In this paper, we propose an ambient backscatter-enabled decode-and-forward (DF) cognitive relay network with wireless energy-harvesting capabilities. In our proposed scheme, a source node communicates with its destination node via a radio frequency-powered DF relay. It is assumed that the relay node is equipped with two different interfaces and can concurrently harvest/decode and backscatter the received source signals. A power-splitting-based approach is adopted for the information processing and the energy harvesting at the relay. The analytical expressions for the outage probability at all of the receiving nodes are derived. It has been shown that the analytical results match the simulation results. It has also been shown that using the ambient backscatter for secondary communications can significantly improve the overall network performance in terms of the achievable throughput and the energy efficiency.

Life on Earth depends on healthy oceans, which supply a large percentage of the planet’s oxygen, food, and energy. However, the oceans are under threat from climate change, which is devastating the marine ecosystem and the economic and social systems that depend on it. The Internet of Underwater Things (IoUT), a global interconnection of underwater objects, enables round-the-clock monitoring of the oceans. It provides high-resolution data for training machine learning (ML) algorithms for rapidly evaluating potential climate change solutions and speeding up decision making. The sensors in conventional IoUT are battery powered, which limits their lifetime, and constitutes environmental hazards when they die. In this article, we propose a sustainable scheme to improve the throughput and enable wireless charging of underwater networks, enabling them to potentially operate indefinitely. The scheme is based on simultaneous wireless information and power transfer (SWIPT) from an autonomous underwater vehicle (AUV) used for data collection. We model the problem of jointly maximizing throughput and harvested power as a Markov decision process (MDP), and develop a model-free reinforcement learning (RL) solution. The model’s reward function incentivises the AUV to find optimal trajectories that maximize throughput and power transfer to the underwater nodes while minimising its own energy consumption. To the best of our knowledge, this is the first attempt at using RL for this application. The scheme is implemented in an open 3-D RL environment specifically developed in MATLAB for this study. The performance results show up 207% improvement in energy efficiency compared to those of a random trajectory scheme used as a baseline model.

Extensive growth in the number of Internet Of Things (IoT) devices has significantly increased susceptibility to various cyber-attacks and hence emphasized the need for robust intrusion detection systems (IDS) for ensuring IoT network security. While deep learning (DL) methodologies have proven effective in the application of IDS, their success greatly depends on the availability of large datasets and significant computational resources during training. To overcome the limitations associated with this dependence on large datasets and significant computational capacity for training, the current work suggests employing the transfer learning (TL) mechanism by combining Swin Transformers with long short-term memory (LSTM) networks. Utilizing the beneficial properties of Swin Transformers in learning hierarchically structured data combined with the proficiency of LSTM in processing sequential dependencies, the hybrid model generates pre-trained weights in the first phase. These pre-trained weights are further transferred into another instance of the new model for subsequent fine-tuning. Experiments are carried out on several benchmarking datasets, namely NSL-KDD, ToN-IoT, BoTIoT, MQTTIoT, and CICIoT2023, which include both binary and multi-class classification scenarios. The proposed model outperforms state-of-the-art DL models, for example, the Autoencoders, ResNets, CNN, RNN, and LSTM models, and achieved an average of 98.97% in accuracy, of 98.97% in precision, of 99.02% in recall, of 98.97% in F1 score, across all datasets. Experimental results establish that the hybrid approach achieves better detection accuracy and better performance measures compared to the latest state-of-the-art methods, thus proving itself effective in increasing the scalability and adaptability of IDS in IoT.

Reconfigurable Intelligent Surfaces have recently emerged as a revolutionary next-generation wireless networks paradigm that harnesses engineered electromagnetic environments to reshape radio wave propagation. Pioneering research presented in this article establishes the viability of Reconfigurable Intelligent Surfaces-enhanced indoor localisation and charts a roadmap for its integration into next-generation wireless network architectures. Here, we present a comprehensive experimental analysis of a Reconfigurable Intelligent Surfaces-enabled indoor localisation scheme that evaluates the localisation accuracy of different machine learning algorithms under varying Reconfigurable Intelligent Surfaces states, antenna types, and communication setups. The results indicate that incorporating Reconfigurable Intelligent Surfaces can significantly enhance indoor localisation accuracy, achieving an impressive 82.4% success rate. Moreover, this study delves into system performance across varied communication modes and subcarrier configurations. This research is poised to lay the groundwork for implementing Reconfigurable Intelligent Surfaces-empowered joint sensing and communications in future next-generation wireless networks.

The demands of ultra-reliable low-latency communication (URLLC) in “NextG" cellular networks necessitate innovative approaches for efficient resource utilization. The current literature on 6G O-RAN primarily addresses improved mobile broadband (eMBB) performance or URLLC latency optimization individually, often neglecting the intricate balance required to optimize both simultaneously under practical constraints. This paper addresses this gap by proposing a DRL-based resource allocation framework integrated with meta-learning to manage eMBB and URLLC services adaptively. Our approach efficiently allocates heterogeneous network resources, aiming to maximize energy efficiency (EE) while minimizing URLLC latency, even under varying environmental conditions. We highlight the critical importance of accurately estimating the traffic distribution flow in the multi-connectivity (MC) scenario, as its uncertainty can significantly degrade EE. The proposed framework demonstrates superior adaptability across different path loss models, outperforming traditional methods and paving the way for more resilient and efficient 6G networks.