As a network with dynamic network topology and limited on-board resource, the Internet of Drones(IoD) suffers various cyber attacks, such as real-time data tampering, clogging, jamming and etc.. The real-time data tampering, especially the session key leakage, in the IoD can distort time-sensitive information and disrupt system operations. Many existing works based on authentication and key agreement perform low efficient and not secure enough for real-time data transmitting in the system. In this manuscript, we proposed a three-factor authentication and key agreement protocol (HEAT) to solve these issues. The HEAT can solve session key leakage during data transmission by refining the user authentication mechanism and introducing the Elliptic Curve Cryptography into communications between drones and users. Compared with existing protocols, the HEAT achieves less authentication delay and higher data efficiency. The HEAT is proven secure with numerous experiments under the extended Canetti-Krawczyk model.

Cellular traffic prediction provides significant support for the management of intelligent networks. Existing models commonly combine recurrent neural networks (RNNs) with attention mechanisms, convolutional neural networks (CNNs), or graph convolutional networks (GCNs) to capture spatial-temporal correlations of cellular traffic. However, attention mechanisms lack sensitivity to local information; CNNs ignore the interaction among distant regions with similar semantics; GCNs exhibit limitations in exploring high-order (beyond pairwise) spatial correlations. To this end, we develop a hypergraph attention recurrent network (HARN) that exploits locality, semantics, and high-order correlations for cellular traffic prediction. Specifically, we first propose a spatial trend-aware attention to perceive local trends, thus easing the mismatching problem of attention mechanisms. Then, we construct a hypergraph to characterize the interactions between distant regions with similar semantics, and leverage a hypergraph convolution network to extract high-order correlations. More importantly, to extract heterogeneous and varying spatial patterns, we further enhance the hypergraph convolution network by incorporating spatial-temporal representations. Last, extensive experiments on three real-world datasets demonstrate the superiority of HARN over state-of-the-art baselines in terms of mean absolute error and root mean square error, with specific improvements of 1.83% and 5.79% on SMS (short message service) dataset, 3.05% and 11.27% on Call dataset, and 1.36% and 1.65% on Internet dataset, respectively.

Traffic prediction is vital to traffic planning, control, and optimization, which is necessary for intelligent traffic management. Existing methods mostly capture spatiotemporal correlations on a fine-grained traffic graph, which cannot make full use of cluster information in coarse-grained traffic graph. However, the flow variation of clusters in the coarse-grained traffic graph is more stable compared with nodes in the fine-grained traffic graph. And the flow variation of a fine-grained node is generally consistent with the trend of the cluster to which the node belongs. Thus information in the coarse-grained traffic graph can guide feature learning in the fine-grained traffic graph. To this end, we propose a Spatiotemporal Multiscale Graph Convolutional Network (SMGCN) that explores spatiotemporal correlations on a multiscale graph. Specifically, given a fine-grained traffic graph, we first generate a coarse-grained traffic graph by graph clustering, and extract spatiotemporal correlations on both fine-grained and coarse-grained traffic graphs. Then we propose a cross-scale fusion (CF) to implement information diffusion between the fine-grained and coarse-grained traffic graphs. Moreover, we employ an adaptive dynamic graph convolution network to mine both static and dynamic spatial features. We evaluate SMGCN on real-world datasets and obtain a$1.18\% -3.32\%$improvement over state-of-the-arts.

The combination of mobile edge computing and 5G heterogeneous networks (5G HetNets) provides new vehicular task offloading research solutions. Most existing task offloading studies assume that vehicle tasks are unique and there are no redundant tasks between vehicles. However, there is a duplication of tasks for vehicles within the same base station. That causes a waste of computing resources and increases task offloading costs. To address this problem, this paper proposes the task offloading algorithm TOERT to eliminate redundant tasks in 5G HetNets. The TOERT algorithm is designed to eliminate redundant tasks, improve vehicle task completion rates and reduce offloading costs. Specifically, we consider two cases of redundant tasks within the macro cell base station (MCBS). When the task results have been stored in the MCBS, vehicles directly agree on the transaction price with the MCBS to obtain the task results. The MCBS first eliminates redundant tasks between vehicles when task results are not stored. Then, the MCBS determines the appropriate small cell base station (SCBS) to participate in the partial offloading. Finally, the vehicles negotiate with the MCBS to obtain task results. Against the other five algorithms considered for comparison purposes, the TOERT algorithm effectively eliminates redundant tasks, improves the task completion rate and increases the benefits of both the vehicles and the MCBS.

Traffic flow prediction is a challenging task due to complex spatial-temporal correlations. Most existing methods leverage graph convolutional network (GCN) to capture spatial correlations. However, GCN has limited ability in mining global spatial correlations. Multi-layer GCN for aggregating multi-order neighbor information will result in high-degree nodes being prone to over-smoothing. To this end, we develop a graph convolutional recurrent attention network (GCRAN) for traffic flow prediction. Specifically, we take the advantage of Gated Recurrent Units (GRU) and Attention to explore local and global temporal correlations. Moreover, we design a novel local context aware spatial attention to extract local and global spatial correlations simultaneously. Experiments on two public real-world traffic datasets demonstrate that GCRAN outperform state-of-the-art baselines.

The Mobile Edge Computing (MEC)-based task offloading in the Internet of Vehicles (IoV) scenario, which transfers computational tasks to mobile edge nodes and fixed edge nodes with available computing resources, has attracted interest in recent years. The MEC-based task offloading can achieve low latency and low operational cost under the tasks delay constraints. However, most existing research generally focuses on how to divide and migrate these tasks to the other devices. This research ignores delay constraints and offloading node selection for different tasks. In this article, we design the MEC-enabled IoV architecture, in which all vehicles and MEC servers act as offloading nodes. Mobile offloading nodes (i.e., vehicles) and fixed offloading nodes (i.e., MEC servers) provide low latency offloading services cooperatively through roadside units. Then we propose the task offloading scheme that considers task classification and offloading nodes selection (TO-TCONS). Our goal is to minimize the total execution time of tasks. In TO-TCONS Scheme, we divide the task offloading into the same region offloading mode and cross-region offloading mode, which is based on the delay constraints of tasks and the travel time of the target vehicle. Moreover, we propose the mobile offloading nodes selection strategy to select offloading nodes for each task, which evaluates offloading candidates for each task based on computing resources and transmission rates. Simulation results demonstrate that TO-TCONS Scheme is indeed capable of reducing total latency of tasks execution under the delay constraints in MEC-enabled IoV.