Author

Himadri Biswas

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#edge computing Book Sep 2026

Compact Wideband Frequency Selective Surface for Electromagnetic Shielding in Cloud Computing Hardware and Infrastructure

As clouds with their higher processing density and wireless interconnectivity operate at gigahertz frequency range, they are more vulnerable to electromagnetic interferences. Interference of this kind has the effect of putting the system at risk, causing crosstalk between the components, and even possibly compromising data security. The research contained within this paper presents a wideband Frequency Selective Surface FSS that has been specially designed to serve as a barrier to electromagnetic interference in modern cloud hardware. The introduced FSS has a stop band between 8.02 GHz and 14.84 GHz. The interference caused by high-speed interconnections and power supplies is efficiently handled by this specific band. The design provides an impressive 90 dB shielding efficiency and a maximum transmission level of –38.77 dB at the resonance frequency of 12.38 GHz. The results of the study indicate that this FSS can totally block the entry of the unwanted electromagnetic waves. The design is realized on a tiny FR4 substrate of size 18 mm by 18 mm by 1.6 mm, is not dependent on the direction of the incoming signal, and exhibits a consistent reflection phase. The FSS broad operation was verified through simulations employing Floquet port analysis for both transverse electric and transverse magnetic polarizations. The small size and frequency-dependent shielding property of the design ensure its use in server casings, Internet of Things edge devices, and data center links. It presents an easy and effective way to improve the electromagnetic compatibility of the cloud infrastructure of the future.

Kousik Roy, Pratik Dey, Kamlesh Kumar Chaurasia et al. · 0 citations

#federated learning Book Sep 2026

A Comprehensive Analysis of Dynamic Task Scheduling for Cloud Computing Environment

Cloud computing has revolutionized the way computational resources are made available through scalable, flexible, and on-demand services. As promising as it is, efficient task scheduling remains an important challenge because of varied workloads, heterogeneous infrastructures, and multi-objective optimization requirements for cost, execution time, and power efficiency. Legacy scheduling approaches frequently fail to meet these demands, and thus the idea of hybrid frameworks fusing artificial intelligence (AI) and optimization techniques has been developed. In this paper, dynamic task scheduling techniques that use deep learning, metaheuristic optimization, and heuristic algorithms to be more efficient are discussed. Specific focus has been placed on energy-efficient models like adaptive Particle Swarm Optimization (PSO) and multi-objective scheduling models with a balance between performance and sustainability. The developed AI-based model utilizes deep learning to predict the workload, optimization techniques to achieve multi-objective trade-offs, and reinforcement learning to adapt in real-time. The research helps in the development of multi-objective, adaptive, and sustainable task scheduling for cloud platforms with future prospects on federated learning, edge/fog integration, and security-conscious scheduling techniques.

Bidyut Roy, Abhijit Paul, Himadri Biswas · 0 citations