Industry 4.0 integrates industrial Internet of Things (IIoT), artificial intelligence, and cloud computing. The advent of the 5G era has undoubtedly provided a new impetus for the development of many Industry 4.0 applications, but it also presents some key security hurdles. The network scale is becoming larger and larger, the network environment is becoming increasingly complex, and security risks are prominent. Frequent issues, such as malicious attacks, privacy information disclosure, and data transmission security. In order to improve the reliability and security of cyberspace, this article proposes a blockchain-based hierarchical IIoT security solution mechanism. In addition, we propose a random reputation voting mechanism and blockchain (RRV-BC) scheme based on verifiable random function and reputation voting to reduce the communication cost during blockchain consensus communication. Meanwhile, the node credit scoring mechanism is introduced to dynamically evaluate the node credit. The simulation results show that the scheme improves the reliability of data communication and the fault tolerance of consensus mechanism by an average of 5% compared with the traditional practical byzantine fault tolerance (PBFT) protocol method.

With the increasing penetration of Internet of Vehicles (IoV) in people’s lives, the safety and reliability of its communication has to attract our attention. Frequency hopping orthogonal frequency division multiplexing (FH-OFDM) is applied to vehicle networking communication with its excellent communication performance. But the sub-carrier frequency hopping pattern in the traditional FH-OFDM information transmission technology has the risk of being easily detected, which seriously affects the security, concealment and anti-jamming of its information transmission. However, existing anti-jamming methods cannot solve this problem. This paper presents a data driven frequency hopping OFDM (DDFH-OFDM) information transmission method for IoV. This method combines message-driven frequency-hopping technology with frequency-hopping OFDM system. The selection of subcarriers is controlled by using the encrypted partial transmission data instead of the pseudorandom sequence. At the same time, the frequency transmission block mapping rule is designed to make the distribution of frequency-hopping subcarriers more uniform. Through simulation, under the same bit error rate (BER), compared with the FH-OFDM information transmission method, the proposed method has good anti-noise, anti-broadband interference and anti-narrowband interference performance, which further improves the communication performance of IoV. It provides a reference technology for anti-jamming communication applications in IoV scenes.

Multimodal automatic modulation recognition (MAMR) has emerged as a prominent research area. The effective fusion of features from different modalities is crucial for MAMR tasks. An effective multimodal fusion mechanism should maximize the extraction and integration of complementary information. Recently, fusion methods based on cross-modal attention have shown high performance. However, they overlook the differences in information intensity between different modalities, suffering from quadratic complexity. To this end, we propose an efficient Alignment before Fusion Transformer Network (AbFTNet) based on an in-phase quadrature (I/Q) and Fractional Fourier Transform (FRFT). Specifically, we first align and correlate the feature representations of different single modalities to achieve mutual information maximization. The single modality feature representations are obtained using the self-attention mechanism of the Transformer. Then, we design an efficient cross-modal aggregation promoting (CAP) module. By designing the aggregation center, we integrate two modalities to achieve the adaptive complementary learning of modal features. This operation bridges the gap in information intensity between different modalities, enabling fair interaction. To verify the effectiveness of the proposed methods, we conduct experiments on the RML2016.10a dataset. The experimental results show that multimodal fusion features significantly outperform single-modal features in classification accuracy across different signal-to-noise ratios (SNRs). Compared to other methods, AbFTNet achieves an average accuracy of 64.59%, with a 1.36% improvement over the TLDNN method, reaching the state of the art.

Under the condition of low SNR, enhancing the precision of parameter estimation for linear frequency modulation (LFM) signals and diminishing the complexity of the relevant methods represent crucial challenges that are presently being confronted. To address this problem, a parameter estimation method for LFM signals based on the High-Resolution Representation network (HRRnet) is proposed. The fundamental concept underlying this method lies in the employment of a strategy that combines the expansion of the receptive field with the fusion of multi-scale features. This enables the efficient extraction of both global and local information, which in turn augments the expressive power of the inherent signal characteristics and consequently mitigates the impact of noise interference. Based on this strategy, a high-resolution representation of the time–frequency spectrum of the signals is performed to improve the distinguishability of the time–frequency spectrum, and it further improve the accuracy of parameter estimation for LFM signals. In addition, the network utilizes dilated convolution to expand the receptive field while reducing the dependence on network depth, so as to control the network complexity and further optimize the computational efficiency. Experimental results show that when the SNR is greater than −12 dB and the tolerable error is equal to 0.1, the average accuracy of the HRRnet method for estimating the initial frequency and frequency modulation coefficient of LFM signals can reach above 95.53% and 91.19%, respectively, and its number of parameters and computational complexity are reduced to more than 20.47% and 20.37% of those of the existing methods.

In order to improve the transmission data rate and the three-resistance characteristics of the network ammunition communication link, this paper proposes a new communication technology with two dimensions. In this technology, orthogonal frequency division multiplexing (OFDM) is used for onedimensional data transmission, and the G-function-driven subcarrier frequency change rule in differential frequency hopping (DFH) is used for two-dimensional data transmission, which breaks through the conventional idea of using carrier amplitude frequency phase modulation information, and establishes a new two-dimensional communication model based on DFH-OFDM. The theoretical analysis and simulation results show that the proposed two-dimensional new communication technology, without changing the original communication system and signal characteristics, effectively reduces the probability of signal interception, improves the communication system capacity, and has good three-resistance performance.

Unmanned aerial vehicle (UAV) swarms, equipped for distributed sensing and rapid response, can form coalitions to undertake complex missions such as post-disaster relief, communication support, and payload delivery. However, typical coalition formation methods assign each UAV to a single task, limiting cross-task resource sharing. To address this, we investigate overlapping coalition formation (OCF) for UAV swarms, where a single UAV is permitted to participate in multiple coalitions, enabling resource reuse and reducing idleness. We formulate OCF as a multi-objective combinatorial optimization problem that jointly balances task fulfillment ratio, coalition synchronization deviation, and operational cost, while explicitly accounting for inter-coalition resource contention and execution precedence. Specifically, we first construct a hypergraph representation of UAVs and tasks and employ a hypergraph attention network to capture their high-order interactions. Next, we propose a structure-aware hierarchical value decomposition method for policy learning, which progressively aggregates individual- and coalition-level information, models member complementarity and inter-coalition cooperative–competitive relations, and generates a global value estimate that is sensitive to changes in coalition structure. Furthermore, we integrate Monte Carlo Tree Search, utilizing the learned value as a heuristic to efficiently explore the feasible region, and close the loop with candidate-structure demonstration replay and policy distillation, enabling search to refine the learned policy. In multi-scale rescue simulations, the proposed approach improves task utility by up to 11.4% over the best-performing baseline and increases energy efficiency by more than 228% compared to a non-overlapping coalition variant.