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Information Fusion in Sensor Networks
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Information Fusion in Sensor Networks

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

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 24324

Special Issue Editor

Prof. Dr. Henry Leung

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: intelligent sensors; machine learning; data analytics; information fusion; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of sensor technologies, the use of multiple sensors has become a trend in many areas such as intelligent vehicles, security, biomedical imaging, remote sensing, and defense. These sensors may be on the same platform or installed on different platforms. In either case, information from these sensors has to be fused to provide a consistent interpretation of the environment and to provide decision support to users. In this Special Issue, lower level information fusion including detection, tracking, recognition, sensor registration, and fusion, as well as higher-level information fusion such as situation awareness, path planning, scheduling, and resource allocation will be considered for sensor networks and internet of things with applications to different fields such as autonomous cars, smart cities, UAV, camera networks, and robotics.

Prof. Dr. Henry Leung
Guest Editor

Manuscript Submission Information

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Keywords

  • multisensory tracking
  • object recognition
  • situation assessment
  • resource management
  • detection and estimation
  • sensor registration and fusion
  • sensor networks
  • internet of things
  • autonomous vehicles

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Published Papers (4 papers)

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Research

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36 pages, 3444 KiB  
Article
A Sheaf Theoretical Approach to Uncertainty Quantification of Heterogeneous Geolocation Information
by Cliff A. Joslyn, Lauren Charles, Chris DePerno, Nicholas Gould, Kathleen Nowak, Brenda Praggastis, Emilie Purvine, Michael Robinson, Jennifer Strules and Paul Whitney
Sensors 2020, 20(12), 3418; https://doi.org/10.3390/s20123418 - 17 Jun 2020
Cited by 5 | Viewed by 4807
Abstract
Integration of multiple, heterogeneous sensors is a challenging problem across a range of applications. Prominent among these are multi-target tracking, where one must combine observations from different sensor types in a meaningful and efficient way to track multiple targets. Because different sensors have [...] Read more.
Integration of multiple, heterogeneous sensors is a challenging problem across a range of applications. Prominent among these are multi-target tracking, where one must combine observations from different sensor types in a meaningful and efficient way to track multiple targets. Because different sensors have differing error models, we seek a theoretically justified quantification of the agreement among ensembles of sensors, both overall for a sensor collection, and also at a fine-grained level specifying pairwise and multi-way interactions among sensors. We demonstrate that the theory of mathematical sheaves provides a unified answer to this need, supporting both quantitative and qualitative data. Furthermore, the theory provides algorithms to globalize data across the network of deployed sensors, and to diagnose issues when the data do not globalize cleanly. We demonstrate and illustrate the utility of sheaf-based tracking models based on experimental data of a wild population of black bears in Asheville, North Carolina. A measurement model involving four sensors deployed among the bears and the team of scientists charged with tracking their location is deployed. This provides a sheaf-based integration model which is small enough to fully interpret, but of sufficient complexity to demonstrate the sheaf’s ability to recover a holistic picture of the locations and behaviors of both individual bears and the bear-human tracking system. A statistical approach was developed in parallel for comparison, a dynamic linear model which was estimated using a Kalman filter. This approach also recovered bear and human locations and sensor accuracies. When the observations are normalized into a common coordinate system, the structure of the dynamic linear observation model recapitulates the structure of the sheaf model, demonstrating the canonicity of the sheaf-based approach. However, when the observations are not so normalized, the sheaf model still remains valid. Full article
(This article belongs to the Special Issue Information Fusion in Sensor Networks)
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45 pages, 1681 KiB  
Article
Performance Analysis of Distributed Estimation for Data Fusion Using a Statistical Approach in Smart Grid Noisy Wireless Sensor Networks
by Chatura Seneviratne, Patikiri Arachchige Don Shehan Nilmantha Wijesekara and Henry Leung
Sensors 2020, 20(2), 567; https://doi.org/10.3390/s20020567 - 20 Jan 2020
Cited by 18 | Viewed by 3909
Abstract
Internet of Things (IoT) can significantly enhance various aspects of today’s electric power grid infrastructures for making reliable, efficient, and safe next-generation Smart Grids (SGs). However, harsh and complex power grid infrastructures and environments reduce the accuracy of the information propagating through IoT [...] Read more.
Internet of Things (IoT) can significantly enhance various aspects of today’s electric power grid infrastructures for making reliable, efficient, and safe next-generation Smart Grids (SGs). However, harsh and complex power grid infrastructures and environments reduce the accuracy of the information propagating through IoT platforms. In particularly, information is corrupted due to the measurement errors, quantization errors, and transmission errors. This leads to major system failures and instabilities in power grids. Redundant information measurements and retransmissions are traditionally used to eliminate the errors in noisy communication networks. However, these techniques consume excessive resources such as energy and channel capacity and increase network latency. Therefore, we propose a novel statistical information fusion method not only for structural chain and tree-based sensor networks, but also for unstructured bidirectional graph noisy wireless sensor networks in SG environments. We evaluate the accuracy, energy savings, fusion complexity, and latency of the proposed method by comparing the said parameters with several distributed estimation algorithms using extensive simulations proposing it for several SG applications. Results prove that the overall performance of the proposed method outperforms other fusion techniques for all considered networks. Under Smart Grid communication environments, the proposed method guarantees for best performance in all fusion accuracy, complexity and energy consumption. Analytical upper bounds for the variance of the final aggregated value at the sink node for structured networks are also derived by considering all major errors. Full article
(This article belongs to the Special Issue Information Fusion in Sensor Networks)
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16 pages, 1897 KiB  
Article
Investigation of Parameter Effects on Virtual-Spring-Force Algorithm for Wireless-Sensor-Network Applications
by Zhiyong Yu, Rongxin Tang, Kai Yuan, Hai Lin, Xin Qian, Xiaohua Deng and Shiyun Liu
Sensors 2019, 19(14), 3082; https://doi.org/10.3390/s19143082 - 12 Jul 2019
Cited by 6 | Viewed by 2799
Abstract
Virtual-force algorithms (VFAs) have been widely studied for accurate node deployment in wireless-sensor-network (WSN) applications. Their main purpose is to achieve the maximum coverage area with the minimum number of sensor nodes in the target area. Recently, we reported a new VFA based [...] Read more.
Virtual-force algorithms (VFAs) have been widely studied for accurate node deployment in wireless-sensor-network (WSN) applications. Their main purpose is to achieve the maximum coverage area with the minimum number of sensor nodes in the target area. Recently, we reported a new VFA based on virtual spring force (VFA-SF) and discussed in detail the corresponding efficiency via statistical analysis. The optimized strategy by adding an external central force (VFA-SF-OPT) was presented, which effectively eliminates the coverage hole or twisted structure in the final network distribution. In this paper, the parameter effects on VFA-SF and the VFA-SF-OPT were further investigated: (1) Node velocity dramatically affects the convergence rate of the node-deployment process. (2) A suitable external central force improves equilibrium distance and reduces energy consumption. (3) The effects of VFA-SF and VFA-SF-OPT for different types of obstacles are discussed. Generally, by choosing suitable parameters, both VFA-SF and VFA-SF-OPT can effectively improve node deployment and energy consumption for the whole sensor network. The results give important insight in parameter selection and information fusion in the application of a large-scale WSN. Full article
(This article belongs to the Special Issue Information Fusion in Sensor Networks)
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Review

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41 pages, 2175 KiB  
Review
Multi-Sensor Fusion for Activity Recognition—A Survey
by Antonio A. Aguileta, Ramon F. Brena, Oscar Mayora, Erik Molino-Minero-Re and Luis A. Trejo
Sensors 2019, 19(17), 3808; https://doi.org/10.3390/s19173808 - 3 Sep 2019
Cited by 77 | Viewed by 11828
Abstract
In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors [...] Read more.
In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examine their relative merits, either as they are reported and sometimes even replicated and a comparison of these methods is made, as well as an assessment of the trends in the area. Full article
(This article belongs to the Special Issue Information Fusion in Sensor Networks)
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