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Computer architecture

Biodegradable nanorobots meet edge computing: Sustainable intelligence in the soil

January 8, 2026 by Arya Upadhyay

Abstract: This article explores the integration of biodegradable nanorobots and edge computing as a sustainable approach to environmental monitoring. Traditional cloud-based systems consume extensive energy for data transmission and computation, creating a paradox where digital progress contributes to ecological degradation. By embedding computation directly into eco-friendly, … [Read more...] about Biodegradable nanorobots meet edge computing: Sustainable intelligence in the soil

Network as a service: A new vista of opportunities

January 1, 2022 by Junaid Qadir, Nadeem Ahmed, Faqir Zarrar Yousaf and Ali Taqweem

Image of a server.

The networking industry, compared to the computer industry, has been slow in evolving from a closed ecosystem with limited abstractions to a more open ecosystem with well-defined, sophisticated, high-level abstractions. This has resulted in an ossified Internet architecture that inhibits innovation and is unnecessarily complex. Fortunately, there has been an exciting flux of … [Read more...] about Network as a service: A new vista of opportunities

Flight data acquisition and a unified analysis tool for the Vector-P unmanned aerial vehicle

January 1, 2022 by Guilherme Coelho da Silva Stanisce Correa and Osamu Saotome

Image of three plane on a blue background.

This article presents a data acquisition system designed for an unmanned aerial vehicle (UAV). The previous version of the UAV data acquisition system relied on using commercial solutions like Micropilot and Icasim. By developing a custom data acquisition system, students can adapt it according to their needs. This allows rapid prototyping of a solution by using commercial … [Read more...] about Flight data acquisition and a unified analysis tool for the Vector-P unmanned aerial vehicle

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About the Magazine

IEEE Potentials Magazine is the publication dedicated to undergraduate and graduate students and young professionals. IEEE Potentials explores career strategies, the latest in research, and important technical developments. Through its articles, it also relates theories to practical applications, highlights technology’s global impact, and generates international forums that foster the sharing of diverse ideas about the profession.

POPULAR ARTICLE

Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.

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