Active queue management (AQM) is a technique to avoid serious congestion of the transmission control protocol (TCP) flows at a router. AQM based on control theory, which utilizes congestion controllers such as proportional-derivative (PD) controller or proportional-integral-derivative (PID) controller, has been previously proposed. In addition, disturbance observer (DOB) has been utilized to compensate for modeling error of a TCP/AQM congestion control system. However, the DOB-based controllers cannot cope with a large time delay in TCP/AQM networks. Although one of the effective time delay compensators is Smith predictor (SP), the implementation of the DOB and SP in an integrated manner has not been accomplished, because of saturation due to the input limit of packet drop probability. In this paper, a novel TCP/AQM congestion control system with the DOB and SP considering the saturation function is proposed to compensate for the modeling error and time delay simultaneously. Simulation results show that the proposed controller provides better throughput and goodput than conventional controllers. One of the simulations assume that the propagation delay and bottleneck link capacity are set to 100 ms and 100 Mbps, respectively. Under this assumption, it is confirmed that the proposed controller achieves a goodput of 99.55 Mbps whereas the classical PID controller and PD controller with DOB achieve 99.23 Mbps and 99.24 Mbps, respectively. INDEX TERMS Active queue management (AQM), control theory, disturbance observer (DOB), quality of services (QoS), Smith predictor (SP), time delay, transmission control protocol (TCP).

Smart monitoring, particularly at intersections, is a promising service that is being considered for the concept of smart cities. A network of light detection and ranging (LIDAR) sensors, which generates point cloud data in real time, can be used to detect people's mobility in smart monitoring. Due to the sheer volume of point cloud data, data transmission requires a significant amount of communication resources. In order to monitor people's mobility in real time, it is necessary to reduce the amount of transmission data to shorten delay. Point cloud compression is one method for reducing the amount of data. However, prior works addressing point cloud compression mainly focused on accuracy for the compression of an entire point cloud without considering its spatial characteristics. The more dynamically a spatial region changes, the more important it is when detecting moving objects such as cars, trucks, pedestrians, and bikes in smart monitoring. This paper proposes a prioritized transmission scheme that applies multiple point cloud compression methods to point cloud data according to the spatial importance of the data, i.e., how dynamically spatial regions change. This paper assumes data transmission of point cloud data from multiple LIDAR devices to an edge server and addresses the intra-frame geometry compression of point cloud data. The proposed scheme splits the point cloud into multiple classes according to the spatial importance and applies multiple point cloud compression methods to each class. A numerical study using a real point cloud dataset obtained at an intersection demonstrates the dependencies of quality, volume, and processing time on possible compression format options. The results verify that the proposed scheme reduces the amount of point cloud data drastically while satisfying the quality and processing time requirements.

Traffic collision is an extremely serious issue in the world today. The World Health Organization (WHO) reported the number of road traffic deaths globally has plateaued at 1.25 million a year. In an attempt to decrease the occurrence of such traffic collisions, various driving systems for detecting pedestrians and vehicles have been proposed, but they are inadequate as they cannot detect vehicles and pedestrians in blind places such as sharp bends and blind intersections. Therefore, mobile networks such as long term evolution (LTE), LTE-Advanced, and 5G networks are attracting a great deal of attention as platforms for connected car services. Such platforms enable individual devices such as vehicles, drones, and sensors to exchange real-time information (e.g., location information) with each other. To guarantee effective connected car services, it is important to deliver a data block within a certain maximum tolerable delay (called a deadline in this work). The Third Generation Partnership Project (3GPP) stipulates that this deadline be 100 ms and that the arrival ratio within the deadline be 0.95. We investigated an intersection at which vehicle collisions often occur to evaluate a realistic environment and found that schedulers such as proportional fairness (PF) and payload-size and deadline-aware (PayDA) cannot satisfy the deadline and arrival ratio within the deadline, especially as network loads increase. They fail because they do not consider three key elements -radio quality, chunk size, and the deadline -when radio resources are allocated. In this paper, we propose a deadline-aware scheduling scheme that considers chunk size and the deadline in addition to radio quality and uses them to prioritize users in order to meet the deadline. The results of a simulation on ns-3 showed that the proposed method can achieve approximately four times the number of vehicles satisfying network requirements compared to PayDA.

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