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Smart Objects in the Internet of Things: From Sensor to...
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Smart Objects in the Internet of Things: From Sensor to Data Processing

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Big Data, Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 44796

Special Issue Editors

Dr. Marco Picone

E-Mail Website
Guest Editor
Department of Sciences and Methods for Engineering (DISMI), University of Modena and Reggio Emilia, Via Amendola 2, Pad. Morselli, 42121 Reggio Emilia, Italy
Interests: Internet of Things; edge computing; distributed systems; digital twins; mobile computing; pervasive computing
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Mamei

E-Mail Website
Guest Editor
Dipartimento di Scienze e Metodi dell’Ingegneria, University of Modena and Reggio Emilia, 42122 Reggio Emilia, Italy
Interests: pervasive computing; data analysis; mobility data; geospatial applications; Internet of things
Dr. Màrius Montón

E-Mail Website
Co-Guest Editor
Internet of Things Partners S.L., Barcelona, Spain
Interests: how to apply last research advances to IoT applications in the industry

Special Issue Information

Dear Colleagues,

The information technology ecosystem has been recently and significantly revolutionized by the advent of the Internet of things (IoT), and its quick and pervasive evolution. The IoT will consist of billions of interconnected devices denoted as “smart objects”, able to generate and consume a massive amount of heterogenous data. The massive amount of information being created by the IoT has the power to revolutionize everything from industry to healthcare, allowing them to work more efficiently and profitably. In this challenging context, data collection and data processing are two of the crucial pillars that will allow for extracting technological and business values from IoT applications and real deployments.

Furthermore, the mission of critical application will need real-time actionable intelligence and powerful user interfaces in order to make decisions and/or support decision makers in complex tasks. The seamless and efficient cooperation between data collection and processing, analytics, edge computing, and networking will be a fundamental enabler for the next generation of applications.

This Special Issue focuses on the innovative developments, technologies, and challenges related to Internet of things data generation, collection, modeling, and processing. The Special Issue is seeking the latest findings from research and ongoing projects. Additionally, review articles that provide readers with current research trends and solutions are also welcome. The potential topics include, but are not limited to, the following:

  • Data discovery in IoT applications
  • IoT data analytics
  • Machine learning for IoT
  • Integrating IoT data with external data sources
  • Data generation, collection, and synchronization technologies
  • Data processing for IoT
  • Data science applications and services
  • Data presentation and user interfaces (conversational interfaces, VR/AR)
  • IoT application orchestration and coordination
  • Distributed data management
  • IoT Real-time application
  • Real-time data processing for IoT
  • Edge computing and efficient data processing
  • Heterogeneity management
  • Data modeling and seamless interoperability
  • Application and data integration for IoT applications

Dr. Marco Picone
Dr. Marco Mamei
Dr. Màrius Montón
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. Journal of Sensor and Actuator Networks 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 2000 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

  • Internet of things
  • edge computing
  • fog computing
  • data processing
  • big data
  • machine learning
  • analytics

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Published Papers (6 papers)

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Research

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26 pages, 7479 KiB  
Article
Learning-Based Coordination Model for On-the-Fly Self-Composing Services Using Semantic Matching
by Houssem Ben Mahfoudh, Ashley Caselli and Giovanna Di Marzo Serugendo
J. Sens. Actuator Netw. 2021, 10(1), 5; https://doi.org/10.3390/jsan10010005 - 20 Jan 2021
Cited by 2 | Viewed by 2709
Abstract
Forecasts announce that the number of connected objects will exceed 20 billion by 2025. Objects, such as sensors, drones or autonomous cars participate in pervasive applications of various domains ranging from smart cities, quality of life, transportation, energy, business or entertainment. These inter-connected [...] Read more.
Forecasts announce that the number of connected objects will exceed 20 billion by 2025. Objects, such as sensors, drones or autonomous cars participate in pervasive applications of various domains ranging from smart cities, quality of life, transportation, energy, business or entertainment. These inter-connected devices provide storage, computing and activation capabilities currently under-exploited. To this end, we defined “Spatial services”, a new generation of services seamlessly supporting users in their everyday life by providing information or specific actions. Spatial services leverage IoT, exploit devices capabilities (sensing, acting), the data they locally store at different time and geographic locations, and arise from the spontaneous interactions among those devices. Thanks to a learning-based coordination model, and without any pre-designed composition, reliable and pertinent spatial services dynamically and fully automatically arise from the self-composition of available services provided by connected devices. In this paper, we show how we extended our learning-based coordination model with semantic matching, enhancing syntactic self-composition with semantic reasoning. The implementation of our coordination model results in a learning-based semantic middleware. We validated our approach on various experiments: deployments of the middleware in various settings; instantiation of a specific scenario and various other case studies; experiments with hundreds of synthetic services; and specific experiments for setting up key learning parameters. We also show how the learning-based coordination model using semantic matching favours service composition, by exploiting three ontological constructions (is-a, isComposedOf, and equivalentTo), de facto removing the syntactic barrier preventing pertinent compositions to arise. Spatial services arise from the interactions of various objects, provide complex and highly adaptive services to users in seamless way, and are pertinent in a variety of domains such as smart cities or emergency situations. Full article
(This article belongs to the Special Issue Smart Objects in the Internet of Things: From Sensor to Data Processing)
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24 pages, 10700 KiB  
Article
On the Use of Cameras for the Detection of Critical Events in Sensors-Based Emergency Alerting Systems
by Daniel G. Costa, Francisco Vasques, Paulo Portugal and Ana Aguiar
J. Sens. Actuator Netw. 2020, 9(4), 46; https://doi.org/10.3390/jsan9040046 - 10 Oct 2020
Cited by 9 | Viewed by 3248
Abstract
The adoption of emergency alerting systems can bring countless benefits when managing urban areas, industrial plants, farms, roads and virtually any area that is subject to the occurrence of critical events, supporting in rescue operations and reducing their negative impacts. For such systems, [...] Read more.
The adoption of emergency alerting systems can bring countless benefits when managing urban areas, industrial plants, farms, roads and virtually any area that is subject to the occurrence of critical events, supporting in rescue operations and reducing their negative impacts. For such systems, a promising approach is to exploit scalar sensors to detect events of interest, allowing for the distributed monitoring of different variables. However, the use of cameras as visual sensors can enhance the detection of critical events, which can be employed along with scalar sensors for a more comprehensive perception of the environment. Although the particularities of visual sensing may be challenging in some scenarios, the combination of scalar and visual sensors for the early detection of emergency situations can be valuable for many scenarios, such as smart cities and industry 4.0, bringing promising results. Therefore, in this article, we extend a sensors-based emergency detection and alerting system to also exploit visual monitoring when identifying critical events. Implementation and experimental details are provided to reinforce the use of cameras as a relevant sensor unit, bringing promising results for emergencies management. Full article
(This article belongs to the Special Issue Smart Objects in the Internet of Things: From Sensor to Data Processing)
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17 pages, 11505 KiB  
Article
Open Sensor Manager for IIoT
by Riku Ala-Laurinaho, Juuso Autiosalo and Kari Tammi
J. Sens. Actuator Netw. 2020, 9(2), 30; https://doi.org/10.3390/jsan9020030 - 19 Jun 2020
Cited by 8 | Viewed by 5508
Abstract
Data collection in an industrial environment enables several benefits: processes and machinery can be monitored; the performance can be optimized; and the machinery can be proactively maintained. To collect data from machines or production lines, numerous sensors are required, which necessitates a management [...] Read more.
Data collection in an industrial environment enables several benefits: processes and machinery can be monitored; the performance can be optimized; and the machinery can be proactively maintained. To collect data from machines or production lines, numerous sensors are required, which necessitates a management system. The management of constrained IoT devices such as sensor nodes is extensively studied. However, the previous studies focused only on the remote software updating or configuration of sensor nodes. This paper presents a holistic Open Sensor Manager (OSEMA), which addresses also generating software for different sensor models based on the configuration. In addition, it offers a user-friendly web interface, as well as a REST API (Representational State Transfer Application Programming Interface) for the management. The manager is built with the Django web framework, and sensor nodes rely on ESP32-based microcontrollers. OSEMA enables secure remote software updates of sensor nodes via encryption and hash-based message authentication code. The collected data can be transmitted using the Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport (MQTT). The use of OSEMA is demonstrated in an industrial domain with applications estimating the usage roughness of an overhead crane and tracking its location. OSEMA enables retrofitting different sensors to existing machinery and processes, allowing additional data collection. Full article
(This article belongs to the Special Issue Smart Objects in the Internet of Things: From Sensor to Data Processing)
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20 pages, 4527 KiB  
Article
A Low-Cost Monitoring System and Operating Database for Quality Control in Small Food Processing Industry
by Ombretta Paladino, Francesca Fissore and Matteo Neviani
J. Sens. Actuator Netw. 2019, 8(4), 52; https://doi.org/10.3390/jsan8040052 - 20 Oct 2019
Cited by 11 | Viewed by 7329
Abstract
The use of completely automated systems for collecting sensor data with the aim of monitoring and controlling the quality of small-scale food processes is not widespread. Small and micro-enterprises usually do not carry out their own precompetitive research or prototype development as regards [...] Read more.
The use of completely automated systems for collecting sensor data with the aim of monitoring and controlling the quality of small-scale food processes is not widespread. Small and micro-enterprises usually do not carry out their own precompetitive research or prototype development as regards to automation technologies. This study proposes a web-based, low-cost monitoring and supervisory control and data acquisition (SCADA) system whose kernel is available for free, as a possible solution that could be adopted by these food producers. It is mainly based on open SW/HW so as its configuration is adaptable to the application and type of plant. It presents a modular architecture and its main functionalities encompass the acquisition, management, aggregation and visualization of process data, providing an operating database. It also provides food tracking and process quality control: The time series are browsable due to QR-Code generation and different early warning detection strategies are implemented. A tool for solving migration problems based on Fick’s equation is offered as a packaging decision support system. Full article
(This article belongs to the Special Issue Smart Objects in the Internet of Things: From Sensor to Data Processing)
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Review

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31 pages, 3992 KiB  
Review
Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges
by Martins O. Osifeko, Gerhard P. Hancke and Adnan M. Abu-Mahfouz
J. Sens. Actuator Netw. 2020, 9(2), 21; https://doi.org/10.3390/jsan9020021 - 25 Apr 2020
Cited by 39 | Viewed by 7853
Abstract
Smart, secure and energy-efficient data collection (DC) processes are key to the realization of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges in this domain have motivated research efforts towards providing cognitive solutions for IoT usage. One such solution, [...] Read more.
Smart, secure and energy-efficient data collection (DC) processes are key to the realization of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges in this domain have motivated research efforts towards providing cognitive solutions for IoT usage. One such solution, termed cognitive sensing (CS) describes the use of smart sensors to intelligently perceive inputs from the environment. Further, CS has been proposed for use in FIoT in order to facilitate smart, secure and energy-efficient data collection processes. In this article, we provide a survey of different Artificial Intelligence (AI)-based techniques used over the last decade to provide cognitive sensing solutions for different FIoT applications. We present some state-of-the-art approaches, potentials, and challenges of AI techniques for the identified solutions. This survey contributes to a better understanding of AI techniques deployed for cognitive sensing in FIoT as well as future research directions in this regard. Full article
(This article belongs to the Special Issue Smart Objects in the Internet of Things: From Sensor to Data Processing)
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29 pages, 1660 KiB  
Review
IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities
by João B. A. Gomes, Joel J. P. C. Rodrigues, Ricardo A. L. Rabêlo, Neeraj Kumar and Sergey Kozlov
J. Sens. Actuator Netw. 2019, 8(4), 57; https://doi.org/10.3390/jsan8040057 - 13 Dec 2019
Cited by 79 | Viewed by 16957
Abstract
Ambient gas detection and measurement had become essential in diverse fields and applications, from preventing accidents, avoiding equipment malfunction, to air pollution warnings and granting the correct gas mixture to patients in hospitals. Gas leakage can reach large proportions, affecting entire neighborhoods or [...] Read more.
Ambient gas detection and measurement had become essential in diverse fields and applications, from preventing accidents, avoiding equipment malfunction, to air pollution warnings and granting the correct gas mixture to patients in hospitals. Gas leakage can reach large proportions, affecting entire neighborhoods or even cities, causing enormous environmental impacts. This paper elaborates on a deep review of the state of the art on gas-sensing technologies, analyzing the opportunities and main characteristics of the transducers, as well as towards their integration through the Internet of Things (IoT) paradigm. This should ease the information collecting and sharing processes, granting better experiences to users, and avoiding major losses and expenses. The most promising wireless-based solutions for ambient gas monitoring are analyzed and discussed, open research topics are identified, and lessons learned are shared to conclude the study. Full article
(This article belongs to the Special Issue Smart Objects in the Internet of Things: From Sensor to Data Processing)
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