Sourav Banerjee

The work presents a generative AI-driven framework for enhancing energy efficiency in spectrum sensing within Cognitive Radio Sensor Networks (CRSNs) for consumer electronics. Using Generative Adversarial Networks (GANs), the approach generates synthetic training data to compensate for limited real-world spectrum observations and dynamically adapts to changing radio environments. This reduces dependence on resource-intensive data collection while optimizing key parameters, such as sensor sleeping rates and detection thresholds, through a neural network model. This proposed work explores the integration of Generative Artificial Intelligence (AI) into spectrum sensing for CRSNs, by proposing a novel framework to improve energy efficiency while maintaining high detection accuracy. The work has included generative models, such as Variational Autoencoders (VAEs) and GANs, which synthesize the realistic spectrum data to train lightweight sensing models by reducing the dependency on extensive real-world data collection along with minimizing computational overhead. The study evaluates the effectiveness of the framework using mathematical modeling, which shows a significant reduction in energy consumption compared to conventional methods, without compromising the sensing reliability. In addition, the paper discusses practical implications for consumer electronics applications by highlighting scalability and adaptability to dynamic spectrum conditions and potential deployment challenges.

In the zestful domain of blockchain technology, non-fungible tokens (NFTs) have embellished a seminal procedure for setting up digital asset holding. This paper proposes a novel framework for multi-chain NFTs, wherein a single smart contract is deployed across multiple blockchain networks, prolonging the alike contract address to clinch congruous and lone NFT adjuncts. The launching burn-and-mint contraption eases coherent NFT conducts beyond divergent chains, magnifying user trail while conserving safety, solidity, and derivation. By examining the effects of this scheme, the challenges of achieving interoperability among blockchain systems are identified, and potential solutions are proposed to facilitate ethical digital asset management. This work presents prospects of multi-chain NFTs to augment liquidity in the digital asset mart, presenting the latest opportunities for creators, collations, and capitalists. Furthermore, the approach of these NFTs in aiding upheld initiatives, like endowing climate projects and tokenizing actual assets, is inspected. The discovery grant to the proceeding discourse on blockchain upheaval, providing perceptions into the evolution of NFT technology and its pivotal role in elevating economic insertion and overseeing expedient usage.

Cloud computing is currently playing an essential role in supporting emerging sectors such as smart energy, intelligent transportation, and large-scale distributed systems. The scalability and adaptability features of cloud computing are efficiently enable resource utilization and continuous data exchange in dynamic and heterogeneous environments. However, the increasing demand for cloud services has raised the energy consumption of cloud data centers, which has a critical environmental impact. Therefore, sustainable resource management tactics have become crucial in today's world. Dynamic Virtual Machine (VM) consolidation is one of the major tactics for sustainable resource management in the green cloud computing environment. Herein, a novel dynamic Energy-Aware Cosine Similarity Learning Network (ECSLN) is proposed to predict the overutilized host. Further, an Impact Factor-Based VM Selection (IFBVMS) method is proposed to select VMs for migration, and an ECSLN-Packed Placement method for VM placement into hosts. The main aim of the proposed VM consolidation is to maximize energy efficiency while preserving compliance with Service Level Agreements (SLAs) for sustainable environments. The experimental evaluation using real-world traces like PlanetLab, Bitbrains, and Alibaba Cluster 2020 workload validates that the proposed VM consolidation methods significantly reduce energy consumption and SLA violation compared to the existing methods for sustainable environments. The evaluation shows that the proposed ECSLN-based consolidation framework enables green cloud infrastructure by intelligently balancing energy consumption, SLA violation, and performance of cloud data centers.