Towards Self-Organizing Internet of Things - Aware Systems for Online Sales

2015 ◽  
Author(s):  
Yukiko Yamamoto ◽  
Takashi Kawabe ◽  
Setsuo Tsuruta ◽  
Ernesto Damiani ◽  
Atsuo Yoshitaka ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Online Sales ◽  
Self Organizing

scholarly journals Functioning of the Internet of Things Networks with Moving Nodes

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 31-36
Author(s):  
A. Marochkina ◽  
А. Paramonov
Keyword(s):  
Internet Of Things ◽  
Network Traffic ◽  
The Internet ◽  
Traffic Stream ◽  
The Internet Of Things ◽  
Area Of Application ◽  
Self Organizing

The area of application for the Internet of Things networks is vast. One of the main uses for such a net-work is the organization of network traffic. A traffic stream can be considered as a self-organizing net-work with moving nodes. This article describes the various features of such networks. Models with vari-ous mobility, velocity and density parameters of nodes are considered for studying the routes in this networks.

scholarly journals An Efficient Tree-Based Self-Organizing Protocol for Internet of Things

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 3535-3546 ◽  
Author(s):  
Tie Qiu ◽  
Xize Liu ◽  
Lin Feng ◽  
Yu Zhou ◽  
Kaiyu Zheng
Keyword(s):  
Internet Of Things ◽  
Self Organizing

An Efficient Multi-Path Self-Organizing Strategy in Internet of Things

2013 ◽  
Vol 73 (4) ◽  
pp. 1613-1629 ◽  
Author(s):  
Tie Qiu ◽  
Weifeng Sun ◽  
Yuanchao Bai ◽  
Yu Zhou
Keyword(s):  
Internet Of Things ◽  
Self Organizing

scholarly journals Growing self-organizing tree-based kernel smoother for machine learning and data mining

2021 ◽  
Author(s):  
Bohan Zheng
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
Multivariate Regression ◽  
Regression Tree ◽  
Local Regression ◽  
Self Organizing Maps ◽  
Electrical Vehicle ◽  
Self Organizing

With Internet of Things (IoT) being prevalently adopted in recent years, traditional machine learning and data mining methods can hardly be competent to deal with the complex big data problems if applied alone. However, hybridizing those who have complementary advantages could achieve optimized practical solutions. This work discusses how to solve multivariate regression problems and extract intrinsic knowledge by hybridizing Self-Organizing Maps (SOM) and Regression Trees. A dual-layer SOM map is developed in which the first layer accomplishes unsupervised learning and then regression tree layer performs supervised learning in the second layer to get predictions and extract knowledge. In this framework, SOM neurons serve as kernels with similar training samples mapped so that regression tree could achieve regression locally. In this way, the difficulties of applying and visualizing local regression on high dimensional data are overcome. Further, we provide an automated growing mechanism based on a few stop criteria without adding new parameters. A case study of solving Electrical Vehicle (EV) range anxiety problem is presented and it demonstrates that our proposed hybrid model is quantitatively precise and interpretive. key words: Multivariate Regression, Big Data, Machine Learning, Data Mining, Self-Organizing Maps (SOM), Regression Tree, Electrical Vehicle (EV), Range Estimation, Internet of Things (IoT)

scholarly journals Self-Organizing Approximation Command Filtered Backstepping Control for Higher Order SISO Systems in Internet of Things

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 5400-5411 ◽  
Author(s):  
Qiusheng He ◽  
Wei Chen ◽  
Haitao Wang ◽  
Qingsong Hu ◽  
Tongfeng Sun ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Higher Order ◽  
Backstepping Control ◽  
Siso Systems ◽  
Self Organizing

Azimuth Tree-Based Self-organizing Protocol for Internet of Things

2020 ◽  
pp. 342-356
Author(s):  
Veena Anand ◽  
Prerana Agrawal ◽  
Pothuri Surendra Varma ◽  
Sudhakar Pandey ◽  
Siddhant Kumar
Keyword(s):  
Internet Of Things ◽  
Self Organizing

Design of Real time Vital Signal Streaming Service Based on Self-Organizing Internet of Things Platform

2017 ◽  
Vol 23 (7) ◽  
pp. 434-439
Author(s):  
Hyunho Kim ◽  
Taeyoung Son ◽  
Soonju Kang
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Streaming Service ◽  
Self Organizing

Research of Energy-Saving Wireless Self-Organizing Multy-Protocol Network for Internet of Things

2019 ◽  
Author(s):  
Margarita V. Ushakova ◽  
Yuriy A. Ushakov ◽  
Petr N. Polezhaev ◽  
Alexandr E. Shukhman
Keyword(s):  
Internet Of Things ◽  
Energy Saving ◽  
Self Organizing
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