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23 pages, 4541 KiB

Open AccessArticle

A Wireless Indoor Environmental Quality Logger Processing the Indoor Global Comfort Index

by Stefano Riffelli

Sensors 2022, 22(7), 2558; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072558 - 27 Mar 2022

Cited by 4 | Viewed by 2528

Indoor environmental quality (IEQ) has a high-level of impact on one’s health and productivity. It is widely accepted that IEQ is composed of four categories: thermal comfort, indoor air quality (IAQ), visual comfort, and acoustic comfort. The main physical parameters that primarily represent [...] Read more.

Indoor environmental quality (IEQ) has a high-level of impact on one’s health and productivity. It is widely accepted that IEQ is composed of four categories: thermal comfort, indoor air quality (IAQ), visual comfort, and acoustic comfort. The main physical parameters that primarily represent these comfort categories can be monitored using sensors. To this purpose, the article proposes a wireless indoor environmental quality logger. In the literature, global comfort indices are often assessed objectively (using sensors) or subjectively (through surveys). This study adopts an integrated approach that calculates a predicted indoor global comfort index (P-IGCI) using sensor data and estimates a real perceived indoor global comfort index (RP-IGCI) based on questionnaires. Among the 19 different tested algorithms, the stepwise multiple linear regression model minimized the distance between the two comfort indices. In the case study involving a university classroom setting—thermal comfort and indoor air quality were identified as the most relevant IEQ elements from a subjective point of view. The model also confirms this findings from an objective perspective since temperature and CO₂ merge as the measured physical parameters with the most impacts on overall comfort. Full article

(This article belongs to the Special Issue Embedded Systems and Internet of Things)

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21 pages, 1448 KiB

Open AccessArticle

User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications

by Nahar Sultana, Farhana Huq, Md. Abdur Razzaque and Md. Mustafizur Rahman

Sensors 2022, 22(3), 1192; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031192 - 04 Feb 2022

Cited by 4 | Viewed by 1769

Abstract

Narrowband Internet of Things (NB-IoT) is a promising technology for healthcare applications since it reduces the latency necessary in acquiring healthcare data from patients, as well as handling remote patients. Due to the interference, limited bandwidth, and heterogeneity of generated data packets, developing [...] Read more.

Narrowband Internet of Things (NB-IoT) is a promising technology for healthcare applications since it reduces the latency necessary in acquiring healthcare data from patients, as well as handling remote patients. Due to the interference, limited bandwidth, and heterogeneity of generated data packets, developing a data transmission framework that offers differentiated Quality of Services (QoS) to the critical and non-critical data packets is challenging. The existing literature studies suffer from insufficient access scheduling considering heterogeneous data packets and relationship among them in healthcare applications. In this paper, we develop an optimal resource allocation framework for NB-IoT that maximizes a user’s utility through event prioritization, rate enhancement, and interference mitigation. The proposed Priority Aware Utility Maximization (PAUM) system also ensures weighted fair access to resources. The suggested system outperforms the state-of-the-art works significantly in terms of utility, delay, and fair resource distribution, according to the findings of the performance analysis performed in NS-3. Full article

(This article belongs to the Special Issue Embedded Systems and Internet of Things)

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20 pages, 4485 KiB

Open AccessArticle

FlexiS—A Flexible Sensor Node Platform for the Internet of Things

by Duc Minh Pham and Syed Mahfuzul Aziz

Sensors 2021, 21(15), 5154; https://0-doi-org.brum.beds.ac.uk/10.3390/s21155154 - 29 Jul 2021

Cited by 7 | Viewed by 2576

Abstract

In recent years, significant research and development efforts have been made to transform the Internet of Things (IoT) from a futuristic vision to reality. The IoT is expected to deliver huge economic benefits through improved infrastructure and productivity in almost all sectors. At [...] Read more.

In recent years, significant research and development efforts have been made to transform the Internet of Things (IoT) from a futuristic vision to reality. The IoT is expected to deliver huge economic benefits through improved infrastructure and productivity in almost all sectors. At the core of the IoT are the distributed sensing devices or sensor nodes that collect and communicate information about physical entities in the environment. These sensing platforms have traditionally been developed around off-the-shelf microcontrollers. Field-Programmable Gate Arrays (FPGA) have been used in some of the recent sensor nodes due to their inherent flexibility and high processing capability. FPGAs can be exploited to huge advantage because the sensor nodes can be configured to adapt their functionality and performance to changing requirements. In this paper, FlexiS, a high performance and flexible sensor node platform based on FPGA, is presented. Test results show that FlexiS is suitable for data and computation intensive applications in wireless sensor networks because it offers high performance with low energy profile, easy integration of multiple types of sensors, and flexibility. This type of sensing platforms will therefore be suitable for the distributed data analysis and decision-making capabilities the emerging IoT applications require. Full article

(This article belongs to the Special Issue Embedded Systems and Internet of Things)

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25 pages, 4697 KiB

Open AccessArticle

Model-Driven Architectural Framework towards Safe and Secure Nuclear Power Reactors

by Bassem Ouni, Christophe Aussagues, Saadia Dhouib and Chokri Mraidha

Sensors 2021, 21(15), 5136; https://0-doi-org.brum.beds.ac.uk/10.3390/s21155136 - 29 Jul 2021

Viewed by 1810

Abstract

Sensor-based digital systems for Instrumentation and Control (I&C) of nuclear reactors are quite complex in terms of architecture and functionalities. A high-level framework is highly required to pre-evaluate the system’s performance, check the consistency between different levels of abstraction and address the concerns [...] Read more.

Sensor-based digital systems for Instrumentation and Control (I&C) of nuclear reactors are quite complex in terms of architecture and functionalities. A high-level framework is highly required to pre-evaluate the system’s performance, check the consistency between different levels of abstraction and address the concerns of various stakeholders. In this work, we integrate the development process of I&C systems and the involvement of stakeholders within a model-driven methodology. The proposed approach introduces a new architectural framework that defines various concepts, allowing system implementations and encompassing different development phases, all actors, and system concerns. In addition, we define a new I&C Modeling Language (ICML) and a set of methodological rules needed to build different architectural framework views. To illustrate this methodology, we extend the specific use of an open-source system engineering tool, named Eclipse Papyrus, to carry out many automation and verification steps at different levels of abstraction. The architectural framework modeling capabilities will be validated using a realistic use case system for the protection of nuclear reactors. The proposed framework is able to reduce the overall system development cost by improving links between different specification tasks and providing a high abstraction level of system components. Full article

(This article belongs to the Special Issue Embedded Systems and Internet of Things)

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15 pages, 1875 KiB

Open AccessArticle

Software Architecture of a Fog Computing Node for Industrial Internet of Things

by Ioan Ungurean and Nicoleta Cristina Gaitan

Sensors 2021, 21(11), 3715; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113715 - 26 May 2021

Cited by 9 | Viewed by 3240

Abstract

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with [...] Read more.

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC. Full article

(This article belongs to the Special Issue Embedded Systems and Internet of Things)

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20 pages, 7000 KiB

Open AccessArticle

An Intelligent Self-Service Vending System for Smart Retail

by Kun Xia, Hongliang Fan, Jianguang Huang, Hanyu Wang, Junxue Ren, Qin Jian and Dafang Wei

Sensors 2021, 21(10), 3560; https://0-doi-org.brum.beds.ac.uk/10.3390/s21103560 - 20 May 2021

Cited by 9 | Viewed by 5210

Abstract

The traditional weighing and selling process of non-barcode items requires manual service, which not only consumes manpower and material resources but is also more prone to errors or omissions of data. This paper proposes an intelligent self-service vending system embedded with a single [...] Read more.

The traditional weighing and selling process of non-barcode items requires manual service, which not only consumes manpower and material resources but is also more prone to errors or omissions of data. This paper proposes an intelligent self-service vending system embedded with a single camera to detect multiple products in real-time performance without any labels, and the system realizes the integration of weighing, identification, and online settlement in the process of non-barcode items. The system includes a self-service vending device and a multi-device data management platform. The flexible configuration of the structure gives the system the possibility of identifying fruits from multiple angles. The height of the system can be adjusted to provide self-service for people of different heights; then, deep learning skill is applied implementing product detection, and real-time multi-object detection technology is utilized in the image-based checkout system. In addition, on the multi-device data management platform, the information docking between embedded devices, WeChat applets, Alipay, and the database platform can be implemented. We conducted experiments to verify the accuracy of the measurement. The experimental results demonstrate that the correlation coefficient

R^{2}

between the measured value of the weight and the actual value is 0.99, and the accuracy of non-barcode item prediction is 93.73%. In Yangpu District, Shanghai, a comprehensive application scenario experiment was also conducted, proving that our system can effectively deal with the challenges of various sales situations. Full article

(This article belongs to the Special Issue Embedded Systems and Internet of Things)

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23 pages, 13197 KiB

Open AccessArticle

Toward Smart Soil Sensing in v4.0 Agriculture: A New Single-Shape Sensor for Capacitive Moisture and Salinity Measurements

by Christophe Escriba, Eli Gabriel Aviña Bravo, Julien Roux, Jean-Yves Fourniols, Michel Contardo, Pascal Acco and Georges Soto-Romero

Sensors 2020, 20(23), 6867; https://0-doi-org.brum.beds.ac.uk/10.3390/s20236867 - 30 Nov 2020

Cited by 6 | Viewed by 3107

Abstract

Modern agriculture imposes the need for better knowledge of the soil moisture content to rationalize the amount of water needed to irrigate farmlands. In this context, since current technological solutions do not correspond to the cost or use criteria, this paper presents a [...] Read more.

Modern agriculture imposes the need for better knowledge of the soil moisture content to rationalize the amount of water needed to irrigate farmlands. In this context, since current technological solutions do not correspond to the cost or use criteria, this paper presents a design for a new original capacitive bi-functional sensor to measure soil moisture and salinity. In this paper, we outline the design stages from simulation to finished elements of the optimal design to deployment in the fields, considering the mechanical integration constraints necessary for industrialization. The measurement electronics were developed based on the sensor’s electric model to obtain a double measurement. An on-site (field lot) measurement program was then carried out to validate the system’s good performance in real-time. Finally, this performance was matched with that of leading commercially available sensors on the market. This work demonstrates that, after deployment of the sensors, the overall system makes it possible to obtain a precise image of cultivated soil’s hydric condition, with the best response time. Full article

(This article belongs to the Special Issue Embedded Systems and Internet of Things)

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Review

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44 pages, 1693 KiB

Open AccessReview

An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications

by Taiwo Samuel Ajani, Agbotiname Lucky Imoize and Aderemi A. Atayero

Sensors 2021, 21(13), 4412; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134412 - 28 Jun 2021

Cited by 72 | Viewed by 13817

Abstract

Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, [...] Read more.

Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more. However, there exists a critical drawback in the efficient implementation of ML algorithms targeting embedded applications. Machine learning algorithms are generally computationally and memory intensive, making them unsuitable for resource-constrained environments such as embedded and mobile devices. In order to efficiently implement these compute and memory-intensive algorithms within the embedded and mobile computing space, innovative optimization techniques are required at the algorithm and hardware levels. To this end, this survey aims at exploring current research trends within this circumference. First, we present a brief overview of compute intensive machine learning algorithms such as hidden Markov models (HMM), k-nearest neighbors (k-NNs), support vector machines (SVMs), Gaussian mixture models (GMMs), and deep neural networks (DNNs). Furthermore, we consider different optimization techniques currently adopted to squeeze these computational and memory-intensive algorithms within resource-limited embedded and mobile environments. Additionally, we discuss the implementation of these algorithms in microcontroller units, mobile devices, and hardware accelerators. Conclusively, we give a comprehensive overview of key application areas of EML technology, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain. Full article

(This article belongs to the Special Issue Embedded Systems and Internet of Things)

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