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Day 1 11/12/2020
Room #1

Welcome Message by Naveen Chilamkurti 09:00 - 09:05

Melbourne time zone

Welcome Message by EAI Conference Manager 09:05 - 09:10

Wlecome Message by EAI Community Manager 09:10 - 09:15

Keynote Speech Keynote speaker Abbas Jamalipour 09:15 - 10:15

Edge Computing and Aerial Support in 6G Wireless Networks

Coffe Break 10:15 - 10:25

Technical Session 10:25 - 12:25

10:25 - 12:25
Spectrum Sensing and Prediction for 5G Radio

In future wireless networks, it is crucial to find a way to precisely evaluate the degree of spectrum occupation and the exact parameters of free spectrum band at a given moment. This approach enables a secondary user (SU) to dynamically access the spectrum without interfering primary user's (PU) transmission. The known methods of signal detection or spectrum sensing (SS) enable making decision on spectrum occupancy by SU. The machine learning (ML), especially deep learning (DL) algorithms have already proved their ability to improve classic SS methods. However, SS can be insufficient to use the free spectrum efficiently. As an answer to this issue, the prediction of future spectrum state has been introduced. In this paper, three DL algorithms, namely NN, RNN and CNN have been proposed to accurately predict the 5G spectrum occupation in the time and frequency domain with the accuracy of a single resource block (RB). The results have been obtained for two different datasets: the 5G downlink signal with representation of daily traffic fluctuations and the sensor-network uplink signal characteristic for IoT. The obtained results prove DL algorithms usefulness for spectrum occupancy prediction and show significant improvement in detection and prediction for both low signal-to-noise ratio (SNR) and for high SNR compared with reference detection/prediction method discussed in the paper.
Authors: Małgorzata Wasilewska (Poznan University of Technology), Hanna Bogucka (Poznan University of Technology), Adrian Kliks (Poznan University of Technology),
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10:25 - 12:25
Towards Preventing Neighborhood Attacks: Proposal of A New Anonymization’s Approach for Social Networks Data

Anonymization is a crucial process to ensure that published social network data does not reveal sensitive user information. Several anonymization approaches for databases have been adopted to anonymize social network data and prevent the various possible attacks on these networks. In this paper, we will identify an important type of attack on privacy in social networks: "neighborhood attacks". But it is observed that the existing anonymization methods can cause significant errors in certain tasks of analysis of structural properties such as the distance between certain pairs of nodes, the average distance measure "APL", the diameter, the radius, etc. This paper aims at proposing a new approach of anonymization for preventing attacks from neighbors while preserving as much as possible the social distance on which other structural properties are based, notably APL. The approach is based on the principle of adding links to have isomorphic neighborhoods, protect published data from neighborhood attacks and preserve utility on the anonymized social graph. Our various experimental results on real and synthetic data show that the algorithm that combines the addition of false nodes with the addition of links, allows to obtain better results compared to the one based only on the addition of links. They also indicate that our algorithm preserves average distances from the existing algorithm because we add edges between the closest nodes.
Authors: Requi DJOMO (University of Douala), Thomas DJOTIO Thomas (University of Yaounde 1),
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10:25 - 12:25
Research on User Privacy Security of China’s Top Ten Online Game Platforms

The privacy agreement presented to online game users is the basic guarantee of the running of an online game. An online game platform can have access to users’ private information by setting various mandatory clauses. This paper takes the ten most popular online game platforms in China in recent years as examples, using documentary analysis and quantitative analysis to analyze their privacy clauses. The research results show that there are loopholes in protection of users’ private information by online platforms that have gained access and rights to use them. Based on this, it is conducive to the protection of users’ private information through improving information security protection system of online game platforms, adding the option for access denial of privacy information in the process of user registration, and mandatorily prolonging the time assigned for users to read the privacy agreements.
Authors: Lan Yu Cui (Yulin normal university), Mi Qian Su (Yulin Normal University), Yu Chen Wang (Yulin Normal University), Zu Mei Mo (Yulin Normal University), Xiao Yue Liang (Yulin Normal University), Jian He (Yulin Normal University), Xiu Wen Ye (Yulin Normal University),
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10:25 - 12:25
Hybrid Deep-readout Echo State Network and Support Vector Machine with Feature Selection for Human Activity Recognition

Developing sophisticated automated systems for assisting numerous humans such as patients and elder people is a promising future direction. Such smart systems are based on recognizing Activities of Daily Living (ADLs) for providing a suitable decision. Activity recognition systems are currently employed in developing many smart technologies (e.g., smart mobile phone) and their uses have been increased dramatically with availability of Internet of Things (IoT) technology. Numerous machine learning techniques are presented in literature for improving performance of activity recognition. Whereas, some techniques have not been sufficiently exploited with this research direction. In this paper, we shed the light on this issue by presenting a technique based on employing Echo State Network (ESN) for human activity recognition. The presented technique is based on combining ESN with Support Vector Machine (SVM) for improving performance of activity recognition. We also applied feature selection method to the collected data to decrease time complexity and increase the performance. Many experiments are conducted in this work to evaluate performance of the presented technique with human activity recognition. Experiment results have shown that the presented technique provides remarkable performance.
Authors: Shadi Abudalfa (University College of Applied Sciences, Palestine), Kevin Bouchard (UQAC),
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10:25 - 12:25
Performance Evaluation of Energy Detection, Matched Filtering and KNN under Different Noise Models

Due to the broadcast nature of radio transmission, both authorized and unauthor-ized users can access the network, which leads to the increasingly prominent se-curity problems of wireless network. At the same time, it is more difficult to de-tect and identify users in wireless network environment due to the influence of noise. In this paper, the performance of energy detection(ED), matched filtering (MF) and K-nearest neighbor algorithm (KNN) are analyzed under different noise and uncertain noise separately. The Gaussian noise, α-stable distribution noise and Laplace distribution noise models are simulated respectively under the different uncertainty of noise when the false alarm probability is 0.01. The results show that the performance of the detectors is significantly affected by different noise models. In any case, the detection probability of KNN algorithm is the highest; the performance of MF is much better than ED under different noise models; KNN is not sensitive to noise uncertainty; MF has better performance on noise uncertainty which makes ED performance decline fleetly.
Authors: Xiaoyan Wang (School of Information Science and Engineering of Yunnan University, Kunming 650091, China), Jingjing Yang (School of Information Science and Engineering of Yunnan University, Kunming 650091, China), Tengye Yu (Radio Management Office of Honghe Prefecture, Yunnan Province, Mengzi 661199, China), Rui Li (Radio Management Office of Honghe Prefecture, Yunnan Province, Mengzi 661199, China), Ming Huang (School of Information Science and Engineering of Yunnan University, Kunming 650091, China),
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Closing Remarks by Naveen Chilamkurti 12:25 - 12:30

Day 2 12/12/2020