Specific emitter identification (SEI) separates the radio frequency fingerprint (RFF) from signals, which is of great significance in solving Internet of Things (IoT) security problems. However, the scarcity of high-quality, diverse, and labeled data in real-world scenarios limits the application of SEI. Under such conditions, the SEI is referred to as few-shot SEI (FS-SEI). To surmount this challenge, we propose a diffusion model-based data augmentation method capable of generating a substantial volume of diverse, high-quality data. Specifically, we develop a multi-scale convolutional block attention module denoising diffusion probabilistic model (MSCBAM-DDPM), which enhances feature capture capabilities, laying the foundation for the generation of diverse data. Furthermore, we propose an adaptive two-stage multi-domain loss function that guides the model to learn the characteristics of the original data and further derive other similar features, thereby achieving the goal of generating diverse and high-quality data. Finally, we theoretically derive the feasibility of the proposed loss function and further demonstrate the excellent diversity and quality of the data generated by our method, as well as its considerable gain for FS-SEI, through extensive experiments on real-world signal datasets.

In traditional public blockchain networks, transaction fees are allocated only to full nodes (miners), neglecting relay nodes and diminishing participation incentives for lightweight nodesparticularly in energy-constrained wireless blockchain environments. This paper proposes a novel dual auction mechanism to allocate transaction fees for both relay and validation activities in the wireless blockchain network. The proposed one consists of two sub-auction stages: the relay sub-auction and the validation subauction. In the relay sub-auction, relay nodes select transactions to forward based on rewards. Additionally, nodes adjust the relay probability using a no-regret algorithm to enhance efficiency. In the validation sub-auction, full nodes use the Vickrey-Clarke-Groves (VCG) mechanism to select transactions and construct the block. Our mechanism demonstrably satisfies Incentive Compatible (IC), Individual Rational (IR), and Computationally Efficient (CE) while maintaining bounded social welfare optimization. Furthermore, we consider the impact of network complexity on blockchain performance. Extensive simulation results demonstrate that the proposed one reduces energy and bandwidth resource consumption without compromising the throughput and security of the wireless blockchain network.

Specific emitter identification (SEI) is a physical-layer authentication technique that identifies devices by extracting radio frequency fingerprints (RFFs) from received signals. Open-set SEI (OS-SEI) refers to classifying known classes while rejecting unknown classes, which typically requires a sufficient amount of labeled training samples. However, in open-world scenarios, labeled samples are often limited, and unlabeled samples may contain unknown classes. Moreover, open-world recognition not only requires detecting unknown class samples but also identifying specific novel classes within these unknown samples and integrating them into the recognition model. Current OS-SEI methods can only categorize all unknown samples as a single class, lacking the ability to further differentiate these unknown classes. To address these challenges, we formulate a novel semi-supervised open-world SEI (SSOW-SEI) problem, which aims to overcome the shortcomings of OS-SEI in utilizing unlabeled data, distinguishing unknown classes, and addressing class distribution mismatches between labeled and unlabeled data. Furthermore, we develop an end-to-end similarity-adaptive (SAA) framework for SSOW-SEI. Specifically, after automatically extracting sample features, SAA first identifies novel classes by measuring pairwise similarities between the features, and then recognizes known classes using adaptive cross-entropy, which balances the learning rate between known and novel classes to prevent model bias toward known classes. Additionally, entropy regularization is applied to mitigate model overfitting. Extensive experimental results demonstrate that the proposed SAA framework effectively leverages limited labeled data, handles large volumes of unlabeled data, and accurately identifies both known and novel classes. The results also highlight its strong generalization, stability, and enhanced adaptability to novel classes.

Blockchain has been widely implemented as a trusted platform. Previous works mainly focus on the computing capacity of devices while communication factors play a vital role in blockchain performance during dynamic wireless environments. High-speed movement causes frequent wireless connection interruptions and leads to severe performance degradation of blockchain. Besides, resource-constrained mobile devices are unwilling to selflessly contribute their energy and bandwidth for blockchain, hindering applications in dynamic mobile networks. This paper proposes a reverse auction mechanism to incentivize mobile devices to provide robust wireless connections. Devices submit their connection provision and expected rewards as bids. The mobile blockchain system uses smart contracts to autonomously execute the reverse auction to determine winners and allocate payments based on actual connections. We prove that the reverse auction mechanism is Individual Rationality (IR), Incentive Compatibility (IC), and Computational Efficiency (CE), and derive the approximation ratio 2$\sigma$of the mechanism. Extensive simulation results demonstrate that the proposed mechanism decreases up to half the energy and bandwidth consumption, but achieves a similar TPS and stale rate compared to the selfless scheme, where devices contribute all wireless connections for nothing in return. The proposed auction mechanism achieves more than 96% of the optimal social welfare.

Data-driven cross-layer secure design is expected to provide effective support for high-reliability and high-speed 6G network services. In this paper, we study the joint anti-jamming decision problem for phy-layer scheduling and congestion control in the transport layer. To address the challenges of the extremely huge action space and the simultaneous existence of multi-dimensional and multi-scale action variables, we propose a hierarchical DRL anti-jamming algorithm. Firstly, we unify the time scales of the variables by defining the state space, action space, and reward function, and construct them as a Markov decision process. Second, we make decisions in different dimensions sequentially through a hierarchical learning algorithm, which compresses the size of the action space while maintaining the correlation between variables. Simulation results show that the proposed cross-layer design method can realize a good adaptation between the lower layer and the transport layer in the context of anti-jamming requirements, which significantly improves the user QoS and system throughput compared with the baseline algorithms.

Specific emitter identification (SEI) is a device identification technology that extracts radio frequency (RF) fingerprint from received signals. However, channel effects on RF fingerprint can vary between the training and testing stage, and SEI based on deep learning (DL) will be unable to withstand channel changes. To address this problem, we propose a channel-robust SEI scheme driven by causal learning. We analyze received signals from the causal perspective and construct a structural causal model (SCM) of SEI. In the SCM, received signals are considered as mixtures of the causal element and interference element, and only the former affects identification. Additionally, we design a new RF fingerprint feature representation called the centralized logarithmic power spectrum (CLPS) to reduce the impact of channel effects. Furthermore, we propose a causal purification network (CPNet) driven by causality to further alleviate channel effects. CPNet weakens the spurious associations between the channel and emitter labels through feature decorrelation and feature purification, strengthens the correlation between RF fingerprint and labels, and improves the generalization of SEI. Finally, our approach is evaluated extensively using 20 ZigBee devices under different channel environments. Experimental results demonstrate that our scheme can effectively alleviate channel effects, improve SEI performance under various channel environments, and exhibit good generalization and stability.

Neighbor discovery is the procedure to establish a first contact between two wireless devices. For duty-cycled low-power devices, energy consumption is closely related to neighbor discovery latency. Actually, in recent protocols, such as Bluetooth low energy (BLE) or ANT+, neighbor discovery latency is determined by the parameters used by the devices, such as advertising interval, scan window, scan interval, and so on. A fundamental problem of the BLE-like protocol is that the exact relation between parameters and discovery latency has not been fully analyzed. In this article, we propose a Euclidean-division-based low-complexity precise analytical approach that can derive the mathematical expressions of both worst-case latency and average latency for any parameter groups. It is confirmed by simulation results that our solution can make highly accurate predictions about the value of latencies. Simulation results also show that the proposed solution has an extremely low complexity. Moreover, we derive the lower bound of latency for given duty cycles, which provides useful guidelines for the choice of energy-efficient parameter groups for BLE.

Blockchain is considered the critical backbone technology for secure and trusted Internet of Things (IoT) in the future 6G network. However, deploying a blockchain system in a complex wireless IoT network is challenging due to the limited resources, complex wireless environment, and the property of self-interested IoT devices. The existing incentive mechanism of blockchain is not compatible with the wireless IoT network. In this article, to incentivize IoT devices to join the construction of the wireless blockchain network, we propose a multidimensional contract to optimize the blockchain utility while addressing the issues of adverse selection and moral hazard. Specifically, the proposed contract considers the IoT device’s hash power and communication cost and especially explores the connectivity of devices from the perspective of complex network theory. We investigate the energy consumption and the block confirmation probability of the wireless blockchain network via simulations under varied network sizes and average link probability. Numerical results demonstrate that our proposed contract mechanism is feasible, achieves 35% more utility than existing approaches, and increases utility by four times compared with the original PoW-based incentive mechanism.

Unmanned aerial vehicles (UAVs) will be widely deployed due to their flexibility, mobility, and miniaturization, providing the necessary support for spectrum sharing between different communication systems in the space–air–ground-integrated IoT network (SAGIN). However, there are potential security threats to spectrum sharing among different communication systems due to the openness of a wireless network, the unreliability of node behavior, and the trust barriers of the networks. In this article, a secure spectrum sharing scheme based on lightweight UAV-blockchain (LUBC) is proposed to address the above security issues. First, a spectrum sharing model based on the overlay mode is developed to improve the spectrum efficiency of SAGIN, where UAVs relay signals from the satellite to the ground users in exchange for spectrum access opportunities and serve their own users simultaneously in the nonorthogonal multiple access (NOMA) mode. Second, a secure spectrum sharing framework based on LUBC is proposed to solve the security and privacy issues of spectrum trading in SAGIN. Then, aiming at maximizing the primary user’s throughput under the premise of meeting the minimum power allocation factor of UAV network, the spectrum auction based on NOMA is formulated as a multirelay selection optimization problem, which is solved by the blockchain-based sequential Vickrey auction mechanism. Finally, the security evaluation and numerical results are conducted to verify the security and effectiveness of the proposed spectrum sharing scheme for SAGIN.

Due to the prominent advantages of decentralization, transparency, security, and traceability, blockchain technologies have attracted ever-increasing attention from academia and industry, which can be applied to establish secure and reliable resource sharing platforms for future networks and applications. Especially, with the promising 6G technology which has large bandwidth and space-air-ground integrated coverage, blockchains have been evolved into 6G-enabled blockchain and envisioned to build various decentralized data and resource management systems. However, for 6G-enabled wireless blockchain networks, there still exist many challenges for their development and prosperity, e.g., large block propagation delay and propagation incentive. Therefore, this paper focuses on addressing the block propagation challenges. Firstly, inspired by epidemic models, we classify consensus nodes into five different states and establish a block propagation model for public blockchains that depicts block propagation laws. Then, considering consensus nodes are limited rational, we propose an Incentive Mechanism based on evolutionary game for Block Propagation (marked as BPIM) to minimize the block propagation delay. Numerical results demonstrate that compared with traditional routing algorithms, BPIM has better block propagation efficiency and greater incentive strength.

In traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this article, we propose an auction-based market model for incentivizing data owners to participate in federated learning. We design two auction mechanisms for the federated learning platform to maximize the social welfare of the federated learning services market. Specifically, we first design an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency. To improve the social welfare, we develop an automated strategy-proof mechanism based on deep reinforcement learning and graph neural networks. The communication traffic congestion and the unique characteristics of federated learning are particularly considered in the proposed model. Extensive experimental results demonstrate that our proposed auction mechanisms can efficiently maximize the social welfare and provide effective insights and strategies for the platform to organize the federated training.

Artificial intelligence is being increasingly applied in manufacturing to maximize industrial productivity. Image clustering, as a fundamental research direction in unsupervised learning, has been used in various fields. Since no label information is required in clustering, it can perform a preliminary analysis of the data while saving lots of manpower. In this paper, we propose a novel end-to-end clustering network called Self-supervised Pairing Image Clustering (SPIC) for industrial application, which produces clustering prediction for input images in an advanced pair classification network. For training this network, a self-supervised pairing module is built to form balanced pairs accurately and efficiently without label information. Since the existence of trivial solutions cannot be avoided in most of unsupervised learning methods, two additional information theoretic-constraints regularize the training that ensures the clustering prediction to be unambiguous and close to the real data distribution during training. Experimental results indicate that the proposed SPIC outperforms the state-of-art approaches on manufacturing datasets-NEU and DAGM. It also shows the execellent generalization capability on other genral public datasets, such as MNIST, Omniglot, CIFAR10, and CIFAR100.

As an emerging decentralized secure data management platform, blockchain has gained much popularity recently. To maintain a canonical state of blockchain data record, proof-of-work based consensus protocols provide the nodes, referred to as miners, in the network with incentives for confirming new block of transactions through a process of “block mining” by solving a cryptographic puzzle. Under the circumstance of limited local computing resources, e.g., mobile devices, it is natural for rational miners, i.e., consensus nodes, to offload computational tasks for proof of work to the cloud/fog computing servers. Therefore, we focus on the trading between the cloud/fog computing service provider and miners, and propose an auction-based market model for efficient computing resource allocation. In particular, we consider a proof-of-work based blockchain network, which is constrained by the computing resource and deployed as an infrastructure for decentralized data management applications. Due to the competition among miners in the blockchain network, the allocative externalities are particularly taken into account when designing the auction mechanisms. Specifically, we consider two bidding schemes: the constant-demand scheme where each miner bids for a fixed quantity of resources, and the multi-demand scheme where the miners can submit their preferable demands and bids. For the constant-demand bidding scheme, we propose an auction mechanism that achieves optimal social welfare. In the multi-demand bidding scheme, the social welfare maximization problem is NP-hard. Therefore, we design an approximate algorithm which guarantees the truthfulness, individual rationality and computational efficiency. Through extensive simulations, we show that our proposed auction mechanisms with the two bidding schemes can efficiently maximize the social welfare of the blockchain network and provide effective strategies for the cloud/fog computing service provider.

Blockchain, an emerging decentralized security system, has been applied in many applications, such as bitcoin, smart grid, and Internet-of-Things. However, running the mining process may cost too much energy consumption and computing resource usage on handheld devices, which restricts the use of blockchain in mobile environments. In this paper, we consider deploying edge computing service to support the mobile blockchain. We propose an auction-based edge computing resource allocation mechanism for the edge computing service provider. Since there is competition among miners, the allocative externalities are taken into account in the model. In our auction mechanism, we maximize the social welfare while guaranteeing the truthfulness, individual rationality and computational efficiency. Through extensive simulations, we evaluate the performance of our auction mechanism which shows that the proposed mechanism can efficiently solve the social welfare maximization problem for the edge computing service provider.

With the advancement and emergence of new network services, such as social network, Internet of Things, and crowd-sensing, large volume of diverse data is collected, shared, and leveraged to develop analytics services. The data analytics service has become a key commodity that can be traded among various economic entities. In this paper, we address the optimal pricing mechanisms and data management for data analytics services and further discuss the perishable services in the time varying environment. We first propose a data market model and define the data utility based on the impact of data size on the performance of data analytics, e.g., prediction and verification accuracy. For perishable services, we study the perishability of data that affects the service quality and provide a quality decay function. The data analytics services are considered as digital goods and uniquely characterized by “unlimited supply” compared to conventional goods. Therefore, we apply the Bayesian profit maximization mechanism in selling data analytics services, which is truthful, rational, and computationally efficient. The optimal service price, data amount, and service update interval are obtained to maximize the profit under different customer's valuation distributions. Finally, experimental results on realworld datasets show that our data market model and pricing mechanism effectively solve the profit maximization problem and provide useful strategies for the data analytics service provider.

A big data service is any data-originated resource that is offered over the Internet. The performance of a big data service depends on the data bought from the data collectors. However, the problem of optimal pricing and data allocation in big data services is not well-studied. In this paper, we propose an auction-based big data market model. We first define the data cost and utility based on the impact of data size on the performance of big data analytics, e.g., machine learning algorithms. The big data services are considered as digital goods and uniquely characterized with ''unlimited supply'' compared to conventional goods which are limited. We therefore propose a Bayesian profit maximization auction which is truthful, rational, and computationally efficient. The optimal service price and data size are obtained by solving the profit maximization auction. Finally, experimental results on a real-world taxi trip dataset show that our big data market model and auction mechanism effectively solve the profit maximization problem of the service provider.