Depth estimation is a fundamental task in 3D computer vision, crucial for applications such as 3D reconstruction, free-viewpoint rendering, robotics, autonomous driving, and AR/VR technologies. Traditional methods relying on hardware sensors like LiDAR are often limited by their high costs, low resolution, and sensitivity to the environment, limiting their applicability to real-world scenarios. Recent advances in vision-based methods offer a promising alternative, yet they face challenges in generalization and stability due to either the low capacity of model architectures or reliance on domain-specific and small-scale datasets. The emergence of scaling laws and foundation models in other domains has inspired the development of “depth foundation models”: deep neural networks trained on large datasets with strong zeroshot generalization capabilities. This paper surveys the evolution of deep learning architectures and paradigms for depth estimation across monocular, stereo, multiview, and monocular video settings. We explore the potential of these models to address existing challenges and we also provide a comprehensive overview of large-scale datasets that can facilitate their development. By identifying key architectures and training strategies, we aim to highlight the path towards robust depth foundation models, offering insights for future research and applications.

Metaverse is drawing increasing attention from both academia and industry. Interoperability among different metaverse systems has become essential. A cross-metaverse interoperability protocol can enable interoperability across metaverses. However, cross-metaverse protocols often suffer significant cost overhead and transaction latency. For example, in STYLE, a leading cross-metaverse platform, 74% of transaction latency and 97% of the cost overhead are attributed to the relay blockchain rather than the two participating metaverses. To make cross-metaverse efficient, in this paper, we propose a fast and cheap cross-metaverse interoperability protocol namedCrossMeta.CrossMetacan enable direct communication among heterogeneous metaverses rather than depending on a relay blockchain. This is achieved through two components: i) a committee that relays transactions from the source metaverse to the destination metaverse, along with availability proofs, and ii) a smart contract that verifies the proofs provided by the committee. To ensure an honest majority within the selected committee, we propose a dynamic committee selection method based on the chain quality property. Furthermore, we demonstrate that honest brokers achieve a Nash equilibrium. Additionally, we prove that the proposedCrossMetaprotocol satisfies the security properties of atomicity and liveness. To demonstrate the practicality ofCrossMeta, we implemented a prototype of theCrossMetausing two real-world metaverse platforms, i.e.,Axie InfinityandSandbox. The evaluation results show thatCrossMetaoutperforms other cross-metaverse solutions regarding transaction latency and gas fees.

The Cyber Threat Intelligence (CTI) marketplace is an emerging platform for CTI service requesters to countermeasure advanced cyber attacks, where CTI service providers are employed on payment. To create a trustworthy CTI marketplace environment, consortium-blockchain-based CTI service platforms have been widely proposed, where the blockchain system becomes the third role, crucially impacting the CTI service quality. How to sustainably promote CTI service quality in this tripartite marketplace is a challenging issue, which has not been well investigated in the literature. In this study, we propose a two-stage tripartite dynamic game-model-based incentive mechanism, where the participation incentives of the three parties are promoted under the constraints of Individual Rationality (IR) and Incentive Compatibility (IC). The sustainability of CTI service is quantitatively formalized through the CTI market demand, which impacts the future profits of the three parties. The Nash equilibrium of the proposed incentive mechanism is solved, where the CTI requester offers an optimal price to achieve effective defense against cyber attacks, and the blockchain platform and CTI service providers collaboratively contribute high-quality CTI services. Empirical experimental results show that the higher the quality of CTI services provided in the marketplace, the greater the market demand for CTI, resulting in a sustainable CTI marketplace.

The accurate imputation of missing load data in building energy consumption is essential for optimizing energy management and scheduling in Internet of Things (IoT)-based smart energy management systems. However, in real-world applications, building load data often suffers from the issue of missing critical samples due to IoT device failures and maintenance. To address this problem, we propose an effective scheme by designing a load data augmentation model named the DAM based on deep neural networks. In the DAM, the partial missing data are generated in each round, followed by stacking with the semi-dataset to perform a new generation round. After several rounds, the missing critical load data are recovered with high precision. A building load dataset collected from a real IoT-based energy-efficiency management system is used for evaluation in this work. Experimental results demonstrate that the proposed scheme can effectively replenish the missing critical data and exhibit excellent stability. Additionally, we compare the prediction performance of the DAM approach with other comparison methods. The results show that our proposed approach outperforms the comparison methods, achieving the highest R2 score of 0.963. Hence, the DAM approach presents an effective solution for addressing the problem of missing critical data in IoT-based smart energy management systems, which is vital for optimizing energy dispatch.

In Internet of Things (IoT) environments, anti-honeypot attacks exploit device fingerprinting and protocol analysis to undermine traditional honeypot defenses. To this end, we introduce an innovative camouflage defense strategy that dynamically disguises real IoT devices as honeypots to mislead attackers and protect critical resources. We formalize this adversarial interaction using a multi-stage signaling game, thereby reversing the information asymmetry that typically favors attackers. Subsequently, we theoretically derive the Perfect Bayesian Nash Equilibrium (PBNE) under conditions of incomplete information, thereby confirming the existence of mixed-strategy equilibria. Practically, we develop a signaling game-based multi-agent proximal policy optimization (SG-MAPPO) algorithm by integrating LSTM networks and Bayesian updates, overcoming challenges in long-term strategy optimization and parameter convergence in dynamic IoT contexts. Experiments demonstrate that SG-MAPPO outperforms existing methods in terms of training efficiency and stability, effectively reducing attacker success rates and providing a proactive defense solution for IoT security.

Permissioned blockchains play a significant role in various application scenarios. Applications built on heterogeneous permissioned blockchains need to migrate data from one chain to another, aiming to keep their competitiveness and security. Thus, data migration across heterogeneous chains is a building block of permissioned blockchains. However, existing data migration protocols across heterogeneous chains are rarely used in practice since data migration technologies are insecure. To this end, we propose a data migration protocol across permissioned blockchains, namedDataFly. We design apeg consensus mechanism, which provides consistent data-migration functionality between any two permissioned blockchains. To preserve the confidentiality of data, we invoke two classical cryptographic methods, i.e., i) ECDSA feature and ii) theintegrated signature and public key encryptionscheme. Through combining those two methods, data can be securely migrated from one permissioned blockchain to another without exposing the migrated data to anyone except associated parties. To demonstrate the practicality ofDataFly, we implement a prototype ofDataFlyusing existing popular permissioned blockchains, i.e., Hyperledger Fabric and private enterprise Ethereum. Measurement results demonstrate thatDataFlyoutperforms related works in terms of transaction latency and gas costs.

Numerous blockchain simulators have been proposed to allow researchers to simulate mainstream blockchains. However, we have not yet found a testbed that enables researchers to develop and evaluate their new consensus algorithms or new protocols for blockchain sharding systems. To fill this gap, we developed BlockEmulator, which is designed as an experimental platform, particularly for emulating blockchain sharding mechanisms. BlockEmulator adopts a lightweight blockchain architecture so developers can only focus on implementing their new protocols or mechanisms. Using layered modules and useful programming interfaces offered by BlockEmulator, researchers can implement a new protocol with minimum effort. Through experiments, we test various functionalities of BlockEmulator in two steps. First, we prove the correctness of the emulation results yielded by BlockEmulator by comparing the theoretical analysis with the observed experiment results. Second, other experimental results demonstrate that BlockEmulator can facilitate measuring a series of metrics, including throughput, transaction confirmation latency, cross-shard transaction ratio, the queuing status of transaction pools, workload distribution across blockchain shards, etc. We have made BlockEmulator open-source in Github.

Non-identically distributed (Non-IID) data is a ma-jor challenge in federated learning (FL). Although many related studies have proposed methods to improve FL model performance, they often incur significant resource consumption. These studies typically save gradient states for training correction, some requiring clients to synchronize these states. Given that clients' extra gradient states could be substantial, even several times larger than the model's size, maintaining and synchronizing such large-size gradient states consume considerable memory and communication resources. This paper rigorously explores a substantial reduction in Non-IID methods' resource consumption on clients by reconstructing Non-IID methods' local corrections on the server. A crucial insight driving this reconstruction is to ensure symmetrical execution time for corrections. Motivated by this principle, we introduce Fleet, a lightweight FL framework. Fleet's server performs a two-stage symmetric gradient correction, while clients perform original gradient descents. Experimen-tal results demonstrate Fleet's superior performance over state-of-the-art methods, with resource consumption comparable to lightweight FedAvg on clients. Especially, Fleet excels in training deep models using large datasets. The experimental findings also support Fleet's dynamic scheduling as a plug-and-play module, showcasing its practical potential in real-world applications.

Cross-shard Transactions (CTXs) widely exist in sharded blockchains. CTXs have to endure large confirmation latency because they need to participate in consensus in both their source and destination shards. To diminish CTXs, plenty of state-of-the-art blockchain protocols have been proposed. For example, in BrokerChain [1], some intermediary broker accounts can help turn CTXs into intra-shard transactions through their voluntary liquidity services. Thereby, the original CTXs can be confirmed in blockchain shards quickly. However, we found that BrokerChain is impractical for a sharded blockchain because it does not consider how to recruit a sufficient number of broker accounts. Thus, blockchain clients do not have the motivation to provide token liquidity for others. To address this challenge, we design Broker2Earn, which is essentially a decentralized finance (DeFi) protocol that works as an incentive mechanism for blockchain users who choose to become brokers. Via participating in Broker2Earn, brokers can earn native revenues when they collateralize their tokens to the protocol. Furthermore, Broker2Earn can also benefit the sharded blockchain since it can efficiently spend each staked liquidity provided by brokers on diminishing CTXs. We formulate the core module of Broker2Earn into a revenue-maximization problem, which is proven NP-hard. To solve this problem, we design an online approximation algorithm using the relax-and-rounding technique. We also rigorously analyze the approximation ratio of our online algorithm. Finally, we conduct extensive experiments using real-world Ethereum transactions on both a transaction-driven simulator and an open-source blockchain testbed. The evaluation results show that the proposed Broker2Earn protocol demonstrates a near-optimal performance that outperforms other baselines, in terms of broker revenues and the usage of system liquidity.

Metaverse seamlessly integrates the real world with the virtual world and allows avatars to carry out rich activities including creation, display, entertainment, social, and trading. It integrates the most fundamental technologies, such as Blockchain , Interaction, Games, Artificial Intelligence, Networks, and the Internet of Things , named BIGANT. Interaction technologies are significant to allow users to interact with virtual entities in physical environments via sensors, such as AR, MR, and VR. However, there are still great challenges regarding how to access the metaverse in a more intelligent, faster, and effective way, especially in capturing human positions and activities. Intelligent wireless sensing technology, integrating AI , can serve as an intelligent, flexible, non-contact way to access the metaverse and expedite the establishment of a bridge between the real physical world and the metaverse. Hence, this paper elaborates on the existing work and discusses potential important trends and hotspots in wireless sensing, especially localization, activity recognition, and pattern analysis. After that, we discussed how intelligent wireless sensing will evolve in the metaverse, together with current challenges and open issues in this topic. Through this overview, we wish readers can better understand how intelligent wireless sensing accelerates the accessing to metaverse and the insights behind the wireless sensing in the metaverse.

Federated Learning (FL) has attracted extraordinary attention from the industry and academia due to its advantages in privacy protection and collaboratively training on isolated datasets. Since machine learning algorithms usually try to find an optimal hypothesis to fit the training data, attackers also can exploit the shared models and reversely analyze users’ private information. However, there is still no good solution to solve the privacy-accuracy trade-off, by making information leakage more difficult and meanwhile can guarantee the convergence of learning. In this work, we propose a Loss Differential Strategy (LDS) for parameter replacement in FL. The key idea of our strategy is to maintain the performance of the Private Model to be preserved through parameter replacement with multi-user participation, while the efficiency of privacy attacks on the model can be significantly reduced. To evaluate the proposed method, we have conducted comprehensive experiments on four typical machine learning datasets to defend against membership inference attack. For example, the accuracy on MNIST is near 99%, while it can reduce the accuracy of attack by 10.1% compared with FedAvg. Compared with other traditional privacy protection mechanisms, our method also outperforms them in terms of accuracy and privacy preserving.

Distributed machine learning (ML) was originally introduced to solve a complex ML problem in a parallel way for more efficient usage of computation resources. In recent years, such learning has been extended to satisfy other objectives, namely, performing learning in situ on the training data at multiple locations and keeping the training datasets private while still allowing sharing of the model. However, these objectives have led to considerable research on the vulnerabilities of distributed learning both in terms of privacy concerns of the training data and the robustness of the learned overall model due to bad or maliciously crafted training data. This article provides a comprehensive survey of various privacy, security, and robustness issues in distributed ML.

For data isolated islands and privacy issues, federated learning has been extensively invoking much interest since it allows clients to collaborate on training a global model using their local data without sharing any with a third party. However, the existing federated learning frameworks always need sophisticated condition configurations (e.g., sophisticated driver configuration of standalone graphics card like NVIDIA, compile environment) that bring much inconvenience for large-scale development and deployment. To facilitate the deployment of federated learning and the implementation of related applications, we innovatively propose WebFed, a novel browser-based federated learning framework that takes advantage of the browser’s features (e.g., Cross-platform, JavaScript Programming Features) and enhances the privacy protection by applying local differential privacy. Finally, We conduct experiments on heterogeneous devices to evaluate the performance of the proposed WebFed framework.

Driven by the advanced technologies of vehicular communications and networking, the Internet of Vehicles (IoV) has become an emerging paradigm in smart world. However, privacy and security are still quite critical issues for the current IoV system because of various sensitive information and the centralized interaction architecture. To address these challenges, a decentralized architecture is proposed to develop a blockchain-supported IoV (BS-IoV) system. In the BS-IoV system, the Roadside Units (RSUs) are redesigned for Mobile Edge Computing (MEC). Except for information collection and communication, the RSUs also need to audit the data uploaded by vehicles, packing data as block transactions to guarantee high-quality data sharing. However, since block generating is critical resource-consuming, the distributed database will cost high computing power. Additionally, due to the dynamical variation environment of traffic system, the computing resource is quite difficult to be allocated. In this paper, to solve the above problems, we propose a Deep Reinforcement Learning (DRL) based algorithm for resource optimization in the BS-IoV system. Specifically, to maximize the satisfaction of the system and users, we formulate a resource optimization problem and exploit the DRL-based algorithm to determine the allocation scheme. The evaluation of the proposed learning scheme is performed in the SUMO with Flow, which is a professional simulation tool for traffic simulation with reinforcement learning functions interfaces. Evaluation results have demonstrated good effectiveness of the proposed scheme.