Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled...Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.展开更多
With the ever-expanding applications of vehicles and the development of wireless communication technology,the burgeoning unmanned aerial vehicle(UAV)assisted vehicular internet of things(UVIoTs)has emerged,where the g...With the ever-expanding applications of vehicles and the development of wireless communication technology,the burgeoning unmanned aerial vehicle(UAV)assisted vehicular internet of things(UVIoTs)has emerged,where the ground vehicles can experience more efficient wireless services by employing UAVs as a temporary mobile base station.However,due to the diversity of UAVs,there exist UAVs such as jammers to degenerate the performance of wireless communication between the normal UAVs and vehicles.To solve above the problem,in this paper,we propose a game based secure data transmission scheme in UVIoTs.Specifically,we exploit the offensive and defensive game to model the interactions between the normal UAVs and jammers.Here,the strategy of the normal UAV is to determine whether to transmit data,while that of the jammer is whether to interfere.We then formulate two optimization problems,i.e.,maximizing the both utilities of UAVs and jammers.Afterwards,we exploit the backward induction method to analyze the proposed countermeasures and finally solve the optimal solution.Lastly,the simulation results show that the proposed scheme can improve the wireless communication performance under the attacks of jammers compared with conventional schemes.展开更多
implementation of wireless technologies based on the vehicular ad hoc sensor network （VASNET） may provide support for the search and rescue （SAR） team to operate effectively in natural disaster events, such as lan...implementation of wireless technologies based on the vehicular ad hoc sensor network （VASNET） may provide support for the search and rescue （SAR） team to operate effectively in natural disaster events, such as landslide, earthquake, flooding, and tsunami. The operations of SAR team are very challenging in such events due to the possible damages of the existing telecommunication infrastructures. The existing deployment of the cellular communications infrastructure may be partially or completely destroyed after the occurrence of these natural disasters. Thus, the current VASNET infrastructure must be able to support the infrastructure-less network by integrating other green wireless technologies that can benefit the SAR team, which can indirectly save more human lives and reduce the number of casualties. Therefore, the integration of green Internet of things （loT） and VASNET is proposed to form a heterogeneous framework for data dissemination in SAR operations. In addition, this paper also discusses the existing lot framework in disaster scenarios with future research direction for IoT using on any aspect, especially related to the natural disaster scenarios.展开更多
With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number ...With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also increases.In addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient.Thus,there is a need to augment them with intelligent network intrusion detection techniques.Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times.However,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection methods.Deep learning solutions are lucrative options as they remove the necessity for feature selection.Therefore,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more heightened.This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers.Vehicular data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper.The proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing works.By running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.展开更多
Wireless Ad Hoc Networks consist of devices that are wirelessly connected.Mobile Ad Hoc Networks(MANETs),Internet of Things(IoT),and Vehicular Ad Hoc Networks(VANETs)are the main domains of wireless ad hoc network.Int...Wireless Ad Hoc Networks consist of devices that are wirelessly connected.Mobile Ad Hoc Networks(MANETs),Internet of Things(IoT),and Vehicular Ad Hoc Networks(VANETs)are the main domains of wireless ad hoc network.Internet is used in wireless ad hoc network.Internet is based on Transmission Control Protocol(TCP)/Internet Protocol(IP)network where clients and servers interact with each other with the help of IP in a pre-defined environment.Internet fetches data from a fixed location.Data redundancy,mobility,and location dependency are the main issues of the IP network paradigm.All these factors result in poor performance of wireless ad hoc networks.The main disadvantage of IP is that,it does not provide in-network caching.Therefore,there is a need to move towards a new network that overcomes these limitations.Named Data Network(NDN)is a network that overcomes these limitations.NDN is a project of Information-centric Network(ICN).NDN provides in-network caching which helps in fast response to user queries.Implementing NDN in wireless ad hoc network provides many benefits such as caching,mobility,scalability,security,and privacy.By considering the certainty,in this survey paper,we present a comprehensive survey on Caching Strategies in NDN-based Wireless AdHocNetwork.Various cachingmechanism-based results are also described.In the last,we also shed light on the challenges and future directions of this promising field to provide a clear understanding of what caching-related problems exist in NDN-based wireless ad hoc networks.展开更多
文摘Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.
基金This work is supported in part by NSFC(nos.U1808207,U20A20175)the Project of Shanghai Municipal Science and Technology Commission(18510761000).
文摘With the ever-expanding applications of vehicles and the development of wireless communication technology,the burgeoning unmanned aerial vehicle(UAV)assisted vehicular internet of things(UVIoTs)has emerged,where the ground vehicles can experience more efficient wireless services by employing UAVs as a temporary mobile base station.However,due to the diversity of UAVs,there exist UAVs such as jammers to degenerate the performance of wireless communication between the normal UAVs and vehicles.To solve above the problem,in this paper,we propose a game based secure data transmission scheme in UVIoTs.Specifically,we exploit the offensive and defensive game to model the interactions between the normal UAVs and jammers.Here,the strategy of the normal UAV is to determine whether to transmit data,while that of the jammer is whether to interfere.We then formulate two optimization problems,i.e.,maximizing the both utilities of UAVs and jammers.Afterwards,we exploit the backward induction method to analyze the proposed countermeasures and finally solve the optimal solution.Lastly,the simulation results show that the proposed scheme can improve the wireless communication performance under the attacks of jammers compared with conventional schemes.
文摘implementation of wireless technologies based on the vehicular ad hoc sensor network （VASNET） may provide support for the search and rescue （SAR） team to operate effectively in natural disaster events, such as landslide, earthquake, flooding, and tsunami. The operations of SAR team are very challenging in such events due to the possible damages of the existing telecommunication infrastructures. The existing deployment of the cellular communications infrastructure may be partially or completely destroyed after the occurrence of these natural disasters. Thus, the current VASNET infrastructure must be able to support the infrastructure-less network by integrating other green wireless technologies that can benefit the SAR team, which can indirectly save more human lives and reduce the number of casualties. Therefore, the integration of green Internet of things （loT） and VASNET is proposed to form a heterogeneous framework for data dissemination in SAR operations. In addition, this paper also discusses the existing lot framework in disaster scenarios with future research direction for IoT using on any aspect, especially related to the natural disaster scenarios.
基金The work of Vinay Chamola and F.Richard Yu was supported in part by the SICI SICRG Grant through the Project Artificial Intelligence Enabled Security Provisioning and Vehicular Vision Innovations for Autonomous Vehicles,and in part by the Government of Canada's National Crime Prevention Strategy and Natural Sciences and Engineering Research Council of Canada(NSERC)CREATE Program for Building Trust in Connected and Autonomous Vehicles(TrustCAV).
文摘With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also increases.In addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient.Thus,there is a need to augment them with intelligent network intrusion detection techniques.Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times.However,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection methods.Deep learning solutions are lucrative options as they remove the necessity for feature selection.Therefore,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more heightened.This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers.Vehicular data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper.The proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing works.By running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2022R1A2C1003549).
文摘Wireless Ad Hoc Networks consist of devices that are wirelessly connected.Mobile Ad Hoc Networks(MANETs),Internet of Things(IoT),and Vehicular Ad Hoc Networks(VANETs)are the main domains of wireless ad hoc network.Internet is used in wireless ad hoc network.Internet is based on Transmission Control Protocol(TCP)/Internet Protocol(IP)network where clients and servers interact with each other with the help of IP in a pre-defined environment.Internet fetches data from a fixed location.Data redundancy,mobility,and location dependency are the main issues of the IP network paradigm.All these factors result in poor performance of wireless ad hoc networks.The main disadvantage of IP is that,it does not provide in-network caching.Therefore,there is a need to move towards a new network that overcomes these limitations.Named Data Network(NDN)is a network that overcomes these limitations.NDN is a project of Information-centric Network(ICN).NDN provides in-network caching which helps in fast response to user queries.Implementing NDN in wireless ad hoc network provides many benefits such as caching,mobility,scalability,security,and privacy.By considering the certainty,in this survey paper,we present a comprehensive survey on Caching Strategies in NDN-based Wireless AdHocNetwork.Various cachingmechanism-based results are also described.In the last,we also shed light on the challenges and future directions of this promising field to provide a clear understanding of what caching-related problems exist in NDN-based wireless ad hoc networks.
