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Sensors

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

Smart assisted living: A data-centric and machine learning approach to intelligent home systems

November 3, 2025 by Md Zia Uddin

Abstract: The evolution of smart homes is marked as one of the most profound integrations of artificial intelligence (AI) into everyday life. Novelty gadgets or remote-controlled devices no longer define modern intelligent living spaces; instead, they are recognized as cognitive ecosystems capable of perceiving environmental and behavioral cues; learning from routine patterns; … [Read more...] about Smart assisted living: A data-centric and machine learning approach to intelligent home systems

Advancing conservation: Internet of Things-driven initiatives for marine animal rescue due to climate changes [Climate Change]

March 6, 2024 by Rahul Sanmugam Gopi, Rajkumar Ramasamy, M. David Honesty Babu

Beneath the expansive oceans lies a complex ecosystem where marine animals, from whales to delicate coral polyps, sustain the vitality of the marine environment. However, these creatures are facing significant challenges due to evolving climate changes. Rising sea temperatures, ocean acidification, habitat degradation, and increasing natural disasters threaten their … [Read more...] about Advancing conservation: Internet of Things-driven initiatives for marine animal rescue due to climate changes [Climate Change]

Reshaping engineered clinical decision support systems

July 28, 2023 by Andrei Perez

The continuous innovation and development of sensing technologies, image processing techniques, and deep learning frameworks have caught the attention of many researchers, who are developing cutting-edge systems for medical applications. This article talks about the recent usage of camera-based biomedical signal processing and the basic concepts you should understand or … [Read more...] about Reshaping engineered clinical decision support systems

Blockchain meets lightweight cryptography

February 10, 2023 by Safiullah Khan

The Internet of Things (IoT) typically comprises sensors that have inadequate computational resources. On the contrary, blockchain technology is thought to be expensive in terms of resource consumption due to the consensus procedures involved. As a result, the application of blockchain to the IoT environment is a challenging task. The most appropriate solution lies in replacing … [Read more...] about Blockchain meets lightweight cryptography

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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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