Wireless sensor networks(WSNs)are characterized by heterogeneous traffic types(audio,video,data)and diverse application traffic requirements.This paper introduces three traffic classes following the defined model of h...Wireless sensor networks(WSNs)are characterized by heterogeneous traffic types(audio,video,data)and diverse application traffic requirements.This paper introduces three traffic classes following the defined model of heterogeneous traffic differentiation in WSNs.The requirements for each class regarding sensitivity to QoS(Quality of Service)parameters,such as loss,delay,and jitter,are described.These classes encompass real-time and delay-tolerant traffic.Given that QoS evaluation is a multi-criteria decision-making problem,we employed the AHP(Analytical Hierarchy Process)method for multi-criteria optimization.As a result of this approach,we derived weight values for different traffic classes based on key QoS factors and requirements.These weights are assigned to individual traffic classes to determine transmission priority.This study provides a thorough comparative analysis of the proposed model against existing methods,demonstrating its superior performance across various traffic scenarios and its implications for future WSN applications.The results highlight the model’s adaptability and robustness in optimizing network resources under varying conditions,offering insights into practical deployments in real-world scenarios.Additionally,the paper includes an analysis of energy consumption,underscoring the trade-offs between QoS performance and energy efficiency.This study presents the development of a differentiated services model for heterogeneous traffic in wireless sensor networks,considering the appropriate QoS framework supported by experimental analyses.展开更多
Redesign of motorized and non-motorized transport in cities and sustainable mobility possibilities and utilization of already occupied areas for motorized and non-motorized transport in cities. Reserved area for publi...Redesign of motorized and non-motorized transport in cities and sustainable mobility possibilities and utilization of already occupied areas for motorized and non-motorized transport in cities. Reserved area for public transport may be the redesign much more to take advantage of without taking up new space in cities. Redesigned solutions shown in the work of public mass transport and the redesign of non-motorized transport, bicycle and pedestrian paths point to the improved use and safety of movement of passengers, cyclists and pedestrians. This paper presents five redesigned concept designs as improving existing forms of transport and movement of cyclists and pedestrians in cities. Redesigned conceptual designs of motorized and non-motorized transport in cities should serve as ideas for the growing problems of urban development in the segment of insufficient surface for pedestrians and cyclists, as well as sustainable mobility transport people in the city.展开更多
Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep lear...Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep learning(DL),numerous driver behaviour learning(DBL)methods have been proposed and applied in connected vehicles(CV)and intelligent transportation systems(ITS).This study provides a review of DBL,which mainly focuses on typical applications in CV and ITS.First,a comprehensive review of the state-of-the-art DBL is presented.Next,Given the constantly changing nature of real driving scenarios,most existing learning-based models may suffer from the so-called“catastrophic forgetting,”which refers to their inability to perform well in previously learned scenarios after acquiring new ones.As a solution to the aforementioned issue,this paper presents a framework for continual driver behaviour learning(CDBL)by leveraging continual learning technology.The proposed CDBL framework is demonstrated to outperform existing methods in behaviour prediction through a case study.Finally,future works,potential challenges and emerging trends in this area are highlighted.展开更多
文摘Wireless sensor networks(WSNs)are characterized by heterogeneous traffic types(audio,video,data)and diverse application traffic requirements.This paper introduces three traffic classes following the defined model of heterogeneous traffic differentiation in WSNs.The requirements for each class regarding sensitivity to QoS(Quality of Service)parameters,such as loss,delay,and jitter,are described.These classes encompass real-time and delay-tolerant traffic.Given that QoS evaluation is a multi-criteria decision-making problem,we employed the AHP(Analytical Hierarchy Process)method for multi-criteria optimization.As a result of this approach,we derived weight values for different traffic classes based on key QoS factors and requirements.These weights are assigned to individual traffic classes to determine transmission priority.This study provides a thorough comparative analysis of the proposed model against existing methods,demonstrating its superior performance across various traffic scenarios and its implications for future WSN applications.The results highlight the model’s adaptability and robustness in optimizing network resources under varying conditions,offering insights into practical deployments in real-world scenarios.Additionally,the paper includes an analysis of energy consumption,underscoring the trade-offs between QoS performance and energy efficiency.This study presents the development of a differentiated services model for heterogeneous traffic in wireless sensor networks,considering the appropriate QoS framework supported by experimental analyses.
文摘Redesign of motorized and non-motorized transport in cities and sustainable mobility possibilities and utilization of already occupied areas for motorized and non-motorized transport in cities. Reserved area for public transport may be the redesign much more to take advantage of without taking up new space in cities. Redesigned solutions shown in the work of public mass transport and the redesign of non-motorized transport, bicycle and pedestrian paths point to the improved use and safety of movement of passengers, cyclists and pedestrians. This paper presents five redesigned concept designs as improving existing forms of transport and movement of cyclists and pedestrians in cities. Redesigned conceptual designs of motorized and non-motorized transport in cities should serve as ideas for the growing problems of urban development in the segment of insufficient surface for pedestrians and cyclists, as well as sustainable mobility transport people in the city.
基金Supported by the National Key Research and Development Program of China(No.2022ZD0115503).
文摘Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep learning(DL),numerous driver behaviour learning(DBL)methods have been proposed and applied in connected vehicles(CV)and intelligent transportation systems(ITS).This study provides a review of DBL,which mainly focuses on typical applications in CV and ITS.First,a comprehensive review of the state-of-the-art DBL is presented.Next,Given the constantly changing nature of real driving scenarios,most existing learning-based models may suffer from the so-called“catastrophic forgetting,”which refers to their inability to perform well in previously learned scenarios after acquiring new ones.As a solution to the aforementioned issue,this paper presents a framework for continual driver behaviour learning(CDBL)by leveraging continual learning technology.The proposed CDBL framework is demonstrated to outperform existing methods in behaviour prediction through a case study.Finally,future works,potential challenges and emerging trends in this area are highlighted.
