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Special Issue "Big Data Analytics and Intelligent Computation to Advance Novel Applications"

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

Deadline for manuscript submissions: 31 May 2022.

Special Issue Editors

Dr. Jianxin Li
E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Burwood, VIC 3125, Australia
Interests: social network data analytics; complex network data mining; knowledge-based deep learning; graph data computation; advanced query processing
Dr. Bin Wang
E-Mail Website
Guest Editor
School of Computer Science, Northeastern University, Shenyang 110819, China
Interests: spatial data management and computation; efficient query processing; data encryption and privacy; traffic and streaming data computing
Dr. Jihong Park
E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Burwood, VIC 3125, Australia
Interests: efficient distributed machine learning; distributed control; distributed ledger technology; applications for beyond 5G/6G communication systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Big data analytics and intelligent computation are a hot topic to advance data-based services in real applications. However, most big data platforms, algorithms, and computational solutions are too general to be applied in novel application scenarios. For instance, in some sensor network applications, there are specific requirements regarding data analytics and computational resources. The sensor data volume is often huge and cannot fully be stored for processing. Sensor data are dynamic and updated over time, while sensor data are mostly consumed in streaming-style strategies. These specific requirements have become the key bottlenecks in utilizing the existing research outputs in big data analytics and intelligent computation. Therefore, to address these novel challenges, new technologies and feasible solutions of big data analytics and intelligent computation are now being pushed to a new frontier.

This Special Issue aims to establish an emerging forum and attract high-quality research submissions from worldwide scholars to develop novel data analytic models, efficient computing algorithms, deep learning solutions, as well as practical frameworks and systems. We also encourage authors to deploy the existing big data analytics solutions with a reasonable extension to adapt their appropriateness in novel application scenarios. The research findings will help industry practitioners and organizations to make smart decisions in real applications and reduce the computational cost.

Dr. Jianxin Li
Dr. Bin Wang
Dr. Jihong Park
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 2200 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

  • Fault-tolerant prediction models 
  • Fault-tolerant recommendation models 
  • Big data feature learning 
  • Big-data-based energy management system and framework 
  • Communication reduction in distributed environment 
  • Novel applications of IoT devices 
  • Novel framework evaluation and metric 
  • Novel system evaluation and metric 
  • Network data mining and clustering 
  • Novel query types in streaming data 
  • Noisy data cleaning

Published Papers (1 paper)

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Research

Article
Inter-Level Feature Balanced Fusion Network for Street Scene Segmentation
Sensors 2021, 21(23), 7844; https://doi.org/10.3390/s21237844 - 25 Nov 2021
Viewed by 269
Abstract
Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of simple [...] Read more.
Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of simple concatenation or element-wise addition will lead to the problem of unbalanced fusion and low utilization of inter-level features. To solve this problem, we propose the Inter-Level Feature Balanced Fusion Network (IFBFNet) to guide the inter-level feature fusion towards a more balanced and effective direction. Our overall network architecture is based on the encoder–decoder architecture. In the encoder, we use a relatively deep convolution network to extract rich semantic information. In the decoder, skip-connections are added to connect and fuse low-level spatial features to restore a clearer boundary expression gradually. We add an inter-level feature balanced fusion module to each skip connection. Additionally, to better capture the boundary information, we added a shallower spatial information stream to supplement more spatial information details. Experiments have proved the effectiveness of our module. Our IFBFNet achieved a competitive performance on the Cityscapes dataset with only finely annotated data used for training and has been greatly improved on the baseline network. Full article
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