Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective funct...Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.展开更多
Air route network optimization,one of the essential parts of the airspace planning,is an effective way to optimize airspace resources,increase airspace capacity,and alleviate air traffic congestion.However,little has ...Air route network optimization,one of the essential parts of the airspace planning,is an effective way to optimize airspace resources,increase airspace capacity,and alleviate air traffic congestion.However,little has been done on the optimization of air route network in the fragmented airspace caused by prohibited,restricted,and dangerous areas（PRDs）.In this paper,an air route network optimization model is developed with the total operational cost as the objective function while airspace restriction,air route network capacity,and non-straight-line factors（NSLF） are taken as major constraints.A square grid cellular space,Moore neighbors,a fixed boundary,together with a set of rules for solving the route network optimization model are designed based on cellular automata.The empirical traffic of airports with the largest traffic volume in each of the 9 flight information regions in China's Mainland is collected as the origin-destination（OD） airport pair demands.Based on traffic patterns,the model generates 35 air routes which successfully avoids 144 PRDs.Compared with the current air route network structure,the number of nodes decreases by 41.67%,while the total length of flight segments and air routes drop by 32.03% and 5.82% respectively.The NSLF decreases by 5.82% with changes in the total length of the air route network.More importantly,the total operational cost of the whole network decreases by 6.22%.The computational results show the potential benefits of the model and the advantage of the algorithm.Optimization of air route network can significantly reduce operational cost while ensuring operation safety.展开更多
Air route crossing waypoint optimization is one of the effective ways to improve airspace utilization,capacity and resilience in dealing with air traffic congestion and delay.However,research is lacking on the optimiz...Air route crossing waypoint optimization is one of the effective ways to improve airspace utilization,capacity and resilience in dealing with air traffic congestion and delay.However,research is lacking on the optimization of multiple Crossing Waypoints(CWPs)in the fragmented airspace separated by Prohibited,Restricted and Dangerous areas(PRDs).To tackle this issue,this paper proposes an Artificial Potential Field(APF)model considering attractive forces produced by the optimal routes and repulsive forces generated by obstacles.An optimization framework based on the APF model is proposed to optimize the different airspace topologies varying the number of CWPs,air route segments and PRDs.Based on the framework,an adaptive method is developed to dynamically control the optimization process in minimizing the total air route cost.The proposed model is applied to a busy controlled airspace.And the obtained results show that after optimization the safety-related indicators:conflict number and controller workload reduced by 7.75%and 6.51%respectively.As for the cost-effectiveness indicators:total route length,total air route cost and non-linear coefficient,declined by 1.74%,3.13%and 1.70%respectively.While the predictability indicator,total flight delay,saw a notable reduction by 7.96%.The proposed framework and methodology can also provide an insight in the understanding of the optimization to other network systems.展开更多
Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,local...Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,localization,heterogeneous network,self-organization,and self-sufficient operation.In this background,the current study focuses on specially-designed communication link establishment for high connection stability of wireless mobile sensor networks,especially in disaster area network.Existing protocols focus on location-dependent communications and use networks based on typically-used Internet Protocol(IP)architecture.However,IP-based communications have a few limitations such as inefficient bandwidth utilization,high processing,less transfer speeds,and excessive memory intake.To overcome these challenges,the number of neighbors(Node Density)is minimized and high Mobility Nodes(Node Speed)are avoided.The proposed Geographic Drone Based Route Optimization(GDRO)method reduces the entire overhead to a considerable level in an efficient manner and significantly improves the overall performance by identifying the disaster region.This drone communicates with anchor node periodically and shares the information to it so as to introduce a drone-based disaster network in an area.Geographic routing is a promising approach to enhance the routing efficiency in MANET.This algorithm helps in reaching the anchor(target)node with the help of Geographical Graph-Based Mapping(GGM).Global Positioning System(GPS)is enabled on mobile network of the anchor node which regularly broadcasts its location information that helps in finding the location.In first step,the node searches for local and remote anticipated Expected Transmission Count(ETX),thereby calculating the estimated distance.Received Signal Strength Indicator(RSSI)results are stored in the local memory of the node.Then,the node calculates the least remote anticipated ETX,Link Loss Rate,and information to the new location.Freeway Heuristic algorithm improves the data speed,efficiency and determines the path and optimization problem.In comparison with other models,the proposed method yielded an efficient communication,increased the throughput,and reduced the end-to-end delay,energy consumption and packet loss performance in disaster area networks.展开更多
This study proposes the use of a novel integrated framework for 2D en route airspace sub-sectorization.The integrated framework combines the multi-commodity flow optimization approach,complex network cluster-ing appro...This study proposes the use of a novel integrated framework for 2D en route airspace sub-sectorization.The integrated framework combines the multi-commodity flow optimization approach,complex network cluster-ing approach,and Minimum Bounding Geometry(MBG)-coupled Rule-based Approach for boundary design.A decomposition-based discrete particle swarm optimization(DPSO)is used to solve the clustering problem.The output of the flow optimization is used as a guiding standard for the DPSO.Experimentations were performed using the Indian airspace sector to validate the framework and DPSO was run for different maximum number of generations(maxgen).The findings reveal that the multi-commodity flow approach captures system-wide flow operations.Clustering results corresponding to maxgen=100 and maxgen=150 perform best in terms of equitable and balanced distribution of cluster size and traffic load.The MBG-coupled Rule-based Approach leads to com-pact and convex sub-sector boundary design.Major implications of this research include dynamic adaptability of the integrated framework,increased sensitivity of sector design to network evolution,and a computationally tractable framework.The higher controllability of the proposed framework also offers an increased acceptance among practitioners.展开更多
The complexity and diversity of the cloud business and the continuous growth of new services put forward higher requirements for businessoriented adaptive reconstruction of cloud networks. Therefore, by introducing th...The complexity and diversity of the cloud business and the continuous growth of new services put forward higher requirements for businessoriented adaptive reconstruction of cloud networks. Therefore, by introducing the construction idea of reconfiguration network into cloud network, this paper designs a business-oriented dynamic reconfiguration model of cloud computing network. In the design process of the model, the formal description of the model reconfigurable goal, the target-tree decomposition method and the target ordergraph relation representation method were proposed. A rapid-reconfiguration method based on similar node transformation, a specific reconfiguration process of the model and reconfiguration optimization algorithm were also presented in detail. The model provided an effective resolution to better realize the flexibility, scalability, security and self-adaptability of the network in the cloud environment, which ensures the reconfiguration continuity of the cloud network to meet ever-changing business requirements. Finally, the performance of the model is verified, which proves the high efficiency of the model the dynamic reconfiguration.展开更多
A Wireless Sensor Network(WSN)is constructed with numerous sensors over geographical regions.The basic challenge experienced while designing WSN is in increasing the network lifetime and use of low energy.As sensor no...A Wireless Sensor Network(WSN)is constructed with numerous sensors over geographical regions.The basic challenge experienced while designing WSN is in increasing the network lifetime and use of low energy.As sensor nodes are resource constrained in nature,novel techniques are essential to improve lifetime of nodes in WSN.Nodes energy is considered as an important resource for sensor node which are battery powered based.In WSN,energy is consumed mainly while data is being transferred among nodes in the network.Several research works are carried out focusing on preserving energy of nodes in the network and made network to live longer.Moreover,this network is threatened by attacks like vampire attack where the network is loaded by fake traffic.Here,Dual Encoding Recurrent Neural network(DERNNet)is proposed for classifying the vampire nodes s node in the network.Moreover,the Grey Wolf Optimization(GWO)algorithm helps for transferring the data by determining best solutions to optimally select the aggregation points;thereby maximizing battery/lifetime of the network nodes.The proposed method is evaluated with three standard approaches namely Knowledge and Intrusion Detection based Secure Atom Search Routing(KIDSASR),Risk-aware Reputation-based Trust(RaRTrust)model and Activation Function-based Trusted Neighbor Selection(AF-TNS)in terms of various parameters.These existing methods may lead to wastage of energy due to vampire attack,which further reduce the lifetime and increase average energy consumed in the network.Hence,the proposed DERNNet method achieves 31.4%of routing overhead,23%of end-to-end delay,78.6%of energy efficiency,94.8%of throughput,28.2%of average latency,92.4%of packet delivery ratio,85.2%of network lifetime,and 94.3%of classification accuracy.展开更多
针对水下传感器网络中节点能耗不均衡和能量有限的问题,提出一种能耗均衡与节能的自适应水下路由协议ECBES(energy consumption balanced and energy saving adaptive underwater routing protocol)。构建双区非均匀分层拓扑。基于能耗...针对水下传感器网络中节点能耗不均衡和能量有限的问题,提出一种能耗均衡与节能的自适应水下路由协议ECBES(energy consumption balanced and energy saving adaptive underwater routing protocol)。构建双区非均匀分层拓扑。基于能耗均衡因子,利用拓扑和节点剩余能量计算节点转发优先级,实现自适应转发节点选择,均衡网络能耗。与此同时,通过候选转发区域各分区域中节点参与转发数据包的比例确定次优候选转发区域,将次优候选转发区域作为初始策略,利用策略迭代思想确定最优候选转发区域,保证投递率的同时减少不同网络规模中重复数据包的转发,降低网络的整体能耗。仿真结果表明,ECBES相比VBF、ES-VBF和ALRP,在不同节点数量下,节点死亡率均最低,在保证数据包投递率的同时,能耗最少。展开更多
基金supported by the the Youth Science and Technology Innovation Fund (Science)(Nos.NS2014070, NS2014070)
文摘Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.
基金co-supported by the National Natural Science Foundation of China(No.61304190)the Natural Science Foundation of Jiangsu Province(No.BK20130818)the Fundamental Research Funds for the Central Universities of China(No.NJ20150030)
文摘Air route network optimization,one of the essential parts of the airspace planning,is an effective way to optimize airspace resources,increase airspace capacity,and alleviate air traffic congestion.However,little has been done on the optimization of air route network in the fragmented airspace caused by prohibited,restricted,and dangerous areas（PRDs）.In this paper,an air route network optimization model is developed with the total operational cost as the objective function while airspace restriction,air route network capacity,and non-straight-line factors（NSLF） are taken as major constraints.A square grid cellular space,Moore neighbors,a fixed boundary,together with a set of rules for solving the route network optimization model are designed based on cellular automata.The empirical traffic of airports with the largest traffic volume in each of the 9 flight information regions in China's Mainland is collected as the origin-destination（OD） airport pair demands.Based on traffic patterns,the model generates 35 air routes which successfully avoids 144 PRDs.Compared with the current air route network structure,the number of nodes decreases by 41.67%,while the total length of flight segments and air routes drop by 32.03% and 5.82% respectively.The NSLF decreases by 5.82% with changes in the total length of the air route network.More importantly,the total operational cost of the whole network decreases by 6.22%.The computational results show the potential benefits of the model and the advantage of the algorithm.Optimization of air route network can significantly reduce operational cost while ensuring operation safety.
基金the Civil Aviation Authority of Singapore and the Nanyang Technological University,Singapore under their collaboration in the Air Traffic Management Research Institute。
文摘Air route crossing waypoint optimization is one of the effective ways to improve airspace utilization,capacity and resilience in dealing with air traffic congestion and delay.However,research is lacking on the optimization of multiple Crossing Waypoints(CWPs)in the fragmented airspace separated by Prohibited,Restricted and Dangerous areas(PRDs).To tackle this issue,this paper proposes an Artificial Potential Field(APF)model considering attractive forces produced by the optimal routes and repulsive forces generated by obstacles.An optimization framework based on the APF model is proposed to optimize the different airspace topologies varying the number of CWPs,air route segments and PRDs.Based on the framework,an adaptive method is developed to dynamically control the optimization process in minimizing the total air route cost.The proposed model is applied to a busy controlled airspace.And the obtained results show that after optimization the safety-related indicators:conflict number and controller workload reduced by 7.75%and 6.51%respectively.As for the cost-effectiveness indicators:total route length,total air route cost and non-linear coefficient,declined by 1.74%,3.13%and 1.70%respectively.While the predictability indicator,total flight delay,saw a notable reduction by 7.96%.The proposed framework and methodology can also provide an insight in the understanding of the optimization to other network systems.
文摘Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,localization,heterogeneous network,self-organization,and self-sufficient operation.In this background,the current study focuses on specially-designed communication link establishment for high connection stability of wireless mobile sensor networks,especially in disaster area network.Existing protocols focus on location-dependent communications and use networks based on typically-used Internet Protocol(IP)architecture.However,IP-based communications have a few limitations such as inefficient bandwidth utilization,high processing,less transfer speeds,and excessive memory intake.To overcome these challenges,the number of neighbors(Node Density)is minimized and high Mobility Nodes(Node Speed)are avoided.The proposed Geographic Drone Based Route Optimization(GDRO)method reduces the entire overhead to a considerable level in an efficient manner and significantly improves the overall performance by identifying the disaster region.This drone communicates with anchor node periodically and shares the information to it so as to introduce a drone-based disaster network in an area.Geographic routing is a promising approach to enhance the routing efficiency in MANET.This algorithm helps in reaching the anchor(target)node with the help of Geographical Graph-Based Mapping(GGM).Global Positioning System(GPS)is enabled on mobile network of the anchor node which regularly broadcasts its location information that helps in finding the location.In first step,the node searches for local and remote anticipated Expected Transmission Count(ETX),thereby calculating the estimated distance.Received Signal Strength Indicator(RSSI)results are stored in the local memory of the node.Then,the node calculates the least remote anticipated ETX,Link Loss Rate,and information to the new location.Freeway Heuristic algorithm improves the data speed,efficiency and determines the path and optimization problem.In comparison with other models,the proposed method yielded an efficient communication,increased the throughput,and reduced the end-to-end delay,energy consumption and packet loss performance in disaster area networks.
基金PMRF PM/MHRD-20-16823.03 for the financial support。
文摘This study proposes the use of a novel integrated framework for 2D en route airspace sub-sectorization.The integrated framework combines the multi-commodity flow optimization approach,complex network cluster-ing approach,and Minimum Bounding Geometry(MBG)-coupled Rule-based Approach for boundary design.A decomposition-based discrete particle swarm optimization(DPSO)is used to solve the clustering problem.The output of the flow optimization is used as a guiding standard for the DPSO.Experimentations were performed using the Indian airspace sector to validate the framework and DPSO was run for different maximum number of generations(maxgen).The findings reveal that the multi-commodity flow approach captures system-wide flow operations.Clustering results corresponding to maxgen=100 and maxgen=150 perform best in terms of equitable and balanced distribution of cluster size and traffic load.The MBG-coupled Rule-based Approach leads to com-pact and convex sub-sector boundary design.Major implications of this research include dynamic adaptability of the integrated framework,increased sensitivity of sector design to network evolution,and a computationally tractable framework.The higher controllability of the proposed framework also offers an increased acceptance among practitioners.
基金the National Natural Science Foundations of China (grant No. 61502531 and No. 61702550)the National Key Research and Development Plan (grant No. 2018YFB0803603 and No. 2016YFB0501901).
文摘The complexity and diversity of the cloud business and the continuous growth of new services put forward higher requirements for businessoriented adaptive reconstruction of cloud networks. Therefore, by introducing the construction idea of reconfiguration network into cloud network, this paper designs a business-oriented dynamic reconfiguration model of cloud computing network. In the design process of the model, the formal description of the model reconfigurable goal, the target-tree decomposition method and the target ordergraph relation representation method were proposed. A rapid-reconfiguration method based on similar node transformation, a specific reconfiguration process of the model and reconfiguration optimization algorithm were also presented in detail. The model provided an effective resolution to better realize the flexibility, scalability, security and self-adaptability of the network in the cloud environment, which ensures the reconfiguration continuity of the cloud network to meet ever-changing business requirements. Finally, the performance of the model is verified, which proves the high efficiency of the model the dynamic reconfiguration.
文摘A Wireless Sensor Network(WSN)is constructed with numerous sensors over geographical regions.The basic challenge experienced while designing WSN is in increasing the network lifetime and use of low energy.As sensor nodes are resource constrained in nature,novel techniques are essential to improve lifetime of nodes in WSN.Nodes energy is considered as an important resource for sensor node which are battery powered based.In WSN,energy is consumed mainly while data is being transferred among nodes in the network.Several research works are carried out focusing on preserving energy of nodes in the network and made network to live longer.Moreover,this network is threatened by attacks like vampire attack where the network is loaded by fake traffic.Here,Dual Encoding Recurrent Neural network(DERNNet)is proposed for classifying the vampire nodes s node in the network.Moreover,the Grey Wolf Optimization(GWO)algorithm helps for transferring the data by determining best solutions to optimally select the aggregation points;thereby maximizing battery/lifetime of the network nodes.The proposed method is evaluated with three standard approaches namely Knowledge and Intrusion Detection based Secure Atom Search Routing(KIDSASR),Risk-aware Reputation-based Trust(RaRTrust)model and Activation Function-based Trusted Neighbor Selection(AF-TNS)in terms of various parameters.These existing methods may lead to wastage of energy due to vampire attack,which further reduce the lifetime and increase average energy consumed in the network.Hence,the proposed DERNNet method achieves 31.4%of routing overhead,23%of end-to-end delay,78.6%of energy efficiency,94.8%of throughput,28.2%of average latency,92.4%of packet delivery ratio,85.2%of network lifetime,and 94.3%of classification accuracy.
文摘针对水下传感器网络中节点能耗不均衡和能量有限的问题,提出一种能耗均衡与节能的自适应水下路由协议ECBES(energy consumption balanced and energy saving adaptive underwater routing protocol)。构建双区非均匀分层拓扑。基于能耗均衡因子,利用拓扑和节点剩余能量计算节点转发优先级,实现自适应转发节点选择,均衡网络能耗。与此同时,通过候选转发区域各分区域中节点参与转发数据包的比例确定次优候选转发区域,将次优候选转发区域作为初始策略,利用策略迭代思想确定最优候选转发区域,保证投递率的同时减少不同网络规模中重复数据包的转发,降低网络的整体能耗。仿真结果表明,ECBES相比VBF、ES-VBF和ALRP,在不同节点数量下,节点死亡率均最低,在保证数据包投递率的同时,能耗最少。
