Aug 2026· Geotechnology, Mining and Rational Use of Natural Resources (GeoTech-VII 2026)· Vol 14297, pp. 1429706 - 1429706-10· 0 citations· 15 references
Engineering
TL;DR
A structured overview of the functional architecture of Smart Dust nodes, including MEMS-based sensing modules, ultra-low-power processing cores, and heterogeneous communication interfaces, as well as integrated power-management subsystems designed for operation under extreme energy constraints are provided.
Abstract
Smart Dust technology represents a new class of distributed sensor networks based on large populations of autonomous micronodes with volumes on the order of a few cubic millimeters. This paper provides a structured overview of the functional architecture of such nodes, including MEMS-based sensing modules, ultra-low-power processing cores, and heterogeneous communication interfaces, as well as integrated power-management subsystems designed for operation under extreme energy constraints. Particular attention is given to energy-management strategies such as duty cycling, event-driven activation, and multimodal energy harvesting, together with the role of TinyML-based local inference in reducing communication overhead at the network edge. The discussion further addresses fundamental physical limits of miniaturization, reliability and calibration issues, security and privacy challenges in large-scale autonomous deployments, and the prospects of biodegradable platforms for environmentally sustainable implementations, highlighting the need for interdisciplinary co-design across microsystems engineering, materials science, and machine learning.
Results demonstrate that smart sensor-based systems outperform conventional sensing systems in efficiency, fault detection, responsiveness, energy savings, and data accuracy, making them a key enabler of Industry 4.0 and future autonomous engineering applications.
V. Sethi· International Journal of Mod...· 0 citations
A solar-powered smart sensing node designed for autonomous operation that integrates TinyML and dual-mode wireless connectivity via LoRaWAN and Wi-Fi for intelligent monitoring and dual-mode wireless connectivity via LoRaWAN and Wi-Fi for intelligent monitoring is presented.
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Measuring the water table level is a critical factor in irrigated agriculture in arid regions, as it can significantly influence the exchange of water and nutrients with crops. This work presents the design, implementation, and field validation of an open-source, solar-powered IoT device for autonomous groundwater leve...
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With the rapid development of today's infrastructure networks, there is now more need than ever before for self‐reliant systems capable of lowering the reliance on human intervention and maintenance. The current methods used for smart infrastructures rely on energy‐consuming sensors that can pose problems related to th...
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