Device-free passive human activity recognition is playing an important role in many applications such as smart homes, identification, health care, etc. However, existing human activity recognition systems either require dedicated device or do not meet the scenarios of the signals through the wall. To address this challenge, we present TW-See, a device-free passive human activity recognition system with Wi-Fi signals, which does not require any dedicated device and meets the scenarios of the signals through the wall. TW-See mainly exploits two key techniques to recognize different human activities. First, we propose an Opposite robust PCA (Or-PCA) approach to obtain the correlation between human activity and its resulting changes in CSI values, which can eliminate the influence of the background environment on the correlation extraction. Second, we propose a normalized variance sliding windows algorithm to segment human action time from Or-PCA waveforms, which can distinguish the true or false of the start and end time of human actions. Furthermore, we implemented TW-See with commodity Wi-Fi devices and evaluated it in several different environments. Experiment results show that TW-See achieves average 94.46% accuracy, when the signals pass through the wall.

The integration of Roadside Units (RSUs) is vital for the development of autonomous driving technologies. Challenges arise from sinking computing capabilities into RSUs and vehicles in the paradigm of Vehicle Edge Computing (VEC), particularly due to heterogeneous computation and communication capacities of network nodes and multiple sources of computing tasks (node-mounted and offloading tasks). These challenges complicate network stability from the perspective of a long-term optimization evolving over time, considering unpredictable task distribution and environmental states. To tackle these challenges, we approach the problem of partial task offloading to minimize task delay while meeting the demand of system stability over time as a dynamic long-term optimization. Utilizing Lyapunov stochastic optimization tools, we successfully decouple the long-term delay minimization and stability constraint, transforming it into a per-slot scheduling problem. Since the per-slot scheduling problem with complicated Lyapunov drift functions can not be solved by numerical optimization at each time step, our solution leverages a proposed deep reinforcement learning algorithm, leading to extensive simulations that demonstrate the superior effectiveness and efficiency of our proposal compared to existing schemes.

With the rapid advancement of intelligent transportation systems, Road Side Units (RSUs) in the Internet of Vehicles (IoV) play a crucial role in sensing environments information for vehicles to make driving decisions and transportation management. By offloading computation tasks from RSUs to other nodes such as cloud servers, other RSUs, and vehicles, the network computation resources can be fully utilized, however, at the expense of increasing task delay. In multi-hop task offloading, tasks can be forwarded to vehicles outside the coverage area of the source RSU where tasks are generated. However, due to the movement of vehicles, stable transmission among nodes is not guaranteed, posing a challenge in determining the next node for task forwarding. In this paper, we ensure the connectivity among nodes by establishing a mobility model and design a mechanism for selecting forwarding vehicles. The goal is to find those vehicles that can communicate stably with the source RSU and determine the optimal communication path. We formulate task offloading as a 0-1 mathematical model with the objective of minimizing the task delay. Subsequently, we propose a solution based on asynchronous deep reinforcement learning A3C. Through extensive simulations, we validate the effectiveness of our approach.

Owing to its outstanding parallel computing capabilities, quantum computing (QC) has been a subject of continuous attention. With the gradual maturation of QC platforms, it has increasingly played a significant role in various fields such as transportation, pharmaceuticals, and industrial manufacturing, achieving unprecedented milestones. In modern society, wireless communication stands as an indispensable infrastructure, with its essence lying in optimization. Although artificial intelligence (AI) algorithms such as reinforcement learning (RL) and mathematical optimization have greatly enhanced the performance of wireless communication, the rapid attainment of optimal solutions for wireless communication problems remains an unresolved challenge. QC, however, presents a new alternative. This paper aims to elucidate the fundamentals of QC and explore its applications in wireless communications and networking. First, we provide a tutorial on QC, covering its basics, characteristics, and popular QC algorithms. Next, we introduce the applications of QC in communications and networking, followed by its applications in miscellaneous areas such as security and privacy, localization and tracking, and video streaming. Finally, we discuss remaining open issues before concluding.

Federated learning (FL) opens a new promising paradigm for the Industrial Internet of Things (IoT) since it can collaboratively train machine learning models without sharing private data. However, deploying FL frameworks in real IoT scenarios faces three critical challenges, i.e., statistical heterogeneity, resource constraint, and fairness. To address these challenges, we design a fair and efficient FL method, termed FedASA, which can address the challenge of statistical heterogeneity in resource-constrained scenarios by determining the shared architecture adaptively. In FedASA, we first present a cell-wised shared architecture selection strategy, which can adaptively construct the shared architecture for each device. We then design a cell-based aggregation algorithm for aggregating heterogeneous shared architectures. In addition, we provide a theoretical analysis of the federated error bound, which provides a theoretical guarantee for the fairness. At the same time, we prove the convergence of FedASA at the first-order stationary point. We evaluate the performance of FedASA through extensive simulation and experiments. Experimental results in cross-location scenarios show that FedASA outperformed the state-of-the-art approaches, improving accuracy by up to 13.27% with better fairness and faster convergence and communication requirement has been reduced by 81.49%.

Based on emerging Artificial Intelligence (AI) tasks, cloud-edge-terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats such as malicious network reconnaissance, side-channel attacks, and Distributed Denial of Service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of Moving Target Defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this paper, we firstly provide a thorough analysis of the threats in the cloud-edge-terminal network. Then we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this paper is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network.