Accurate air quality prediction can help cope with air pollution and improve the life quality. With the development of the deployments of low-cost air quality sensors, increasing data related to air ...quality has provided chances to find out more accurate prediction methods. Air quality is affected by many external factors such as the position, wind, meteorological information, and so on. Meanwhile, these factors are spatio-temporal dynamic and there are many dynamic contextual relationships between them. Many methods for air quality prediction do not consider these complex spatio–temporal correlations and dynamic contextual relationships. In this paper, we propose a dual-path dynamic directed graph convolutional network (DP-DDGCN) for air quality prediction. We first create a dual-path transposed dynamic directed graph according to static distance relationships of stations and the dynamic relationships generated by wind speed and directions. Then based on the dual-path dynamic directed graph, we can capture the dynamic spatial dependencies more comprehensively. After that we apply gated recurrent units (GRUs) and add the future meteorological features, to extract the complex temporal dependencies of historical air quality data. Using dual-path dynamic directed graph blocks and the GRUs, we finally construct a dynamic spatio-temporal gated recurrent block to capture the dynamic spatio-temporal contextual correlations. Based on real-world datasets, which record a large amount of PM2.5 concentration data, we compare the proposed model with the benchmark models. The experimental results show that our proposed model has the best performance in predicting the PM2.5 concentrations.
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•The distance and wind features are applied to model the dynamic directed graphs.•A dual-path dynamic directed graph-based framework is developed for air quality forecasting.•Meteorological information has a latent impact on the PM2.5 correlations.•Both the outgoing and incoming graph model a comprehensive spatial relationship.•The proposed DP-DDGCN model outperforms the state-of-the-art baselines.
The sixth-generation (6G) wireless communication system is expected to integrate communication, intelligence, sensing, positioning, control, and calculation to adapt to time-critical, ultra-reliable, ...and energy-saving data delivery, as well as accurate positioning of personnel and equipment, serving the Internet of Things (IoT). On one hand, reconfigurable intelligent surface (RIS) can intelligently manipulate radio waves and is considered to be one of the candidate technologies for the 6G wireless communication. Hence, there are more and more surveys on RIS-assisted communications. On the other hand, the potential of RIS in positioning has attracted growing attention, and articles on RIS-assisted positioning have been blown out. Therefore, it is time to review this literature to understand the potential of RIS positioning, research status, and point out the direction for future research. This paper first explains the working principle and channel model of RIS and summarizes some characteristics of RIS suitable for positioning. Then we give a concise review and classification of existing RIS positioning research. Finally, we put forward our views on the future research challenges and attractive directions for RIS-aided wireless positioning technology.
Internet of Things (IoT) is playing an increasingly important role in Intelligent Transportation Systems (ITS) for real-time sensing and communication. In ITS, vehicle types, volume and speeds ...provide important information for road traffic management. However, the present methods for on-road traffic monitoring are lacking in providing cost-effective means to meet the demands. In this paper, we propose MagMonitor, a novel method for on-road traffic surveillance through a single small and easy-to-install magnetic sensor. The developed magnetic sensor system is wireless-connected, cost-effective, and environmental-friendly. First, a magnetic model of a moving vehicle is presented. The model employs multiple magnetic dipoles for modelling moving vehicle and varies depending on the on-road vehicle types. Through modelling of local magnetic field perturbations caused by moving vehicles, we extract the characteristics of magnetic waveforms for vehicle identification and speed estimation. The proposed model and estimation technique are validated with real field experimental data. Furthermore, we analyze and compare the performance of the proposed estimation technique with other speed estimation algorithms, which shows the superior accuracy of the proposed technique.
Due to the wireless broadcasting and broad coverage in satellite-supported Internet of things (IoT) networks, the IoT nodes are susceptible to eavesdropping threats. Considering the distance ...difference between satellite and nearby destinations is negligible, the main and wiretapping channels between satellite and IoT node are similar, it poses great challenges to reach physical layer security in satellite-assisted IoT networks. In this paper, to guarantee secure transmissions for satellite-assisted IoT downlink communications, the multi-domain resource multiplexing based secure approach is proposed. Particularly, the self-induced co-channel interference between adjacent nodes is leveraged to increase the difference of signal transmission quality over both main and wiretapping channels. By comprehensively optimizing multi-domain resources, i.e., frequency, power, and spatial domains, secure transmissions from satellite to IoT nodes are reached. Specifically, the problem to maximize the sum secrecy rate of IoT nodes is formulated with a constraint of common communication rate of IoT nodes. To solve this non-convex problem, an alternating optimization (AO) algorithm with two inner successive convex approximation (SCA) algorithms are executed to solve the power allocation, spectral multiplexing, and precoding. In addition, simulation results are carried out to evaluate the secrecy rate performance and verify the efficiency of our proposed approach.
Driven by the ever-increasing demands of vehicular services, edge computing has become a promising paradigm to facilitate edge services in vehicular networks by using edge computing devices (ECDs). ...To enhance the service experience, we develop a reservation service framework, where the reservation service request of a vehicle needs to be relayed to one of the ECDs which is ahead of its driving direction. However, due to the various behaviors of vehicles, not all the vehicles are trustworthy and willing to join in the service request relay process. Therefore, how to exploit the cooperation between ECDs and vehicles to relay the service request by considering the dynamic traffic status and the behaviors of vehicles becomes a challenge. As an effort to address this problem, we propose a trusted relay selection scheme for edge services to facilitate the proposed reservation service framework. Specifically, we first design the request relay mechanism based on the dynamic traffic status to guarantee the efficiency of the relay process. Then, the reputation management mechanism is presented to constrain the behaviors of vehicles, where a vehicle with high reputation value can enjoy the price discount for computing service. Based on the designed request relay and reputation management mechanisms, a reputation-based auction approach is then proposed to select relay vehicles (RVs) to reduce the cost of the relay service. Simulation results show that the proposed reservation service framework can manage vehicles efficiently and lead to the lowest cost for the relay services compared with the conventional schemes.
With the popularity of image sensors in various mobile devices, image blurring caused by hand shaking or out of focus becomes ubiquitous, which deteriorates image quality and poses challenges for ...vision tasks, including object detection, image classification and image segmentation. Designing an efficient blur detection algorithm which can automatically detect and locate blurred regions becomes necessary. In this letter, we design an end-to-end convolution neural network called heterogeneous attention nested U-shaped network (HANUN) for blur detection. We introduce pyramid pooling into encoders to enhance the feature extraction at different scales and reduce the gradual information loss. Inspired by the nested network design, small U-shaped networks are embedded into our decoders to increase the network depth and promote feature fusion with different receptive field scales. In addition, we incorporate a channel attention mechanism in the proposed network to highlight the informative features for detecting the blurry regions. Experimental results show that HANUN outperforms other state-of-the-art algorithms for blur detection tasks on public datasets and real-world images.
A fundamental issue of the vehicular digital twin (DT) is efficiently synchronizing the data between the DT and the vehicular user (VUE). In this paper, we consider the heterogeneous vehicular ...networks (HetVNets) in which a VUE can connect to the network through different networks. The HetVNets can improve the efficiency of communication by providing seamless connections. However, the uneven distribution of VUEs and the dynamics of HetVNets make the environment more complex. Therefore, we propose the network selection algorithm for data synchronization between VUEs and DTs in the HetVNets, where the behaviour between the VUEs is considered as a competition for wireless resources. A learning-based prediction model residing in the DT is developed where the DT can predict the waiting time of each relay and transmit the predicted results to the VUE for decision-making. We model the network selection problem as a potential game considering both the transmission time and the waiting time obtained from the prediction model and prove the existence of Nash equilibrium (NE). We analyze the performance of the proposed algorithm, and simulation results show that our approach can effectively find the optimal strategy while achieving a fast convergence speed and high-level performance compared to the baselines.
Efficient route planning and traffic scheduling are the key enabling technologies to achieve intelligent transportation systems (ITS). However, the existing schemes that use driving time or driving ...distance as indicators cannot meet the personalized requirements of vehicles. In addition, the existing research usually optimizes the route planning and traffic scheduling separately, which is difficult to effectively improve the traffic efficiency in practical applications. In this paper, by considering the personalized requirements of vehicles and the target of the traffic manager (TM), a digital-twin (DT) enabled integrated framework is proposed to jointly optimize the personalized route planning and global traffic scheduling. Specifically, based on the collection of traffic data, we first develop a DT architecture for route planning and traffic scheduling. With this DT architecture, we then consider the traffic condition of each road section and design a global traffic scheduling algorithm to help the DT of the TM (DT-TM) set the optimal reward for each road section to motivate and guide the DTs of vehicles (DT-Vs) to select their routes. After that, by considering the preference of each DT-V and the reward of each road section, a personalized route planning algorithm is designed to plan the optimal route for the DT-V. Since the reward of each road section set by the DT-TM and the route selected by each DT-V affect each other, we thus design a joint optimization algorithm based on dynamic iteration to obtain an equilibrium solution for the DT-Vs and the DT-TM. The simulation results show that the proposed framework can schedule the traffic more effectively and bring higher utility to both the vehicles and the TM than the benchmark schemes.
Preemptive scheduling efficiently addresses the coexistence of enhanced Mobile Broad Band (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC). While URLLC puncturing influences eMBB ...performance, further investigation is necessary to study the trade-offs between stability, delay, and efficiency. However, existing studies overlook the imbalance in eMBB/URLLC load distribution and personalized fluctuations in eMBB performance, leading to sub-optimal results. To tackle this, we propose an unmanned aerial vehicle (UAV) relay-assisted eMBB/URLLC multiplexing framework. Specifically, considering the utilization of UAVs for connecting separated next-generation Node Bs (gNBs) and the individual subject experience of services, we first formulate the multiplexing problem as an optimization problem. The objective is to maximize eMBB throughput and minimize personalized fluctuations in eMBB performance and UAV consumption, subject to URLLC constraints. Then, the challenging problem is decomposed into the eMBB problem and the URLLC problem. For the former, we further decompose it into three sub-problems and solve them using optimization methods. For the latter, we propose a deep reinforcement learning-based algorithm to obtain an optimal strategy for relaying and puncturing URLLC into eMBB intelligently. Simulation results demonstrate that our proposals outperform benchmark schemes regarding eMBB throughput, UAV consumption, eMBB performance fluctuation, URLLC satisfaction, and learning efficiency.
Unmanned aerial vehicle (UAV) relay networks with flexible and controllable characteristics are expected to complement the capacity of the gNB. This paper studies the multiplexing of enhanced Mobile ...BroadBand (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) in a multi-UAV relay network, where the strict latency requirement of URLLC can be achieved by the preemptive multiplexing of eMBB resources. However, this may affect eMBB reliability due to the transmission interruptions. Moreover, given the limited energy resources of UAVs, there is an inherent tradeoff among reliability, delay, spectral efficiency, and energy efficiency. To address these challenges, this paper develops a hierarchical UAV-assisted eMBB/URLLC multiplexing scheduling framework. For the eMBB scheduler, we first utilize multiple UAVs to assist the gNB in relaying eMBB traffic and formulate the eMBB resource allocation problem as an optimization problem. Then, we propose a decomposition-relaxation-optimization algorithm to maximize eMBB data rates while considering the personalized fairness of resource allocation and UAV power consumption. For the URLLC scheduler, we further consider the multiplexing of eMBB/URLLC traffic based on the optimization of eMBB resources. To reduce the performance fluctuations of eMBB, we propose a novel cross-slot strategy to schedule URLLC within two time slots rather than one time slot as in existing works. With this strategy, a deep reinforcement learning-based algorithm is proposed to obtain the optimal strategy for the preemption of URLLC on eMBB. Simulation results show that the proposed algorithms outperform the benchmark schemes in terms of convergence rate, eMBB reliability, personalized resource fairness, UAV consumption, and URLLC satisfaction.
