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

Deadline for manuscript submissions: closed (9 July 2018) | Viewed by 93360

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Special Issue Editors

Prof. Dr. Paulo Novais

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Algoritmi Center, Campus of Gualtar, 4710-057 Braga, Portugal
Interests: ambient intelligence; ambient assisted living; conflict resolution; behavioral analysis; intelligent tutors
Special Issues, Collections and Topics in MDPI journals

Dr. Tiago Oliveira

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Guest Editor

National Institute of Informatics, Tokyo 101-8430, Japan
Interests: knowledge representation and ontologies; logic programming; data mining and machine learning; bayesian networks; argumentation frameworks; decision support systems
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Dr. Luke Wallace

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School of Science, Geospatial Science, RMIT University, Melbourne, VIC 3000, Australia
Interests: 3D remote sensing; remote sensing of forested environments; laser scanning; vegetation structure; wildfire
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in pervasive sensing technology have led to the development of sensor networks that can be seamlessly integrated into the living environment without interfering with daily life. The domains of actuation of these sensor networks are diverse, including robotics, health care, wellness applications, environmental management, disaster prevention, energy management, process control, and so forth. The collection of substantial amounts of data in these domains, from heterogeneous sources, has the potential to change them and improve the outcomes of the activities developed within them. However, the extraction of useful knowledge from these data, which in many cases can be considered big data, faces important challenges such as dealing with corrupted sensor readings, data reduction, the need for expensive human intervention, among others. The challenge in transforming the valuable insights provided by the data analytics process into decision models capable of refining the decision-making process also present difficulties. Additionally, the way in which an agent actuates in the environment, based on the outcomes provided by the decision models, must also respect the nuances and constraints imposed by the domain.

In this context, this Special Issue on Smart Decision-making invites authors to submit their works on data collection and analytics strongly tied to the development of decision models that enhance the intervention of agents in the domain. Works presenting mechanisms for belief-revision and adaption based on data are also welcome and within the scope of the special issue. Topics of interest include, but are not limited to:

Multi-sensor data fusion;
Data-analytics;
Self-adapting systems;
Belief-revision;
Decision making frameworks;
Problem solving with incomplete information;
Default reasoning;
Argumentation;
Semantic models and ontologies for dynamic environments;
Models of trust and reputation;
Reasoning under uncertainty;
Fault tolerant architectures;
Applications (reasoning techniques in dynamic environments such as a medical setting, energy management, process control, etc);

Dr. Paulo Novais
Dr. Tiago Oliveira
Dr. Luke Wallace
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

Data Fusion
Data Analytics
Decision-making
Uncertainty
Dynamic Environments

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Published Papers (14 papers)

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Research

15 pages, 4053 KiB

Open AccessArticle

Sensor-Based Real-Time Detection in Vulcanization Control Using Machine Learning and Pattern Clustering

by Jonghyuk Kim and Hyunwoo Hwangbo

Sensors 2018, 18(9), 3123; https://doi.org/10.3390/s18093123 - 16 Sep 2018

Cited by 5 | Viewed by 3830

Abstract

Recent paradigm shifts in manufacturing have resulted from the need for a smart manufacturing environment. In this study, we developed a model to detect anomalous signs in advance and embedded it in an existing programmable logic controller system. For this, we investigated the innovation process for smart manufacturing in the domain of synthetic rubber and its vulcanization process, as well as a real-time sensing technology. The results indicate that only analysis of the pattern of input variables can lead to significant results without the generation of target variables through manual testing of chemical properties. We have also made a practical contribution to the realization of a smart manufacturing environment by building cloud-based infrastructure and models for the pre-detection of defects. Full article

(This article belongs to the Special Issue Smart Decision-Making)

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15 pages, 1155 KiB

Open AccessArticle

Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement

by Tiago Oliveira, Ana Silva, Ken Satoh, Vicente Julian, Pedro Leão and Paulo Novais

Sensors 2018, 18(9), 2983; https://doi.org/10.3390/s18092983 - 6 Sep 2018

Cited by 6 | Viewed by 3898

Abstract

Prediction in health care is closely related with the decision-making process. On the one hand, accurate survivability prediction can help physicians decide between palliative care or other practice for a patient. On the other hand, the notion of remaining lifetime can be an incentive for patients to live a fuller and more fulfilling life. This work presents a pipeline for the development of survivability prediction models and a system that provides survivability predictions for years one to five after the treatment of patients with colon or rectal cancer. The functionalities of the system are made available through a tool that balances the number of necessary inputs and prediction performance. It is mobile-friendly and facilitates the access of health care professionals to an instrument capable of enriching their practice and improving outcomes. The performance of survivability models was compared with other existing works in the literature and found to be an improvement over the current state of the art. The underlying system is capable of recalculating its prediction models upon the addition of new data, continuously evolving as time passes. Full article

(This article belongs to the Special Issue Smart Decision-Making)

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19 pages, 12255 KiB

Open AccessArticle

A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User

by Marcos Maroto-Gómez, Álvaro Castro-González, José Carlos Castillo, María Malfaz and Miguel A. Salichs

Sensors 2018, 18(8), 2691; https://doi.org/10.3390/s18082691 - 16 Aug 2018

Cited by 18 | Viewed by 4401

Abstract

Nowadays, many robotic applications require robots making their own decisions and adapting to different conditions and users. This work presents a biologically inspired decision making system, based on drives, motivations, wellbeing, and self-learning, that governs the behavior of the robot considering both internal and external circumstances. In this paper we state the biological foundations that drove the design of the system, as well as how it has been implemented in a real robot. Following a homeostatic approach, the ultimate goal of the robot is to keep its wellbeing as high as possible. In order to achieve this goal, our decision making system uses learning mechanisms to assess the best action to execute at any moment. Considering that the proposed system has been implemented in a real social robot, human-robot interaction is of paramount importance and the learned behaviors of the robot are oriented to foster the interactions with the user. The operation of the system is shown in a scenario where the robot Mini plays games with a user. In this context, we have included a robust user detection mechanism tailored for short distance interactions. After the learning phase, the robot has learned how to lead the user to interact with it in a natural way. Full article

(This article belongs to the Special Issue Smart Decision-Making)

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19 pages, 3889 KiB

Open AccessArticle

PHAROS—PHysical Assistant RObot System

by Angelo Costa, Ester Martinez-Martin, Miguel Cazorla and Vicente Julian

Sensors 2018, 18(8), 2633; https://doi.org/10.3390/s18082633 - 11 Aug 2018

Cited by 59 | Viewed by 7974

Abstract

The great demographic change leading to an ageing society demands technological solutions to satisfy the increasing varied elderly needs. This paper presents PHAROS, an interactive robot system that recommends and monitors physical exercises designed for the elderly. The aim of PHAROS is to be a friendly elderly companion that periodically suggests personalised physical activities, promoting healthy living and active ageing. Here, it is presented the PHAROS architecture, components and experimental results. The architecture has three main strands: a Pepper robot, that interacts with the users and records their exercises performance; the Human Exercise Recognition, that uses the Pepper recorded information to classify the exercise performed using Deep Leaning methods; and the Recommender, a smart-decision maker that schedules periodically personalised physical exercises in the users’ agenda. The experimental results show a high accuracy in terms of detecting and classifying the physical exercises (97.35%) done by 7 persons. Furthermore, we have implemented a novel procedure of rating exercises on the recommendation algorithm. It closely follows the users’ health status (poor performance may reveal health problems) and adapts the suggestions to it. The history may be used to access the physical condition of the user, revealing underlying problems that may be impossible to see otherwise. Full article

(This article belongs to the Special Issue Smart Decision-Making)

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17 pages, 1053 KiB

Open AccessArticle

Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems

by Khac-Hoai Nam Bui, Jason J. Jung and David Camacho

Sensors 2018, 18(7), 2206; https://doi.org/10.3390/s18072206 - 9 Jul 2018

Cited by 20 | Viewed by 5789

Abstract

Recently, the concept of Internet of Agent has been introduced as a potential technology that pushes intelligence, data processing, analytics and communication capabilities down to the point where the data originates. In this paper, we introduce a novel approach for a Decentralized Home Energy Management System by applying the Internet of Agent concept. In particular, we first present an Internet of Agent framework in terms of sensing, communicating and collaborating among connected appliances. Then, the decentralized management based on consensual negotiation mechanism with several intelligent techniques are proposed for dynamic scheduling connected appliance. Specifically, by applying the Internet of Agent framework, connected appliances are regarded as smart agents that are able to make individual decisions by reaching agreement over the exchange of operations on competitive resources. Furthermore, in this study, the load balancing problem in which load shifting is able to reduce the electricity demand during peak hours is taken into account in order to emphasize the effectiveness of our approach. For the experiment, we develop a simulation of smart home environment to evaluate our approach using NetLogo, a tool which provides real-time analysis in the modeling and simulation domain of complex systems. Full article

(This article belongs to the Special Issue Smart Decision-Making)

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15 pages, 16528 KiB

Open AccessArticle

Detection of Cattle Using Drones and Convolutional Neural Networks

by Alberto Rivas, Pablo Chamoso, Alfonso González-Briones and Juan Manuel Corchado

Sensors 2018, 18(7), 2048; https://doi.org/10.3390/s18072048 - 27 Jun 2018

Cited by 113 | Viewed by 9535

Abstract

Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle. Full article

(This article belongs to the Special Issue Smart Decision-Making)

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32 pages, 9108 KiB

Open AccessArticle

A Mission Planning Approach for Precision Farming Systems Based on Multi-Objective Optimization

by Zhaoyu Zhai, José-Fernán Martínez Ortega, Néstor Lucas Martínez and Jesús Rodríguez-Molina

Sensors 2018, 18(6), 1795; https://doi.org/10.3390/s18061795 - 2 Jun 2018

Cited by 36 | Viewed by 6482

Abstract

As the demand for food grows continuously, intelligent agriculture has drawn much attention due to its capability of producing great quantities of food efficiently. The main purpose of intelligent agriculture is to plan agricultural missions properly and use limited resources reasonably with minor human intervention. This paper proposes a Precision Farming System (PFS) as a Multi-Agent System (MAS). Components of PFS are treated as agents with different functionalities. These agents could form several coalitions to complete the complex agricultural missions cooperatively. In PFS, mission planning should consider several criteria, like expected benefit, energy consumption or equipment loss. Hence, mission planning could be treated as a Multi-objective Optimization Problem (MOP). In order to solve MOP, an improved algorithm, MP-PSOGA, is proposed, taking advantages of the Genetic Algorithms and Particle Swarm Optimization. A simulation, called precise pesticide spraying mission, is performed to verify the feasibility of the proposed approach. Simulation results illustrate that the proposed approach works properly. This approach enables the PFS to plan missions and allocate scarce resources efficiently. The theoretical analysis and simulation is a good foundation for the future study. Once the proposed approach is applied to a real scenario, it is expected to bring significant economic improvement. Full article

(This article belongs to the Special Issue Smart Decision-Making)

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31 pages, 4399 KiB

Open AccessArticle

Agent-Based Intelligent Interface for Wheelchair Movement Control

by Alberto L. Barriuso, Javier Pérez-Marcos, Diego M. Jiménez-Bravo, Gabriel Villarrubia González and Juan F. De Paz

Sensors 2018, 18(5), 1511; https://doi.org/10.3390/s18051511 - 11 May 2018

Cited by 14 | Viewed by 6517

Abstract

People who suffer from any kind of motor difficulty face serious complications to autonomously move in their daily lives. However, a growing number research projects which propose different powered wheelchairs control systems are arising. Despite of the interest of the research community in the area, there is no platform that allows an easy integration of various control methods that make use of heterogeneous sensors and computationally demanding algorithms. In this work, an architecture based on virtual organizations of agents is proposed that makes use of a flexible and scalable communication protocol that allows the deployment of embedded agents in computationally limited devices. In order to validate the proper functioning of the proposed system, it has been integrated into a conventional wheelchair and a set of alternative control interfaces have been developed and deployed, including a portable electroencephalography system, a voice interface or as specifically designed smartphone application. A set of tests were conducted to test both the platform adequacy and the accuracy and ease of use of the proposed control systems yielding positive results that can be useful in further wheelchair interfaces design and implementation. Full article

(This article belongs to the Special Issue Smart Decision-Making)

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24 pages, 6267 KiB

Open AccessArticle

Smart Waste Collection System with Low Consumption LoRaWAN Nodes and Route Optimization

by Álvaro Lozano, Javier Caridad, Juan Francisco De Paz, Gabriel Villarrubia González and Javier Bajo

Sensors 2018, 18(5), 1465; https://doi.org/10.3390/s18051465 - 8 May 2018

Cited by 71 | Viewed by 13955

Abstract

New solutions for managing waste have emerged due to the rise of Smart Cities and the Internet of Things. These solutions can also be applied in rural environments, but they require the deployment of a low cost and low consumption sensor network which can be used by different applications. Wireless technologies such as LoRa and low consumption microcontrollers, such as the SAM L21 family make the implementation and deployment of this kind of sensor network possible. This paper introduces a waste monitoring and management platform used in rural environments. A prototype of a low consumption wireless node is developed to obtain measurements of the weight, filling volume and temperature of a waste container. This monitoring allows the progressive filling data of every town container to be gathered and analysed as well as creating alerts in case of incidence. The platform features a module for optimising waste collection routes. This module dynamically generates routes from data obtained through the deployed nodes to save energy, time and consequently, costs. It also features a mobile application for the collection fleet which guides every driver through the best route—previously calculated for each journey. This paper presents a case study performed in the region of Salamanca to evaluate the efficiency and the viability of the system’s implementation. Data used for this case study come from open data sources, the report of the Castilla y León waste management plan and data from public tender procedures in the region of Salamanca. The results of the case study show a developed node with a great lifetime of operation, a large coverage with small deployment of antennas in the region, and a route optimization system which uses weight and volume measured by the node, and provides savings in cost, time and workforce compared to a static collection route approach. Full article

(This article belongs to the Special Issue Smart Decision-Making)

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30 pages, 13854 KiB

Open AccessArticle

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario

by Joanna Alvarado-Uribe, Andrea Gómez-Oliva, Ari Yair Barrera-Animas, Germán Molina, Miguel Gonzalez-Mendoza, María Concepción Parra-Meroño and Antonio J. Jara

Sensors 2018, 18(3), 890; https://doi.org/10.3390/s18030890 - 17 Mar 2018

Cited by 13 | Viewed by 5503

Abstract

Nowadays, Physical Web together with the increase in the use of mobile devices, Global Positioning System (GPS), and Social Networking Sites (SNS) have caused users to share enriched information on the Web such as their tourist experiences. Therefore, an area that has been significantly improved by using the contextual information provided by these technologies is tourism. In this way, the main goals of this work are to propose and develop an algorithm focused on the recommendation of Smart Point of Interaction (Smart POI) for a specific user according to his/her preferences and the Smart POIs’ context. Hence, a novel Hybrid Recommendation Algorithm (HyRA) is presented by incorporating an aggregation operator into the user-based Collaborative Filtering (CF) algorithm as well as including the Smart POIs’ categories and geographical information. For the experimental phase, two real-world datasets have been collected and preprocessed. In addition, one Smart POIs’ categories dataset was built. As a result, a dataset composed of 16 Smart POIs, another constituted by the explicit preferences of 200 respondents, and the last dataset integrated by 13 Smart POIs’ categories are provided. The experimental results show that the recommendations suggested by HyRA are promising. Full article

(This article belongs to the Special Issue Smart Decision-Making)

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16 pages, 1932 KiB

Open AccessArticle

A Modified Distributed Bees Algorithm for Multi-Sensor Task Allocation

by Itshak Tkach, Aleksandar Jevtić, Shimon Y. Nof and Yael Edan

Sensors 2018, 18(3), 759; https://doi.org/10.3390/s18030759 - 2 Mar 2018

Cited by 19 | Viewed by 5519

Abstract

Multi-sensor systems can play an important role in monitoring tasks and detecting targets. However, real-time allocation of heterogeneous sensors to dynamic targets/tasks that are unknown a priori in their locations and priorities is a challenge. This paper presents a Modified Distributed Bees Algorithm (MDBA) that is developed to allocate stationary heterogeneous sensors to upcoming unknown tasks using a decentralized, swarm intelligence approach to minimize the task detection times. Sensors are allocated to tasks based on sensors’ performance, tasks’ priorities, and the distances of the sensors from the locations where the tasks are being executed. The algorithm was compared to a Distributed Bees Algorithm (DBA), a Bees System, and two common multi-sensor algorithms, market-based and greedy-based algorithms, which were fitted for the specific task. Simulation analyses revealed that MDBA achieved statistically significant improved performance by 7% with respect to DBA as the second-best algorithm, and by 19% with respect to Greedy algorithm, which was the worst, thus indicating its fitness to provide solutions for heterogeneous multi-sensor systems. Full article

(This article belongs to the Special Issue Smart Decision-Making)

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22 pages, 6903 KiB

Open AccessArticle

A Context-Aware Indoor Air Quality System for Sudden Infant Death Syndrome Prevention

by Daniel H. De La Iglesia, Juan F. De Paz, Gabriel Villarrubia González, Alberto L. Barriuso and Javier Bajo

Sensors 2018, 18(3), 757; https://doi.org/10.3390/s18030757 - 2 Mar 2018

Cited by 12 | Viewed by 6043

Abstract

Context-aware monitoring systems designed for e-Health solutions and ambient assisted living (AAL) play an important role in today’s personalized health-care services. The majority of these systems are intended for the monitoring of patients’ vital signs by means of bio-sensors. At present, there are very few systems that monitor environmental conditions and air quality in the homes of users. A home’s environmental conditions can have a significant influence on the state of the health of its residents. Monitoring the environment is the key to preventing possible diseases caused by conditions that do not favor health. This paper presents a context-aware system that monitors air quality to prevent a specific health problem at home. The aim of this system is to reduce the incidence of the Sudden Infant Death Syndrome, which is triggered mainly by environmental factors. In the conducted case study, the system monitored the state of the neonate and the quality of air while it was asleep. The designed proposal is characterized by its low cost and non-intrusive nature. The results are promising. Full article

(This article belongs to the Special Issue Smart Decision-Making)

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14 pages, 2890 KiB

Open AccessArticle

Measurement of Axial Rigidity and Postural Instability Using Wearable Sensors

by Dung Phan, Malcolm Horne, Pubudu N. Pathirana and Parisa Farzanehfar

Sensors 2018, 18(2), 495; https://doi.org/10.3390/s18020495 - 7 Feb 2018

Cited by 18 | Viewed by 6132

Abstract

Axial Bradykinesia is an important feature of advanced Parkinson’s disease (PD). The purpose of this study is to quantify axial bradykinesia using wearable sensors with the long-term aim of quantifying these movements, while the subject performs routine domestic activities. We measured back movements during common daily activities such as pouring, pointing, walking straight and walking around a chair with a test system engaging a minimal number of Inertial Measurement (IM) based wearable sensors. Participants included controls and PD patients whose rotation and flexion of the back was captured by the time delay between motion signals from sensors attached to the upper and lower back. PD subjects could be distinguished from controls using only two sensors. These findings suggest that a small number of sensors and similar analyses could distinguish between variations in bradykinesia in subjects with measurements performed outside of the laboratory. The subjects could engage in routine activities leading to progressive assessments of therapeutic outcomes. Full article

(This article belongs to the Special Issue Smart Decision-Making)

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25 pages, 5500 KiB

Open AccessArticle

L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification

by Jaesung Choi, Eungyeol Song and Sangyoun Lee

Sensors 2018, 18(1), 306; https://doi.org/10.3390/s18010306 - 20 Jan 2018

Cited by 6 | Viewed by 6256

Abstract

The decision tree is one of the most effective tools for deriving meaningful outcomes from image data acquired from the visual sensors. Owing to its reliability, superior generalization abilities, and easy implementation, the tree model has been widely used in various applications. However, in image classification problems, conventional tree methods use only a few sparse attributes as the splitting criterion. Consequently, they suffer from several drawbacks in terms of performance and environmental sensitivity. To overcome these limitations, this paper introduces a new tree induction algorithm that classifies images on the basis of local area learning. To train our predictive model, we extract a random local area within the image and use it as a feature for classification. In addition, the self-organizing map, which is a clustering technique, is used for node learning. We also adopt a random sampled optimization technique to search for the optimal node. Finally, each trained node stores the weights that represent the training data and class probabilities. Thus, a recursively trained tree classifies the data hierarchically based on the local similarity at each node. The proposed tree is a type of predictive model that offers benefits in terms of image’s semantic energy conservation compared with conventional tree methods. Consequently, it exhibits improved performance under various conditions, such as noise and illumination changes. Moreover, the proposed algorithm can improve the generalization ability owing to its randomness. In addition, it can be easily applied to ensemble techniques. To evaluate the performance of the proposed algorithm, we perform quantitative and qualitative comparisons with various tree-based methods using four image datasets. The results show that our algorithm not only involves a lower classification error than the conventional methods but also exhibits stable performance even under unfavorable conditions such as noise and illumination changes. Full article

(This article belongs to the Special Issue Smart Decision-Making)

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