In recent years, RF fingerprinting (RFF) has emerged as a promising technology for wireless device authentication. However, temporal variations in device load and temperature, along with channel effects, lead to inconsistencies in RFF distributions between training and testing phases. As a result, deep learning (DL)-based recognition models often suffer from degraded performance. To address this problem, we propose the first test-time-adaptation (TTA) approach to improve the domain generalization ability of RFF recognition models. We first analyze the causes of time-varying RFF distribution shifts, such as carrier frequency offset (CFO), and develop a physical impairment-based data augmentation strategy. Based on this, we further propose a physically information-aware prototype to guide the model for TTA. Our method requires no model retraining or labeled test samples, and is a lightweight, nonparametric solution. Finally, our approach is extensively evaluated using mobile phones with the IEEE 802.11 orthogonal frequency division multiplexing (OFDM) system, which demonstrates that our scheme can effectively improve RFF average recognition performance by about 7.8%.

The emergence of abundant non-stationary radio signal (NSRS) data presents significant opportunities for applications in wireless communications, radar systems, remote sensing, and healthcare. While deep learning models have shown promise in capturing sequence dependencies, deriving generic and fine-grained representations of NSRS data remains challenging due to its complex, dynamic nature and the scarcity of labeled data. The NSRS data are often frequency-sensitive and exhibit minuscule inter-class distances, posing significant challenges for precise classification. To address these issues, we propose a novelDualModalityPatchContrastive (DMPC) framework. This framework leverages a stochastic patching paradigm for diverse local pattern extraction and a time-frequency cross-view optimization for frequency-sensitive feature mining. Furthermore, an Attentive Patch Aggregation (APA) mechanism enhances fine-grained inference under few-shot conditions through patch-level feature voting. Extensive experiments demonstrate the effectiveness of our approach in addressing the unique challenges of NSRS data.

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 proliferation of smart devices has propelled the advancement of IoT-based spatial crowdsourcing. The issue of location privacy in task allocation for IoT-based spatial crowdsourcing has attracted significant attention. Therefore, the main goals of privacy-preserving spatial crowdsourcing (PriSC) are: 1) achieving better location privacy for both participants and tasks and 2) achieving better allocation performance, i.e., accuracy and average moving distance. The homomorphic encryption-based approaches can achieve these goals, yet they suffer from heavy computation and large communication overhead. Although the differential privacy (DP)-based approaches are very efficient, these approaches leverage allocation performance to achieve better location privacy. Motivated by the deficiencies of these existing approaches, we propose a lightweight hash-based spatial crowdsourcing protocol, which not only protects both task location and participant location from the server but also reduces service providers’ computation and communication overhead. Besides, our design is independent of the concrete hash function and thus can be instantiated by any collision-resistant cryptographic hash function. Experiment results demonstrate that our protocol outperforms related works in terms of accuracy and average moving distance.

Industrial cyber-physical systems (ICPSs) have significantly improved production efficiency, e.g., Siemens applied ICPS in its Amberg factory, which achieved 75% production automation. However, the security problems involved have not been completely solved, e.g., the source location privacy (SLP). Currently, with the expansion of the ICPS network size and the increasing number of devices, the existing defense process uses these device nodes as false nodes to enhance the security of the ICPS. However, this approach also results in additional energy consumption and transmission delays. To address the above challenges, we propose a solution to mix false sources and phantom strategies [i.e., phantom-backbone–fake (PBF)]. First, a relay phantom node selection algorithm is proposed because adversaries are prone to track source nodes through fixed routes. We aim to optimize the path from the source node to the relay node and then to the sink node to minimize transmission delays. Ultimately, we devise a comprehensive strategy that considers both energy efficiency and distance indicators for the selection of appropriate false nodes. Through simulation and analysis, we demonstrate that our approach improves security and overall network performance.

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.

Efficient and secure data sharing in the Internet of Vehicles (IoV) is critical for the advancement of intelligent transportation, particularly in the context of emerging 5G/6G networks and driverless technology. This has prompted researchers to explore Blockchain-based Internet of Vehicles (BIoV) solutions to address common issues in intelligent transportation systems (ITS). However, at the edge layer, existing reputation and consensus mechanisms in the BIoV model face challenges such as significant energy consumption, limited scalability, and pronounced centralization. To tackle these issues, we introduceTRCO, a Permissioned Blockchain-basedTrusted andRobustConsensusOptimization orienting ITS.TRCOis a three-layer BIoV architecture applicable to any BIoV scenario, offering robust scalability with the addition of new sub-regions. To improve the quality of data sharing for edge layer, we propose a decentralized reputation evaluation mechanism and integrate PoS and PBFT consensus algorithms to enhance performance by evaluating the communication behavior of edge servers. Security analysis and simulation experiments demonstrate thatTRCOachieves high scalability and reliability, effectively filters Byzantine edge servers, and enhances the decentralization and consensus efficiency of the system.

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.

Blockchain interoperability has become an essential functionality, which enables asset/data transfers across different blockchains. Sidechains have been deemed as a key technique to provide interoperability. However, sidechains are rarely used in practice, this is because sidechain technologies are impractical and non cost-efficient. To make sidechains practical, in this paper, we design a universal sidechain construction named USSC, which applies to a variety of blockchains without forking them. USSC also enables interoperability across heterogeneous blockchains regardless of underlying consensus. This is facilitated by three components: i) a committee selection method, ii) a cross-chain certificate, and iii) a cross-chain bridge based on smart contracts. The proposed committee-selection method guarantees an honest majority within a committee. Through a concrete implementation of USSC, we outline how the proof-of-stake (PoS) and the proof-of-work (PoW) blockchains enable asset transfers. Furthermore, our USSC is more storage-efficient because it produces a smaller size of certificate and only needs partial nodes instead of all sidechain nodes following a blockchain. Thus, USSC can reduce the overhead of storage and communication of nodes. In addition, we prove that USSC achieves a secure sidechain construction with desirable security properties. Finally, we develop a proof-of-concept implementation of USSC using Cardano and Ethereum. Experimental results demonstrate that USSC outperforms PoW and PoS sidechains, in terms of the certificate size.

Abstract Over the past 70 years, the semiconductor industry has undergone transformative changes, largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional (2D) materials like transition metal dichalcogenides (TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

With the application of blockchain in Internet of Things (IoT), various IoT scenario-oriented blockchains have been proposed, meaning that multichain future within IoT ecosystem is growing. Cross-chain interoperability thus is essential. Sidechains is one of the crucial technologies for cross-chain interoperability, which allows blockchains to interoperate with each other. However, sidechains encounters practicability challenges impeding adoption, such as poor generality and inefficiency. In this article, we propose practical and secure sidechain construction (PSSC)in the form of parent-child chains, a practical and secure sidechains construction for heterogeneous blockchains orienting IoT scenarios. To achieve practicability, we first utilize succinct noninteractive argument of knowledge (SNARK), which is succinct noninteractive proof (arguments) of knowledge, to reproduce the state transition of blockchains, so that PSSC can get rid of the constraints on consensus mechanisms of blockchains; we then design an impawn-punish mechanism based on smart contract, which transfers the work verifying SNARK proof from mainchain to sidechain, preventing fork threat to mainchain. Due to employing SNARK based on recursive proof composition to generate cross-chain proof, our PSSC is more efficient with succinct cross-chain proof, reducing the overhead of storage of nodes. Besides, we formally prove our PSSC satisfies the three fundamental security properties of sidechains: 1) persistence; 2) liveness; and 3) firewall. Finally, we develop a proof-of-concept implementation of PSSC, and the experimental results show that the proof size of PSSC is about 28.8 kB, which is roughly$2094\times $,$9.36\times $,$6.56\times $,$4.23\times $, and$3.27\times $smaller than BTCRelay proof, PoW sidechains proof, PoS sidechains, zkRelay proof, and fast sidechains, respectively.

The emergence of 5G/6G networks has sparked new potentials for Internet of Things (IoT) scenarios, such as vehicle ad hoc networks (VANETs), inspiring numerous scholars to leverage Blockchain-based Internet of Vehicles (BIoV) solutions to address prevailing issues in VANETs. However, the dynamic and decentralized nature of VANETs presents significant challenges in terms of security and privacy, hindering data providers from engaging in the data-sharing process. Furthermore, the reliability of edge nodes and system architecture in the BIoV paradigm faces several challenges, including limited consensus participation, high resource consumption, poor scalability, and centralization. To mitigate these challenges, we propose RTSP, a robust and trusted service protocol for VANETs, based on a distributed ledger technology. RTSP advocates a novel three-tier BIoV architecture suitable for any permissioned BIoV application scenario. To improve the quality of data sharing, we design a decentralized reputation mechanism. This mechanism mitigates the performance bottleneck induced by consensus transactions by measuring the historical behavior of roadside units (RSUs). Empirical evidence from simulation experiments and security performance analyses substantiates RTSP’s capabilities. It can hinder the number of faulty RSUs from increasing while simultaneously improving data-sharing efficiency, simplifying communication complexity, and enhancing system scalability and consensus stability.

With the popularity of the mobile Internet, data is increasingly becoming a new resource. Therefore, the trading of such data resources has become an increasing demand. In this paper, we propose a fair privacy-preserving data trading protocol based on blockchain. Firstly, our data trading protocol achieves fairness by carefully combining the probabilistic approaches and the fully homomorphic encryption techniques. Moreover, our protocol allows online arbitration when misbehavior occurs in the trading process is detected. Note that previous data trading protocols need a Trusted Third Party (TTP) or an offline arbitrator to solve disputes, weakening the trust of those protocols. Secondly, the data validity verification process of our protocol is more flexible. Most Importantly, different from all previous designs which only achieve privacy against communication channel eavesdroppers, our protocol achieves privacy against any eavesdropper and the passive arbitrator. The above-distinguishing properties of our protocol are mainly benefited from the homomorphic encryption and double encryption techniques. In addition, our data trading protocol can be instantiated with post-quantum primitives and thus achieves post-quantum security. To demonstrate the feasibility of the proposed protocol, we conduct a comprehensive evaluation with the instantiated cryptographic primitives based on the Ethereum test network.

We study the problem of object reconstruction in a multi-agent collaboration scenario. Specifically, we focus on the reconstruction of specific goals through several cooperative agents equipped with vision sensors to achieve higher efficiency than single agents. Our main insight is that a complete 3D object can be split into several local 3D models and assigned to different agents. In addition, we can use the salient characteristics of the collaboration agent itself to help realize the integration of local models. We develop a novel pipeline that first restores local 3D models from the images obtained from different agents, then the relative poses between collaborative agents are estimated by aligning intrinsic features. After that, all local models are integrated using the estimated parameters. Extensive experiments show that our proposed method is capable of accurately reconstructing 3D objects in the real world in a multi-agent collaborative manner. The full reconstruction pipeline is released to the public as an open-source project.

Abstract The rapid development of autonomous vehicle technology has brought a new experience to people's daily travel. However, if a traffic accident involving autonomous vehicles occurs, it will face difficulties in vehicle accident forensic‐preservation, leakage of vehicle owner's privacy, and identifying legal liabilities. This article proposes an accident responsibility identification model for the Internet of Vehicles based on lightweight blockchain to solve the above problems. This model uses Car Forensics Master to collect evidence from the accident vehicle, and at the same time collects evidence from maintenance service providers, automobile manufacturers, transportation management departments, insurance companies, and other vehicle accident related parties and stores them in the preservation chain. We also use VPKI to protect the autonomous vehicle identity privacy. In order to improve the efficiency of the model and set up authorized access to related entities, the identification of accident liability is jointly completed by the preservation chain and the accident identification chain. In addition, we prove that the protocol proposed in the model has ideal security properties. Finally, we implement the smart contracts in the model through the Solidity language, and evaluate its performance.

Automatic modulation classification (AMC) is an important technology for the monitoring, management, and control of communication systems. In recent years, machine learning approaches are becoming popular to improve the effectiveness of AMC for radio signals. However, the automatic modulation open-set recognition (AMOSR) scheme that aims to identify the known modulation types and recognize the unknown modulation signals is not well studied. Therefore, in this paper, we propose a novel multi-modal marginal prototype framework for radio frequency (RF) signals (MMPRF) to improve AMOSR performance. First, MMPRF addresses the problem of simultaneous recognition of closed and open sets by partitioning the feature space in the way of one versus other and marginal restrictions. Second, we exploit the wireless signal domain knowledge to extract a series of signal-related features to enhance the AMOSR capability. In addition, we propose a GAN-based unknown sample generation strategy to allow the model to understand the unknown world. Finally, we conduct extensive experiments on several publicly available radio modulation data, and experimental results show that our proposed MMPRF outperforms the state-of-the-art AMOSR methods.

In recent years, a surging number of unmanned aerial vehicles (UAVs) are pervasively utilized in many areas. However, the increasing number of UAVs may cause privacy and security issues such as voyeurism and espionage. It is critical for individuals or organizations to manage their behaviors and proactively prevent the misbehaved invasion of unauthorized UAVs through effective anomaly detection. The UAV anomaly detection framework needs to cope with complex signals in the noisy-prone environments and to function with very limited labeled samples. This paper proposesBisSiam, a novel framework that is capable of identifying UAV presence, types and operation modes.BisSiamconverts UAVs signals to bispectrum as the input and exploits a siamese network based contrastive learning model to learn the vector encoding. A sampling mechanism is proposed for optimizing the sample size involved in the model training whilst ensuring the model accuracy without compromising the training efficiency. Finally, we present a similarity-based fingerprint matching mechanism for detecting unseen UAVs without the need of retraining the whole model. Experiment results show that our approach outperforms other baselines and can reach 92.85% accuracy of UAV type detection in unsupervised learning scenarios. 91.4% accuracy can be achieved whenBisSiamis used for detecting the UAV type of the out-of-sample UAVs.

We propose FBSChain, a faster blockchain sharding protocol based on quorum-based BFT algorithm (running as sharding consensus) and use a vertical committee structure to assist in the decoupling of transaction confirmation and block generation. We utilize a set of clients to drive all these related processes. In addition, we deploy an improved common-coin scheme without any private setup to generate the randomness for reconfiguration. The protocol can perform better with less computational and communication costs and improve system scalability without compromising security performance.

Abstract Data is a kind of important asset in the digital economy and is driving the rise of data markets. Meanwhile, data markets promote data trading efficiently and improve the utilization of data. However, several challenges about data trading need to be addressed. Here, we resolve these challenges via our blockchain-based fair and decentralized data trading model. Disputes about data correctness is settled by the decentralized arbitration mechanism in our model. To ensure the fairness of data trading, we integrate a sale contract and a deterministic public-key encryption algorithm. The decentralization feature of blockchain cuts off the single-point failure for the data trading platform. In addition, we prove that the proposed protocol achieves the desirable security properties that a secure data trading protocol should have. Moreover, utilizing the smart contract in Solidity and program in Java, we implement our model and then evaluate its performance.

Summary Ground‐penetrating radar (GPR) technology is being widely used for urban roadbed damage detection. In this paper, we present the development of a cloud‐based difference detection method using big GPR data for roadbed damage detection. Unlike many processing methods relying on standalone servers, the proposed method can achieve high accuracy and processing efficiency by distributing the computation over a server cluster. The method includes a difference detection algorithm for GPR image enhancement and damage extraction, the Hadoop Distributed File System, and MapReduce distributed parallel computing framework for big GPR data parallel processing and interpretation. Moreover, to automatize roadbed damage classification, a criterion with seven grades is established based on the wave group shape, amplitude, phase, and attenuation characteristics. We validate the proposed method through a detection experiment that was conducted on the Fourth Ring Road in Beijing, China. The detection revealed 187 damage from the efficient processing of big GPR data, thus suggesting the ability of the proposed method to provide timely support for road safety in cities.