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Sensor Systems in Smart Environments

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

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 27795

Special Issue Editors

National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Via Pietro Bucci, 8-9C, 87036 Rende (CS), Italy
Interests: cognitive IoT; urban computing; swarm intelligence; edge computing; reinforcement learning; cognitive buildings
Special Issues, Collections and Topics in MDPI journals
Boise State University, 1910 University Drive, Boise, ID 83725, USA
Interests: artificial intelligence; data science; precision agriculture; cyber security; national security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A “smart” environments incorporates intelligent systems (e.g., smart home, smart factory, smart city, smart building, smart parking, smart agriculture, intelligent traffic systems, smart car etc.) employing pervasive computing, machine learning, artificial intelligence, cognitive sensor systems, wireless and sensor networking to realize cyber and physical components for sensing, reasoning and controlling the environment. Within a smart environment, autonomous agents can take on an important mediating role between human users and the environment. This is particularly true if high-level cognitive functions and computational intelligence are employed to handle the uncertainties of the complex environment so as to allow agents to act appropriately within different contexts of interaction. The development of a smart environment introduces several challenges, which include system integration, system maintenance, geographical and functional extensibility, social networking, mobile computing, context-aware applications and services, human-in-the-loop modeling and simulation, big data analysis, cloud and edge-based IoT frameworks and environment, field experiments and testbeds. The main goal of this Special Issue is to present and discuss recent advances in the area of sensor systems, in particular in regard to technologies, architectures, algorithms and protocols for smart environments with emphasis on real smart environment applications. Suitable topics include, but are not limited, to the following:

  • Security and privacy for smart IoT and CPS
  • Wireless sensor networks (WSN) in smart cities
  • Smart cities and Internet of Everything
  • Smart city sewage, water and electricity management
  • Smart city healthcare service monitoring
  • Smart city emergency altering, management and infrastructure
  • Smart city crime watching and alerting systems
  • Smart city education, training and social services
  • Smart transportation system planning, evaluation, and technologies
  • Smart home, smart building and social community networks/infrastructures
  • Precision agriculture
  • Agent-based modeling
  • Big data analytics and machine learning
  • Cloud computing for Internet of Things
  • Aggregate computing
  • Self-organization swarm intelligence
  • Cognitive edge computing
  • Evolutionary computation
  • Expert and knowledge-based systems
  • Heuristic algorithms
  • Hybrid models of NN
  • Industry 4.0 & the connected factory
  • Intelligent internet systems
  • Machine learning
  • Data science
  • Next generation networks and CPS
  • Neural networks and applications
  • Optimization - quantum machine learning

Dr. Giandomenico Spezzano
Dr. Edoardo Serra
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

  • Intelligent sensor systems
  • Cognitive sensor systems
  • Cyber-physical systems
  • Machine learning
  • Computational intelligence
  • Data science

Published Papers (5 papers)

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Research

28 pages, 5188 KiB  
Article
Towards Customer-Centric Additive Manufacturing: Making Human-Centered 3D Design Tools through a Handheld-Based Multi-Touch User Interface
by Ivan Rodriguez-Conde and Celso Campos
Sensors 2020, 20(15), 4255; https://0-doi-org.brum.beds.ac.uk/10.3390/s20154255 - 30 Jul 2020
Cited by 6 | Viewed by 3112
Abstract
Seeking a more flexible and efficient production, additive manufacturing (AM) has emerged as a major player in the industrial field, streamlining the fabrication of custom tangible assets by directly 3D printing them. However, production still takes too long due to printing, but also [...] Read more.
Seeking a more flexible and efficient production, additive manufacturing (AM) has emerged as a major player in the industrial field, streamlining the fabrication of custom tangible assets by directly 3D printing them. However, production still takes too long due to printing, but also due to the product design stage, in which the customer works together with an expert to create a 3D model of the targeted product by means of computer-aided design (CAD) software. Skipping intermediate agents and making customers responsible for the design process will reduce waiting times and speed up the manufacturing process. This work is conceived as a first step towards that optimized AM model, being aimed at bringing CAD tools closer to clients through an enhanced user experience, and consequently at simplifying pre-manufacturing design tasks. Specifically, as an alternative to the traditional user interface operated with the keyboard and mouse duo, standard in CAD and AM, the paper presents a comprehensive multi-touch interaction system conceived as a customer-centric human-machine interface. To depict the proposed solutions, we adopt furniture manufacturing as a case study and, supported by a CAD-like software prototype for 3D modeling of custom cabinets introduced in a previous work of the authors, we assess our approach’s validity in terms of usability by conducting in-lab and remote user studies. The comparison between the designed multi-touch interaction and its desktop alternative yields promising results, showing improved performance and higher satisfaction of the end-user for the touch-based approach, that lay the groundwork for a smarter factory vision based on remotely-operated AM. Full article
(This article belongs to the Special Issue Sensor Systems in Smart Environments)
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27 pages, 824 KiB  
Article
Resource Usage and Performance Trade-offs for Machine Learning Models in Smart Environments
by Davy Preuveneers, Ilias Tsingenopoulos and Wouter Joosen
Sensors 2020, 20(4), 1176; https://0-doi-org.brum.beds.ac.uk/10.3390/s20041176 - 20 Feb 2020
Cited by 24 | Viewed by 3677
Abstract
The application of artificial intelligence enhances the ability of sensor and networking technologies to realize smart systems that sense, monitor and automatically control our everyday environments. Intelligent systems and applications often automate decisions based on the outcome of certain machine learning models. They [...] Read more.
The application of artificial intelligence enhances the ability of sensor and networking technologies to realize smart systems that sense, monitor and automatically control our everyday environments. Intelligent systems and applications often automate decisions based on the outcome of certain machine learning models. They collaborate at an ever increasing scale, ranging from smart homes and smart factories to smart cities. The best performing machine learning model, its architecture and parameters for a given task are ideally automatically determined through a hyperparameter tuning process. At the same time, edge computing is an emerging distributed computing paradigm that aims to bring computation and data storage closer to the location where they are needed to save network bandwidth or reduce the latency of requests. The challenge we address in this work is that hyperparameter tuning does not take into consideration resource trade-offs when selecting the best model for deployment in smart environments. The most accurate model might be prohibitively expensive to computationally evaluate on a resource constrained node at the edge of the network. We propose a multi-objective optimization solution to find acceptable trade-offs between model accuracy and resource consumption to enable the deployment of machine learning models in resource constrained smart environments. We demonstrate the feasibility of our approach by means of an anomaly detection use case. Additionally, we evaluate the extent that transfer learning techniques can be applied to reduce the amount of training required by reusing previous models, parameters and trade-off points from similar settings. Full article
(This article belongs to the Special Issue Sensor Systems in Smart Environments)
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16 pages, 6867 KiB  
Article
A Self-Powered Wireless Water Quality Sensing Network Enabling Smart Monitoring of Biological and Chemical Stability in Supply Systems
by Marco Carminati, Andrea Turolla, Lorenzo Mezzera, Michele Di Mauro, Marco Tizzoni, Gaia Pani, Francesco Zanetto, Jacopo Foschi and Manuela Antonelli
Sensors 2020, 20(4), 1125; https://0-doi-org.brum.beds.ac.uk/10.3390/s20041125 - 19 Feb 2020
Cited by 46 | Viewed by 8094
Abstract
A smart, safe, and efficient management of water is fundamental for both developed and developing countries. Several wireless sensor networks have been proposed for real-time monitoring of drinking water quantity and quality, both in the environment and in pipelines. However, surface fouling significantly [...] Read more.
A smart, safe, and efficient management of water is fundamental for both developed and developing countries. Several wireless sensor networks have been proposed for real-time monitoring of drinking water quantity and quality, both in the environment and in pipelines. However, surface fouling significantly affects the long-term reliability of pipes and sensors installed in-line. To address this relevant issue, we presented a multi-parameter sensing node embedding a miniaturized slime monitor able to estimate the micrometric thickness and type of slime. The measurement of thin deposits in pipes is descriptive of water biological and chemical stability and enables early warning functions, predictive maintenance, and more efficient management processes. After the description of the sensing node, the related electronics, and the data processing strategies, we presented the results of a two-month validation in the field of a three-node pilot network. Furthermore, self-powering by means of direct energy harvesting from the water flowing through the sensing node was also demonstrated. The robustness and low cost of this solution enable its upscaling to larger monitoring networks, paving the way to water monitoring with unprecedented spatio-temporal resolution. Full article
(This article belongs to the Special Issue Sensor Systems in Smart Environments)
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17 pages, 981 KiB  
Article
A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction
by Faraz Malik Awan, Yasir Saleem, Roberto Minerva and Noel Crespi
Sensors 2020, 20(1), 322; https://0-doi-org.brum.beds.ac.uk/10.3390/s20010322 - 06 Jan 2020
Cited by 74 | Viewed by 8700
Abstract
Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on [...] Read more.
Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander’s parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots. Full article
(This article belongs to the Special Issue Sensor Systems in Smart Environments)
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17 pages, 4691 KiB  
Article
E-Cabin: A Software Architecture for Passenger Comfort and Cruise Ship Management
by Paolo Barsocchi, Erina Ferro, Davide La Rosa, Atieh Mahroo and Daniele Spoladore
Sensors 2019, 19(22), 4978; https://0-doi-org.brum.beds.ac.uk/10.3390/s19224978 - 15 Nov 2019
Cited by 8 | Viewed by 3526
Abstract
A cruise ship is a concentrate of technologies aimed at providing passengers with the best leisure experience. As tourism in the cruise sector increases, ship owners turned their attention towards novel Internet of things solutions able, from one hand, to provide passengers with [...] Read more.
A cruise ship is a concentrate of technologies aimed at providing passengers with the best leisure experience. As tourism in the cruise sector increases, ship owners turned their attention towards novel Internet of things solutions able, from one hand, to provide passengers with personalized and comfortable new services and, from the other hand, to enable energy saving behaviors and a smart management of the vessel equipment. This paper introduces the E-Cabin system, a software architecture that leverages sensor networks and reasoning techniques and allows a customized cabin indoor comfort. The E-Cabin architecture is scalable and easily extendible; sensor networks can be added or removed, rules can be added to/changed in the reasoner software, and new services can be supported based on the analysis of the collected data, without altering the system architecture. The system also allows the ship manager to monitor each cabin status though a simple and intuitive dashboard, thus providing useful insights enabling a smart scheduling of maintenance activities, energy saving, and security issues detection. This work delves into the E-Cabin’s system architecture and provides some usability tests to measure the dashboard’s efficacy. Full article
(This article belongs to the Special Issue Sensor Systems in Smart Environments)
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