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Ubiquitous Sensing and Intelligent Systems

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

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 20315
Please contact the Guest Editor or the Section Managing Editor at ([email protected]) for any queries.

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Special Issue Editor

Dr. Catarina Silva

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Guest Editor

Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, 3030-790 Coimbra, Portugal
Interests: social networks; machine learning; text classification; dynamic environments; crowdsourcing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, there are two trends that are becoming pervasive: sensing and intelligence. The undertakings that are being pursued that involve both a ubiquitous source of data and an intelligent system constitute an immense source of advancement and achievements.

On one hand, sensing data are everywhere, from abundant sources, e.g., people, cameras, homes, agriculture, and cars. Such an overwhelming amount of data is avidly scrutinized in the quest for pattern detection, decision support information, trend detection, and anomaly detection, among many others.

On the other hand, intelligent systems, supported by seemingly never-ending computational power, are booming, with new approaches and algorithms appearing everyday extending the state-of-art.

The interdisciplinary challenges that can arise have been a source of major investigation. Researchers can find in this Special Issue a venue for presenting both theoretical and practical approaches allowing the presentation and discussion of innovative solutions by addressing a number of open challenges, such as sensor fusion, decision support, privacy, explainability, and trend detection, shaping the path for transformative research.

Prof. Dr. Catarina Silva
Guest Editor

Manuscript Submission Information

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Keywords

Intelligent systems based on ubiquitous sensors
Privacy issues in sensor-based systems
Sensor fusion for decision support, prognostics, and diagnostics
Fairness, accountability, and transparency in sensor-based intelligent systems
Visualization approaches
Preprocessing, feature engineering, and data reduction
Data-driven approaches
Remote sensing
Pattern recognition
Big data
Biosensing
IoT
High-tech agriculture
Industrial applications
Health applications
Transformative research

Published Papers (6 papers)

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Research

26 pages, 7233 KiB

Open AccessEditor’s ChoiceArticle

Cloud Data-Driven Intelligent Monitoring System for Interactive Smart Farming

by Kristina Dineva and Tatiana Atanasova

Sensors 2022, 22(17), 6566; https://doi.org/10.3390/s22176566 - 31 Aug 2022

Cited by 17 | Viewed by 3466

Abstract

Smart farms, as a part of high-tech agriculture, collect a huge amount of data from IoT devices about the conditions of animals, plants, and the environment. These data are most often stored locally and are not used in intelligent monitoring systems to provide opportunities for extracting meaningful knowledge for the farmers. This often leads to a sense of missed transparency, fairness, and accountability, and a lack of motivation for the majority of farmers to invest in sensor-based intelligent systems to support and improve the technological development of their farm and the decision-making process. In this paper, a data-driven intelligent monitoring system in a cloud environment is proposed. The designed architecture enables a comprehensive solution for interaction between data extraction from IoT devices, preprocessing, storage, feature engineering, modelling, and visualization. Streaming data from IoT devices to interactive live reports along with built machine learning (ML) models are included. As a result of the proposed intelligent monitoring system, the collected data and ML modelling outcomes are visualized using a powerful dynamic dashboard. The dashboard allows users to monitor various parameters across the farm and provides an accessible way to view trends, deviations, and patterns in the data. ML models are trained on the collected data and are updated periodically. The data-driven visualization enables farmers to examine, organize, and represent collected farm’s data with the goal of better serving their needs. Performance and durability tests of the system are provided. The proposed solution is a technological bridge with which farmers can easily, affordably, and understandably monitor and track the progress of their farms with easy integration into an existing IoT system. Full article

(This article belongs to the Special Issue Ubiquitous Sensing and Intelligent Systems)

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18 pages, 1219 KiB

Open AccessArticle

Attitudes towards Participation in a Passive Data Collection Experiment

by Bence Ságvári, Attila Gulyás and Júlia Koltai

Sensors 2021, 21(18), 6085; https://doi.org/10.3390/s21186085 - 10 Sep 2021

Cited by 7 | Viewed by 2558

Abstract

In this paper, we present the results of an exploratory study conducted in Hungary using a factorial design-based online survey to explore the willingness to participate in a future research project based on active and passive data collection via smartphones. Recently, the improvement of smart devices has enabled the collection of behavioural data on a previously unimaginable scale. However, the willingness to share this data is a key issue for the social sciences and often proves to be the biggest obstacle to conducting research. In this paper we use vignettes to test different (hypothetical) study settings that involve sensor data collection but differ in the organizer of the research, the purpose of the study and the type of collected data, the duration of data sharing, the number of incentives and the ability to suspend and review the collection of data. Besides the demographic profile of respondents, we also include behavioural and attitudinal variables to the models. Our results show that the content and context of the data collection significantly changes people’s willingness to participate, however their basic demographic characteristics (apart from age) and general level of trust seem to have no significant effect. This study is a first step in a larger project that involves the development of a complex smartphone-based research tool for hybrid (active and passive) data collection. The results presented in this paper help improve our experimental design to encourage participation by minimizing data sharing concerns and maximizing user participation and motivation. Full article

(This article belongs to the Special Issue Ubiquitous Sensing and Intelligent Systems)

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17 pages, 5600 KiB

Open AccessArticle

Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification

by Joana Costa, Catarina Silva, Miguel Santos, Telmo Fernandes and Sérgio Faria

Sensors 2021, 21(15), 5162; https://doi.org/10.3390/s21155162 - 30 Jul 2021

Cited by 10 | Viewed by 2786

Abstract

Intelligent approaches in sports using IoT devices to gather data, attempting to optimize athlete’s training and performance, are cutting edge research. Synergies between recent wearable hardware and wireless communication strategies, together with the advances in intelligent algorithms, which are able to perform online pattern recognition and classification with seamless results, are at the front line of high-performance sports coaching. In this work, an intelligent data analytics system for swimmer performance is proposed. The system includes (i) pre-processing of raw signals; (ii) feature representation of wearable sensors and biosensors; (iii) online recognition of the swimming style and turns; and (iv) post-analysis of the performance for coaching decision support, including stroke counting and average speed. The system is supported by wearable inertial (AHRS) and biosensors (heart rate and pulse oximetry) placed on a swimmer’s body. Radio-frequency links are employed to communicate with the heart rate sensor and the station in the vicinity of the swimming pool, where analytics is carried out. Experiments were carried out in a real training setup, including 10 athletes aged 15 to 17 years. This scenario resulted in a set of circa 8000 samples. The experimental results show that the proposed system for intelligent swimming analytics with wearable sensors effectively yields immediate feedback to coaches and swimmers based on real-time data analysis. The best result was achieved with a Random Forest classifier with a macro-averaged

F_{1}

of 95.02%. The benefit of the proposed framework was demonstrated by effectively supporting coaches while monitoring the training of several swimmers. Full article

(This article belongs to the Special Issue Ubiquitous Sensing and Intelligent Systems)

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19 pages, 4321 KiB

Open AccessArticle

An Approximate GEMM Unit for Energy-Efficient Object Detection

by Ratko Pilipović, Vladimir Risojević, Janko Božič, Patricio Bulić and Uroš Lotrič

Sensors 2021, 21(12), 4195; https://doi.org/10.3390/s21124195 - 18 Jun 2021

Cited by 5 | Viewed by 3255

Abstract

Edge computing brings artificial intelligence algorithms and graphics processing units closer to data sources, making autonomy and energy-efficient processing vital for their design. Approximate computing has emerged as a popular strategy for energy-efficient circuit design, where the challenge is to achieve the best tradeoff between design efficiency and accuracy. The essential operation in artificial intelligence algorithms is the general matrix multiplication (GEMM) operation comprised of matrix multiplication and accumulation. This paper presents an approximate general matrix multiplication (AGEMM) unit that employs approximate multipliers to perform matrix–matrix operations on four-by-four matrices given in sixteen-bit signed fixed-point format. The synthesis of the proposed AGEMM unit to the 45 nm Nangate Open Cell Library revealed that it consumed only up to 36% of the area and 25% of the energy required by the exact general matrix multiplication unit. The AGEMM unit is ideally suited to convolutional neural networks, which can adapt to the error induced in the computation. We evaluated the AGEMM units’ usability for honeybee detection with the YOLOv4-tiny convolutional neural network. The results implied that we can deploy the AGEMM units in convolutional neural networks without noticeable performance degradation. Moreover, the AGEMM unit’s employment can lead to more area- and energy-efficient convolutional neural network processing, which in turn could prolong sensors’ and edge nodes’ autonomy. Full article

(This article belongs to the Special Issue Ubiquitous Sensing and Intelligent Systems)

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30 pages, 12418 KiB

Open AccessArticle

Exploring a Novel Multiple-Query Resistive Grid-Based Planning Method Applied to High-DOF Robotic Manipulators

by Jesus Huerta-Chua, Gerardo Diaz-Arango, Hector Vazquez-Leal, Javier Flores-Mendez, Mario Moreno-Moreno, Roberto C. Ambrosio-Lazaro and Carlos Hernandez-Mejia

Sensors 2021, 21(9), 3274; https://doi.org/10.3390/s21093274 - 10 May 2021

Cited by 2 | Viewed by 2193

Abstract

The applicability of the path planning strategy to robotic manipulators has been an exciting topic for researchers in the last few decades due to the large demand in the industrial sector and its enormous potential development for space, surgical, and pharmaceutical applications. The automation of high-degree-of-freedom (DOF) manipulator robots is a challenging task due to the high redundancy in the end-effector position. Additionally, in the presence of obstacles in the workspace, the task becomes even more complicated. Therefore, for decades, the most common method of integrating a manipulator in an industrial automated process has been the demonstration technique through human operator intervention. Although it is a simple strategy, some drawbacks must be considered: first, the path’s success, length, and execution time depend on operator experience; second, for a structured environment with few objects, the planning task is easy. However, for most typical industrial applications, the environments contain many obstacles, which poses challenges for planning a collision-free trajectory. In this paper, a multiple-query method capable of obtaining collision-free paths for high DOF manipulators with multiple surrounding obstacles is presented. The proposed method is inspired by the resistive grid-based planner method (RGBPM). Furthermore, several improvements are implemented to solve complex planning problems that cannot be handled by the original formulation. The most important features of the proposed planner are as follows: (1) the easy implementation of robotic manipulators with multiple degrees of freedom, (2) the ability to handle dozens of obstacles in the environment, (3) compatibility with various obstacle representations using mathematical models, (4) a new recycling of a previous simulation strategy to convert the RGBPM into a multiple-query planner, and (5) the capacity to handle large sparse matrices representing the configuration space. A numerical simulation was carried out to validate the proposed planning method’s effectiveness for manipulators with three, five, and six DOFs on environments with dozens of surrounding obstacles. The case study results show the applicability of the proposed novel strategy in quickly computing new collision-free paths using the first execution data. Each new query requires less than 0.2 s for a 3 DOF manipulator in a configuration space free-modeled by a 7291 × 7291 sparse matrix and less than 30 s for five and six DOF manipulators in a configuration space free-modeled by 313,958 × 313,958 and 204,087 × 204,087 sparse matrices, respectively. Finally, a simulation was conducted to validate the proposed multiple-query RGBPM planner’s efficacy in finding feasible paths without collision using a six-DOF manipulator (KUKA LBR iiwa 14R820) in a complex environment with dozens of surrounding obstacles. Full article

(This article belongs to the Special Issue Ubiquitous Sensing and Intelligent Systems)

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23 pages, 3603 KiB

Open AccessArticle

Developing a Modern Greenhouse Scientific Research Facility—A Case Study

by Davor Cafuta, Ivica Dodig, Ivan Cesar and Tin Kramberger

Sensors 2021, 21(8), 2575; https://doi.org/10.3390/s21082575 - 7 Apr 2021

Cited by 8 | Viewed by 4467

Abstract

Multidisciplinary approaches in science are still rare, especially in completely different fields such as agronomy science and computer science. We aim to create a state-of-the-art floating ebb and flow system greenhouse that can be used in future scientific experiments. The objective is to create a self-sufficient greenhouse with sensors, cloud connectivity, and artificial intelligence for real-time data processing and decision making. We investigated various approaches and proposed an optimal solution that can be used in much future research on plant growth in floating ebb and flow systems. A novel microclimate pocket-detection solution is proposed using an automatically guided suspended platform sensor system. Furthermore, we propose a methodology for replacing sensor data knowledge with artificial intelligence for plant health estimation. Plant health estimation allows longer ebb periods and increases the nutrient level in the final product. With intelligent design and the use of artificial intelligence algorithms, we will reduce the cost of plant research and increase the usability and reliability of research data. Thus, our newly developed greenhouse would be more suitable for plant growth research and production. Full article

(This article belongs to the Special Issue Ubiquitous Sensing and Intelligent Systems)

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