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Day 1 01/10/2020
Room #1

Welcome message by the General Chair Prof. Fadi Al-Turjman 09:00 - 09:00

Starts at 9:00 Turkey time

Welcome message by the Conference Manager 09:00 - 09:00

Welcome message by the EAI Community Manager 09:00 - 09:00

Keynote: Associate Prof. Ahmed Abdelgawad 09:00 - 09:00

Title: All What You Need to Know About Internet of Things (IoT): Build your own IoT architecture

Coffee break 09:00 - 09:00

15 minutes

Session 1 09:00 - 09:00

09:00 - 09:00
Share: a design pattern for the composition of IoT dynamic services

The Internet-of-Things (IoT) is one of the modern technological revolutions that enables communication amongst a plethora of different devices. To date 30 billion devices are connected to the internet more than 75 billion devices are foreseen to be connected worldwide by 2025, a five fold increase in ten years. Devices can have different brands and developers and can be designed to function on a proprietary ecosystem, with separate applications, gateways and tools to support them. This fragmentation can be disastrous in certain industries, such as the medical ones and limit integration between different systems. In this paper, we envision a solution to overcome this interaction problems. We propose \emph{Share} a novel programming standard through a design pattern. This allows on the fly service composition of resource constrained IoT devices. To this ending, IoT devices exchange integration codes which specify the data format and the interaction protocol. The design by contract scheme (DCS) is used to make sure that the matching services verify the constraints dictated by the composition. Unlike other on the fly approaces, \emph{Share} can run on very small and resource costranied devices. \emph{Share} has been implemented by using LUA programming language and has been validated on the ESP30 embedded device.
Authors: leonardo mostarda (Scuola di Scienze e Tecnologie,Universita' degli Studi di, Camerino, Italy), Diletta Cacciagrano (University of Camerino), Fadi AL-TURJMAN (Research Center for AI and IoT, Near East University, Nicosia), culomne rosario (university of Camerino),
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09:00 - 09:00
From Traditional House Price Appraisal to Computer Vision-Based: A survey

As realtors and clients are trying to find a solution that will ease and provide a solution of finding and prizing of houses, a more efficient easy and reliable method which is a computer vision-based house prizing appraisal method has captured the attention of researchers and they are contributing in the study area, in this survey we looked at the trend of housing appraisal from traditional method to the computer vision-based housing appraisal.
Authors: Naser Saleh Mohamed Naser (Department of Electrical and Electronic Engineering, Near East University, Nicosia), Sertan Serte (Department of Electrical and Electronic Engineering, Near East University, Nicosia), Fadi Al-Turjman (Research Center for AI and IoT, Near East University, Nicosia),
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09:00 - 09:00
STUDENT GRADE PREDICTION USING MACHINE LEARNING

The work proposed in this paper, is the application of machine learn-ing techniques in recognizing patterns and predicting student success rate on the bases of their performance on their previous grades. This method was imple-mented with their previous academic data for student present in the tertiary in-stitution. However, the education system of students in Portugal have enhanced during the past decades. Precisely, the inadequate achievement of success in critical courses like Portuguese language and also Mathematics is a grave issue. In this paper we intend to analyze student’s success in tertiary institution using ML techniques. Real-world raw data were received by using existing data from the school. The two core course were modeled, also four ML techniques were tested. The results gotten shows that student success rate can greatly be instigat-ed by their previous performance. With the direct outcome of the research, more adequate predicting tool can also be developed, which improves education qual-ity and enhances resource management for schools. This study is said to in-crease student performance greatly if taken in to consideration.
Authors: Adedoyin Ahmed Hussain (Computer Engineering Dept., Research Centre for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey.), Kamil Dimililer (Electrical & Electronic Engineering Dept., Research Centre for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey.),
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Lunch 09:00 - 09:00

30 minutes

Session 2 09:00 - 09:00

09:00 - 09:00
Classification of IoT Device Communication through Machine Learning Techniques

The Internet of Things (IoT) also called the Internet of Everything is a system of interconnected smart devices that are uniquely identifiable over the network and are capable of autonomous communication of data over the network with or without human-to-computer interaction. These devices have a high level of diversity, heterogeneity, and operates over different computational power, and it is highly necessary to develop a framework that allows us to automatically classify these devices into different classes for better management, security and privacy purposes. Several solutions have been made to solve the problem of automatic device classification, such as network traffic analysis, network protocols analysis, etc. The signal of a device is an important feature that could be utilized to classify various network devices. We proposed a framework to identify network devices based on signal analysis. We have a training data set, produced by collecting Wi-Fi and Bluetooth signals from a geographical area, and established a machine learning model, trained it with the existing data set and used it for the prediction of a network device type (e.g., a Wi-Fi or Bluetooth device) with 100\% accuracy. Furthermore, we applied clustering techniques on the acquired signals to predict the total number of active Wi-Fi devices in a given region and achieved 100\% accuracy for detecting the number of active Wi-Fi devices.
Authors: Zaib Ullah (University of Camerino, Italy), Sheraz Ahmad (University of Camerino, Italy), K. N. R. Surya Vara Prasad (Department of Electrical and Computer Engineering, University of British Columbia, Canada), Leonardo Mostarda (University of Camerino), Fadi Al-Turjman (Research Center for AI and IoT, Near East University, Nicosia),
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09:00 - 09:00
A Framework of Developing Healthcare Application Systems Using 6LoWPAN Based Wireless Sensor Networks

There were an increasing number of applications of Wireless Sensor Networks (WSNs) in healthcare emerging. It has never been clearer to see the advantages and benefits of the application of WSNs in the quality of healthcare in a wide variety of areas. The sensing and communications technology of today have also reached a point where these WSNs can be readily implemented and deployed to function although there are some limits and hinderance from the security concerns. In this paper, we provide a protocol stack that applicable to the WSNs for healthcare systems, and to outline a framework to implement WSNs in three different healthcare settings. Following the proposed framework, we have simulated a WSN-based healthcare application for the settings of hospitals and/or nursing homes for the performance study.
Authors: Zhongwei Zhang (University of Southern Queensland), Jianxiong Wang (University of Southern Queensland), Xiaohua Hu (Hainan Normal University),
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09:00 - 09:00
Active Noise Cancellation for IoT-Driven Electronic Stethoscope: A Comparative Study of Adaptive Filters

Early detection of bowel motility after major abdominal surgery has a significant importance in patient’s healing process. Development of IoT based technologies allowed researchers to develop wearable devices that can be used in healthcare and patient monitoring. There are many studies exist to monitor gastrointestinal tract motility and automatic detection of bowel activity. However, detection of bowel activity suffers from ambient noise. Active noise cancellation (ANC) methods allows us to remove unwanted signal by the addition of a second sound that can be reference to ambient noise. ANC applications mainly used adaptive filter algorithms. In this paper, simulations of ANC application are performed in order to remove ambient noises from bowel sound. Simulation setup is created based on previously developed bio-acoustic sensing device. Performance of four different adaptive filter algorithms; Least Mean Square (LMS), Normalized LMS (NLMS), Recursive Least Square (RLS) and Adaptive Lattice Filter (ALF) are tested. Simulation results are proposed.
Authors: Erdinc Turk (Akdeniz University), Umit Ulusar (Akdeniz University), Guner Ogunc (Akdeniz University), Murat Canpolat (Akdeniz University), Muhittin Yaprak (Akdeniz University),
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Day 2 02/10/2020
Room #1

Session 3 08:00 - 09:00

Starts at 9:00 Turkey time
09:00 - 09:00
RapidAuth: Fast Authentication for Sustainable IoT

The exponential growth in the number of Internet of Things (IoT) devices, the sensitive nature of data they produce, and the simple nature of these devices makes IoT systems vulnerable to a wide range cyber-threats. Physical attacks are one of the major concerns for IoT device security. Security solutions for the IoT have to be accurate and quick since many real time applications depend on the data generated by these devices. In this article we undertake IoT authentication problem by proposing a fast protocol RapidAuth, which also restricts physical attacks. The proposed protocol uses Physical Unclonable Functions to achieve the security goals and requires the exchange of only two messages between the server and the IoT device. The analysis of RapidAuth proves its' robustness against various types of attacks as well as its' efficiency in terms of computation, communication, memory overheads and energy consumption.
Authors: Shehzad Ashraf Chaudhry (Istanbul Gelisim University, Turkey), Muhammad Naveed Aman (National University of Singapore), Fadi Al-Turjman (Near East University),
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09:00 - 09:00
Analysis of Machine Learning Techniques for Lightweight DDoS Attack Detection on IoT Networks

As botnet style distributed denial of service (DDoS) attacks continue to proliferate the Internet of Things (IoT) landscape, researchers have struggled to provide a definitive way of addressing concerns related to the IoT’s security. In this paper, we work from the axiom that DDoS attacks are easiest to detect at the target of the attack but are best mitigated closer to the attacker by implementing four machine learning models that detect botnet-infected DDoS attackers on their access network. These models operate on network packet counts, which can easily be gathered by an access router, and run in real-time or near real-time, even on a low power device, namely a Raspberry Pi. We introduce a novel method for visualizing network activity as graph-ical heatmaps and use convolutional neural network (CNN) models de-signed for embedded devices and mobile platforms to classify network traffic as benign or malicious. We compare this approach using a support vector machine (SVM) and a long short-term memory recurrent neural network (LSTM). Based on our results, we conclude that the use of lightweight CNNs to analyze network traffic through graphical heatmaps provides highly accurate botnet-based DDoS attack detection for IoT access networks, with an average accuracy of 99.8%, despite our training dataset being be-tween 73x – 2170x smaller than those seen in related works, and runtimes ranging from 334 ms to 2 seconds on a Raspberry Pi.
Authors: Eric McCullough (Missouri State University), Razib Iqbal (Missouri State University), Ajay Katangur (Missouri State University),
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09:00 - 09:00
A New Intrusion Detection Scheme using CatBoost Classifier

Advancements in the network infrastructure has caused a positive influence in our day to day life. Many reform initiatives have been taken all over the world which are related to the digitization of the countries methodologies of handling in-formation. The usage of modern techniques also has a drawback, which allows data theft. Hence, a secure system is required which can detect any kind of fraud-ulent activity and alert the administrator. Such a system is called an Intrusion De-tection System (IDS). There are many types of IDSs available at our disposal and a lot of research has also been done on their various types. This paper presents the implementation of IDS based on CatBoost technique which is a part of en-semble machine learning strategy. The results of the implementation have been evaluated on the evaluation metrics like accuracy, precision, recall, and F1-score. The programming environment used is Python. The implementation has been experimented on the NSL-KDD dataset and the results have been ana-lyzed on the detection accuracy, which shows proposed scheme has reached an accuracy of 99.46% on the NSL-KDD dataset.
Authors: Nitesh Singh Bhati (Delhi Technical Campus Greater Noida), Manju Khari (AIACT&R Delhi),
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Coffee break 08:30 - 09:00

15 minutes

Session 4 09:00 - 09:00

09:00 - 09:00
IOT Based Energy Monitoring of PV Plants - An Overview

With the increasing demand of electric power and pressure of mitigating GHG emissions, electric utilities are inclined towards increasing the renewable capacity in their electricity mix. Solar photovoltaic systems, being one of the major contributors in sustainable energy production, cover a vital portion of global cumulative installed renewable capacity. To ensure the optimal efficiency and avoid any forthcoming outage, monitoring of photovoltaic plants is an essential element of integrating renewable into current generation systems. Authors review the types of photovoltaic plants based on configuration and the parameters that are optimal for energy monitoring. It also includes the measuring techniques for the different parameters of monitoring. Familiarity with these parameters and their measuring techniques is essential in development of an efficient photovoltaic energy monitoring system. Various components of these monitoring systems are exposed to extreme weather conditions which reduce their life span. In addition, the efficiency of the photovoltaic modules degrade over time and the cost and complexity of energy monitoring systems limits their usage at a larger scale.
Authors: Ahmad Rasheed (Eastern Mediterranean University), Fadi AL-TURJMAN (Research Center for AI and IoT, Near East University, Nicosia),
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09:00 - 09:00
Light Communication for Controlling Industrial Robots

Optical Wireless Communication (OWC) is regarded as an auspicious communication approach that can outperform the existing wireless technology. It utilizes LED lights, whose subtle variation in radiant intensity generate a binary data stream. This is perceived by a photodiode, that converts it to electric signals for further interpretation. This article aims at exploring the use of this emerging technology in order to control wirelessly industrial robots, overcoming the need for wires, especially in environments where radio waves are not working due to environmental factors or not allowed for safety reasons. We performed experiments to ensure the suitability and efficiency of OWC based technology for the aforementioned scope and "in vitro" tests in various LoS and NLoS configurations to observe the system throughput and reliability. The technology performance in the "clear LoS" and in the presence of a transparent barrier, were also analyzed.
Authors: leonardo mostarda (Scuola di Scienze e Tecnologie,Universita' degli Studi di, Camerino, Italy), Diletta Cacciagrano (University of Camerino), Fadi AL-TURJMAN (Research Center for AI and IoT, Near East University, Nicosia), Zaib Ullah (University of Camerino, Italy), Mattia Paccamiccio (university of Camerino),
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09:00 - 09:00
Design Of A Navigation System For The Blind/Visually Impaired

Since individuals with needs in the general public increased, the work introduced is a navigation system that will give a solid and durable obstacle detection and environmental imager and navigation for the user. It provides minimal cost system to permit navigation. The obstacle detection is to distinguish the deterrent and guide the visually impaired (VIP) about a suitable pathway. The framework utilizes sensor based obstacle detection, and sends back buzzer or audio sound as a reaction that warns the VIP about position. The primary technique utilized by each blind or visually impaired is the strolling stick for identifying deterrent in which its functionality is restricted, it doesn't secure territories close to the head let alone all obstacle. This framework acquires data about impediments close to the head and provides the right pathway for the VIP. When utilized with a mobile stick, the VIP is completely ensured against a snag, and the route is made simple. The environmental imager and navigation mode is the sound and visual guide for the VIP which permits users to just touch a button and proposed destination to the caregiver. This includes GPS and live video feed direction. The general system is versatile and can be conveyed by a VIP. The accuracy achieved for the system differs from 94.15% to 99.72%. The percentage rate of the snag discovery for either indoor or outside varies from 95.40% to 99.67%. This examination will Increase the VIP mobility significantly.
Authors: Yoney Kirsal Ever (Near East University), Adedoyin Hussain (Computer Engineering Dept., and Research Centre for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey.), Fadi AL-TURJMAN (Research Center for AI and IoT, Near East University, Nicosia), Eser Gemikonakli (University of Kyrenia),
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09:00 - 09:00
6G Applications and Standards - An Overview

Reliable data access is essential to an increasingly smart, au-tomated and pervasive digital environment. Mobile net-works are very important and, in a fully connected, smart digital world, everything needs to be linked, from people to vehicles, sensors, software, cloud services and even robotic agents. 5 G wireless networks currently being deployed of-fer significant enhancements beyond LTE, but may not sat-isfy the full networking requirements of the growing digital society. This paper outlines technology which are intended to convert the sixth-generation 6 G wireless network and which we consider to be an enabler for several potential cases of 6 G use. We offer a detailed system-level perspec-tive on 6 G scenarios and specifications, frameworks, standards, research activities and 6 G technology that can either be addressed by enhancing the 5 G architecture or implementing entirely new communication paradigms.
Authors: Suleiman Abdullahi Ali (Near East University), Fadi Fadi Al-Turjman (Research center for AI and IoT, Near East University, Nicosia,),
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Closing speech by the General Chair 09:00 - 09:00