In today’s digital world, the Internet is an essential component of communication networks. It provides a platform for quickly exchanging information among communicating parties. There is a risk of unauthorized persons gaining access to our sensitive information while it is being transmitted. Cryptography is one of the most effective and efficient strategies for protecting our data and it are utilized all around the world. The efficiency of a cryptography algorithm is determined by a number of parameters, one of which is the length of the key. For cryptography, key (public/private) is an essential part. To provide robust security, RSA takes larger key size. If we use larger key size, the processing performance will be slowed. As a result, processing speed will decrease and memory consumption will increase. Due to this, cryptographic algorithms with smaller key size and higher security are becoming more popular. Out of the cryptographic algorithms, Elliptic Curve Cryptography (ECC) provides equivalent level of safety which RSA provides, but it takes smaller key size. On the basis of key size, our work focused on, studied, and compared the efficacy in terms of security among the well-known public key cryptography algorithms, namely ECC (Elliptic Curve Cryptography) and RSA (Rivets Shamir Adelman).

The Internet of Things (IoT) depends primarily on low-cost wireless sensors with limited energy capacity to allow pervasive monitoring and intelligent control. Nevertheless, unmanned aerial vehicle (UAV) can be used to connect remote terminals that are outside wireless coverage to IoT networks. This solution provides a means of extending the reach of IoT networks, offering more opportunities for monitoring and control. Despite this benefit, the UAV also suffers from low capacity onboard battery. To overcome these problems, solar energy is integrated with UAV, and wireless-powered communication (WPC) techniques are used for IoT terminals. Also, the non-orthogonal multiple access (NOMA) technique can be employed to address the massive connectivity issue of IoT terminals. By leveraging these advantages, we jointly optimize the three-dimensional UAV trajectory and time allocation for WPC powered IoT devices (IoTDs) underlaying solar-powered UAV. To achieve the target, in this paper, introduces a multiagent federated reinforcement learning (MAFAL) algorithm, which concentrates on maximizing energy efficiency (EE) while minimizing energy consumption, guaranteeing quality of service (QoS), fairness, and trajectory planning. The proposed algorithm aims to optimize the overall performance of the system by learning from the collective experience of multiple agents. Simulation result demonstrated that the proposed method achieves 56.84%, 68.45%, and 73.63% higher EE as compared to MAD2PG, DDPG, and DQN, respectively.

Mobile Edge Computing (MEC) is well known for its critical role in controlling the growth of Internet of Things (IoT) applications, even though energy demand forecasting is inherently challenging because of multiple task-specific factors. Including a microgrid as a backup energy source offers a chance to improve the effectiveness of energy distribution, but it also raises concerns about using non-renewable energy sources. This study explores the complex task of managing energy scheduling in a microgrid-enabled MEC network. It does this by minimizing residual planned energy using the conditional value at risk (CVaR) measure and illuminate the complexity of this NP-hard problem. To address this complex problem, a multi-agent stochastic game framework is implemented to achieve a Nash equilibrium for collective policies and prove model convergence, revealing the novel Asynchronous Federated Learning framework for Microgrid-Enabled MEC Network (AFFIN). A device selection strategy and a multi-agent Asynchronous Advantage Actor-Critic (A3C) algorithm with shared neural networks are employed to enhance learning efficiency. The proposed scheme enables localized model training and parameter sharing with the MEC server. The results demonstrated the exceptional effectiveness of incorporating CVaR into energy scheduling, surpassing the performance of random agent and single-agent models in terms of optimizing energy distribution in the MEC network.

The emergence of intelligent vehicular networks has catalyzed the advancement of communication architectures capable of meeting the rigorous demands of ultra-reliable low-latency communication (URLLC), enhanced spectral efficiency, and interference mitigation. This paper presents a novel cross-layer interference management framework for Vehicle Road Cooperation Systems (VRCS), built on the integration of Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) with Generative Artificial Intelligence (GAI)-enhanced Tiny Federated Learning (TinyFL). High mobility and dynamic topologies in VRCS often result in severe co-channel interference, adversely affecting link reliability and latency. To address this, the proposed system leverages the full-space beamforming capabilities of STAR-RIS to intelligently reconfigure the wireless propagation environment. Complementing this, a lightweight and privacy-preserving TinyFL architecture is employed, tailored to the computational constraints of vehicular nodes. To further improve model generalization and adaptability under non-IID and time-varying scenarios, GAI is integrated to synthesize diverse and realistic training data. The framework supports decentralized and intelligent mode selection, link scheduling, and resource allocation through a Markov Decision Process (MDP)-driven reinforcement learning formulation spanning the physical, MAC, and network layers. This design ensures scalable, autonomous decision-making while preserving data privacy. Extensive simulations demonstrate the superiority of the proposed system, achieving up to 32.4% improvement in interference suppression, 27.8% gain in throughput, and 41.6% faster learning convergence compared to conventional FL, FRL, and TinyFRL baselines. These results highlight the effectiveness of combining STAR-RIS with edge AI to address the critical challenges of scalability, latency, and adaptability in next-generation vehicular communication systems.