With the rapid development of vehicular networks, vehicle-to-everything (V2X) communications have huge number of tasks to be calculated, which brings challenges to the scarce network resources. Cloud servers can alleviate the terrible situation regarding the lack of computing abilities of vehicular user equipment (VUE), but the limited resources, the dynamic environment of vehicles, and the long distances between the cloud servers and VUE induce some potential issues, such as extra communication delay and energy consumption. Fortunately, mobile edge computing (MEC), a promising computing paradigm, can ameliorate the above problems by enhancing the computing abilities of VUE through allocating the computational resources to VUE. In this paper, we propose a joint optimization algorithm based on a deep reinforcement learning algorithm named the double deep Q network (double DQN) to minimize the cost constituted of energy consumption, the latency of computation, and communication with the proper policy. The proposed algorithm is more suitable for dynamic scenarios and requires low-latency vehicular scenarios in the real world. Compared with other reinforcement learning algorithms, the algorithm we proposed algorithm improve the performance in terms of convergence, defined cost, and speed by around 30%, 15%, and 17%.

The unmanned aerial vehicle (UAV) technique combined with the cellular network is a promising and reliable technique to provide a low-cost, flexible, and easy-to-expandable network. In this article, we propose a UAV-assisted cooperative transmission network (UCTN), where UAVs and the base station (BS) transmit data jointly through the cooodination of the software-defined network (SDN). As the UAV has limited on-board energy, we aim to maximize the network energy efficiency (EE) by jointly optimizing the UAV–user association, UAV location, BS resource allocation, and the load allocation between the two systems. The formulated problem is a mixed-integer nonconvex optimization problem, which is intractable. To solve it, alternative optimization (AO) and Dinkelbach’s method are adopted to recast the original problem into a tractable form. After that, the corresponding problem is divided into three subproblems and can be solved iteratively. An energy-constrained coalition game is proposed to solve the UAV–user association problem, and the UAV hovering point is carefully designed to balance the coalition serving time. Furthermore, the successive convex approximation (SCA) technique is adopted to optimize the BS transmit power and bandwidth allocation. Numerical experiments demonstrate that the proposed algorithm achieves significant performance gains over other benchmark schemes.

Owing to the striking features, such as controllable mobility, low cost, and so on, unmanned aerial vehicles (UAVs) are deemed to be the promising solution to complete data collection tasks of Internet of Things devices (IoTDs). The limited onboard energy, however, undeniably impedes the progress of collecting data. Furthermore, this task is complicated further due to the various amount of data generated by the different types of IoTDs. The goal of this paper is to design an applicable data collection scheme for IoT networks using a laser-powered UAV to maximize system energy efficiency. We propose an improved clustering algorithm called logarithm kernel-based mean shift (LKMS) inspired by the idea behind the mean shift algorithm. Based on the LKMS, we propose a novel algorithm to determine the optimal visiting order and enter points (EPs) of IoTD clusters, paving the way for the following optimization. To manage to solve the variables-coupling and non-convex formulated problem, we artificially divide the entire flying procedure into two phases, the flying and charging (FC) phase as well as the collecting data (CD) phase, depending on whether the UAV is harvesting energy. The block coordinate descent (BCD) and the successive convex approximation (SCA) methods are used to decouple the variables and solve the non-convex subproblems. Simulation results validate the effectiveness of our proposed scheme.

This work studies a practical scenario where the unmanned aerial vehicle (UAV) is dispatched from the data center to collect the data from the Internet of Things (IoT) devices and return to the data center. These IoT devices with different amounts of data and are sparsely located in an untraveled area. In this scenario, we consider minimizing the UAV's cruise time by jointly optimizing the clustering strategy of the IoT devices, the association strategy between the UAV and the IoT devices, the UAV's trajectory, and the bandwidth allocation strategy. Traditionally, the minimal cruise time is obtained by visiting all IoT devices one by one and this will lead to huge cost. To address this problem, affinity propagation (AP) algorithm is proposed to cluster all these IoT devices first of all. Then, to avoid visiting all devices one by one and realize parallel computation of data collection time, the entire UAV's flying process is divided into two phases, namely, the inter-cluster phase and the intra-cluster phase. For the inter-cluster phase, the minimal cruise time is formulated to minimize total distance among clusters using the traveling salesman problem with a neighborhood (TSPN)-based algorithm. Subsequently, for the intra-cluster phase, we formulate the collecting time minimization problem in each cluster as a mix-integer non-convex optimization problem. To address these issues, we design a two-loop algorithm involving the bisection search, the altering optimization (AO), and the successive convex approximation (SCA) methods. Simulation results validate the effectiveness of our proposed scheme.

This paper investigates the unmanned aerial vehicle (UAV)‐assisted wireless communication network that collects the data information of Internet of things (IoT) devices deployed in the region, where the cellular networks cannot cover. Due to the numerous variety and number of IoT devices, a large amount of data generated by IoT networks needs to be collected by UAV. The goal of this paper is to minimize the UAV's cruise time with the joint optimization of IoT devices communication scheduling, UAV trajectory, and transmit bandwidth allocation. To facilitate data collection by UAVs, the data‐distance‐k‐means (d2‐k‐means) algorithm is proposed to divide IoT devices into multiple initial clusters. However, the formulated problem is mixed‐integer joint non‐convex, so it is difficult to solve directly. Since it may be with relatively high computational complexity, as an alternative, a block coordinate descent (BCD)‐based method is designed. To tackle the non‐convex problem, a successive convex approximation (SCA)‐based algorithm is also proposed. Numerical results demonstrate that the proposed scheme is able to achieve significant performance over other schemes for scenarios of UAV‐assisted wireless IoT networks to collect massive amount of data.

This paper considers a scenario in which an unmanned aerial vehicle (UAV) collects the sensed data from the terrestrial Internet of things (IoT) devices in an untraveled and base station (BS)-uncovered area.These IoT devices, in this scenario, are of various types and deployed in a spare manner.Consequently, how to design an effective and suitable resource allocation scheme is a key part of dispatching a UAV to collect the sensed data from IoT devices.The goal of this paper is to minimize the UAV's cruise time with the joint optimization of IoT devices' communication scheduling, UAV trajectory, and bandwidth allocation.To facilitate data collection by UAVs, a logarithmic kernel-based mean shift (LKMS) clustering algorithm is proposed to group IoT devices into multiple clusters.Based on the clustering result, a mixed-integer joint non-convex problem is formulated.To avoid the difficulties caused by solving the aforementioned problem directly, the block coordinate descent (BCD)-based method, as an alternative, is adopted to decouple the variables and decompose the non-convex problem.To tackle the non-convex subproblems, a successive convex approximation (SCA)-based algorithm is also proposed.Numerical results demonstrate that the proposed scheme is able to achieve significant performance over other schemes for scenarios of UAV-assisted wireless IoT networks to collect a huge amount of data.