Biosensors

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

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.

Biosensors

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