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Special Issue "Emerging Sensors Techniques and Technologies for Intelligent Environments"

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

Deadline for manuscript submissions: 15 June 2022.

Special Issue Editors

Dr. Ionut Anghel
E-Mail Website
Guest Editor
Senior researcher at Distributed Systems Research Laboratory; Associate professor at Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400027, 26-28 Baritiu Street, Cluj-Napoca, Romania
Interests: green IT; smart homes; context aware adaptive systems; blockchain; decentralized distributed systems
Special Issues, Collections and Topics in MDPI journals
Dr. Tudor Cioara
E-Mail Website
Guest Editor
Senior researcher at Distributed Systems Research Laboratory; Professor at Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400027, 26-28 Baritiu street, Cluj-Napoca, Romania
Interests: blockchain; smart environments; complex distributed systems; machine learning; energy efficient systems
Special Issues, Collections and Topics in MDPI journals
Dr. Marcel Antal
E-Mail Website
Guest Editor
Researcher at Distributed Systems Research Laboratory; Senior lecturer at Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400027, 26-28 Baritiu Street, Cluj-Napoca, Romania
Interests: IoT; blockchain; big data analytics; multidisciplinary optimization; complex systems modelling; smart energy grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The trending techniques, models, algorithms, and technologies for managing indoor and outdoor intelligent environments rely heavily on data acquisition through a diversity of heterogeneous Internet of Things (IoT) devices and sensors. They process and analyze large and heterogeneous streams of data with the general objective of extracting knowledge and making reinforced decisions for adapting to complex events from such environments.
In this context, innovative services supported by sensor-based monitoring can be developed, enabling the intelligent management of smart cities, smart grids, ambient assisted living, factories of the future, logistics chains, etc. These services can also benefit from the emerging promising technologies that can provide the required infrastructure for adding the envisioned intelligence: cloud-based models, deep learning, fog, and edge computing, blockchain, and digital twins.
This Special Issue offers opportunities for publishing innovative solutions, techniques, and technologies for the development and management of intelligent environments. Potential interesting topics for this Special Issue include, but are not limited to, the following:

  • IoT-based indoor or outdoor smart environments;
  • Blockchain-enabled solutions for smart cities and smart grids;
  • Smart grid monitoring, management, and optimization;
  • Fog and edge computing in the IoT;
  • Technologies for age-friendly cities;
  • Ambient assisted living systems;
  • Solutions for industrial IoT and factories of the future;
  • Big data and machine learning techniques;
  • Intelligent transport systems and logistic chains;
  • Intelligent healthcare systems;
  • Local energy communities and demand response.

Dr. Ionut Anghel
Dr. Tudor Cioara
Dr. Marcel Antal
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 papers will be 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 2200 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.

Published Papers (9 papers)

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Research

Article
Security Management Suitable for Lifecycle of Personal Information in Multi-User IoT Environment
Sensors 2021, 21(22), 7592; https://doi.org/10.3390/s21227592 - 16 Nov 2021
Viewed by 302
Abstract
In recent years, as all actions of Internet users become information, the importance of personal information is emphasized, but in reality, the management of personal information is still insufficient. With the advent of the concept of sharing systems such as the sharing economy, [...] Read more.
In recent years, as all actions of Internet users become information, the importance of personal information is emphasized, but in reality, the management of personal information is still insufficient. With the advent of the concept of sharing systems such as the sharing economy, the numbers of IoT application services (for example, a healthcare service using sharing IoT devices, or a vehicle sharing system with IoT devices) using users’ personal information are increasing, but the risk of using personal information is not managed. To solve this issue, the European GDPR stipulates the content of personal information protection. In this paper, we present a method to securely manage personal information in IoT devices in IoT application environments in accordance with the GDPR. We first describe the lifecycle stages of personal information occurring in IoT application services and propose a method to securely manage personal information at each stage of the lifecycle according to the flow of personal information in IoT devices. We also evaluated the usefulness and applicability of the proposed scheme through two service scenarios. Since the proposed method satisfies the requirements for personal information management in IoT application environments, it is expected to contribute to the development of the IoT business field that handles personal information. Full article
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Article
Pollution and Weather Reports: Using Machine Learning for Combating Pollution in Big Cities
Sensors 2021, 21(21), 7329; https://doi.org/10.3390/s21217329 - 03 Nov 2021
Viewed by 355
Abstract
Air pollution has become the most important issue concerning human evolution in the last century, as the levels of toxic gases and particles present in the air create health problems and affect the ecosystems of the planet. Scientists and environmental organizations have been [...] Read more.
Air pollution has become the most important issue concerning human evolution in the last century, as the levels of toxic gases and particles present in the air create health problems and affect the ecosystems of the planet. Scientists and environmental organizations have been looking for new ways to combat and control the air pollution, developing new solutions as technologies evolves. In the last decade, devices able to observe and maintain pollution levels have become more accessible and less expensive, and with the appearance of the Internet of Things (IoT), new approaches for combating pollution were born. The focus of the research presented in this paper was predicting behaviours regarding the air quality index using machine learning. Data were collected from one of the six atmospheric stations set in relevant areas of Bucharest, Romania, to validate our model. Several algorithms were proposed to study the evolution of temperature depending on the level of pollution and on several pollution factors. In the end, the results generated by the algorithms are presented considering the types of pollutants for two distinct periods. Prediction errors were highlighted by the RMSE (Root Mean Square Error) for each of the three machine learning algorithms used. Full article
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Article
Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings
Sensors 2021, 21(9), 2946; https://doi.org/10.3390/s21092946 - 22 Apr 2021
Viewed by 754
Abstract
The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. [...] Read more.
The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was created to differentiate between natural energy peaks and outliers, so as to first apply a data cleaning. Then, a calculation of the impact in the energy baseline for the demand response computation was implemented, with improved precision, as related to other referenced methods and to the original data processing. For the demo pilot project implemented in the Technical University of Cluj-Napoca block of buildings, without the energy baseline data cleaning, in some cases it was impossible to compute the established key performance indicators (peak power reduction, energy savings, cost savings, CO2 emissions reduction) or the resulted values were far much higher (>50%) and not realistic. Therefore, in real case business models, it is crucial to use outlier’s removal. In the past years, both companies and academic communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability, and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection. Full article
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Article
Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers
Sensors 2021, 21(8), 2879; https://doi.org/10.3390/s21082879 - 20 Apr 2021
Viewed by 656
Abstract
Data centers consume lots of energy to execute their computational workload and generate heat that is mostly wasted. In this paper, we address this problem by considering heat reuse in the case of a distributed data center that features IT equipment (i.e., servers) [...] Read more.
Data centers consume lots of energy to execute their computational workload and generate heat that is mostly wasted. In this paper, we address this problem by considering heat reuse in the case of a distributed data center that features IT equipment (i.e., servers) installed in residential homes to be used as a primary source of heat. We propose a workload scheduling solution for distributed data centers based on a constraint satisfaction model to optimally allocate workload on servers to reach and maintain the desired home temperature setpoint by reusing residual heat. We have defined two models to correlate the heat demand with the amount of workload to be executed by the servers: a mathematical model derived from thermodynamic laws calibrated with monitored data and a machine learning model able to predict the amount of workload to be executed by a server to reach a desired ambient temperature setpoint. The proposed solution was validated using the monitored data of an operational distributed data center. The server heat and power demand mathematical model achieve a correlation accuracy of 11.98% while in the case of machine learning models, the best correlation accuracy of 4.74% is obtained for a Gradient Boosting Regressor algorithm. Also, our solution manages to distribute the workload so that the temperature setpoint is met in a reasonable time, while the server power demand is accurately following the heat demand. Full article
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Article
Design, Implementation, and Configuration of Laser Systems for Vehicle Detection and Classification in Real Time
Sensors 2021, 21(6), 2082; https://doi.org/10.3390/s21062082 - 16 Mar 2021
Viewed by 584
Abstract
The use of real-time vehicle detection and classification systems is essential for the accurate management of traffic and road infrastructure. Over time, diverse systems have been proposed for it, such as the widely known magnetic loops or microwave radars. However, these types of [...] Read more.
The use of real-time vehicle detection and classification systems is essential for the accurate management of traffic and road infrastructure. Over time, diverse systems have been proposed for it, such as the widely known magnetic loops or microwave radars. However, these types of sensors do not offer all the information currently required for exhaustive and comprehensive traffic control. Thus, this paper presents the design, implementation, and configuration of laser systems to obtain 3D profiles of vehicles, which collect more precise information about the state of the roads. Nevertheless, to obtain reliable information on vehicle traffic by means of these systems, it is fundamental to correctly carry out a series of preliminary steps: choose the most suitable type of laser, select its configuration properly, determine the optimal location, and process the information provided accurately. Therefore, this paper details a series of criteria to help make these crucial and difficult decisions. Furthermore, following these guidelines, a complete laser system implemented for vehicle detection and classification is presented as result, which is characterized by its versatility and the ability to control up to four lanes in real time. Full article
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Communication
Large-Scale Water Quality Prediction Using Federated Sensing and Learning: A Case Study with Real-World Sensing Big-Data
Sensors 2021, 21(4), 1462; https://doi.org/10.3390/s21041462 - 20 Feb 2021
Cited by 3 | Viewed by 779
Abstract
Green tide, which is a serious water pollution problem, is caused by the complex relationships of various factors, such as flow rate, several water quality indicators, and weather. Because the existing methods are not suitable for identifying these relationships and making accurate predictions, [...] Read more.
Green tide, which is a serious water pollution problem, is caused by the complex relationships of various factors, such as flow rate, several water quality indicators, and weather. Because the existing methods are not suitable for identifying these relationships and making accurate predictions, a new system and algorithm is required to predict the green tide phenomenon and also minimize the related damage before the green tide occurs. For this purpose, we consider a new network model using smart sensor-based federated learning which is able to use distributed observation data with geologically separated local models. Moreover, we design an optimal scheduler which is beneficial to use real-time big data arrivals to make the overall network system efficient. The proposed scheduling algorithm is effective in terms of (1) data usage and (2) the performance of green tide occurrence prediction models. The advantages of the proposed algorithm is verified via data-intensive experiments with real water quality big-data. Full article
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Article
Intent Detection and Slot Filling with Capsule Net Architectures for a Romanian Home Assistant
Sensors 2021, 21(4), 1230; https://doi.org/10.3390/s21041230 - 09 Feb 2021
Viewed by 797
Abstract
As virtual home assistants are becoming more popular, there is an emerging need for supporting languages other than English. While more wide-spread or popular languages such as Spanish, French or Hindi are already integrated into existing home assistants like Google Home or Alexa, [...] Read more.
As virtual home assistants are becoming more popular, there is an emerging need for supporting languages other than English. While more wide-spread or popular languages such as Spanish, French or Hindi are already integrated into existing home assistants like Google Home or Alexa, integration of other less-known languages such as Romanian is still missing. This paper explores the problem of Natural Language Understanding (NLU) applied to a Romanian home assistant. We propose a customized capsule neural network architecture that performs intent detection and slot filling in a joint manner and we evaluate how well it handles utterances containing various levels of complexity. The capsule network model shows a significant improvement in intent detection when compared to models built using the well-known Rasa NLU tool. Through error analysis, we observe clear error patterns that occur systematically. Variability in language when expressing one intent proves to be the biggest challenge encountered by the model. Full article
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Article
Reliable Link Level Routing Algorithm in Pipeline Monitoring Using Implicit Acknowledgements
Sensors 2021, 21(3), 968; https://doi.org/10.3390/s21030968 - 01 Feb 2021
Viewed by 890
Abstract
End-to-end reliability for Wireless Sensor Network communications is usually provided by upper stack layers. Furthermore, most of the studies have been related to star, mesh, and tree topologies. However, they rarely consider the requirements of the multi-hop linear wireless sensor networks, with thousands [...] Read more.
End-to-end reliability for Wireless Sensor Network communications is usually provided by upper stack layers. Furthermore, most of the studies have been related to star, mesh, and tree topologies. However, they rarely consider the requirements of the multi-hop linear wireless sensor networks, with thousands of nodes, which are universally used for monitoring applications. Therefore, they are characterized by long delays and high energy consumption. In this paper, we propose an energy efficient link level routing algorithm that provides end-to-end reliability into multi-hop wireless sensor networks with a linear structure. The algorithm uses implicit acknowledgement to provide reliability and connectivity with energy efficiency, low latency, and fault tolerance in linear wireless sensor networks. The proposal is validated through tests with real hardware. The energy consumption and the delay are also mathematically modeled and analyzed. The test results show that our algorithm decreases the energy consumption and minimizes the delays when compared with other proposals that also apply the explicit knowledge technique and routing protocols with explicit confirmations, maintaining the same characteristics in terms of reliability and connectivity. Full article
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Article
Blockchain and Demand Response: Zero-Knowledge Proofs for Energy Transactions Privacy
Sensors 2020, 20(19), 5678; https://doi.org/10.3390/s20195678 - 05 Oct 2020
Cited by 4 | Viewed by 1522
Abstract
Nowadays, the adoption of demand response programs is still lagging due to the prosumers’ lack of awareness, fear of losing control and privacy of energy data, etc. Programs decentralization, by adopting promising technologies such as blockchain, may bring significant advantages in terms of [...] Read more.
Nowadays, the adoption of demand response programs is still lagging due to the prosumers’ lack of awareness, fear of losing control and privacy of energy data, etc. Programs decentralization, by adopting promising technologies such as blockchain, may bring significant advantages in terms of transparency, openness, improved control, and increased active participation of prosumers. Nevertheless, even though in general the transparency of the public blockchain is a desirable feature in the energy domain, the prosumer energy data is sensitive and rather private, thus, a privacy-preserving solution is required. In this paper, we present a decentralized implementation of demand response programs on top of the public blockchain which deals with the privacy of the prosumer’s energy data using zero-knowledge proofs and validates on the blockchain the prosumer’s activity inside the program using smart contracts. Prosumer energy data is kept private, while on the blockchain it is stored a zero-knowledge proof that is generated by the prosumer itself allowing the implementation of functions to validate potential deviations from the request and settle prosumer’s activity. The solution evaluation results are promising in terms of ensuring the privacy of prosumer energy data stored in the public blockchain and detecting potential data inconsistencies. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Reconstruction of Incomplete Journeys by Reverse Pairing: Characterizing Multi-modal Mobility in Public Transport Systems
Authors: Juan José Vinagre Díaz
Affiliation: GB2S – Grupo de Biometría, Bioseñales, Seguridad y Smart Mobility, Universidad Politécnica de Madrid
Abstract: Automatic fare collection (AFC) systems have opened a promising research area to characterize the public transport mobility. They collect intensive and detailed smart card data (SCD) to be exploited and provide meaningful information about the operation of the service and the behavior of passengers. However, in most cases SCD are incomplete as they are gathered by entry-only AFC systems. In order to obtain valuable information like origin-destination (OD) matrices, we need to reconstruct complete journeys. Research in this field has adopted the trip chaining model as the basic approach. In this paper we propose a different perspective in order to increase the accuracy of the inferred trips. Our reverse paring method reconstructs incomplete multi-modal journeys by finding spatial similarities between the outbound and return routes of the same user. We tested this method using 4 months of real SCD provided by the Consorcio Regional de Transportes de Madrid, the public transport agency in the Community of Madrid (Spain). From the basis of these reconstructed individual journeys, we developed a software tool that provides novel performance metrics and visualization utilities. On one hand, we generate a visual space-time characterization of the overall operation of transport networks. On the other, we supply enhanced OD matrices that show mobility patterns between zones together with average traversed distances, travel times, and operation speeds, which model the real efficacy of the public transport system.

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