route for the *** solve these problems,this paper proposes a trusted edge resource allocation framework for assisted driving service,which includes two stages:the blockchain generation stage(the first stage)and assisted driving service stage(the second stage).Furthermore,in the first stage,a delay-and-throughput-oriented block generation model for the mobile terminal is *** the second stage,a balanced offloading algorithm for assisted driving service based on edge collaboration is proposed to solve the problems of unbalanced load of cluster mobile edge computing(MEC)servers and low resource utilization of the *** this paper optimizes the throughput of blockchain and delay of the transportation network through deep reinforcement learning(DRL)***,compared with joint computation and communication resources’allocation(JCCR)and resource allocation method based on binary offloading(RAB),our proposed scheme can optimize the delay by 7.4%and 26.7%,and support various application services of the vehicular networks more effectively.

关键词： Blockchain load balancing vehicular networks resource allocation

Brain Pathology in COVID-19:Clinical Manifestations and Potential Mechanisms

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Neuroscience Bulletin 2024年第3期40卷 383-400页

作者： Zhixing xu Hui Wang siya Jiang Jiao Teng Dongxu Zhou Zhong Chen Chengping Wen Zhenghao xu First School of Clinical Medicine Zhejiang Chinese Medical UniversityHangzhou 310053China School of Pharmaceutical Sciences Zhejiang Chinese Medical UniversityHangzhou 310053China Second School of Clinical Medicine Zhejiang Chinese Medical UniversityHangzhou 310053China Afliated Lin’an People’s Hospital of Hangzhou Medical College First People’s Hospital of Hangzhou Lin’an DistrictLin’anHangzhou 311300China School of Life Sciences Zhejiang Chinese Medical UniversityHangzhou 310053China Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province College of Pharmaceutical ScienceZhejiang Chinese Medical UniversityHangzhou 310053China Laboratory of Rheumatology and Institute of TCM Clinical Basic Medicine College of Basic Medical ScienceZhejiang Chinese Medical UniversityHangzhou 310053China

Neurological manifestations of coronavirus disease 2019(COVID-19)are less noticeable than the respiratory symptoms,but they may be associated with disability and mortality in *** though Omicron caused less severe disease than Delta,the incidence of neurological manifestations is *** than 30%of patients experienced“brain fog”,delirium,stroke,and cognitive impairment,and over half of these patients presented abnormal neuroimaging *** this review,we summarize current advances in the clinical findings of neurological manifestations in COVID-19 patients and compare them with those in patients with influenza *** also illustrate the structure and cellular invasion mechanisms of SARS-CoV-2 and describe the pathway for central SARS-CoV-2 *** addition,we discuss direct damage and other pathological conditions caused by SARS-CoV-2,such as an aberrant interferon response,cytokine storm,lymphopenia,and hypercoagulation,to provide treatment *** review may offer new insights into preventing or treating brain damage in COVID-19.

关键词： COVID-19 SARS-CoV-2 Brain Neurological pathology

补充维生素D对非酒精性脂肪肝病患者肠道菌群的影响

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华南理工大学学报（自然科学版） 2022年第11期50卷 62-73页

作者：韩源平徐思雅蒋希乐四川大学生命科学学院四川成都610065 四川大学华西医院四川成都610041

为探索补充维生素D(VD)对于NAFLD患者肠道菌群的影响。本研究共纳入29例NAFLD患者,采用随机分组法分为对照组(只接受内科治疗,n=15)和VD组(在内科治疗的基础上,补充维生素D_(2),n=14)。根据中国药典的剂量标准,对VD组NAFLD患者一次性给... 详细信息

为探索补充维生素D(VD)对于NAFLD患者肠道菌群的影响。本研究共纳入29例NAFLD患者,采用随机分组法分为对照组(只接受内科治疗,n=15)和VD组(在内科治疗的基础上,补充维生素D_(2),n=14)。根据中国药典的剂量标准,对VD组NAFLD患者一次性给予60万国际单位VD_(2),肌肉注射。30天后测定患者血清生化指标,采集患者粪便,进行16S r RNA测序分析,比较治疗前后肠道菌群在多样性、丰度的变化,以及肠道菌群与生化指标的相关性。研究结果表明:补充VD之后,NAFLD患者血清丙氨酸氨基转移酶(ALT)水平显著降低(P0.05),但毛螺菌科丰度显著降低(P<0.01),丹毒丝菌科(P<0.01)、丹毒丝菌科未命名属(P<0.05)丰度显著升高;毛螺菌科相对丰度水平与血清甘油三酯(TG)(P<0.05)呈显著正相关,丹毒丝菌科(P<0.01)和丹毒丝菌科未命名属(P<0.05)与血清25(OH)D水平呈显著正相关,而与ALT、天门冬氨酸氨基转移酶(AST)呈显著负相关(ALT:P<0.000 1;AST:P<0.05);补充VD能够显著降低血清ALT水平,对NAFLD患者肝损伤有一定改善作用;VD能显著降低患者肠道菌群毛螺菌科的丰度,显著提高丹毒丝菌科和丹毒丝菌科未命名属的丰度,进而降低血清TG、ALT和AST水平。

关键词：非酒精性脂肪肝病维生素D 肠道菌群血清TG水平血清ALT水平血清AST水平