Urban traffic volume detection is an essential part of trafficplanning in terms of urban planning in China. To improve the statisticsefficiency of road traffic volume, this thesis proposes a method for predictingmotor...Urban traffic volume detection is an essential part of trafficplanning in terms of urban planning in China. To improve the statisticsefficiency of road traffic volume, this thesis proposes a method for predictingmotor vehicle traffic volume on urban roads in small and medium-sizedcities during the traffic peak hour by using mobile signal technology. Themethod is verified through simulation experiments, and the limitations andthe improvement methods are discussed. This research can be divided intothree parts: Firstly, the traffic patterns of small and medium-sized cities areobtained through a questionnaire survey. A total of 19745 residents weresurveyed in Luohe, a medium-sized city in China and five travel modes oflocal people were obtained. Secondly, after the characteristics of residents’rest and working time are investigated, a method is proposed in this studyfor the distribution of urban residential and working places based on mobilephone signaling technology. Finally, methods for predicting traffic volume ofthese travel modes are proposed after the characteristics of these travel modesand methods for the distribution of urban residential and working placesare analyzed. Based on the actual traffic volume data observed at offlineintersections, the project team takes Luohe city as the research object and itverifies the accuracy of the prediction method by comparing the predictiondata. The prediction simulation results of traffic volume show that the averageerror rate of traffic volume is unstable. The error rate ranges from 10% to 30%.In this thesis, simulation experiments and field investigations are adopted toanalyze why these errors occur.展开更多
There has been an exponential rise in mobile data traffic in recent times due to the increasing popularity of portable devices like tablets,smartphones,and laptops.The rapid rise in the use of these portable devices h...There has been an exponential rise in mobile data traffic in recent times due to the increasing popularity of portable devices like tablets,smartphones,and laptops.The rapid rise in the use of these portable devices has put extreme stress on the network service providers while forcing telecommunication engineers to look for innovative solutions to meet the increased demand.One solution to the problem is the emergence of fifth-generation(5G)wireless communication,which can address the challenges by offering very broad wireless area capacity and potential cut-power consumption.The application of small cells is the fundamental mechanism for the 5Gtechnology.The use of small cells can enhance the facility for higher capacity and reuse.However,it must be noted that small cells deployment will lead to frequent handovers of mobile nodes.Considering the importance of small cells in 5G,this paper aims to examine a new resource management scheme that can work to minimize the rate of handovers formobile phones through careful resources allocation in a two-tier network.Therefore,the resource management problem has been formulated as an optimization issue thatwe aim to overcome through an optimal solution.To find a solution to the existing problem of frequent handovers,a heuristic approach has been used.This solution is then evaluated and validated through simulation and testing,during which the performance was noted to improve by 12%in the context of handover costs.Therefore,this model has been observed to be more efficient as compared to the existing model.展开更多
Drone applications in 5th generation(5G)networks mainly focus on services and use cases such as providing connectivity during crowded events,human-instigated disasters,unmanned aerial vehicle traffic management,intern...Drone applications in 5th generation(5G)networks mainly focus on services and use cases such as providing connectivity during crowded events,human-instigated disasters,unmanned aerial vehicle traffic management,internet of things in the sky,and situation awareness.4G and 5G cellular networks face various challenges to ensure dynamic control and safe mobility of the drone when it is tasked with delivering these services.The drone can fly in three-dimensional space.The drone connectivity can suffer from increased handover cost due to several reasons,including variations in the received signal strength indicator,co-channel interference offered to the drone by neighboring cells,and abrupt drop in lobe edge signals due to antenna nulls.The baseline greedy handover algorithm only ensures the strongest connection between the drone and small cells so that the drone may experience several handovers.Intended for fast environment learning,machine learning techniques such as Q-learning help the drone fly with minimum handover cost along with robust connectivity.In this study,we propose a Q-learning-based approach evaluated in three different scenarios.The handover decision is optimized gradually using Q-learning to provide efficient mobility support with high data rate in time-sensitive applications,tactile internet,and haptics communication.Simulation results demonstrate that the proposed algorithm can effectively minimize the handover cost in a learning environment.This work presents a notable contribution to determine the optimal route of drones for researchers who are exploring UAV use cases in cellular networks where a large testing site comprised of several cells with multiple UAVs is under consideration.展开更多
文摘Urban traffic volume detection is an essential part of trafficplanning in terms of urban planning in China. To improve the statisticsefficiency of road traffic volume, this thesis proposes a method for predictingmotor vehicle traffic volume on urban roads in small and medium-sizedcities during the traffic peak hour by using mobile signal technology. Themethod is verified through simulation experiments, and the limitations andthe improvement methods are discussed. This research can be divided intothree parts: Firstly, the traffic patterns of small and medium-sized cities areobtained through a questionnaire survey. A total of 19745 residents weresurveyed in Luohe, a medium-sized city in China and five travel modes oflocal people were obtained. Secondly, after the characteristics of residents’rest and working time are investigated, a method is proposed in this studyfor the distribution of urban residential and working places based on mobilephone signaling technology. Finally, methods for predicting traffic volume ofthese travel modes are proposed after the characteristics of these travel modesand methods for the distribution of urban residential and working placesare analyzed. Based on the actual traffic volume data observed at offlineintersections, the project team takes Luohe city as the research object and itverifies the accuracy of the prediction method by comparing the predictiondata. The prediction simulation results of traffic volume show that the averageerror rate of traffic volume is unstable. The error rate ranges from 10% to 30%.In this thesis, simulation experiments and field investigations are adopted toanalyze why these errors occur.
基金This work was supported by the Taif University Researchers Supporting Project number(TURSP-2020/79),Taif University,Taif,Saudi Arabia.
文摘There has been an exponential rise in mobile data traffic in recent times due to the increasing popularity of portable devices like tablets,smartphones,and laptops.The rapid rise in the use of these portable devices has put extreme stress on the network service providers while forcing telecommunication engineers to look for innovative solutions to meet the increased demand.One solution to the problem is the emergence of fifth-generation(5G)wireless communication,which can address the challenges by offering very broad wireless area capacity and potential cut-power consumption.The application of small cells is the fundamental mechanism for the 5Gtechnology.The use of small cells can enhance the facility for higher capacity and reuse.However,it must be noted that small cells deployment will lead to frequent handovers of mobile nodes.Considering the importance of small cells in 5G,this paper aims to examine a new resource management scheme that can work to minimize the rate of handovers formobile phones through careful resources allocation in a two-tier network.Therefore,the resource management problem has been formulated as an optimization issue thatwe aim to overcome through an optimal solution.To find a solution to the existing problem of frequent handovers,a heuristic approach has been used.This solution is then evaluated and validated through simulation and testing,during which the performance was noted to improve by 12%in the context of handover costs.Therefore,this model has been observed to be more efficient as compared to the existing model.
基金The project supported by National Natural Science Foundation of China under Grant No. 50272022 and the Sunshine Foundation of Wuhan City under Grant No. 20045006071-40
文摘我们认为在社会上交往的活动个人以内的一个传染病的模型在 2D 网络学习流行繁殖的稳定的状态的行为。Usingmean 地近似和大规模模拟，我们与传染疾病在下面灭绝的批评阀值δ _ c 和 p_c 恢复平常的流行行为。为在δ _ C 上面远的人口密度δ，总体上在接触率λ和人口密度δ之间有线性关系，这被发现。同时，从 mean-Geldapproximation 获得的结果与我们的数字结果相比，并且这二结果总的来说然而并非完全是类似的，这被发现一样。
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2018R1D1A1B07049877)and the Strengthening R&D Capability Program of Sejong University.
文摘Drone applications in 5th generation(5G)networks mainly focus on services and use cases such as providing connectivity during crowded events,human-instigated disasters,unmanned aerial vehicle traffic management,internet of things in the sky,and situation awareness.4G and 5G cellular networks face various challenges to ensure dynamic control and safe mobility of the drone when it is tasked with delivering these services.The drone can fly in three-dimensional space.The drone connectivity can suffer from increased handover cost due to several reasons,including variations in the received signal strength indicator,co-channel interference offered to the drone by neighboring cells,and abrupt drop in lobe edge signals due to antenna nulls.The baseline greedy handover algorithm only ensures the strongest connection between the drone and small cells so that the drone may experience several handovers.Intended for fast environment learning,machine learning techniques such as Q-learning help the drone fly with minimum handover cost along with robust connectivity.In this study,we propose a Q-learning-based approach evaluated in three different scenarios.The handover decision is optimized gradually using Q-learning to provide efficient mobility support with high data rate in time-sensitive applications,tactile internet,and haptics communication.Simulation results demonstrate that the proposed algorithm can effectively minimize the handover cost in a learning environment.This work presents a notable contribution to determine the optimal route of drones for researchers who are exploring UAV use cases in cellular networks where a large testing site comprised of several cells with multiple UAVs is under consideration.
