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.

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.

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.

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 UAV-assisted edge computing is introduced for IoT. Meanwhile, it faces privacy disclosure issue that the the data generated on UTs is transmitted to edge/cloud servers for processing. So, the federated learning (FL) is adopted to train 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 UAV-assisted edge computing environment. In this paper, 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 the node selection algorithm is designed. Finally, the extensive simulation experiments are conducted to verify the feasibility and efficiency of proposed algorithm for FL.

In the mobile edge computing (MEC) environment, the edge caching can provide the timely data response service for the intelligent scenarios. However, due to the limited storage capacity of edge nodes and the malicious node behavior, the question of how to select the cached contents and realize the decentralized security data caching faces challenges. In this paper, a blockchain-based decentralized and proactive caching strategy is proposed in an MEC environment to address this problem. The novelty is that the blockchain was adopted in an MEC environment with a proactive caching strategy based on node utility, and the corresponding optimization problem was built. The blockchain was adopted to build a secure and reliable service environment. The employed methodology is that the optimal caching strategy was achieved based on the linear relaxation technology and the interior point method. Additionally, in a content caching system, there is a trade-off between cache space and node utility, and the caching strategy was proposed to solve this problem. There was also a trade-off between the consensus process delay of blockchain and the caching latency of content. An offline consensus authentication method was adopted to reduce the influence of the consensus process delay on the content caching. The key finding was that the proposed algorithm can reduce latency and can ensure the security data caching in an IoT environment. Finally, the simulation experiment showed that the proposed algorithm can achieve up to 49.32%, 43.11%, and 34.85% improvements on the cache hit rate, the average content response latency, and the average system utility, respectively, compared to the random content caching algorithm, and it achieved up to 9.67%, 8.11%, and 5.95% increases, successively, compared to the greedy content caching algorithm.