Submit to Sensors Review for Sensors Edit a Special Issue

Journal Browser

► Journal Browser

Special Issue "Low Power and Energy Efficient Sensing Applications"

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 7076

Share This Special Issue

Special Issue Editors

Prof. Dr. Vedran Bilas

E-Mail Website
Guest Editor

Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia
Interests: electromagnetic induction sensing; sensor interfaces and signal processing; networked embedded sensors; low power electronic systems
Special Issues, Collections and Topics in MDPI journals

Dr. Michele Magno

E-Mail Website
Guest Editor

Department of Electronic Informatic Technology and Electric Engineering (ITIT), ETH Zürich, 8092 Zürich, Switzerland
Interests: low power embedded systems; sensors systems; wireless sensor networks; energy harvesting; low power machine learning; microcontrollers; energy efficiency
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ongoing “smartization” in many aspects of our lives builds on numerous sensing systems embedded in wearable and mobile devices, vehicles, machines, appliances, environment or infrastructure. Those sensing systems are smart and low-power, continuously locally processing the sensor data. This Special Issue aims to present recent research and technology advancements and experiences in applications of low power sensing focusing on small form devices, hardware, and algorithms enabling smart sensors with very low power consumption, energy efficiency, and eventually achieving battery-less or perpetual operation.

Prof. Dr. Vedran Bilas
Dr. Michele Magno
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Low-power smart sensors
Energy-efficient sensor interfaces
Embedded systems for low-power sensing
Low-power sensor signal processing
Machine learning in resource-constraint processors
Energy harvesting for smart sensors
Self-sustaining sensors
Zero-power sensors
Sensing applications and deployments

Published Papers (3 papers)

Download All Papers

Research

Open AccessArticle

Low-Power Sensor Interface with a Switched Inductor Frequency Selective Envelope Detector

Marko Gazivoda

and

Vedran Bilas

Sensors 2021, 21(6), 2124; https://doi.org/10.3390/s21062124 - 18 Mar 2021

Cited by 2 | Viewed by 1748

Abstract

With the growing need to understand our surroundings and improved means of sensor manufacturing, the concept of Internet of Things (IoT) is becoming more interesting. To enable continuous monitoring and event detection by IoT, the development of low power sensors and interfaces is required. In this work we present a novel, switched inductor based acoustic sensor interface featuring a bandpass filter and envelope detector, perform a sensitivity, frequency selectivity, and power consumption analysis of the circuit, and present its design parameters and their qualitative influence on circuit characteristics. We develop a prototype and present experimental characterization of the interface and its operation with input signals up to 20 mV peak-to-peak, at low acoustic frequencies from 100 Hz to 1 kHz. The prototype achieves a sensitivity of approximately 2 mV/mV in the passband, a four times lower sensitivity in the stopband, and a power consumption of approximately 3.31 µW. We compare the prototype interface to an interface consisting of an active bandpass filter and a passive voltage doubler using a prerecorded speedboat signal. Full article

(This article belongs to the Special Issue Low Power and Energy Efficient Sensing Applications)

► Show Figures

Figure 1

Open AccessArticle

Passive Extraction of Signal Feature Using a Rectifier with a Mechanically Switched Inductor for Low Power Acoustic Event Detection

and

Sensors 2020, 20(18), 5445; https://doi.org/10.3390/s20185445 - 22 Sep 2020

Cited by 2 | Viewed by 2115

Abstract

Analog hardware used for signal envelope extraction in low-power interfaces for acoustic event detection, owing to its low complexity and power consumption, suffers from low sensitivity and performs poorly under low signal to noise ratios (SNR) found in undersea environments. To overcome those problems, in this paper, we propose a novel passive electromechanical solution for the signal feature extraction in low frequency acoustic range (200–1000 Hz), in the form of a piezoelectric vibration transducer, and a rectifier with a mechanically switched inductor. A simulation study of the novel solution is presented, and a proof-of-concept device is developed and experimentally characterized. We demonstrate its applicability and show the advantages of the passive electromechanical device in comparison to the active electrical solution in terms of operation with lower input signals (<20 mV compared to 40 mV), and higher robustness in low SNR conditions (output voltage loss for −10 dB ≤ SNR < 40 dB of 1 mV, compared to 10 mV). In addition to the signal processing performance improvements, compared to our previous work, the utilization of the presented novel passive feature extractor would also decrease power consumption of a detector’s channel by over 76%, enabling life-time extension and/or increased quality of detection with larger number of channels. To the best of our knowledge, this is the first solution presented in the literature that demonstrates the possibility of using a passive electromechanical feature extractor in a low-power analog wake-up event detector interface. Full article

(This article belongs to the Special Issue Low Power and Energy Efficient Sensing Applications)

► Show Figures

Figure 1

Open AccessArticle

Energy per Operation Optimization for Energy-Harvesting Wearable IoT Devices

and

Sensors 2020, 20(3), 764; https://doi.org/10.3390/s20030764 - 30 Jan 2020

Cited by 21 | Viewed by 2797

Abstract

Wearable internet of things (IoT) devices can enable a variety of biomedical applications, such as gesture recognition, health monitoring, and human activity tracking. Size and weight constraints limit the battery capacity, which leads to frequent charging requirements and user dissatisfaction. Minimizing the energy consumption not only alleviates this problem, but also paves the way for self-powered devices that operate on harvested energy. This paper considers an energy-optimal gesture recognition application that runs on energy-harvesting devices. We first formulate an optimization problem for maximizing the number of recognized gestures when energy budget and accuracy constraints are given. Next, we derive an analytical energy model from the power consumption measurements using a wearable IoT device prototype. Then, we prove that maximizing the number of recognized gestures is equivalent to minimizing the duration of gesture recognition. Finally, we utilize this result to construct an optimization technique that maximizes the number of gestures recognized under the energy budget constraints while satisfying the recognition accuracy requirements. Our extensive evaluations demonstrate that the proposed analytical model is valid for wearable IoT applications, and the optimization approach increases the number of recognized gestures by up to 2.4× compared to a manual optimization. Full article

(This article belongs to the Special Issue Low Power and Energy Efficient Sensing Applications)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Special Issue "Low Power and Energy Efficient Sensing Applications"

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (3 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI