This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization pr...This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.展开更多
Lane detection based on machine vision,a key application in intelligent transportation,is generally characterized by gradient information of lane edge and plays an important role in advanced driver assistance systems(...Lane detection based on machine vision,a key application in intelligent transportation,is generally characterized by gradient information of lane edge and plays an important role in advanced driver assistance systems(ADAS).However,gradient information varies with illumination changes.In the complex scenes of urban roads,highlight and shadow have effects on the detection,and non-lane objects also lead to false positives.In order to improve the accuracy of detection and meet the robustness requirement,this paper proposes a method of using top-hat transformation to enhance the contrast and filter out the interference of non-lane objects.And then the threshold segmentation algorithm based on local statistical information and Hough transform algorithm with polar angle and distance constraint are used for lane fitting.Finally,Kalman filter is used to correct lane lines which are wrong detected or missed.The experimental results show that computation times meet the real-time requirements,and the overall detection rate of the proposed method is 95.63%.展开更多
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant 2022JBGP003in part by the National Natural Science Foundation of China(NSFC)under Grant 62071033in part by ZTE IndustryUniversity-Institute Cooperation Funds under Grant No.IA20230217003。
文摘This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.
文摘Lane detection based on machine vision,a key application in intelligent transportation,is generally characterized by gradient information of lane edge and plays an important role in advanced driver assistance systems(ADAS).However,gradient information varies with illumination changes.In the complex scenes of urban roads,highlight and shadow have effects on the detection,and non-lane objects also lead to false positives.In order to improve the accuracy of detection and meet the robustness requirement,this paper proposes a method of using top-hat transformation to enhance the contrast and filter out the interference of non-lane objects.And then the threshold segmentation algorithm based on local statistical information and Hough transform algorithm with polar angle and distance constraint are used for lane fitting.Finally,Kalman filter is used to correct lane lines which are wrong detected or missed.The experimental results show that computation times meet the real-time requirements,and the overall detection rate of the proposed method is 95.63%.
