To ensure flight safety,the complex network method is used to study the influence and invulnerability of air traffic cyber physical system(CPS)nodes.According to the rules of air traffic management,the logical couplin...To ensure flight safety,the complex network method is used to study the influence and invulnerability of air traffic cyber physical system(CPS)nodes.According to the rules of air traffic management,the logical coupling relationship between routes and sectors is analyzed,an air traffic CPS network model is constructed,and the indicators of node influence and invulnerability are established.The K-shell algorithm is improved to identify node influence,and the invulnerability is analyzed under random and selective attacks.Taking Airspace in Eastern China as an example,its influential nodes are sorted by degree,namely,K-shell,the improved K-shell(IKS)and betweenness centrality.The invulnerability of air traffic CPS under different attacks is analyzed.Results show that IKS can effectively identify the influential nodes in the air traffic CPS network,and IKS and betweenness centrality are the two key indicators that affect the invulnerability of air traffic CPS.展开更多
Traffic matrix is an abstract representation of the traffic volume flowing between sets of source and destination pairs.It is a key input parameter of network operations management,planning,provisioning and traffic en...Traffic matrix is an abstract representation of the traffic volume flowing between sets of source and destination pairs.It is a key input parameter of network operations management,planning,provisioning and traffic engineering.Traffic matrix is also important in the context of OpenFlow-based networks.Because even good measurement systems can suffer from errors and data collection systems can fail,missing values are common.Existing matrix completion methods do not consider traffic exhibit characteristics and only provide a finite precision.To address this problem,this paper proposes a novel approach based on compressive sensing and traffic self-similarity to reconstruct the missing traffic flow data.Firstly,we analyze the realworld traffic matrix,which all exhibit lowrank structure,temporal smoothness feature and spatial self-similarity.Then,we propose Self-Similarity and Temporal Compressive Sensing(SSTCS) algorithm to reconstruct the missing traffic data.The extensive experiments with the real-world traffic matrix show that our proposed SSTCS can significantly reduce data reconstruction errors and achieve satisfactory accuracy comparing with the existing solutions.Typically SSTCS can successfully reconstruct the traffic matrix with less than 32%errors when as much as98%of the data is missing.展开更多
WSN has been developing from traditional environment monitoring applications to the ubiquitous information services such as the Congestion-oriented Intelligent Transportation System （ColTS）. However, the mobility of...WSN has been developing from traditional environment monitoring applications to the ubiquitous information services such as the Congestion-oriented Intelligent Transportation System （ColTS）. However, the mobility of nodes makes data dissemination a hard nut to crack. In this paper, we propose MSDD, a multiple mobile sinks data dissemination mechanism for solving the dissemination problem. The main ideas of MSDD are constructing a two-tier grid structure by a designated sink, exploiting a hierarchical monitoring mechanism, and employing a global agent to track the sink locations in order to make the emergencies reported to the sinks immediately, In addition, MSDD supports the query-driven data dissemination. Being compared with TTDD, MSDD is theoretically proved to have less communication overhead. We also validate MSDD outperforms TTDD in reliability and the emergency delivery latency bv simulations.展开更多
基金This work was supported by the Fundamental Research Funds for the Central Universities(No.3122019191).
文摘To ensure flight safety,the complex network method is used to study the influence and invulnerability of air traffic cyber physical system(CPS)nodes.According to the rules of air traffic management,the logical coupling relationship between routes and sectors is analyzed,an air traffic CPS network model is constructed,and the indicators of node influence and invulnerability are established.The K-shell algorithm is improved to identify node influence,and the invulnerability is analyzed under random and selective attacks.Taking Airspace in Eastern China as an example,its influential nodes are sorted by degree,namely,K-shell,the improved K-shell(IKS)and betweenness centrality.The invulnerability of air traffic CPS under different attacks is analyzed.Results show that IKS can effectively identify the influential nodes in the air traffic CPS network,and IKS and betweenness centrality are the two key indicators that affect the invulnerability of air traffic CPS.
基金This work is supported by the Prospcctive Research Project on Future Networks of Jiangsu Future Networks Innovation Institute under Grant No.BY2013095-1-05, the National Ba- sic Research Program of China （973） under Grant No. 2012CB315805 and the National Natural Science Foundation of China under Grants No. 61173167.
文摘Traffic matrix is an abstract representation of the traffic volume flowing between sets of source and destination pairs.It is a key input parameter of network operations management,planning,provisioning and traffic engineering.Traffic matrix is also important in the context of OpenFlow-based networks.Because even good measurement systems can suffer from errors and data collection systems can fail,missing values are common.Existing matrix completion methods do not consider traffic exhibit characteristics and only provide a finite precision.To address this problem,this paper proposes a novel approach based on compressive sensing and traffic self-similarity to reconstruct the missing traffic flow data.Firstly,we analyze the realworld traffic matrix,which all exhibit lowrank structure,temporal smoothness feature and spatial self-similarity.Then,we propose Self-Similarity and Temporal Compressive Sensing(SSTCS) algorithm to reconstruct the missing traffic data.The extensive experiments with the real-world traffic matrix show that our proposed SSTCS can significantly reduce data reconstruction errors and achieve satisfactory accuracy comparing with the existing solutions.Typically SSTCS can successfully reconstruct the traffic matrix with less than 32%errors when as much as98%of the data is missing.
基金This work was supported in part by China National Natural Science Foundation under Grant 61271185,and the Fundamental Research Funds for the Central Universities
文摘WSN has been developing from traditional environment monitoring applications to the ubiquitous information services such as the Congestion-oriented Intelligent Transportation System （ColTS）. However, the mobility of nodes makes data dissemination a hard nut to crack. In this paper, we propose MSDD, a multiple mobile sinks data dissemination mechanism for solving the dissemination problem. The main ideas of MSDD are constructing a two-tier grid structure by a designated sink, exploiting a hierarchical monitoring mechanism, and employing a global agent to track the sink locations in order to make the emergencies reported to the sinks immediately, In addition, MSDD supports the query-driven data dissemination. Being compared with TTDD, MSDD is theoretically proved to have less communication overhead. We also validate MSDD outperforms TTDD in reliability and the emergency delivery latency bv simulations.
