Smart dust technology in distributed sensor networks: architecture, energy strategies, and emerging challenges

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.