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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: closed (30 September 2018).

Special Issue Editors

Guest Editor
Prof. Dr. Juan Manuel Corchado Rodríguez

IoT European Digital Innovation Hub, Full Professor at the University of Salamanca, Visiting Professor at the Osaka Institute of Technology, Visiting Professor at the Universiti Malaysia Kelantan, President of the IEEE SMC (Spanish Chapter), Director BISITE - Bioinformatics Intelligent Systems and Educational, Technology Research Group, Salamanca, Spain
Website 1 | Website 2 | Website 3 | E-Mail
Interests: artificial Intelligence, machine learning, edge computing, distributed computing, Blockchain, consensus model, smart cities, smart grid
Guest Editor
Dr. Javier Prieto

BISITE research group, University of Salamanca, Edificio Multiusos I+D+i, C/ Espejo s/n, 37007 Salamanca, Spain
Website | E-Mail
Interests: artificial intelligence; adaptive Bayesian inference; Smart Homes

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

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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 (9 papers)

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Research

Open AccessArticle
An Intelligent Smart Plug with Shared Knowledge Capabilities
Sensors 2018, 18(11), 3961; https://doi.org/10.3390/s18113961
Received: 30 September 2018 / Revised: 6 November 2018 / Accepted: 12 November 2018 / Published: 15 November 2018
Cited by 3 | PDF Full-text (1960 KB) | HTML Full-text | XML Full-text
Abstract
The massive dissemination of smart devices in current markets provides innovative technologies that can be used in energy management systems. Particularly, smart plugs enable efficient remote monitoring and control capabilities of electrical resources at a low cost. However, smart plugs, besides their enabling [...] Read more.
The massive dissemination of smart devices in current markets provides innovative technologies that can be used in energy management systems. Particularly, smart plugs enable efficient remote monitoring and control capabilities of electrical resources at a low cost. However, smart plugs, besides their enabling capabilities, are not able to acquire and communicate information regarding the resource’s context. This paper proposes the EnAPlug, a new environmental awareness smart plug with knowledge capabilities concerning the context of where and how users utilize a controllable resource. This paper will focus on the abilities to learn and to share knowledge between different EnAPlugs. The EnAPlug is tested in two different case studies where user habits and consumption profiles are learned. A case study for distributed resource optimization is also shown, where a central heater is optimized according to the shared knowledge of five EnAPlugs. Full article
(This article belongs to the Special Issue Wireless Sensors Networks in Activity Detection and Context Awareness)
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Open AccessArticle
Mitigation of CSI Temporal Phase Rotation with B2B Calibration Method for Fine-Grained Motion Detection Analysis on Commodity Wi-Fi Devices
Sensors 2018, 18(11), 3795; https://doi.org/10.3390/s18113795
Received: 10 October 2018 / Revised: 30 October 2018 / Accepted: 31 October 2018 / Published: 6 November 2018
Cited by 1 | PDF Full-text (2066 KB) | HTML Full-text | XML Full-text
Abstract
Limitations of optical devices for motion sensing such as small coverage, sensitivity to obstacles, and privacy exposure result in the need for improvement. As motion sensing based on radio frequency signals is not constrained by the limitation above, channel state information (CSI) from [...] Read more.
Limitations of optical devices for motion sensing such as small coverage, sensitivity to obstacles, and privacy exposure result in the need for improvement. As motion sensing based on radio frequency signals is not constrained by the limitation above, channel state information (CSI) from Wi-Fi devices could be used to improve sensing performance under the above circumstances. Unfortunately, CSI phase cannot be practically obtained due to the temporal phase rotation generated from Wi-Fi chips. Therefore, it would be rather complicated to realize motion analysis, especially the direction of motion. To mitigate the issue, this paper proposes a CSI calibration method that employs a back-to-back channel between Wi-Fi transceivers for phase rotation removal while preserving the original CSI phase. Through experiment, calibrated CSI showed a high similarity to the channel without phase rotation measured using a Vector Network Analyzer (VNA). Another experiment was conducted to observe Doppler frequency due to simple hand gestures using the Wavelet transform. A visual analysis revealed that the Doppler frequency of calibrated CSI could correctly capture the motion pattern. To the best of the authors’ knowledge, this is the first calibration method that maintains the original CSI and is applicable for in-depth motion analysis. Full article
(This article belongs to the Special Issue Wireless Sensors Networks in Activity Detection and Context Awareness)
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Open AccessArticle
A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building
Sensors 2018, 18(11), 3766; https://doi.org/10.3390/s18113766
Received: 28 September 2018 / Revised: 31 October 2018 / Accepted: 2 November 2018 / Published: 4 November 2018
Cited by 4 | PDF Full-text (2760 KB) | HTML Full-text | XML Full-text
Abstract
Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing [...] Read more.
Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved. Full article
(This article belongs to the Special Issue Wireless Sensors Networks in Activity Detection and Context Awareness)
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Open AccessArticle
Sensoring a Generative System to Create User-Controlled Melodies
Sensors 2018, 18(10), 3201; https://doi.org/10.3390/s18103201
Received: 2 August 2018 / Revised: 11 September 2018 / Accepted: 17 September 2018 / Published: 21 September 2018
PDF Full-text (2204 KB) | HTML Full-text | XML Full-text
Abstract
The automatic generation of music is an emergent field of research that has attracted the attention of countless researchers. As a result, there is a broad spectrum of state of the art research in this field. Many systems have been designed to facilitate [...] Read more.
The automatic generation of music is an emergent field of research that has attracted the attention of countless researchers. As a result, there is a broad spectrum of state of the art research in this field. Many systems have been designed to facilitate collaboration between humans and machines in the generation of valuable music. This research proposes an intelligent system that generates melodies under the supervision of a user, who guides the process through a mechanical device. The mechanical device is able to capture the movements of the user and translate them into a melody. The system is based on a Case-Based Reasoning (CBR) architecture, enabling it to learn from previous compositions and to improve its performance over time. The user uses a device that allows them to adapt the composition to their preferences by adjusting the pace of a melody to a specific context or generating more serious or acute notes. Additionally, the device can automatically resist some of the user’s movements, this way the user learns how they can create a good melody. Several experiments were conducted to analyze the quality of the system and the melodies it generates. According to the users’ validation, the proposed system can generate music that follows a concrete style. Most of them also believed that the partial control of the device was essential for the quality of the generated music. Full article
(This article belongs to the Special Issue Wireless Sensors Networks in Activity Detection and Context Awareness)
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Open AccessArticle
A Clustering WSN Routing Protocol Based on k-d Tree Algorithm
Sensors 2018, 18(9), 2899; https://doi.org/10.3390/s18092899
Received: 17 July 2018 / Revised: 27 August 2018 / Accepted: 29 August 2018 / Published: 1 September 2018
Cited by 2 | PDF Full-text (1799 KB) | HTML Full-text | XML Full-text
Abstract
Clustering in wireless sensor networks has been widely discussed in the literature as a strategy to reduce power consumption. However, aspects such as cluster formation and cluster head (CH) node assignment strategies have a significant impact on quality of service, as energy savings [...] Read more.
Clustering in wireless sensor networks has been widely discussed in the literature as a strategy to reduce power consumption. However, aspects such as cluster formation and cluster head (CH) node assignment strategies have a significant impact on quality of service, as energy savings imply restrictions in application usage and data traffic within the network. Regarding the first aspect, this article proposes a hierarchical routing protocol based on the k-d tree algorithm, taking a partition data structure of the space to organize nodes into clusters. For the second aspect, we propose a reactive mechanism for the formation of CH nodes, with the purpose of improving delay, jitter, and throughput, in contrast with the low-energy adaptive clustering hierarchy/hierarchy-centralized protocol and validating the results through simulation. Full article
(This article belongs to the Special Issue Wireless Sensors Networks in Activity Detection and Context Awareness)
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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
Cited by 7 | PDF Full-text (1902 KB) | HTML Full-text | XML Full-text
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
Cited by 3 | PDF Full-text (43436 KB) | HTML Full-text | XML Full-text
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 39 | 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 28 | 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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