With increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). Specifically, UAV-aided MCS allows autonomous data collection anytime anywhere due to the capability of fast deployment and controllable mobility. However, the on-board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we consider the fixed-wing UAV-aided MCS system, and investigate the corresponding joint route planning and task assignment problem from an energy efficiency perspective. The formulated joint optimization problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on either dynamic programming (DP) or genetic algorithms (GA), and the task assignment problem is addressed in the second stage by exploring the Gale-Shapley (GS) algorithm. We provide a comprehensive theoretical analysis, and elaborate the procedures of practical implementation. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.

It is expected that a mixture of autonomous and manual vehicles will persist as a part of the intelligent transportation system (ITS) for many decades. Thus, addressing the safety issues arising from this mix of autonomous and manual vehicles before autonomous vehicles are entirely popularized is crucial. As the ITS system has increased in complexity, autonomous vehicles exhibit problems such as a low intention recognition rate and poor real-time performance when predicting the driving direction; these problems seriously affect the safety and comfort of mixed traffic systems. Therefore, the ability of autonomous vehicles to predict the driving direction in real time according to the surrounding traffic environment must be improved and researchers must work to create a more mature ITS. In this paper, we propose a deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5G-enabled ITS. In this scheme, a driving trajectory dataset and a natural-driving dataset are employed as the network inputs to long-term memory networks in the 5G-enabled ITS: the probability matrix of each intention is calculated by the softmax function. Then, the final intention probability is obtained by fusing the mean rule in the decision layer. Experimental results show that the proposed scheme achieves intention recognition rates of 91.58% and 90.88% for left and right lane changes, respectively, effectively improving both accuracy and real-time intention recognition and improving the lane change problem in a mixed traffic environment.

Timely and accurate cellular traffic prediction is difficult to achieve due to the complex spatial-temporal characteristics of cellular traffic. The latest approaches mainly aim to model local spatial-temporal dependencies of cellular traffic based on deep learning techniques but lack the consideration of diverse global spatial-temporal correlations hidden in cellular traffic. To tackle this issue, we propose a novel multi-view spatial-temporal graph network (MVSTGN), which combines attention and convolution mechanisms into traffic pattern analysis, enabling the comprehensive excavation of spatial-temporal characteristics. Specifically, the MVSTGN realizes the above statement from three spatial-temporal views: 1) From a global spatial view, two spatial attention modules are proposed to capture the global spatial correlations between different regions at node and trend levels; 2) From a global temporal view, a temporal attention module is employed to capture and encode global temporal correlations between traffic at different times; 3) From a local spatial-temporal view, a dense convolution module is developed to further excavate the local spatial-temporal dependencies in cellular traffic. Consequently, a successful cellular traffic prediction strategy is constructed to fully explore the spatial-temporal characteristics from multiple views. The experimental results on a popular real-world cellular traffic dataset demonstrate that the MVSTGN achieves obvious improvements over baselines.

In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance.

Energy harvesting based cognitive machine-to-machine (EH-CM2M) communication has been proposed to overcome the problem of spectrum scarcity and limited battery capacity by enabling M2M transmitters (M2M-TXs) to harvest energy from ambient radio frequency signals, as well as to reuse the resource blocks (RBs) allocated to cellular users (CUs) in an opportunistic manner. However, the complex interference scenarios and the stringent quality of service (QoS) requirements pose new challenges on resource allocation optimization. In this paper, we consider how to maximize the energy efficiency of M2M-TXs via the joint optimization of channel selection, peer discovery, power control, and time allocation. We propose a two-stage three-dimensional matching algorithm. In the first stage, M2M-TXs, M2M receivers (M2M-RXs) and RBs are temporally matched together, and then the joint power control and time allocation problem is solved by combining alternating optimization (AO), nonlinear fractional programming, and linear programming to construct the preference lists. In the second stage, the joint channel selection and peer discovery problem is solved by the proposed pricing-based matching algorithm based on the established preference lists. Simulation results confirm that the proposed algorithm can approach the optimal performance with a low complexity.