In the unmanned aerial vehicle (UAV) assisted Internet of Vehicles (IoV) scenario, with the advantages of rapid deployment and line-of-sight communication, UAVs are widely adopted to provide the computation, communication and storage service for vehicles. However, existing research often overlooks the impact of content caching on task latency and UAV resource optimization in UAV assisted IoV. Hence, this paper explores the relationship between computation offloading and content caching, and formulates a joint optimization problem to reduce task delays in UAV assisted IoV environment. Especially, each computation task is modeled as a directed acyclic graph, in which the subtasks are interdependence. Furthermore, the joint optimization problem is the non-linear integer programming problem, and the Deep Deterministic Policy Gradient algorithm is used to solve the joint optimization problem, regarding the decision variables, available resources and the optimal objective as the action space, the state space and the reward function, successively. Finally, extensive simulation experiments demonstrate the algorithm’s effectiveness on task processing delay in UAV assisted IoV environments.

Mobile edge computing (MEC) offers a promising approach for providing computation and storage services to user terminals (UTs). However, the computational resources deployed on the base stations or fixed locations are insufficient for temporary emergency scenarios. To expand MEC capacity, an air-ground cooperation architecture that leverages the low cost, rapid deployment, and mobility of low-altitude platform is proposed. A joint optimization strategy for air-ground cooperation caching and content delivery is introduced to reduce delays caused by limited wireless backhaul capacity, energy constraints of edge nodes in air (ENAs), and repeated content delivery. This strategy incorporates trajectory planning of UAVs, transmission power allocation, downlink bandwidth allocation, content caching, and user association. Content popularity is predicted using an LSTM network based on historical data. We employ the block coordinate descent (BCD) method to address the optimization problem and design the popularity prediction-based air-ground cooperation caching and content delivery (PP-AG3C) algorithm. Numerical simulations show that our algorithm outperforms benchmark algorithms in average delivery delay, data transmission energy, and cache hit rate. When the number of UTs is 60, compared with PP-AG3C algorithm, The average data transmission energy consumption of TPCU-AG3C algorithm, TCU-AG3C algorithm, TC-AG3C algorithm, RT-AG3C algorithm and FT-AG3C algorithm increased by 30.79%, 49.41%, 76.70%, 152.85%, and 51%, respectively.

Semantic communication is an emerging paradigm for digital twin (DT) synchronization in unmanned aerial vehicle (UAV)-assisted edge computing environments, where machine learning (ML) models are deployed on edge servers and UAVs as semantic encoders and decoders to perform real-time synchronization. However, with limited system resources, additional computation workloads are still brought to all participants for semantic information extraction and recovery. In this work, we propose an optimized tiny ML-based DT synchronization framework to minimize the synchronization latency in UAV-assisted edge computing environments, considering time-average constraints on virtual energy deficit queue stability. Due to the coexistence of tiny ML-based semantic communications, a semantic extraction factor is introduced to formulate the DT synchronization problem as a time-average time minimization problem. By leveraging the Lyapunov optimization framework, the multi-stage DT synchronization problem is transformed into several per-slot resource allocation problems. To solve the per-slot optimization problem efficiently, a deep reinforcement learning-based synchronization (DRLS) algorithm is proposed, where an actor-critic structure is adopted to generate synchronization actions with low time complexity. Finally, we conduct simulation experiments to evaluate the performance of the proposed DRLS scheme. Numerical results demonstrate that our DRLS algorithm can reduce 8.23% of DT synchronization delay and 15.31% of synchronization data dropping rates on average by comparing it with the UAV-edge collaborative synchronization scheme without semantic communications. Besides, the DRLS algorithm can achieve up to 57.14% synchronization energy reduction compared with representative synchronization policies.

Recently, edge computing plays the important role in Internet of Things (IoT). However, for the emergency scenarios, the capacity of edge computing is limited. Thus, the unmanned aerial vehicle (UAV)-assisted edge computing is introduced for IoT. Meanwhile, it faces privacy disclosure issues that the data generated on user terminals (UTs) is transmitted to edge/cloud servers for processing. So, the federated learning (FL) is adopted to train the model. But due to the difference between UTs or data quality, and the dynamic wireless network, the FL faces the bottleneck of training efficiency and accuracy in a UAV-assisted edge computing environment. In this article, a three-layer FL architecture is proposed. The FL training process is optimized by jointly optimizing the UT selection, the UAV selection, the data set selection, and the wait delay selection of one-time training. The convergence gap optimization problem is built by analyzing model training convergence, and the node selection algorithm is designed. Finally, the extensive simulation experiments are conducted to verify the feasibility and efficiency of the proposed algorithm for FL.

The purposes are to explore the safety performance of the Maritime Transportation System (MTS) based on Digital Twins (DTs) Internet of Things (IoT) and develop maritime transportation towards intelligence and digitalization. Because the comprehensive operational security of modern MTS is not yet mature, historical transportation data of the Maritime Silk Road are acquired and preprocessed. Afterward, DTs are introduced, and relay nodes are added to data transmission paths to construct a maritime transportation DTs model based on relay cooperation IoT. Eventually, this model's security performance is validated through simulation experiments. Relay security analysis suggests that interference information is a vital guarantee to assist in information non-disclosure, from which the constructed model can harvest energy to increase the data transmission power, thereby improving communication performance and secrecy rate. Outage probability analysis reveals that the simulated and the theoretical results are almost the same; moreover, given the system's multi-hop paths in the same environment, the more the relays and the greater the fading index, the better the system performance and the lower the outage probability. Once the iterations reach a particular number, the node secrecy rate becomes optimal and cannot cause excessive burden to the system; besides, the power distribution can establish a new equilibrium when the nodes are in different locations, so that system security performance gets improved. The simulated value is closest to the actual result under 100% successful transmission probability and 0.01~0.05 λ value. To sum up, the constructed maritime transportation DTs model presents extraordinary transmission and security performance, providing an experimental basis for intelligent and secure maritime transportation in the future.

The Internet of Things (IoT) is the integration of all information and Internet technology in the information age, which can realize the collection and transmission of intelligent information. A large number of sensors are producing and collecting data involving various industries every day. The amount of stream data generated is huge, and a large number of abnormal data are also generated in the process. Due to the demands of business and life quality improvement, the application of IoT technology to real-time monitoring and correction of massive stream data, especially the correction of abnormal data, is a very valuable research direction, and also the key to ensure the credibility and fidelity of IoT data. This article proposes a recurrent neural network model based on long- and short-term memory network (LSTM) and LSTM+. LSTM+ model not only reduces the regression error compared with the traditional LSTM model, but also can detect abnormal data collected by IoT terminal nodes, and can correct the abnormal data in real time, so as to ensure that the network prediction can have good stability and robustness.

The present work aims to improve the communication security of Internet of Vehicles (IoV) nodes in intelligent transportation through studying the safety of IoV in smart transportation based on Blockchain (BC). An IoV DTs model is built by combining big data with Digital Twins (DTs). Then, regarding the current IoV communication security issues, a secure communication architecture for the IoV system is proposed based on the immutable and trackable BC data. Besides, Wasserstein Distance Based Generative Adversarial Network (WaGAN) model constructs the IoV node risk forecast model. Because the WaGAN model calculates the loss function through Wasserstein distance, the learning rate of the model accelerates remarkably. After ten iterations, the loss rate of the WaGAN model is close to zero. Massive in-vehicle devices in IoV are connected simultaneously to the base station, causing network channel congestion. Therefore, a Group Authentication and Privacy-preserving (GAP) scheme is put forward. As users increase during authentication, the GAP scheme performs better than other authentication access schemes. In summary, the Intelligent Transportation System driven by DTs can promote intelligent transportation management. Besides, introducing BC into IoV can improve access control’s accuracy and response efficiency. The research reported here has significant value for improving the security of the information sharing of the IoV.

In recent years, Sina microblog becomes an important social network platform for users to not only form personal website but share, deliver and keep interest messages via the relationships between users. However, with larger number of users in social networks, the problem of ‘information overload’ has been increasingly highlighted. In this case, community discovery has been the urgent requirements in microblog social networks. In this paper, a microblog community discovery algorithm based on tag correlation and interactive behaviors is presented. For the selection of exact user tags, the model of maximal marginal relevance is used and improved to enrich the variousness and novelty of user interest tags. The method of edge-similarity computation is improved to suit for microblog community discovery. Hadoop platform is applied for data process, which greatly decreases the running time of experiments. Finally, extensive experiments are provided to demonstrate the performance of proposed algorithm. The results indicate that the performance of proposed algorithm is better than that of benchmark algorithms for microblog social networks. The proposed algorithm for community discovery can identify the users belonging to multi communities or no community so that the results of community discovery are more accurate and more suitable for actual environment.