Search arXivSearch

arXiv · 2503.13466

A comprehensive review of sensor technologies, instrumentation, and signal processing solutions for low-power Internet of Things systems with mini-computing devices

Abstract

This article provides a comprehensive overview of sensors commonly used in low-cost, low-power systems, focusing on key concepts such as Internet of Things (IoT), Big Data, and smart sensor technologies. It outlines the evolving roles of sensors, emphasizing their characteristics, technological advancements, and the transition toward "smart sensors" with integrated processing capabilities. The article also explores the growing importance of mini-computing devices in educational environments. These devices provide cost-effective and energy-efficient solutions for system monitoring, prototype validation, and real-world application development. By interfacing with wireless sensor networks and IoT systems, mini-computers enable students and researchers to design, test, and deploy sensor-based systems with minimal resource requirements. Furthermore, this article examines the most widely used sensors, detailing their properties and modes of operation to help readers understand how sensor systems function. The aim of this study is to provide an overview of the most suitable sensors for various applications by explaining their uses and operations in simple terms. This clarity will assist researchers in selecting the appropriate sensors for educational and research purposes or understanding why specific sensors were chosen, along with their capabilities and possible limitations. Ultimately, this research seeks to equip future engineers with the knowledge and tools needed to integrate cutting-edge sensor networks, IoT, and Big Data technologies into scalable, real-world solutions.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Alexandros Gazis, Ioannis Papadongonas, Athanasios Andriopoulos, Constantinos Zioudas, Theodoros Vavouras. 2025-02-28. A comprehensive review of sensor technologies, instrumentation, and signal processing solutions for low-power Internet of Things systems with mini-computing devices. https://doi.org/10.20935/acadeng7541

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Circulant ADMM-Net for Fast High-resolution DoA Estimation

This paper introduces CADMM-Net and CHADMM-Net, two deep neural networks for direction of arrival estimation within the least-absolute shrinkage and selection operator (LASSO) framework. These two networks are based on a structured deep unfolding of the alternating direction method of multipliers (ADMM) algorithm through the use of circulant as well as Hermitian-circulant matrices. Along with a computational complexity of $\mathcal{O}(N\log(N))$ per layer for the inference, where $N$ is the length of the dictionary $\mathbf{A}$, they additionally exhibit a memory footprint of $N$ and approximately half of $N$ for CADMMNet and CHADMM-Net, respectively, compared with $N^{2}$ for ADMM-Net. Furthermore, these structured networks exhibit a competitive performance against ADMM-Net, LISTA, TLISTA, and THLISTA with respect to the detection rate, the angular root-mean square error, and the normalized mean squared error.

eess.SP

Adaptive Probabilistic Constellation Shaping based on Enumerative Sphere Shaping for FSO Channel with Turbulence and Pointing Errors

Free-space optical (FSO) transmission enables fast, secure, and efficient next-generation communications with abundant spectrum resources. However, atmospheric turbulence, pointing errors, path loss, and atmospheric loss induce random attenuation, challenging link reliability. Adaptive coded modulation technology enhances spectrum utilization and reliability. We propose an adaptive probabilistic constellation shaping (A-PCS) coherent system utilizing enumerative sphere shaping (ESS) for distribution matcher (DM). With PCS-64QAM, the system achieves continuous rate control from conventional QPSK-equivalent to 64QAM spectral efficiency, providing quasi-continuous control with granularities of approximately $0.05$~bits/4D for spectral efficiency and $0.1$~dB for the post-FEC SNR threshold, and a maximum control depth of $12.5$~dB. Leveraging ESS for efficient sequence utilization, it offers higher spectral efficiency and finer control granularity than constant composition distribution matcher (CCDM)-based A-PCS systems. We further model and analyze the FSO channel, presenting calculations and comparisons of outage probability and ergodic capacity under varying turbulence intensities and pointing errors. Results demonstrate 99.999~\% reliability at maximum $σ_\mathrm{R}^2 = 1.02$ and $σ_\mathrm{s} = 0.51~\mathrm{m}$, meeting requirements under severe turbulence and large pointing errors. {Furthermore, under non-ideal delayed channel state information (CSI) feedback conditions, the system adapts to varying turbulence coherence times and feedback delays, with results showing that finer modulation granularity (provided by A-PCS-ESS) enhances immunity to feedback delay, maintaining a performance advantage over conventional adaptive schemes across a range of channel environments and delay values.

eess.SP

Rate-Splitting--Inspired Bistatic OFDM-ISAC

Achieving effective uplink bistatic ISAC over an OFDM waveform gives rise to challenging interference structures. These are mostly due to unequal direct- and echo-path contributions and Doppler-induced ICI, rendering orthogonal resource separation and fixed SIC strategies inadequate. To address this problem, we propose a RS-inspired framework where the transmitter splits each communication message into a robust and a supplementary stream, which are jointly superposed over a sensing signal. Furthermore, we present the design of a staged sensing-communication receiver. Based on this framework, we derive tractable per-subcarrier SINR expressions and establish the relation between sensing accuracy and communication reliability based on the Fisher information. Building on these, we formulate a joint power-allocation problem for SE maximization under sensing-performance and power constraints. The resulting non-convex formulation is solved using convex surrogates and fractional programming. Numerical results demonstrate that, compared to NOMA-inspired baselines, the proposed framework provides more effective IFI management and improved robustness to Doppler-induced ICI.

eess.SP