This survey examines the current state of the Metaverse, encompassing its fundamental concepts, technological framework, practical applications, and user experience to evaluate its stage of development. This paper reviews the core concepts of the Metaverse and Extended Reality (XR) and evaluates the latest advancements in hardware and software technologies. Furthermore, it examines the Metaverse’s typical applications in four key domains: education, training, medicine, and mixed life, while summarizing user feedback to identify its advantages and challenges. The feedback indicates that the Metaverse offers notable benefits, including immersive experiences, enhanced training effectiveness, cost efficiency, and improved safety. However, significant challenges remain, such as hardware performance limitations, software inefficiencies, user discomfort, health risks, and social and ethical concerns. The analysis suggests that while the Metaverse has yet to reach full maturity, it holds great potential for future development. To further advance the field, this paper highlights key research priorities in artificial intelligence, quantum computing, and social governance, providing insights for future studies.

Federated learning (FL) is promising for privacy-sensitive fault diagnosis in the Industrial Internet of Things (IIoT). However, real-world deployments must address the coexistence of class-incremental fault evolution and dynamic edge participation, which leads to fragmented class exposure, catastrophic forgetting, and aggregation bias. To address this issue, we propose FedDiDy, a unified framework for federated class-incremental fault diagnosis under dynamic participation. FedDiDy combines a multi-head classifier for task decoupling, a raw-data-free conditional generator for historical knowledge replay, and a perception-aware aggregation mechanism for bias mitigation. Experiments on four datasets under Bernoulli, Cyclic, and Markov participation patterns show that FedDiDy consistently outperforms the compared baselines, with up to 15.1% absolute improvement in average accuracy and 14.44% reduction in forgetting rate.

Attention deficit disorder is a frequently observed symptom in individuals with autism spectrum disorder (ASD). This condition can present significant obstacles for those affected, manifesting in challenges such as sustained focus, task completion, and the management of distractions. These issues can impede learning, social interactions, and daily functioning. This complexity of symptoms underscores the need for tailored approaches in both educational and therapeutic settings to support individuals with ASD effectively. In this study, we have expanded upon our initial virtual reality (VR) prototype, originally created for attention therapy, to conduct a detailed statistical analysis. Our objective was to precisely identify and measure any significant differences in attention-related outcomes between sessions and groups. Our study found that heart rate (HR) and electrodermal activity (EDA) were more responsive to attention shifts than temperature. The ‘Noise’ and ‘Score’ strategies significantly affected eye openness, with the ASD group showing more responsiveness. The control group had smaller pupil sizes, and the ASD group’s pupil size increased notably when switching strategies in Session 1. Distraction log data showed that both ‘Noise’ and ‘Object Opacity’ strategies influenced attention patterns, with the ‘Red Vignette’ strategy showing a significant effect only in the ASD group. The responsiveness of HR and EDA to attention shifts and the changes in pupil size could serve as valuable physiological markers to monitor and guide these interventions. These findings further support evidence that VR has positive implications for helping those with ASD, allowing for more tailored personalized interventions with meaningful impact.

Federated Learning(FL) is currently a widely used collaborative learning framework, and the distinguished feature of FL is that the clients involved in training do not need to share raw data, but only transfer the model parameters to share knowledge, and finally get a global model with improved performance. However, recent studies have found that sharing model parameters may still lead to privacy leakage. From the shared model parameters, local training data can be reconstructed and thus lead to a threat to individual privacy and security. We observed that most of the current attacks are aimed at client-specific data reconstruction, while limited attention is paid to the information leakage of the global model. In our work, we propose a novel FL attack based on shared model parameters that can deduce the data distribution of the global model. Different from other FL attacks that aim to infer individual clients’ raw data, the data distribution inference attack proposed in this work shows that the attackers can have the capability to deduce the data distribution information behind the global model. We argue that such information is valuable since the training data behind a well-trained global model indicates the common knowledge of a specific task, such as social networks and e-commerce applications. To implement such an attack, our key idea is to adopt a deep reinforcement learning approach to guide the attack process, where the RL agent adjusts the pseudo-data distribution automatically until it is similar to the ground truth data distribution. By a carefully designed MDP process, our implementation ensures our attack can have stable performance and experimental results verify the effectiveness of our proposed inference attack.

The distinct attributes of Internet of Things (IoTs) devices, including the disparity between training and testing data distributions and limited availability of training data, pose challenges for deep learning models in effectively addressing unseen domain segmentation tasks. Federated Learning (FL) can increase the participation of various data contributors, thus has great potential to develop a unified framework to shed light on the relationship between unseen domains and generalized domains. In this paper, we proposed an FL-based unseen domain segmentation model. The architecture includes (1) an external memory module as an object feature guide to reduce the feature ambiguity of unseen domain objects. (2) A re-attention activation mechanism for better completing localization of unseen domain objects, enhancing the features of potential targets and suppressing interference features. (3) A self-supervised learning paradigm for achieving specific object feature exploration. Based on flexible splitting and combining, our model is able to capture both personalization and generalization capabilities, the client side retains a strong personalization ability, while the server side has a strong generalization ability. Moreover, taking into account the inherent limitations in computing and storage resources commonly associated with IoT devices, the introduced model leverages the concept of optional dependencies to enable efficient inference within resource-constrained client environments. Our proposed model is validated through extensive experiments. The approach proposed in this paper outperforms the generalization capabilities of state-of-the-art work on several benchmarks.

Today's business environment is characterized by uncertainty and competition, so the capability to adapt to the evolving era and unforeseen challenges is essential in business strategies. Recent studies on extended enterprise indicate that collaboration among different stakeholders is beneficial for surviving these unexpected changes. However, the barriers such as market uncertainty, privacy and trust concerns, and individual contribution evaluation limit the implementation and application of the extended enterprise concept. Federated learning (FL), in which multiple enterprise entities can use a shared model while retaining all training data locally, has emerged as a promising artificial intelligence (AI) solution for accumulating insights from multiple stakeholders and providing collaborative decision-making. Furthermore, the enhanced privacy-protection benefits of FL remove the barriers to implementing extended enterprise collaboration. In particular, an FL central server manages the local updates of multiple enterprise entities (FL clients) and aggregates their contributions to improve the global model training. Meanwhile, to address the time-series graph learning problem in most business environments, we incorporate temporal convolutional network, graph convolutional neural network, and gated recurrent unit architecture into FL to capture the temporal-spatial dependencies in individual data sources. Furthermore, we use traffic flow forecasting as the use case of our proposed framework to verify its effectiveness. Finally, the experimental results on a real traffic flow dataset and the comparison results with the state-of-the-art baseline methods show that our proposed solution achieves superior performance.

The opioid abuse epidemic represents a major public health threat to global populations. The role social media may play in facilitating illicit drug trade is largely unknown due to limited research. However, it is known that social media use among adults in the US is widespread, there is vast capability for online promotion of illegal drugs with delayed or limited deterrence of such messaging, and further, general commercial sale applications provide safeguards for transactions; however, they do not discriminate between legal and illegal sale transactions. These characteristics of the social media environment present challenges to surveillance which is needed for advancing knowledge of online drug markets and the role they play in the drug abuse and overdose deaths. In this paper, we present a computational framework developed to automatically detect illicit drug ads and communities of vendors. The SVM- and CNN- based methods for detecting illicit drug ads, and a matrix factorization based method for discovering overlapping communities have been extensively validated on the large dataset collected from Google+, Flickr and Tumblr. Pilot test results demonstrate that our computational methods can effectively identify illicit drug ads and detect vendor-community with accuracy. These methods hold promise to advance scientific knowledge surrounding the role social media may play in perpetuating the drug abuse epidemic.

More and more applications are promoting cus-tomized or personalized services. In order for these applications to provide meaningful output, it collects users’ personal information over time. Some personal information (e.g. education level or income level) can only be captured by users actively updating their profile to reflect these changes. We refer to these as long-term time series data, as they do not change frequently. If applications can keep up to date on a diverse and large set of personal features, they can provide higher quality service. However, this quality of service comes at the cost of the user sacrificing their privacy. There has been numerous research on protecting privacy of time series data for context aware services, but the privacy leakage of personal information updates during the whole life-cycle of the series has received only scant attention.Motivated users concerned about their privacy, we discuss in detail the privacy leakage risk, focusing on long-term time-series data from the perspective of game theory. Then, we propose a reward-privacy model, targeting the privacy-aware data-updates for the entire life-cycle in context-aware services by leveraging a three-party Stackelberg game. We theoretically prove that a Nash Equilibrium exists in the proposed model, and then use simulations to validate that a Nash Equilibrium exists for different parameters of the productivity function. By using our proposed framework, users have guidance to decide not only the timing of submitting personal updates, but also the granularity or obscurity level for their data.

The ubiquity of devices in Internet of Things (IoT) has opened up a large source for IoT data. Machine learning (ML) models with big IoT data is beneficial to our daily life in monitoring air condition, pollution, climate change, etc. However, centralized conventional ML models rely on all clients' data at a central server, which seriously threatens user privacy. Federated learning (FL) emerges as a promising solution aiming to protect user privacy by enabling model training on a large corpus of decentralized data. The recent studies indicate FL suffers from the heterogeneity issue as it treats all clients' data equally, that is, FL might sacrifice the performance of the majority of clients to accommodate the performance of the minority of clients with low usability data. In order to overcome this issue, a reinforcement learning (RL)-based intelligent central server with the capability of recognizing heterogeneity is implemented, which can help lead the trend toward better performance for majority of clients. To be specific, an FL central server analyses the benefits of different collaboration by capturing the intricate patterns in heterogeneous clients based on rating feedback and then updates clients' weights iteratively, until it establishes a coalition of clients with quasioptimal performance. The experimental results on three real data sets under various heterogeneity levels demonstrate the superior performance of the proposed solution.

The IoT era observes the increasing demand for data to support various applications and services. The Mobile Crowdsensing (MCS) system then emerged. By utilizing the hybrid intelligence of humans and sensors, it is significantly beneficial to keep collecting high‐quality sensing data for all kinds of IoT applications, such as environmental monitoring, intelligent healthcare services, and traffic management. However, the service quality of MCS systems relies on a dedicated designed task allocation framework, which needs to consider the participant resource bottleneck and system utility at the same time. Recent studies tend to use a different solution to solve the two challenges. The incentive mechanism is for resolving the participant shortage problem, and task assignment methods are studied to find the best match of participants and system utility goal of MCS. Thus, existing task allocation frameworks fail to consider the participant’s expectations deeply. We propose a semiopportunistic concept‐based solution to overcome this issue. Similar to the “shared mobility” concept, our proposed task allocation framework can offer the participants routing advice without disturbing their original travel plan. The participant can accomplish the sensing request on his route. We further consider the system constraints to determine a subgroup of participants that can obtain the utility optimization goal. Specifically, we use the Graph Attention Network (GAT) to produce the target sensing area’s virtual representation and provide the participant with a payoff‐maximized route. Such a method makes our solution adapt to most of MCS scenarios’ conditions instead of using fixed system settings. Then, a reinforcement learning‐ (RL‐) based task assignment is adopted, which can help the MCS system towards better performance improvements while support different utility functions. The simulation results on various conditions demonstrate the superior performance of the proposed solution.

Modern applications and services leveraged by interactive cyberphysical systems (CPS) are providing significant convenience to our daily life in various aspects at present. Clients submit their requests including query contents to CPS servers to enjoy diverse services such as health care, automatic driving, and location-based services. However, privacy concerns arise at the same time. Content privacy is recognized and a lot of efforts have been made in the literature of privacy preserving in interactive cyberphysical systems such as location-based services. Nevertheless, neither the cloaking based solutions nor existing client based solutions have achieved effective content privacy by optimizing proper content privacy metrics. In this paper we formulate the problem of achieving the optimal content privacy in interactive cyberphysical systems using k -anonymity solutions based on two content privacy metrics, which are defined using the concepts of entropy and differential privacy. Then we propose an algorithm, Multilayer Alignment (MLA), to establish k -anonymity mechanisms for preserving content privacy in interactive cyberphysical systems. Our proposed MLA is theoretically proved to achieve the optimal content privacy in terms of both the entropy based and the differential privacy mannered content privacy metrics. Evaluation based on real-life datasets is conducted, and the evaluation results validate the effectiveness of our proposed algorithm.

Vehicular ad hoc networks (VANETs) which are deployed along roads make traffic systems safer and efficient. Existing theoretical results on capacity scaling laws provide insights and guidance for the design and deployment of VANETs. In this paper, we propose a novel fundamental framework RVWNM (Real Vehicular Wireless Network Model), which enables a more realistic capacity analysis in VANETs. We first introduce a Euclidean planar graph which can be constructed from any real map of urban area, and represents the practical geometry structure of the urban area. Then, an interference relationship graph is abstracted from the Euclidean planar graph which considers the transmission interference relations among the nodes in the network. Finally, we analyze theoretically the interference relationships in the interference relationship graph. As far as we know, we are the first to use a practical geometry structure to calculate the asymptotic capacity of VANETs. To verify the feasibility of RVWNM, we calculate the asymptotic capacity of urban area VANETs with the consideration of social- proximity based mobility of vehicles.

Capacity scaling laws of wireless networks have attracted a lot of attention. In this paper, we study the multicast capacity of bus-assistant VANETs (vehicular ad hoc networks) with two-hop relay scheme, which has not been addressed before. Assume that n ordinary vehicles and nbbuses are deployed in a grid-like road framework while the number of roads increase linearly with n. All the ordinary vehicles obey the restricted mobility model. Thus, the spatial stationary distribution decays as power law with the distance from the centre spot (home-point) of a restrict region of each vehicle. All the buses deployed in all roads as intermediate nodes. They are used to forward packets for ordinary vehicles. Each ordinary vehicle randomly chooses k - 1 vehicles from the other ordinary vehicles as receivers. The packets could be transmitted directly from source to destination or be transmitted to an intermediate vehicle or bus, then be forwarded to the destination. We found that the social-proximity urban bus-assisted VANET has three routing methods. For each routing method, we derive the matching asymptotic upper and lower bounds of multicast capacity of bus-assisted VANET.