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Applied Sciences
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Advanced Wireless Sensor Networks for Emerging Applications
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Advanced Wireless Sensor Networks for Emerging Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 6751

Special Issue Editor

Prof. Dr. Dongsoo Har

E-Mail Website
Guest Editor
Cho Chun Shik Graduate School of Green Transportation, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
Interests: intelligent transportation system; sensor networking and its applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless sensor networks (WSNs) and their applications have been extensively investigated. To date, studies of WSNs have mostly focused on theoretical and experimental aspects of networking performance while achieving typical goals of monitoring and collecting information in the target area. This nature of behavior of WSNs is essentially unchanged even with lately emerging applications such as smart cities, nanogrids, and autonomous vehicles.

Unlike conventional applications of WSNs relying on a relatively small number of sensor nodes, late applications of WSNs tend to require a large number of sensor nodes for various purposes. For example, the large number of sensor nodes of the WSN embedded in connected vehicles and transportation infrastructure helps to support eco-friendly mobility of smart cities. On the other hand, such a magnitude of sensor nodes introduces diverse technical challenges that must be addressed to obtain the desired performance of WSNs. To this end, more intelligent approaches including the use of deep learning techniques are considered as their solutions.

This Special Issue is focused on techniques in the scientific or engineering field of advanced WSNs. Review articles on advanced WSNs as well as research articles on the state-of-the-art development of WSNs for emerging applications are considered for publication. Topics of interest for this Special Issue include but are not limited to architecture and communication/networking protocols of the IoT for smart cities, smart charging schemes for rechargeable WSNs, applications of WSNs for nanogrids, sensor fusion for autonomous vehicles, and deep learning techniques applied to WSNs for improved performance.

Prof. Dr. Dongsoo Har
Guest Editor

Manuscript Submission Information

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Keywords

  • wireless sensor network
  • IoT
  • sensor fusion
  • deep learning
  • smart city
  • nanogrid

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Published Papers (3 papers)

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Editorial

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4 pages, 187 KiB  
Editorial
Special Issue on Advanced Wireless Sensor Networks for Emerging Applications
by Hojun Jin, Sarvar Hussain Nengroo, Inhwan Kim and Dongsoo Har
Appl. Sci. 2022, 12(14), 7315; https://doi.org/10.3390/app12147315 - 21 Jul 2022
Cited by 3 | Viewed by 1233
Abstract
Wireless sensor networks (WSNs) have been widely used due to their extensive range of applications [...] Full article
(This article belongs to the Special Issue Advanced Wireless Sensor Networks for Emerging Applications)

Research

Jump to: Editorial

15 pages, 2474 KiB  
Article
Learning Dense Features for Point Cloud Registration Using a Graph Attention Network
by Quoc-Vinh Lai-Dang, Sarvar Hussain Nengroo and Hojun Jin
Appl. Sci. 2022, 12(14), 7023; https://doi.org/10.3390/app12147023 - 12 Jul 2022
Cited by 7 | Viewed by 2222
Abstract
Point cloud registration is a fundamental task in many applications such as localization, mapping, tracking, and reconstruction. Successful registration relies on extracting robust and discriminative geometric features. Though existing learning-based methods require high computing capacity for processing a large number of raw points [...] Read more.
Point cloud registration is a fundamental task in many applications such as localization, mapping, tracking, and reconstruction. Successful registration relies on extracting robust and discriminative geometric features. Though existing learning-based methods require high computing capacity for processing a large number of raw points at the same time, computational capacity limitation is not an issue thanks to powerful parallel computing process using GPU. In this paper, we introduce a framework that efficiently and economically extracts dense features using a graph attention network for point cloud matching and registration (DFGAT). The detector of the DFGAT is responsible for finding highly reliable key points in large raw data sets. The descriptor of the DFGAT takes these keypoints combined with their neighbors to extract invariant density features in preparation for the matching. The graph attention network (GAT) uses the attention mechanism that enriches the relationships between point clouds. Finally, we consider this as an optimal transport problem and use the Sinkhorn algorithm to find positive and negative matches. We perform thorough tests on the KITTI dataset and evaluate the effectiveness of this approach. The results show that this method with the efficiently compact keypoint selection and description can achieve the best performance matching metrics and reach the highest success ratio of 99.88% registration in comparison with other state-of-the-art approaches. Full article
(This article belongs to the Special Issue Advanced Wireless Sensor Networks for Emerging Applications)
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25 pages, 6862 KiB  
Article
Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
by Sarvar Hussain Nengroo, Hojun Jin and Sangkeum Lee
Appl. Sci. 2022, 12(14), 6908; https://doi.org/10.3390/app12146908 - 7 Jul 2022
Cited by 16 | Viewed by 2668
Abstract
Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and [...] Read more.
Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and wind energy production is critical for grid managers to limit the impact of random fluctuations. In this study, the kernel recursive least-squares (KRLS) algorithm is proposed for the prediction of PV and wind energy. The wireless sensor network (WSN) typically adopted for data collection with a flexible configuration of sensor nodes is used to transport PV and wind production data to the monitoring center. For efficient transmission of the data production, a link scheduling technique based on sensor node attributes is proposed. Different statistical and machine learning (ML) techniques are examined with respect to the proposed KRLS algorithm for performance analysis. The comparison results show that the KRLS algorithm surpasses all other regression approaches. For both PV and wind power feed-in forecasts, the proposed KRLS algorithm demonstrates high forecasting accuracy. In addition, the link scheduling proposed for the transmission of data for the management of distributed renewable energy resources is compared with a reference technique to show its comparable performance. The efficacy of the proposed KRLS model is better than other regression models in all assessment events in terms of an RMSE value of 0.0146, MAE value of 0.00021, and R2 of 99.7% for PV power, and RMSE value of 0.0421, MAE value of 0.0018, and R2 of 88.17% for wind power. In addition to this, the proposed link scheduling approach results in 22% lower latency and 38% higher resource utilization through the efficient scheduling of time slots. Full article
(This article belongs to the Special Issue Advanced Wireless Sensor Networks for Emerging Applications)
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