On December 18,2023,the Jishishan area in Gansu Province was jolted by a M_(S) 6.2 earthquake,which is the most powerful seismic event that occurred throughout the year in China.The earthquake occurred along the NWtre...On December 18,2023,the Jishishan area in Gansu Province was jolted by a M_(S) 6.2 earthquake,which is the most powerful seismic event that occurred throughout the year in China.The earthquake occurred along the NWtrending Lajishan fault(LJSF),a large tectonic transformation zone.After this event,China Earthquake Networks Center(CENC)has timely published several reports about seismic sources for emergency responses.The earthquake early warning system issued the first alert 4.9 s after the earthquake occurrence,providing prompt notification that effectively mitigated panics,injuries,and deaths of residents.The near real-time focal mechanism solution indicates that this earthquake is associated with a thrust fault.The distribution of aftershocks,the rupture process,and the recorded amplitudes from seismic monitoring and GNSS stations,all suggest that the mainshock rupture predominately propagates to the northwest direction.The duration of the rupture process is~12 s,and the largest slip is located at approximately 6.3 km to the NNW from the epicenter,with a peak slip of 0.12 m at~8 km depth.Seismic station N0028 recorded the highest instrumental intensity,which is 9.4 on the Mercalli scale.The estimated intensity map shows a seismic intensity reaching up to IX near the rupture area,consistent with field survey results.The aftershocks(up to December 22,2023)are mostly distributed in the northwest direction within~20 km of the epicenter.This earthquake caused serious casualties and house collapses,which requires further investigations into the impact of this earthquake.展开更多
The ongoing expansion of the Industrial Internet of Things(IIoT)is enabling the possibility of effective Industry 4.0,where massive sensing devices in heterogeneous environments are connected through dedicated communi...The ongoing expansion of the Industrial Internet of Things(IIoT)is enabling the possibility of effective Industry 4.0,where massive sensing devices in heterogeneous environments are connected through dedicated communication protocols.This brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face,providing ad-hoc functions for scheduling and guaranteeing the network operations.Recently,the large development of SoftwareDefined Networking(SDN)and Artificial Intelligence(AI)technologies have made feasible the design and control of scalable and secure IIoT networks.This paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT networks.After surveying the state-of-the-art research efforts in the subject,the paper introduces a candidate architecture for AI-enabled Software-Defined IIoT Network(AI-SDIN)that divides the traditional industrial networks into three functional layers.And with this aim in mind,key technologies(Blockchain-based Data Sharing,Intelligent Wireless Data Sensing,Edge Intelligence,Time-Sensitive Networks,Integrating SDN&TSN,Distributed AI)and improve applications based on AISDIN are also discussed.Further,the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks.展开更多
Integrated satellite-terrestrial network(ISTN)has been considered a novel network architecture to achieve global three-dimensional coverage and ultra-wide area broadband access anytime and anywhere.Being a promising p...Integrated satellite-terrestrial network(ISTN)has been considered a novel network architecture to achieve global three-dimensional coverage and ultra-wide area broadband access anytime and anywhere.Being a promising paradigm,cloud computing and mobile edge computing(MEC)have been identified as key technology enablers for ISTN to further improve quality of service and business continuity.However,most of the existing ISTN studies based on cloud computing and MEC regard satellite networks as relay networks,ignoring the feasibility of directly deploying cloud computing nodes and edge computing nodes on satellites.In addition,most computing tasks are transferred to cloud servers or offloaded to nearby edge servers,the layered design of integrated satellite-air-terrestrial architecture and the cloud-edge-device cooperative processing problems have not been fully considered.Therefore,different from previous works,this paper proposed a novel satellite-air-terrestrial layered architecture for cloud-edge-device collaboration,named SATCECN.Then this paper analyzes the appropriate deployment locations of cloud servers and edge servers in ISTN,and describes the processing flow of typical satellite computing tasks.For computing resource allocation problems,this paper proposed a device-edge-cloud Multi-node Cross-layer Collaboration Computing(MCCC)method to find the optimal task allo-cation strategy that minimizes the task completion delay and the weighted system energy consumption.Furthermore,the approximate optimal solutions of the optimization model are obtained by using successive convex approxi-mation algorithm,and the outstanding advantages of the proposed method in reducing system energy consumption and task execution delay are verified through experiments.Finally,some potential issues and directions for future research are highlighted.展开更多
Underwater Wireless Sensor Networks(UWSNs)are widely used in many fields,such as regular marine monitoring and disaster warning.However,UWSNs are still subject to various limitations and challenges:ocean interferences...Underwater Wireless Sensor Networks(UWSNs)are widely used in many fields,such as regular marine monitoring and disaster warning.However,UWSNs are still subject to various limitations and challenges:ocean interferences and noises are high,bandwidths are narrow,and propagation delays are high.Sensor batteries have limited energy and are difficult to be replaced or recharged.Accordingly,the design of routing protocols is one of the solutions to these problems.Aiming at reducing and balancing network energy consumption and effectively extending the life cycle of UWSNs,this paper proposes a Hierarchical Adaptive Energy-efficient Clustering Routing(HAECR)strategy.First,this strategy divides hierarchical regions based on the depth of the sensor node in a three-dimensional(3D)space.Second,sensor nodes form different competition radii based on their own relevant attributes and remaining energy.Nodes in the same layer compete freely to form clusters of different sizes.Finally,the transmission path between clusters is determined according to comprehensive factors,such as link quality,and then the optimal route is planned.The simulation experiment is conducted in the monitoring range of the 3D space.The simulation results prove that the HAECR clustering strategy is superior to LEACH and UCUBB in terms of balancing and reducing energy consumption,extending the network lifetime,and increasing the number of data transmissions.展开更多
The rapid development of information technology promotes the transformation and development of future air combat,frommechanization to informatization,intelligence,and multiplatform integration.For the multiplatform av...The rapid development of information technology promotes the transformation and development of future air combat,frommechanization to informatization,intelligence,and multiplatform integration.For the multiplatform avionics system in the unmanned aerial vehicle(UAV)-based network,we aim to address the data routing and sharing issues and propose an integrated communication effectiveness metric.The proposed integrated communication effectiveness is a hierarchical metric consisting of link effectiveness,node effectiveness,and data effectiveness.The link quality,link stability,node honesty,node ability,and data value are concurrently taken into account.We give the normal mathematical expression for the integrated communication effectiveness.We propose a hop-by-hop routing scheme based on a Q-learning algorithm considering the proposed effectiveness metric.Simulation results demonstrate that the proposed scheme is able to find the most efficient routing in the UAV network.展开更多
基金supported by China Earthquake Administration Science for Earthquake Resilience(XH23050YB)Natural Science Foundation of China(42304072).
文摘On December 18,2023,the Jishishan area in Gansu Province was jolted by a M_(S) 6.2 earthquake,which is the most powerful seismic event that occurred throughout the year in China.The earthquake occurred along the NWtrending Lajishan fault(LJSF),a large tectonic transformation zone.After this event,China Earthquake Networks Center(CENC)has timely published several reports about seismic sources for emergency responses.The earthquake early warning system issued the first alert 4.9 s after the earthquake occurrence,providing prompt notification that effectively mitigated panics,injuries,and deaths of residents.The near real-time focal mechanism solution indicates that this earthquake is associated with a thrust fault.The distribution of aftershocks,the rupture process,and the recorded amplitudes from seismic monitoring and GNSS stations,all suggest that the mainshock rupture predominately propagates to the northwest direction.The duration of the rupture process is~12 s,and the largest slip is located at approximately 6.3 km to the NNW from the epicenter,with a peak slip of 0.12 m at~8 km depth.Seismic station N0028 recorded the highest instrumental intensity,which is 9.4 on the Mercalli scale.The estimated intensity map shows a seismic intensity reaching up to IX near the rupture area,consistent with field survey results.The aftershocks(up to December 22,2023)are mostly distributed in the northwest direction within~20 km of the epicenter.This earthquake caused serious casualties and house collapses,which requires further investigations into the impact of this earthquake.
基金This work was supported by the six talent peaks project in Jiangsu Province(No.XYDXX-012)Natural Science Foundation of China(No.62002045),China Postdoctoral Science Foundation(No.2021M690565)Fundamental Research Funds for the Cornell University(No.N2117002).
文摘The ongoing expansion of the Industrial Internet of Things(IIoT)is enabling the possibility of effective Industry 4.0,where massive sensing devices in heterogeneous environments are connected through dedicated communication protocols.This brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face,providing ad-hoc functions for scheduling and guaranteeing the network operations.Recently,the large development of SoftwareDefined Networking(SDN)and Artificial Intelligence(AI)technologies have made feasible the design and control of scalable and secure IIoT networks.This paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT networks.After surveying the state-of-the-art research efforts in the subject,the paper introduces a candidate architecture for AI-enabled Software-Defined IIoT Network(AI-SDIN)that divides the traditional industrial networks into three functional layers.And with this aim in mind,key technologies(Blockchain-based Data Sharing,Intelligent Wireless Data Sensing,Edge Intelligence,Time-Sensitive Networks,Integrating SDN&TSN,Distributed AI)and improve applications based on AISDIN are also discussed.Further,the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks.
基金supported by the Academic Discipline,Post-Graduate Education Project of the Beijing Municipal Commission of Education,and Fundamental Research Funds for the Central Universities under Grant 2022YJS015the National Natural Science Foundation of China under Grant 62173026.
文摘Integrated satellite-terrestrial network(ISTN)has been considered a novel network architecture to achieve global three-dimensional coverage and ultra-wide area broadband access anytime and anywhere.Being a promising paradigm,cloud computing and mobile edge computing(MEC)have been identified as key technology enablers for ISTN to further improve quality of service and business continuity.However,most of the existing ISTN studies based on cloud computing and MEC regard satellite networks as relay networks,ignoring the feasibility of directly deploying cloud computing nodes and edge computing nodes on satellites.In addition,most computing tasks are transferred to cloud servers or offloaded to nearby edge servers,the layered design of integrated satellite-air-terrestrial architecture and the cloud-edge-device cooperative processing problems have not been fully considered.Therefore,different from previous works,this paper proposed a novel satellite-air-terrestrial layered architecture for cloud-edge-device collaboration,named SATCECN.Then this paper analyzes the appropriate deployment locations of cloud servers and edge servers in ISTN,and describes the processing flow of typical satellite computing tasks.For computing resource allocation problems,this paper proposed a device-edge-cloud Multi-node Cross-layer Collaboration Computing(MCCC)method to find the optimal task allo-cation strategy that minimizes the task completion delay and the weighted system energy consumption.Furthermore,the approximate optimal solutions of the optimization model are obtained by using successive convex approxi-mation algorithm,and the outstanding advantages of the proposed method in reducing system energy consumption and task execution delay are verified through experiments.Finally,some potential issues and directions for future research are highlighted.
文摘Underwater Wireless Sensor Networks(UWSNs)are widely used in many fields,such as regular marine monitoring and disaster warning.However,UWSNs are still subject to various limitations and challenges:ocean interferences and noises are high,bandwidths are narrow,and propagation delays are high.Sensor batteries have limited energy and are difficult to be replaced or recharged.Accordingly,the design of routing protocols is one of the solutions to these problems.Aiming at reducing and balancing network energy consumption and effectively extending the life cycle of UWSNs,this paper proposes a Hierarchical Adaptive Energy-efficient Clustering Routing(HAECR)strategy.First,this strategy divides hierarchical regions based on the depth of the sensor node in a three-dimensional(3D)space.Second,sensor nodes form different competition radii based on their own relevant attributes and remaining energy.Nodes in the same layer compete freely to form clusters of different sizes.Finally,the transmission path between clusters is determined according to comprehensive factors,such as link quality,and then the optimal route is planned.The simulation experiment is conducted in the monitoring range of the 3D space.The simulation results prove that the HAECR clustering strategy is superior to LEACH and UCUBB in terms of balancing and reducing energy consumption,extending the network lifetime,and increasing the number of data transmissions.
文摘The rapid development of information technology promotes the transformation and development of future air combat,frommechanization to informatization,intelligence,and multiplatform integration.For the multiplatform avionics system in the unmanned aerial vehicle(UAV)-based network,we aim to address the data routing and sharing issues and propose an integrated communication effectiveness metric.The proposed integrated communication effectiveness is a hierarchical metric consisting of link effectiveness,node effectiveness,and data effectiveness.The link quality,link stability,node honesty,node ability,and data value are concurrently taken into account.We give the normal mathematical expression for the integrated communication effectiveness.We propose a hop-by-hop routing scheme based on a Q-learning algorithm considering the proposed effectiveness metric.Simulation results demonstrate that the proposed scheme is able to find the most efficient routing in the UAV network.
