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Selected Papers from the International Conference on Big Data Computing and Communications

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 6067

Special Issue Editors


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Guest Editor
Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China
Interests: Internet of things; data mining; edge computing; mobile computing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science and Technology, University of Science and Technology of China (USTC) Hefei, Anhui, China
Interests: Networking Algorithm Design and System Implementation; Edge Computing; Mobile Computing

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Guest Editor
School of Cyber Science and Engineering Wuhan University, Wuhan 430000, China
Interests: optimization algorithms; edge and cloud computing; 5G; crowdsourcing; IoT; machine learning; federated learning
School of Software, Dalian University of Technology, Dalian 116621, China
Interests: underwater optical communication; underwater positioning; underwater wireless sensor network
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science and Technology, Jilin University, Changchun 130012, China
Interests: Mobile Crowdsensing; Mobile Computing; Data Mining
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: remote health monitoring; transportation systems
Department of Computer Science and Engineering Shanghai Jiao Tong University, Shanghai 200240, China
Interests: Mobile Edge Computing; UAV Edge Intelligence; Federated Learning for Wireless Networks

Special Issue Information

Dear Colleagues,

The 7th International Conference on Big Data Computing and Communications (BIGCOM 2021) will be held from 13 to 15 August 2021 in Deqing, China. More information can be found by visiting the conference website (http://staff.ustc.edu.cn/~bigcom2021/index.html).

This conference will provide an important platform for scholars, engineers, and students to showcase their innovative research outcomes, transfer new knowledge, and boost interdisciplinary collaboration in areas of the frontier topics in theoretical and applied engineering and computer science.

Big Data lies at the center of modern science and technology, with major advances in analysis and learning from Big Data concurrently reshaping human knowledge, society, and the economy. The overwhelming amounts of data generated in many applications (fundamental sciences, cyber-physical systems, smart cities, sensor networks, and many more) alongside the urge for fast and effective handling and decision-making, in real-time, pose a number of significant challenges on the underlying system design and methods.

This Special Issue of Sensors (IF: 3.275, ISSN 1424-8220) will present a collection of outstanding papers presented at BIGCOM 2021. Conference participants are invited to submit extended versions of their conference papers to this Special Issue.

Potential topics include, but are not limited to, the following:

Computing

Data-Intensive Parallel and Distributed Computing (Clouds, Clusters, Grids, P2P)
Performance Analysis of Big Data Tools and Applications
Computing on Network Edge
Large-scale Optimization Methods

Mining

Modeling and Algorithms for Big Data Analytics and Data Mining
Knowledge Mining from Big data
Network and Graph Mining
High Dimensional Data Mining

Security

Security and Legal Issues in Mobile Applications/Apps
Privacy of Open Data
Trust and Privacy Issues in Mobile Commerce Environment
Cryptographic Protocols

Networking

Artificial Intelligence and Internet of Things
Cyber-Physical Systems
Wireless Networks
Multi-agent Systems

Management

Big Data Storage, Indexing, Searching, and Querying
Big Data Quality Management
Heterogeneous Data Management
Database and Data Warehouse Technologies for Big data

Applications

Infrastructures and Frameworks of Data-Driven Applications
Mobile Device Apps Based on Big Data
Big Data for Healthcare
Big Data for Smart Cities

Prof. Dr. Haipeng Dai
Prof. Dr. Haisheng Tan
Prof. Dr. Ruiting Zhou
Prof. Dr. Chi Lin
Prof. Dr. En Wang
Prof. Dr. Ruipeng Gao
Dr. Yuben Qu
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)

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Research

14 pages, 3903 KiB  
Article
WiFi Signal-Based Gesture Recognition Using Federated Parameter-Matched Aggregation
by Weidong Zhang, Zexing Wang and Xuangou Wu
Sensors 2022, 22(6), 2349; https://doi.org/10.3390/s22062349 - 18 Mar 2022
Cited by 6 | Viewed by 2174
Abstract
Gesture recognition plays an important role in smart homes, such as human–computer interaction, identity authentication, etc. Most of the existing WiFi signal-based approaches exploit a large number of channel state information (CSI) datasets to train a gestures classification model; however, these models require [...] Read more.
Gesture recognition plays an important role in smart homes, such as human–computer interaction, identity authentication, etc. Most of the existing WiFi signal-based approaches exploit a large number of channel state information (CSI) datasets to train a gestures classification model; however, these models require a large number of human participants to train, and are not robust to the recognition environment. To address this problem, we propose a WiFi signal-based gesture recognition system with matched averaging federated learning (WiMA). Since there are differences in the distribution of WiFi signal changes caused by the same gesture in different environments, the traditional federated parameter average algorithm seriously affects the recognition accuracy of the model. In WiMA, we exploit the neuron arrangement invariance of neural networks in parameter aggregation, which can improve the robustness of the gesture recognition model with heterogeneous CSI data of different training environments. We carried out experiments with seven participant users in a distributed gesture recognition environment. Experimental results show that the average accuracy of our proposed system is up to 90.4%, which is very close to the accuracy of state-of-the-art approaches with centralized training models. Full article
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21 pages, 6990 KiB  
Article
WiPg: Contactless Action Recognition Using Ambient Wi-Fi Signals
by Zhanjun Hao, Juan Niu, Xiaochao Dang and Zhiqiang Qiao
Sensors 2022, 22(1), 402; https://doi.org/10.3390/s22010402 - 5 Jan 2022
Cited by 3 | Viewed by 2567
Abstract
Motion recognition has a wide range of applications at present. Recently, motion recognition by analyzing the channel state information (CSI) in Wi-Fi packets has been favored by more and more scholars. Because CSI collected in the wireless signal environment of human activity usually [...] Read more.
Motion recognition has a wide range of applications at present. Recently, motion recognition by analyzing the channel state information (CSI) in Wi-Fi packets has been favored by more and more scholars. Because CSI collected in the wireless signal environment of human activity usually carries a large amount of human-related information, the motion-recognition model trained for a specific person usually does not work well in predicting another person’s motion. To deal with the difference, we propose a personnel-independent action-recognition model called WiPg, which is built by convolutional neural network (CNN) and generative adversarial network (GAN). According to CSI data of 14 yoga movements of 10 experimenters with different body types, model training and testing were carried out, and the recognition results, independent of bod type, were obtained. The experimental results show that the average correct rate of WiPg can reach 92.7% for recognition of the 14 yoga poses, and WiPg realizes “cross-personnel” movement recognition with excellent recognition performance. Full article
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