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Special Issue "Wireless Sensors Networks in Activity Detection and Context Awareness"

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

Deadline for manuscript submissions: 30 September 2018

Special Issue Editors

Guest Editor
Prof. Dr. Juan Manuel Corchado Rodríguez

BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain
Website | E-Mail
Interests: multi-agent systems; artificial intelligence; internet of things; smart energy systems; intelligent distributed systems; information security
Guest Editor
Dr. Javier Prieto Tejedor

Bisite Research Group, University of Salamanca, Postal Address Edificio I+D+i, C/ Patio de Escuelas 1, 37008, Salamanca, Spain
Website | E-Mail
Interests: artificial intelligence; distributed computing; ambient intelligence; IoT

Special Issue Information

Dear Colleagues,

Nowadays, with the boom of Internet-of-Things (IoT) solutions, context-aware systems have become more commonly implemented in our surroundings, which is due to their reduced cost and ease of use and integration. Furthermore, wireless sensor networks (WSNs) are widely used to collect environmental parameters in homes, buildings, vehicles, etc., where they are a source of information that supports the decision-making process and, in particular, aids activity monitoring and learning. However, the rapid deployment of WSNs requires new solutions in both, machine learning algorithms that identify contexts and activities, and distributed computing architectures that enable the ingestion and processing of vast amounts of new data. Regarding the machine learning solutions, new clustering and classification techniques, reinforcement learning methods, or data quality approaches are required. Related to the distributed computing architectures, new fod/edge computing models, energy harvesting methodologies, or device-to-device communication paradigms are needed.

This Special Issue expects innovative work to explore new frontiers and challenges in the field of WSNs in activity monitoring and context awareness research, including the mentioned new machine learning models, distributed computing architectures, as well as new sensor deployments and use-cases of application of activity monitoring and context awareness in smart environments.

The particular topics of interest include, but are not limited to:

  • Sensor deployments for context awareness.
  • Use-cases of context awareness and activity monitoring.
  • Clustering and classification algorithms for activity monitoring.
  • Deep and reinforcement learning in activity monitoring.
  • New audio processing algorithms for context recognition.
  • New image processing algorithms for context recognition.
  • Big Data analytics for context awareness and activity monitoring.
  • Data quality and false data detection in WSN.
  • Fod/edge computing for WSNs for context awareness.
  • Energy harvesting in WSNs for context awareness.
  • New device-to-device paradigms for WSNs in context awareness.
  • Security in WSNs for context awareness.
  • Data privacy in activity monitoring.
  • Blockchain and distributed ledger solutions for data veracity and privacy in WSNs.
  • Multi Agent Systems
  • Organization Based Multiagent Systems
  • Virtual Organizations
  • Industry 4.0
  • NFV and SDN for WSNs.
Prof. Dr. Juan Manuel Corchado Rodríguez
Dr. Javier Prieto Tejedor
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 monthly 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 1800 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

  • context awareness
  • IoT
  • activity monitoring
  • Fod/Edge computing
  • energy harvesting
  • WSN

Published Papers (4 papers)

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Research

Open AccessArticle Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments
Sensors 2018, 18(4), 1288; https://doi.org/10.3390/s18041288
Received: 19 March 2018 / Revised: 17 April 2018 / Accepted: 19 April 2018 / Published: 23 April 2018
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Abstract
Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a
[...] Read more.
Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures. Full article
(This article belongs to the Special Issue Wireless Sensors Networks in Activity Detection and Context Awareness)
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Open AccessArticle IoT On-Board System for Driving Style Assessment
Sensors 2018, 18(4), 1233; https://doi.org/10.3390/s18041233
Received: 23 February 2018 / Revised: 7 April 2018 / Accepted: 12 April 2018 / Published: 17 April 2018
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Abstract
The assessment of skills is essential and desirable in areas such as medicine, security, and other professions where mental, physical, and manual skills are crucial. However, often such assessments are performed by people called “experts” who may be subjective and are able to
[...] Read more.
The assessment of skills is essential and desirable in areas such as medicine, security, and other professions where mental, physical, and manual skills are crucial. However, often such assessments are performed by people called “experts” who may be subjective and are able to consider a limited number of factors and indicators. This article addresses the problem of the objective assessment of driving style independent of circumstances. The proposed objective assessment of driving style is based on eight indicators, which are associated with the vehicle’s speed, acceleration, jerk, engine rotational speed and driving time. These indicators are used to estimate three driving style criteria: safety, economy, and comfort. The presented solution is based on the embedded system designed according to the Internet of Things concept. The useful data are acquired from the car diagnostic port—OBD-II—and from an additional accelerometer sensor and GPS module. The proposed driving skills assessment method has been implemented and experimentally validated on a group of drivers. The obtained results prove the system’s ability to quantitatively distinguish different driving styles. The system was verified on long-route tests for analysis and could then improve the driver’s behavior behind the wheel. Moreover, the spider diagram approach that was used established a convenient visualization platform for multidimensional comparison of the result and comprehensive assessment in an intelligible manner. Full article
(This article belongs to the Special Issue Wireless Sensors Networks in Activity Detection and Context Awareness)
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Open AccessFeature PaperArticle Energy Optimization Using a Case-Based Reasoning Strategy
Sensors 2018, 18(3), 865; https://doi.org/10.3390/s18030865
Received: 16 January 2018 / Revised: 9 March 2018 / Accepted: 12 March 2018 / Published: 15 March 2018
Cited by 3 | PDF Full-text (7761 KB) | HTML Full-text | XML Full-text
Abstract
At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way
[...] Read more.
At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices. Full article
(This article belongs to the Special Issue Wireless Sensors Networks in Activity Detection and Context Awareness)
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Open AccessArticle GreenVMAS: Virtual Organization Based Platform for Heating Greenhouses Using Waste Energy from Power Plants
Sensors 2018, 18(3), 861; https://doi.org/10.3390/s18030861
Received: 22 January 2018 / Revised: 6 March 2018 / Accepted: 13 March 2018 / Published: 14 March 2018
Cited by 2 | PDF Full-text (4149 KB) | HTML Full-text | XML Full-text
Abstract
The gradual depletion of energy resources makes it necessary to optimize their use and to reuse them. Although great advances have already been made in optimizing energy generation processes, many of these processes generate energy that inevitably gets wasted. A clear example of
[...] Read more.
The gradual depletion of energy resources makes it necessary to optimize their use and to reuse them. Although great advances have already been made in optimizing energy generation processes, many of these processes generate energy that inevitably gets wasted. A clear example of this are nuclear, thermal and carbon power plants, which lose a large amount of energy that could otherwise be used for different purposes, such as heating greenhouses. The role of GreenVMAS is to maintain the required temperature level in greenhouses by using the waste energy generated by power plants. It incorporates a case-based reasoning system, virtual organizations and algorithms for data analysis and for efficient interaction with sensors and actuators. The system is context aware and scalable as it incorporates an artificial neural network, this means that it can operate correctly even if the number and characteristics of the greenhouses participating in the case study change. The architecture was evaluated empirically and the results show that the user’s energy bill is greatly reduced with the implemented system. Full article
(This article belongs to the Special Issue Wireless Sensors Networks in Activity Detection and Context Awareness)
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