Deploying transformer-based anomaly detection in Industrial Internet of Things (IIoT) networks poses unique challenges including stringent latency requirements, heterogeneous protocols, and safety-critical operations. The proposed work combines multimodal federated learning with adaptive resource optimization, specifically designed for diverse edge devices by addressing three key challenges: (1) a Cross-Modal Progressive Attention (CMPA) mechanism that enables the efficient fusion of vibration, temperature, and pressure sensor data through trainable routing matrices, thus, reducing the complexity fromO(T2max) toO(TmaxlogTmax); (2) a Resource-Adaptive Federated Optimization (RAFO) algorithm with provable convergence guarantees under non-IID and asynchronous conditions; and (3) an adaptive differential privacy scheme that dynamically adjusts privacy budgets based on model convergence and data sensitivity. The theoretical analysis confirms that the convergence bounds under asynchronous updates and privacy composition is guaranteed. Evaluation is performed on four IIoT datasets (TON-IoT, SWAT, WADI, and HAI) that demonstrates that FedTransformer-Edge attains 91.3% F1-score (±2.1%, 95% CI) while reducing communication overhead by 62% and energy consumption by 48% compared to other state-of-the-art base-lines. The framework maintains real-time performance (3.7ms average latency) on resource-constrained devices with 512MB RAM, validated through deployment on 50 heterogeneous edge devices.

5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the important applications of 5G wireless network which requires minimum latency and higher reliability to support mission-critical applications. Therefore, lossless reliable communication with a high data rate is the key requirement in modern wireless communication systems. These all factors highly depend upon channel conditions. In this work, a channel model is proposed for air-to-surface link exploiting millimeter wave (mmWave) for 5G enabled maritime unmanned aerial vehicle (UAV) communication. Firstly, we will present the formulated channel estimation method which directly aims to adopt channel state information (CSI) of mmWave from the channel model inculcated by UAV operating within the Long Short Term Memory (LSTM)-Distributed Conditional generative adversarial network (DCGAN) i.e. (LSTM-DCGAN) for each beamforming direction. Secondly, to enhance the applications for the proposed trained channel model for the spatial domain, we have designed an LSTM-DCGAN based UAV network, where each one will learn mmWave CSI for all the distributions. Lastly, we have categorized the most favorable LSTM-DCGAN training method and emanated certain conditions for our UAV network to increase the channel model learning rate. Simulation results have shown that the proposed LSTM-DCGAN based network is vigorous to the error generated through local training. A detailed comparison has been done with the other available state-of-the-art CGAN network architectures i.e. stand-alone CGAN (without CSI sharing), Simple CGAN (with CSI sharing), multi-discriminator CGAN, federated learning CGAN and DCGAN. Simulation results have shown that the proposed LSTM-DCGAN structure demonstrates higher accuracy during the learning process and attained more data rate for downlink transmission as compared to the previous state of artworks.

5G-based millimeter Waves (mmWave) systems have the prospective of enabling > 1Gbps communications in the Intelligent Transportation Systems (ITS). ITS relies on vehicle-to-everything (V2X) communications to share information among vehicles. However, the V2X Communications via existing technologies such as DSRC, 3G, 4G and LTE, are not able to achieve such a high data rate. Although 5G-based mmWave can support ultra-low-delay V2X transmissions, it comes with beam alignment difficulties as well as the routing stability issues due to rapid mobility of vehicles. The dynamic vehicle traffic causes frequent beam misalignment which tends to degrade the quality-of-service (QoS) performance. In this paper, we first propose a 3D-based position detection scheme for beam alignment/selection purpose. Then a group-based routing algorithm is performed to select a secure path for achieving trustworthy data transmissions. The road traffic is automatically segmented to divide the vehicles into different groups, and each group head is selected and members are added. Group members are authenticated by the group head via elliptic curve algorithms. Huffman coding is performed to compress the data and encrypt the binary files. This proposed novel intelligent beam control and secure stable routing scheme have been verified in simulations to demonstrate much better performance than existing schemes.