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Special Issue "Data and Information Fusion for Wireless Sensor Networks"

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

Deadline for manuscript submissions: closed (15 January 2019).

Special Issue Editors

Prof. Dr. Jose Manuel Molina López
Website
Guest Editor
Computer Science and Engineering Department, Universidad Carlos III de Madrid, Edificio Sabatini, 28911 Leganés, Spain
Interests: data fusion; machine learning; Internet of Things (IoT); ambient intelligent; AAL; privacy
Special Issues and Collections in MDPI journals
Dr. Jesús García-Herrero
Website
Guest Editor
Computer Science Department, Universidad Carlos III de Madrid, Avenida Gregorio Peces-Barba Martínez, 22. 28270 Colmenarejo Madrid, Spain
Interests: data fusion; surveillance systems; contextual information; data mining

Special Issue Information

Dear Colleagues,

In today’s digital world, information is the key factor to make decisions. Ubiquitous electronic sources, such as sensors and video, provide a steady stream of data, while text-based data from databases, the Internet, email, chat and VOIP, and social media are growing exponentially. The ability to make sense of data by fusing it into new knowledge would provide clear advantages in making decisions.

Fusion systems aim to integrate sensor data and information in databases, knowledge bases, contextual information, etc., in order to describe situations. In a sense, the goal of information fusion is to attain a global view of a scenario in order to make the best decision.

The key aspect in modern DF applications is the appropriate integration of all types of information or knowledge: Observational data, knowledge models (a priori or inductively learned), and contextual information. Each of these categories has a distinctive nature and potential support for the result of the fusion process.

  • Observational Data: Observational data are the fundamental data about a dynamic scenario, as collected from some observational capability (sensors of any type). These data are about the observable entities in the world that are of interest
  • Contextual Information: Contextual information has become fundamental to develop models in complex scenarios. Context and the elements of what could be called Contextual Information could be defined as “the set of circumstances surrounding a task that are potentially of relevance to its completion.” Because of its task-relevance; fusion or estimating/inferring task implies the development of a best-possible estimate taking into account this lateral knowledge.
  • Learned Knowledge: DF systems combine multi-source data to provide inferences, exploiting models of the expected behaviors of entities (physical models like cinematics or logical models like expected behaviors depending on context). In those cases where a priori knowledge for DF process development cannot be formed, one possibility is to try and excise knowledge through online machine learning processes, operating on observational and other data. These are procedural and algorithmic methods for discovering relationships among, and behaviors of, entities of interest.

This Special Issue invites contributions on the following topics (but is not limited to them):

  • Data fusion of distributed sensors
  • Context definition and management
  • Machine learning techniques
  • Integration of data fusion
  • Ambient intelligence
  • Data fusion on autonomous systems
  • Virtual and augmented reality
  • Human computer interaction
  • Visual pattern recognition
  • Environment modeling and reconstruction from images

Prof. Dr. Jose Molina López
Dr. Jesús García-Herrero
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 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.

Published Papers (7 papers)

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Research

Open AccessArticle
Thinger.io: An Open Source Platform for Deploying Data Fusion Applications in IoT Environments
Sensors 2019, 19(5), 1044; https://doi.org/10.3390/s19051044 - 01 Mar 2019
Cited by 2
Abstract
In the last two decades, data and information fusion has experienced significant development due mainly to advances in sensor technology. The sensors provide a continuous flow of data about the environment in which they are deployed, which is received and processed to build [...] Read more.
In the last two decades, data and information fusion has experienced significant development due mainly to advances in sensor technology. The sensors provide a continuous flow of data about the environment in which they are deployed, which is received and processed to build a dynamic estimation of the situation. With current technology, it is relatively simple to deploy a set of sensors in a specific geographic area, in order to have highly sensorized spaces. However, to be able to fusion and process the information coming from the data sources of a highly sensorized space, it is necessary to solve certain problems inherent to this type of technology. The challenge is analogous to what we can find in the field of the Internet of Things (IoT). IoT technology is characterized by providing the infrastructure capacity to capture, store, and process a huge amount of heterogeneous sensor data (in most cases, from different manufacturers), in the same way that it occurs in data fusion applications. This work is not simple, mainly due to the fact that there is no standardization of the technologies involved (especially within the communication protocols used by the connectable sensors). The solutions that we can find today are proprietary solutions that imply an important dependence and a high cost. The aim of this paper is to present a new open source platform with capabilities for the collection, management and analysis of a huge amount of heterogeneous sensor data. In addition, this platform allows the use of hardware-agnostic in a highly scalable and cost-effective manner. This platform is called Thinger.io. One of the main characteristics of Thinger.io is the ability to model sensorized environments through a high level language that allows a simple and easy implementation of data fusion applications, as we will show in this paper. Full article
(This article belongs to the Special Issue Data and Information Fusion for Wireless Sensor Networks)
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Open AccessArticle
Distributed Fusion of Sensor Data in a Constrained Wireless Network
Sensors 2019, 19(5), 1006; https://doi.org/10.3390/s19051006 - 27 Feb 2019
Cited by 1
Abstract
Smart buildings with connected lighting and sensors are likely to become one of the first large-scale applications of the Internet of Things (IoT). However, as the number of interconnected IoT devices is expected to rise exponentially, the amount of collected data will be [...] Read more.
Smart buildings with connected lighting and sensors are likely to become one of the first large-scale applications of the Internet of Things (IoT). However, as the number of interconnected IoT devices is expected to rise exponentially, the amount of collected data will be enormous but highly redundant. Devices will be required to pre-process data locally or at least in their vicinity. Thus, local data fusion, subject to constraint communications will become necessary. In that sense, distributed architectures will become increasingly unavoidable. Anticipating this trend, this paper addresses the problem of presence detection in a building as a distributed sensing of a hidden Markov model (DS-HMM) with limitations on the communication. The key idea in our work is the use of a posteriori probabilities or likelihood ratios (LR) as an appropriate “interface” between heterogeneous sensors with different error profiles. We propose an efficient transmission policy, jointly with a fusion algorithm, to merge data from various HMMs running separately on all sensor nodes but with all the models observing the same Markovian process. To test the feasibility of our DS-HMM concept, a simple proof-of-concept prototype was used in a typical office environment. The experimental results show full functionality and validate the benefits. Our proposed scheme achieved high accuracy while reducing the communication requirements. The concept of DS-HMM and a posteriori probabilities as an interface is suitable for many other applications for distributed information fusion in wireless sensor networks. Full article
(This article belongs to the Special Issue Data and Information Fusion for Wireless Sensor Networks)
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Open AccessArticle
Compressive Sensing Based Radio Tomographic Imaging with Spatial Diversity
Sensors 2019, 19(3), 439; https://doi.org/10.3390/s19030439 - 22 Jan 2019
Cited by 6
Abstract
Radio tomographic imaging (RTI) has emerged as a promising device-free localization technology for locating the targets with no devices attached. RTI deduces the location information from the reconstructed attenuation image characterizing target-induced spatial loss of radio frequency measurements in the sensing area. In [...] Read more.
Radio tomographic imaging (RTI) has emerged as a promising device-free localization technology for locating the targets with no devices attached. RTI deduces the location information from the reconstructed attenuation image characterizing target-induced spatial loss of radio frequency measurements in the sensing area. In cluttered indoor environments, RF measurements of wireless links are corrupted by multipath effects and thus less robust to achieve a high localization accuracy for RTI. This paper proposes to improve the quality of measurements by using spatial diversity. The key insight is that, with multiple antennae equipped, due to small-scale multipath fading, RF measurement variation of each antenna pair behaves differently. Therefore, spatial diversity can provide more reliable and strong measurements in terms of link quality. Moreover, to estimate the location from the image more precisely and make the image more identifiable, we propose using a new reconstruction regularization linearly combining the sparsity and correlation inherent in the image. The proposed reconstruction method can remarkably reduce the image noise and enhance the imaging accuracy especially in the case of a few available measurements. Indoor experimental results demonstrate that compared to existing RTI improvement methods, our RTI solution can reduce the root-mean-square localization error at least 47% while also improving the imaging performance. Full article
(This article belongs to the Special Issue Data and Information Fusion for Wireless Sensor Networks)
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Open AccessArticle
Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets
Sensors 2019, 19(2), 286; https://doi.org/10.3390/s19020286 - 12 Jan 2019
Cited by 2
Abstract
In this paper, a monitoring system of agricultural production is modeled as a Data Fusion System (data from local fairs and meteorological data). The proposal considers the particular information of sales in agricultural markets for knowledge extraction about the associations among them. This [...] Read more.
In this paper, a monitoring system of agricultural production is modeled as a Data Fusion System (data from local fairs and meteorological data). The proposal considers the particular information of sales in agricultural markets for knowledge extraction about the associations among them. This association knowledge is employed to improve predictions of sales using a spatial prediction technique, as shown with data collected from local markets of the Andean region of Ecuador. The commercial activity in these markets uses Alternative Marketing Circuits (CIALCO). This market platform establishes a direct relationship between producer and consumer prices and promotes direct commercial interaction among family groups. The problem is presented first as a general fusion problem with a network of spatially distributed heterogeneous data sources, and is then applied to the prediction of products sales based on association rules mined in available sales data. First, transactional data is used as the base to extract the best association rules between products sold in different local markets, knowledge that allows the system to gain a significant improvement in prediction accuracy in the spatial region considered. Full article
(This article belongs to the Special Issue Data and Information Fusion for Wireless Sensor Networks)
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Open AccessArticle
A Temporal Adaptive Access Mechanism for Data Fusion in an IoT Environment
Sensors 2018, 18(12), 4205; https://doi.org/10.3390/s18124205 - 30 Nov 2018
Cited by 1
Abstract
Data fusion in the Internet of Things (IoT) environment demands collecting and processing a wide variety of data with mixed time characteristics, both real-time and non-real-time data. Most of the previous research on data fusion was about the data processing aspect; however, successful [...] Read more.
Data fusion in the Internet of Things (IoT) environment demands collecting and processing a wide variety of data with mixed time characteristics, both real-time and non-real-time data. Most of the previous research on data fusion was about the data processing aspect; however, successful data transmission is a prerequisite for high-performance data fusion in IoT. On the other hand, research on data transmissions in IoT mainly focuses on networking without sufficiently considering the special requirements of the upper-layer applications, such as the data fusion process, that are consuming the transmitted data. In this paper, we tackle the problem of data transmission for data fusion in an IoT environment by proposing a distributed scheduling mechanism VD-CSMA in wireless sensor networks, which considers the values for data fusion, as well as the delay constraints of packets when determining their priority levels for transmission. Simulation results have shown that VD-CSMA may enhance both throughput and delay performance of data transmission as compared to the typical scheduling schemes used for data fusion in IoT. Full article
(This article belongs to the Special Issue Data and Information Fusion for Wireless Sensor Networks)
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Open AccessArticle
Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking
Sensors 2018, 18(7), 2012; https://doi.org/10.3390/s18072012 - 22 Jun 2018
Cited by 6
Abstract
Networked multiple sensors are used to solve the problem of maneuvering target tracking. To avoid the linearization of nonlinear dynamic functions, and to obtain more accurate estimates for maneuvering targets, a novel adaptive information-weighted consensus filter for maneuvering target tracking is proposed. The [...] Read more.
Networked multiple sensors are used to solve the problem of maneuvering target tracking. To avoid the linearization of nonlinear dynamic functions, and to obtain more accurate estimates for maneuvering targets, a novel adaptive information-weighted consensus filter for maneuvering target tracking is proposed. The pseudo measurement matrix is computed with unscented transform to utilize the information form of measurements, which is necessary for consensus iterations. To improve the maneuvering target tracking accuracy and get a unified estimation in each sensor node across the entire network, the adaptive current statistical model is exploited to update the estimate, and the information-weighted consensus protocol is applied among neighboring nodes for each dynamic model. Based on posterior probabilities of multiple models, the final estimate of each sensor is acquired with weighted combination of model-conditioned estimates. Experimental results illustrate the superior performance of the proposed algorithm with respect tracking accuracy and agreement of estimates in the whole network. Full article
(This article belongs to the Special Issue Data and Information Fusion for Wireless Sensor Networks)
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Open AccessArticle
Design of Compressed Sensing Algorithm for Coal Mine IoT Moving Measurement Data Based on a Multi-Hop Network and Total Variation
Sensors 2018, 18(6), 1732; https://doi.org/10.3390/s18061732 - 28 May 2018
Cited by 4
Abstract
As the application of a coal mine Internet of Things (IoT), mobile measurement devices, such as intelligent mine lamps, cause moving measurement data to be increased. How to transmit these large amounts of mobile measurement data effectively has become an urgent problem. This [...] Read more.
As the application of a coal mine Internet of Things (IoT), mobile measurement devices, such as intelligent mine lamps, cause moving measurement data to be increased. How to transmit these large amounts of mobile measurement data effectively has become an urgent problem. This paper presents a compressed sensing algorithm for the large amount of coal mine IoT moving measurement data based on a multi-hop network and total variation. By taking gas data in mobile measurement data as an example, two network models for the transmission of gas data flow, namely single-hop and multi-hop transmission modes, are investigated in depth, and a gas data compressed sensing collection model is built based on a multi-hop network. To utilize the sparse characteristics of gas data, the concept of total variation is introduced and a high-efficiency gas data compression and reconstruction method based on Total Variation Sparsity based on Multi-Hop (TVS-MH) is proposed. According to the simulation results, by using the proposed method, the moving measurement data flow from an underground distributed mobile network can be acquired and transmitted efficiently. Full article
(This article belongs to the Special Issue Data and Information Fusion for Wireless Sensor Networks)
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