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Development and Application of Methods and Algorithms to Smart Objects and Smart Sensors

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

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 19977

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Prof. Dr. Nunzio Alberto Borghese

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Guest Editor

Department of Computer Science, AIS-Lab Laboratory of Applied Intelligent Systems, Università degli Studi di Milano, 20133 Milan, Italy
Interests: artificial intelligence; applied intelligent systems; e-health platforms; exergames; smart objects
Special Issues, Collections and Topics in MDPI journals

Dr. Matteo Luperto

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Guest Editor

Department of Computer Science, AIS-Lab Laboratory of Applied Intelligent Systems, Università degli Studi di Milano, 20133 Milan, Italy
Interests: robotics; service robots; Internet of Robotics Things; AAL

Special Issue Information

Dear Colleagues,

A smart object is a cyber-physical device that combines its corporeal essence (i.e., the object) with software-based capabilities to provide novel functionalities. Miniaturization in electronics has boosted such technology, providing a huge variety of sensing circuits that can be embedded inside the most diverse objects. This, combined with onboard computing capability that supports intelligent behavior, processing, or allows smart sensors to communicate to a broader network of components in cloud-based AI infrastructures, makes objects smart.

A particular and interesting category of smart objects are smart sensors: objects common in everyday life are transformed into smart sensors with the capability of acquiring valuable data on the user and/or the environment; i.e., novel types of objects with embedded sensing capabilities could be developed to provide new functionalities.

Consequently, smart sensors have proven their potential and are becoming pervasive in many sectors, from automotive to health, from cultural heritage to industry, and from assisted living to smart environments.

We are pleased to invite you to contribute to this Special Issue, whose main goal is to collect reports on robust architectures and methods that leverage new possibilities offered by smart sensors and smart objects at large to provide novel, effective, and powerful sensing devices and methods.

For this Special Issue, original research articles and reviews are welcome. Specific research areas to be covered may include (but are not limited to) the following:

Real-time context awareness
Proactive sensing abilities in smart sensors and in smart sensors networks
Interaction and cooperation with IoT networks
Reactivity and adaptability of smart sensors to environmental changes
Longitudinal analysis of data from series of smart sensors
Real-world application and trials
Connectivity-based functionalities
Learning in embedded smart sensors architectures
Reasoning and decision-making
Autonomy and self-management
Development of novel smart sensors and smart objects
Smart sensors with actuation capabilities
Interdisciplinary application
Anomaly detection
Deep-learning application of smart sensors
Activity recognition using smart sensors and smart sensor networks
Humanlike interaction
Connectivity and data transmission in hazardous environments
Cloud-based architectures
Cross-sensitivity and multimodal error modeling
Energy and battery management
Active and passive sensing methods
Vision-based sensing methods
Artificial olfaction
Dispersed and pervasive functionality
Performance tracking
Security and privacy issues in using smart sensors
Autonomous robotics and sensing

We welcome applications of smart sensors to diverse fields, from AI to industry.

We look forward to receiving your contributions.

Prof. Dr. Nunzio Alberto Borghese
Dr. Matteo Luperto
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 2600 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

smart sensors
smart objects
monitoring
network of sensors
Internet of Things
pervasive monitoring
smart sensor applications

Published Papers (7 papers)

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Research

19 pages, 4892 KiB

Open AccessArticle

Fuzzy Logic Controlled Simulation in Regulating Thermal Comfort and Indoor Air Quality Using a Vehicle Heating, Ventilation, and Air-Conditioning System

by Konguvel Rajeswari Subramaniam, Chi-Tsun Cheng and Toh Yen Pang

Sensors 2023, 23(3), 1395; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031395 - 26 Jan 2023

Cited by 3 | Viewed by 2048

Abstract

Conventional heating ventilation and air-conditioning (HVAC) controllers have been designed to mainly control the temperature of a confined compartment, such as a room or a cabin of a vehicle. Other important parameters related to the thermal comfort and indoor air quality (IAQ) of the confined compartment have often been ignored. In this project, IAQ in the vehicle cabin was represented by its carbon dioxide (CO₂) concentration, and the occupants’ thermal comfort levels were estimated with the predicted mean vote (PMV) index. A new fuzzy logic controller (FLC) was designed and developed using the MATLAB fuzzy logic toolbox and Simulink to provide a nonlinear mapping between the measured values, i.e., PMV, temperature, CO₂, and control parameters (recirculation flaps, blower’s speed, and refrigerant mass flow rate) of a vehicle HVAC system. The new FLC aimed to regulate both in-cabin PMV and CO₂ values without significantly increasing overall energy consumption. To evaluate the effectiveness of the proposed FLC, a cabin simulator was used to mimic the effects of different HVAC variables and indoor/outdoor environmental settings, which represented the in-cabin PMV and IAQ readings. Results demonstrated that the new FLC was effective in regulating the in-cabin PMV level and CO₂ concentration, at desirable levels, by adaptively controlling the opening and closing of the recirculation flap based on in-cabin temperature and CO₂ readings, while maintaining an average-to-good energy consumption level. The proposed FLC could be applied to a large variety of HVAC systems by utilizing low-cost sensors, without the need to significantly modify the internal design of the HVAC system. Full article

(This article belongs to the Special Issue Development and Application of Methods and Algorithms to Smart Objects and Smart Sensors)

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28 pages, 4907 KiB

Open AccessArticle

Design and Evaluation of Personalized Services to Foster Active Aging: The Experience of Technology Pre-Validation in Italian Pilots

by Letizia Lorusso, Miran Mosmondor, Andrej Grguric, Lara Toccafondi, Grazia D’Onofrio, Sergio Russo, Jure Lampe, Tarmo Pihl, Nicolas Mayer, Gianna Vignani, Isabelle Lesterpt, Lucie Vaamonde, Francesco Giuliani, Manuele Bonaccorsi, Carlo La Viola, Erika Rovini, Filippo Cavallo and Laura Fiorini

Sensors 2023, 23(2), 797; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020797 - 10 Jan 2023

Cited by 3 | Viewed by 2112

Abstract

Assistive devices could promote independent living and support the active and healthy aging of an older population; however, several factors can badly influence the long-term use of new technologies. In this context, this paper presents a two-step methodology called “pre-validation” that aims to identify the factors that can bias the use of new services, thus minimizing the risk of an unsuccessful longer trial. The proposed pre-validation methodology is composed of two main phases that aim to assess the usability and the reliability of the technology assessed in a laboratory environment and the usability, acceptability, user experience, and reliability of the technology in real environments. The tested services include the socialization scenario, in which older adults are better connected to the community via technological solutions (i.e., socialization applications), and the monitoring scenario, which allows for the introduction of timely interventions (technologies involved include environmental monitoring sensors, a telepresence robot, wearable sensors, and a personalized dashboard). The obtained results underline an acceptable usability level (average System Usability Scale score > 65) for the tested technologies (i.e., socialization applications and a telepresence robot). Phase Two also underlines the good acceptability, user experience, and usability of the tested services. The statistical analysis underlines a correlation between the stress related to the use of technology, digital skills, and intention of use, among other factors. Qualitative feedback also remarks on a correlation between older adults with low digital skills and an anxiety about using technology. Positive correlation indexes were highlighted between the trust and usability scores. Eventually, future long-term trials with assistive technology should rely on motivated caregivers, be founded on a strong recruitment process, and should reassure older adults—especially the ones with low digital literacy—about the use of technology by proposing personalized training and mentoring, if necessary, to increase the trust. Full article

(This article belongs to the Special Issue Development and Application of Methods and Algorithms to Smart Objects and Smart Sensors)

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14 pages, 6435 KiB

Open AccessArticle

Event-Based Angular Speed Measurement and Movement Monitoring

by George Oliveira de Araújo Azevedo, Bruno José Torres Fernandes, Leandro Honorato de Souza Silva, Agostinho Freire, Rogério Pontes de Araújo and Francisco Cruz

Sensors 2022, 22(20), 7963; https://0-doi-org.brum.beds.ac.uk/10.3390/s22207963 - 19 Oct 2022

Viewed by 2061

Abstract

Computer vision techniques can monitor the rotational speed of rotating equipment or machines to understand their working conditions and prevent failures. Such techniques are highly precise, contactless, and potentially suitable for applications without massive setup changes. However, traditional vision sensors collect a significant amount of data to process and measure the rotation of high-speed systems, and they are susceptible to motion blur. This work proposes a new method for measuring rotational speed processing event-based data applied to high-speed systems using a neuromorphic sensor. This sensor produces event-based data and is designed to work with high temporal resolution and high dynamic range. The main advantages of the Event-based Angular Speed Measurement (EB-ASM) method are the high dynamic range, the absence of motion blurring, and the possibility of measuring multiple rotations simultaneously with a single device. The proposed method uses the time difference between spikes in a Kernel or Window selected in the sensor frame range. It is evaluated in two experimental scenarios by measuring a fan rotational speed and a Router Computer Numerical Control (CNC) spindle. The results compare measurements with a calibrated digital photo-tachometer. Based on the performed tests, the EB-ASM can measure the rotational speed with a mean absolute error of less than 0.2% for both scenarios. Full article

(This article belongs to the Special Issue Development and Application of Methods and Algorithms to Smart Objects and Smart Sensors)

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24 pages, 2105 KiB

Open AccessArticle

Improving Situation Awareness via a Situation Model-Based Intersection of IoT Sensor and Social Media Information Spaces

by Irfan Baig Mirza, Dimitrios Georgakopoulos and Ali Yavari

Sensors 2022, 22(20), 7823; https://0-doi-org.brum.beds.ac.uk/10.3390/s22207823 - 14 Oct 2022

Cited by 3 | Viewed by 1537

Abstract

Existing techniques for distilling situation awareness currently focus on information harvested from either IoT sensors or social media. While the benefits of fusing information from these two distinct information spaces for achieving enhanced situation awareness are well understood, existing techniques and related systems for fusing the IoT sensors and social media information spaces are currently embryonic. Key challenges in intersecting, combining, and fusing these information spaces to distil high-value situation awareness include devising situation models and related techniques for filtering, integrating, and fusing sparse and heterogeneous IoT sensor data and social media postings to provide a richer and more accurate situation awareness. This paper proposes novel, semantically based techniques fusing social media and IoT sensor information spaces and a comprehensive, fully implemented system that utilizes these to provide enhanced situation awareness. More specifically, this paper proposes the design of semantic-based situation models for fusing sensor and social media information spaces and presents techniques for finding similarities across these information spaces and fusing social media posting and IoT sensor data to generate an enhanced situation awareness. Furthermore, the paper presents the design and implementation of a complete system that uses the proposed models and techniques and uses that in an experimental evaluation that illustrates improvements in situation awareness from fusing the IoT sensor and social media information spaces. Full article

(This article belongs to the Special Issue Development and Application of Methods and Algorithms to Smart Objects and Smart Sensors)

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29 pages, 13180 KiB

Open AccessArticle

A Two-Mode Underwater Smart Sensor Object for Precision Aquaculture Based on AIoT Technology

by Chin-Chun Chang, Naomi A. Ubina, Shyi-Chyi Cheng, Hsun-Yu Lan, Kuan-Chu Chen and Chin-Chao Huang

Sensors 2022, 22(19), 7603; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197603 - 07 Oct 2022

Cited by 6 | Viewed by 3192

Abstract

Monitoring the status of culture fish is an essential task for precision aquaculture using a smart underwater imaging device as a non-intrusive way of sensing to monitor freely swimming fish even in turbid or low-ambient-light waters. This paper developed a two-mode underwater surveillance camera system consisting of a sonar imaging device and a stereo camera. The sonar imaging device has two cloud-based Artificial Intelligence (AI) functions that estimate the quantity and the distribution of the length and weight of fish in a crowded fish school. Because sonar images can be noisy and fish instances of an overcrowded fish school are often overlapped, machine learning technologies, such as Mask R-CNN, Gaussian mixture models, convolutional neural networks, and semantic segmentation networks were employed to address the difficulty in the analysis of fish in sonar images. Furthermore, the sonar and stereo RGB images were aligned in the 3D space, offering an additional AI function for fish annotation based on RGB images. The proposed two-mode surveillance camera was tested to collect data from aquaculture tanks and off-shore net cages using a cloud-based AIoT system. The accuracy of the proposed AI functions based on human-annotated fish metric data sets were tested to verify the feasibility and suitability of the smart camera for the estimation of remote underwater fish metrics. Full article

(This article belongs to the Special Issue Development and Application of Methods and Algorithms to Smart Objects and Smart Sensors)

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13 pages, 3941 KiB

Open AccessArticle

Domain Feature Mapping with YOLOv7 for Automated Edge-Based Pallet Racking Inspections

by Muhammad Hussain, Hussain Al-Aqrabi, Muhammad Munawar, Richard Hill and Tariq Alsboui

Sensors 2022, 22(18), 6927; https://0-doi-org.brum.beds.ac.uk/10.3390/s22186927 - 13 Sep 2022

Cited by 39 | Viewed by 6121

Abstract

Pallet racking is an essential element within warehouses, distribution centers, and manufacturing facilities. To guarantee its safe operation as well as stock protection and personnel safety, pallet racking requires continuous inspections and timely maintenance in the case of damage being discovered. Conventionally, a rack inspection is a manual quality inspection process completed by certified inspectors. The manual process results in operational down-time as well as inspection and certification costs and undiscovered damage due to human error. Inspired by the trend toward smart industrial operations, we present a computer vision-based autonomous rack inspection framework centered around YOLOv7 architecture. Additionally, we propose a domain variance modeling mechanism for addressing the issue of data scarcity through the generation of representative data samples. Our proposed framework achieved a mean average precision of 91.1%. Full article

(This article belongs to the Special Issue Development and Application of Methods and Algorithms to Smart Objects and Smart Sensors)

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16 pages, 6780 KiB

Open AccessArticle

LTC-Mapping, Enhancing Long-Term Consistency of Object-Oriented Semantic Maps in Robotics

by Jose-Luis Matez-Bandera, David Fernandez-Chaves, Jose-Raul Ruiz-Sarmiento, Javier Monroy, Nicolai Petkov and Javier Gonzalez-Jimenez

Sensors 2022, 22(14), 5308; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145308 - 15 Jul 2022

Cited by 1 | Viewed by 1744

Abstract

This paper proposes LTC-Mapping, a method for building object-oriented semantic maps that remain consistent in the long-term operation of mobile robots. Among the different challenges that compromise this aim, LTC-Mapping focuses on two of the more relevant ones: preventing duplicate instances of objects (instance duplication) and handling dynamic scenes. The former refers to creating multiple instances of the same physical object in the map, usually as a consequence of partial views or occlusions. The latter deals with the typical assumption made by object-oriented mapping methods that the world is static, resulting in outdated representations when the objects change their positions. To face these issues, we model the detected objects with 3D bounding boxes, and analyze the visibility of their vertices to detect occlusions and partial views. Besides this geometric modeling, the boxes are augmented with semantic information regarding the categories of the objects they represent. Both the geometric entities (bounding boxes) and their semantic content are propagated over time through data association and a fusion technique. In addition, in order to keep the map curated, the non-detection of objects in the areas where they should appear is also considered, proposing a mechanism that removes them from the map once there is evidence that they have been moved (i.e., multiple non-detections occur). To validate our proposal, a number of experiments have been carried out using the Robot@VirtualHome ecosystem, comparing its performance with a state-of-the-art alternative. The results report a superior performance of LTC-Mapping when modeling both geometric and semantic information of objects, and also support its online execution. Full article

(This article belongs to the Special Issue Development and Application of Methods and Algorithms to Smart Objects and Smart Sensors)

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