sensors-logo

Journal Browser

Journal Browser

Selected Papers from the International Conference on Internet of Things and Intelligent Applications (ITIA2020)

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

Deadline for manuscript submissions: closed (30 October 2021) | Viewed by 6287

Special Issue Editors


E-Mail Website
Guest Editor
1. College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
2. School of Engineering, College of Science, University of Lincoln, Lincoln LN6 7TS, UK
Interests: Internet of Things; sensor networks; green computing; cloud and fog computing; fault diagnosis; wireless sensor networks; multimedia communication; middleware; security
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
University of Missouri–Kansas City, Kansas City, USA
Interests: software security; access control; software-defined networking; software engineering

E-Mail Website
Guest Editor
Department of Electronic and Computer Engineering, Brunel University London, Middlesex UB8 3PH, UK
Interests: high-performance computing (grid and cloud computing); big data analytics; intelligent systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Fordham University, New York, USA
Interests: data science; big data; bioinformatics/health informatics; fintech; cybersecurity

Special Issue Information

Dear Colleagues,

The International Conference on Internet of Things and Intelligent Applications (ITIA2020) will be held on November 27–29, 2020, in Zhenjiang, China (http://itia.xintongconference.com/Page).

ITIA2020 will be an excellent international conference for sharing knowledge and results in the theory, methodology, and applications of the Internet of things and artificial intelligence, and their applications in agriculture, education, etc. Authors of selected papers from the conference will be invited to submit extended versions of their original papers and contributions on conference topics (New papers closely related to the conference themes are also welcome).

Prof. Dr. Lei Shu
Prof. Dr. Dianxiang Xu
Prof. Dr. Maozhen Li
Prof. Dr. Henry Han
Dr. Leandros Maglaras
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.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

16 pages, 8501 KiB  
Article
Semantic Point Cloud Segmentation Using Fast Deep Neural Network and DCRF
by Yunbo Rao, Menghan Zhang, Zhanglin Cheng, Junmin Xue, Jiansu Pu and Zairong Wang
Sensors 2021, 21(8), 2731; https://doi.org/10.3390/s21082731 - 13 Apr 2021
Cited by 5 | Viewed by 2060
Abstract
Accurate segmentation of entity categories is the critical step for 3D scene understanding. This paper presents a fast deep neural network model with Dense Conditional Random Field (DCRF) as a post-processing method, which can perform accurate semantic segmentation for 3D point cloud scene. [...] Read more.
Accurate segmentation of entity categories is the critical step for 3D scene understanding. This paper presents a fast deep neural network model with Dense Conditional Random Field (DCRF) as a post-processing method, which can perform accurate semantic segmentation for 3D point cloud scene. On this basis, a compact but flexible framework is introduced for performing segmentation to the semantics of point clouds concurrently, contribute to more precise segmentation. Moreover, based on semantics labels, a novel DCRF model is elaborated to refine the result of segmentation. Besides, without any sacrifice to accuracy, we apply optimization to the original data of the point cloud, allowing the network to handle fewer data. In the experiment, our proposed method is conducted comprehensively through four evaluation indicators, proving the superiority of our method. Full article
Show Figures

Figure 1

13 pages, 3027 KiB  
Article
Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking
by Zhiyuan Li and Ershuai Peng
Sensors 2021, 21(3), 955; https://doi.org/10.3390/s21030955 - 01 Feb 2021
Cited by 8 | Viewed by 2308
Abstract
With the development of smart vehicles and various vehicular applications, Vehicular Edge Computing (VEC) paradigm has attracted from academic and industry. Compared with the cloud computing platform, VEC has several new features, such as the higher network bandwidth and the lower transmission delay. [...] Read more.
With the development of smart vehicles and various vehicular applications, Vehicular Edge Computing (VEC) paradigm has attracted from academic and industry. Compared with the cloud computing platform, VEC has several new features, such as the higher network bandwidth and the lower transmission delay. Recently, vehicular computation-intensive task offloading has become a new research field for the vehicular edge computing networks. However, dynamic network topology and the bursty computation tasks offloading, which causes to the computation load unbalancing for the VEC networking. To solve this issue, this paper proposed an optimal control-based computing task scheduling algorithm. Then, we introduce software defined networking/OpenFlow framework to build a software-defined vehicular edge networking structure. The proposed algorithm can obtain global optimum results and achieve the load-balancing by the virtue of the global load status information. Besides, the proposed algorithm has strong adaptiveness in dynamic network environments by automatic parameter tuning. Experimental results show that the proposed algorithm can effectively improve the utilization of computation resources and meet the requirements of computation and transmission delay for various vehicular tasks. Full article
Show Figures

Figure 1

Back to TopTop