DEVELOPMENT OF KONNECT-X EMBEDDED PLATFORM FOR INDUSTRIAL IoT

2017 ◽  
Vol 6 (1) ◽  
pp. 10
Author(s):  
SHREEKANTH T. ◽  
SOWRABHU D ◽  
◽  
Keyword(s):  
Embedded Platform ◽  
Industrial Iot

Industrial IoT Fog Node Adaptation in Complex Network Environment

2019 ◽  
Author(s):  
Shuaitian Wang ◽  
Yuxi Liu ◽  
Yanling Zhang ◽  
Linghe Kong ◽  
Guihai Chen
Keyword(s):  
Complex Network ◽  
Network Environment ◽  
Industrial Iot

scholarly journals 5G Mobile Services and Scenarios: Challenges and Solutions

2018 ◽  
Vol 10 (10) ◽  
pp. 3626 ◽  
Author(s):  
Yousaf Zikria ◽  
Sung Kim ◽  
Muhammad Afzal ◽  
Haoxiang Wang ◽  
Mubashir Rehmani
Keyword(s):  
Mobile Network ◽  
Future Internet ◽  
Autonomous Driving ◽  
Mobile Services ◽  
Data Traffic ◽  
Fifth Generation ◽  
Wide Range ◽  
Industrial Iot ◽  
5G Network ◽  
Massive Number

The Fifth generation (5G) network is projected to support large amount of data traffic and massive number of wireless connections. Different data traffic has different Quality of Service (QoS) requirements. 5G mobile network aims to address the limitations of previous cellular standards (i.e., 2G/3G/4G) and be a prospective key enabler for future Internet of Things (IoT). 5G networks support a wide range of applications such as smart home, autonomous driving, drone operations, health and mission critical applications, Industrial IoT (IIoT), and entertainment and multimedia. Based on end users’ experience, several 5G services are categorized into immersive 5G services, intelligent 5G services, omnipresent 5G services, autonomous 5G services, and public 5G services. In this paper, we present a brief overview of 5G technical scenarios. We then provide a brief overview of accepted papers in our Special Issue on 5G mobile services and scenarios. Finally, we conclude this paper.

Load Balancing for Reliable Self-Organizing Industrial IoT Networks

2019 ◽  
Vol 15 (9) ◽  
pp. 5052-5063 ◽  
Author(s):  
M. Carmen Lucas-Estan ◽  
Javier Gozalvez
Keyword(s):  
Load Balancing ◽  
Industrial Iot ◽  
Self Organizing

A Novel Framework of Three-Hierarchical Offloading Optimization for MEC in Industrial IoT Networks

2020 ◽  
Vol 16 (8) ◽  
pp. 5424-5434 ◽  
Author(s):  
Zichao Zhao ◽  
Rui Zhao ◽  
Junjuan Xia ◽  
Xianfu Lei ◽  
Dong Li ◽  
...  
Keyword(s):  
Industrial Iot

scholarly journals Autonomous Interference Mapping for Industrial IoT Networks over Unlicensed Bands

2020 ◽  
pp. 0-0
Author(s):  
Simone Grimaldi ◽  
Aamir Mahmood ◽  
Syed Ali Hassan ◽  
Mikael Gidlund ◽  
Gerhard P. Hancke
Keyword(s):  
Industrial Iot ◽  
Unlicensed Bands ◽  
Interference Mapping

scholarly journals A Single-Trial P300 Detector Based on Symbolized EEG and Autoencoded-(1D)CNN to Improve ITR Performance in BCIs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3961
Author(s):  
Daniela De Venuto ◽  
Giovanni Mezzina
Keyword(s):  
Recognition Performance ◽  
Low Complexity ◽  
Single Trial ◽  
Eeg Signals ◽  
Speed Increase ◽  
P300 Speller ◽  
Embedded Platform ◽  
Temporal Features ◽  
High Recognition Accuracy ◽  
Car Driving

In this paper, we propose a breakthrough single-trial P300 detector that maximizes the information translate rate (ITR) of the brain–computer interface (BCI), keeping high recognition accuracy performance. The architecture, designed to improve the portability of the algorithm, demonstrated full implementability on a dedicated embedded platform. The proposed P300 detector is based on the combination of a novel pre-processing stage based on the EEG signals symbolization and an autoencoded convolutional neural network (CNN). The proposed system acquires data from only six EEG channels; thus, it treats them with a low-complexity preprocessing stage including baseline correction, windsorizing and symbolization. The symbolized EEG signals are then sent to an autoencoder model to emphasize those temporal features that can be meaningful for the following CNN stage. This latter consists of a seven-layer CNN, including a 1D convolutional layer and three dense ones. Two datasets have been analyzed to assess the algorithm performance: one from a P300 speller application in BCI competition III data and one from self-collected data during a fluid prototype car driving experiment. Experimental results on the P300 speller dataset showed that the proposed method achieves an average ITR (on two subjects) of 16.83 bits/min, outperforming by +5.75 bits/min the state-of-the-art for this parameter. Jointly with the speed increase, the recognition performance returned disruptive results in terms of the harmonic mean of precision and recall (F1-Score), which achieve 51.78 ± 6.24%. The same method used in the prototype car driving led to an ITR of ~33 bit/min with an F1-Score of 70.00% in a single-trial P300 detection context, allowing fluid usage of the BCI for driving purposes. The realized network has been validated on an STM32L4 microcontroller target, for complexity and implementation assessment. The implementation showed an overall resource occupation of 5.57% of the total available ROM, ~3% of the available RAM, requiring less than 3.5 ms to provide the classification outcome.

scholarly journals Comparing Wi-Fi 6 and 5G downlink performance for Industrial IoT

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Roberto Maldonado ◽  
Anders Karstensen ◽  
Guillermo Pocovi ◽  
Ali Esswie ◽  
Claudio Rosa ◽  
...  
Keyword(s):  
Industrial Iot

Industrial IoT Security Concept with Extended ISO/IEC/IEEE 21450 TEDS

2020 ◽  
Author(s):  
Tobias Mitterer ◽  
Leander B. Hormann ◽  
Hans-Peter Bernhard ◽  
Peter Priller ◽  
Hubert Zangl
Keyword(s):  
Iot Security ◽  
Industrial Iot
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