Faculty of Engineering, Applied Science and Technology

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Browsing Faculty of Engineering, Applied Science and Technology by Title

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    AI-based Energy Model for Adaptive Duty Cycle Scheduling in Wireless Networks
    (Institute of Electrical and Electronics Engineers Inc., 2021) Charef, Nadia ; Mnaouer, Adel Ben ; Bouachir, Ouns
    The vast distribution of low-power devices in IoT applications requires robust communication technologies that ensure high-performance level in terms of QoS, light-weight computation, and security. Advanced wireless technologies (i.e. 5G and 6G) are playing an increasing role in facilitating the deployment of IoT applications. To prolong the network lifetime, energy harvesting is an essential technology in wireless networks. Nevertheless, maintaining energy sustainability is difficult when considering high QoS requirements in IoT. Therefore, an energy management technique that ensures energy efficiency and meets QoS is needed. Energy efficiency in duty cycling solutions needs novel energy management techniques to address these challenges and achieve a trade-off between energy efficiency and delay. Predictive models (i.e., based on AI and ML techniques) represent useful tools that encapsulate the stochastic nature of harvested energy in duty cycle scheduling. The conventional predictive model relies on environmental parameters to estimate the harvested energy. Instead, Artificial Intelligence (AI) allows for recursive prediction models that rely on past behavior of harvested and consumed energy. This is useful to achieve better precision in energy estimation and extend the limit beyond predictive models directed solely for energy sources that exhibit periodic behavior. In this paper, we explore the usage of a ML model to enhance the performance of duty cycle scheduling. The aim is to improve the QoS performance of the proposed solution. To assess the performance of the proposed model, it was simulated using the INET framework of the OMNet++ simulation environment. The results are compared to an enhanced IEEE 802.15.4 MAC protocol from the literature. The results of the comparative study show clear superiority of the proposed AI-based protocol that testified to better use of energy estimation for better management of the duty cycling at the MAC sublayer. © 2021 IEEE.
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    Arithmetic properties of complex fibonacci numbers and fibonacci quaternions
    (SAS International Publications, 2021-09) Leung, Ho-Hon ; Kamalov, Firuz
    In this paper, we investigate certain arithmetic properties of complex Fibonacci numbers and Fibonacci quaternions. More specifically, we look at the divisibility properties of complex Fibonacci numbers and Fibonacci quaternions. Our results make use of some well-known Fibonacci identities. Since quaternions are non-commutative algebra, extra care has been taken to investigate the various divisibility properties of the Fibonacci quaternions. © SAS International Publications.
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    Autocorrelation for time series with linear trend
    (Institute of Electrical and Electronics Engineers Inc., 2021-09-29) Kamalov, Firuz ; Thabtah, Fadi ; Gurrib, Ikhlaas
    The autocorrelation function (ACF) is a fundamental concept in time series analysis including financial forecasting. In this note, we investigate the properties of the sample ACF for a time series with linear trend. In particular, we show that the sample ACF of the time series approaches 1 for all lags as the number of time steps increases. The theoretical results are supported by numerical experiments. Our result helps researchers better understand the ACF patterns and make correct ARMA selection. © 2021 IEEE.
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    Autoencoder-based Intrusion Detection System
    (Institute of Electrical and Electronics Engineers Inc., 2021) Kamalov, Firuz ; Zgheib, Rita ; Leung, Ho Hon ; Al-Gindy, Ahmed ; Moussa, Sherif
    Given the dependence of the modern society on networks, the importance of effective intrusion detection systems (IDS) cannot be underestimated. In this paper, we consider an autoencoder-based IDS for detecting distributed denial of service attacks (DDoS). The advantage of autoencoders over traditional machine learning methods is the ability to train on unlabeled data. As a result, autoencoders are well-suited for detecting unknown attacks. The key idea of the proposed approach is that anomalous traffic flows will have higher reconstruction loss which can be used to flag the intrusions. The results of numerical experiments show that the proposed method outperforms benchmark unsupervised algorithms in detecting DDoS attacks. © 2021 IEEE.
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    An Autonomous Multi-Variable Outdoor Air Quality Mapping Wireless Sensors IoT Node for Qatar
    (Institute of Electrical and Electronics Engineers Inc., 2020-06) Tariq, Hasan ; Abdaoui, Abderrazak ; Touati, Farid ; Al-Hitmi, Mohammed Abdulla E ; Crescini, Damiano ; Mnaouer, Adel Ben
    In all outdoor long haul air quality monitoring and mapping applications, sensing accuracy, the sustainability of telemetry, and resilience in node systems and operation are major challenges. The severity of these challenges varies depending on the moderateness and harshness of the climate of observation. In this work, an autonomous environmentally powered, sensors self-diagnostic/calibration, and context-aware IoT-based telemetry for multi-variable sensing node is being proposed. In this node, photo-voltaic and piezoelectric energy harvesters contributed to self-calibration and sustainable measurement of temperature (in °C), humidity (in %), pressure (in bar), geo-position (in NMEA format), volatile organic compounds-VOC (in ppm), particulate matter PM (in ppm), ozone (in Dobson Unit), Carbon mono-oxide (in ppm), Nitrogen dioxide (in ppm), and Sulphur dioxide (in ppm). Results have shown that the proposed system worked autonomously for days and optimized the real-time air quality mapping for the chosen geo-spatial cluster, i.e. Qatar University. © 2020 IEEE.
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    Autoregressive and neural network models: A comparative study with linearly lagged series
    (Institute of Electrical and Electronics Engineers Inc., 2021-09-29) Kamalov, Firuz ; Gurrib, Ikhlaas ; Thabtah, Fadi
    Time series analysis such as stock price forecasting is an important part of financial research. In this regard, autoregressive (AR) and neural network (NN) models offer contrasting approaches to time series modeling. Although AR models remain widely used, NN models and their variant long short-term memory (LSTM) networks have grown in popularity. In this paper, we compare the performance of AR, NN, and LSTM models in forecasting linearly lagged time series. To test the models we carry out extensive numerical experiments based on simulated data. The results of the experiments reveal that despite the inherent advantage of AR models in modeling linearly lagged data, NN models perform just as well, if not better, than AR models. Furthermore, the NN models outperform LSTMs on the same data. We find that a simple multi-layer perceptron can achieve highly accurate out of sample forecasts. The study shows that NN models perform well even in the case of linearly lagged time series. © 2021 IEEE.
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    Batch-based power-controlled channel assignment for improved throughput in software-defined networks
    (Institute of Electrical and Electronics Engineers Inc., 2019) Salameh, Haythem Bany ; Musa, Ahmed ; Outoom, Ruba ; Halloush, Rami ; Aloqaily, Moayad ; Jararweh, Yaser
    Software-defined networking (SDN) along with transmission power control (TPC) have a great potential in enabling efficient wireless networking. Power control aims at increasing network throughput, while SDN provides cognition and intelligent capabilities to network devices. The key challenge in enabling efficient operation of such networks is how to perform efficient power-controlled MAC protocols that includes channel assignment and power allocation such that network throughput is enhanced while using the least number of channels. Traditional MAC protocols for SDNs employ an exclusive channel-occupancy between neighboring secondary users (SUs), which significantly limits network performance. In this paper, we develop a novel power-controlled spectrum access protocol for SDNs based on the interference-channel occupancy model with the objective of increasing network throughput. It allows several concurrent interference-limited transmissions to simultaneously proceed over the same channel in the same neighborhood. Unlike most of previous power-control MAC protocols that perform the channel assignment and power allocation sequentially, our protocol simultaneously makes distributed channel and power assignment decisions for multiple SU transmissions (batch-based method). Batching can be achieved by using an admission control window for SUs to exchange their collision-avoidance control information. Simulation results reveal that compared with CSMA/CA variants, our protocol greatly improve spectrum efficiency, which improves network throughput while reducing energy consumption. © 2019 IEEE.
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    Cluster aware mobility encounter dataset enlargement
    (Institute of Electrical and Electronics Engineers Inc., 2019) Haldar, Rajarshi ; Bacanli, Salih Safa ; Aloqaily, Moayad ; Mnaouer, Adel Ben ; Turgut, Damla
    The recent emerging fields in data processing and manipulation has facilitated the need for synthetic data generation. This is also valid for mobility encounter dataset generation. Synthetic data generation might be useful to run research-based simulations and also create mobility encounter models. Our approach in this paper is to generate a larger dataset by using a given dataset which includes the clusters of people. Based on the cluster information, we created a framework. Using this framework, we can generate a similar dataset that is statistically similar to the input dataset. We have compared the statistical results of our approach with the real dataset and an encounter mobility model generation technique in the literature. The results showed that the created datasets have similar statistical structure with the given dataset. © 2019 IEEE.
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    Cognitive internet of things for smart water pipeline monitoring system
    (Institute of Electrical and Electronics Engineers Inc., 2019) Abdelhafidh, Maroua ; Mohamed, Fourati ; Fourati, Lamia Chaari ; Mnaouer, Adel Ben ; Mokhtar, Zid ; Permission to reuse the abstract has been secured from the Publisher.
    Water Pipeline Monitoring System (WPMS) is extremely important considering the several pipeline damages and the various hydraulic failures that cause a critical water loss. In this context, Cognitive Water Distribution System integrates Internet of Things (IoT) technology, based on smart sensors, actuators and connected objects, with a reliable Big Data processing for smart and robust Structural Health Monitoring (SHM) of pipelines. In this paper, we propose a cognitive IoT-based architecture where we used Apache Spark framework to maintain a real time processing of the large amount of collected data. This efficient processing of measured data and its correspondent calculated values simplify the transient simulations and leak detection and make it faster and easier. © 2018 IEEE.
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    Comparative analysis of activation functions in neural networks
    (Institute of Electrical and Electronics Engineers Inc., 2021) Kamalov, Firuz ; Nazir, Amril ; Safaraliev, Murodbek ; Cherukuri, Aswani Kumar ; Zgheib, Rita
    Although the impact of activations on the accuracy of neural networks has been covered in the literature, there is little discussion about the relationship between the activations and the geometry of neural network model. In this paper, we examine the effects of various activation functions on the geometry of the model within the feature space. In particular, we investigate the relationship between the activations in the hidden and output layers, the geometry of the trained neural network model, and the model performance. We present visualizations of the trained neural network models to help researchers better understand and intuit the effects of activation functions on the models. © 2021 IEEE.
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    Data and service management in densely crowded environments : challenges, opportunities, and recent developments
    (Institute of Electrical and Electronics Engineers Inc., 2019) Aloqaily, Moayad ; Ridhawi, Ismaeel Al ; Salameh, Haythem Bany ; Jararweh, Yaser
    Densely crowded environments such as stadiums and metro stations have shown shortcomings when users request data and services simultaneously. This is due to the excessive amount of requested and generated traffic from the user side. Based on the wide availability of user smart-mobile devices, and noting their technological advancements, devices are not being categorized only as data/service requesters anymore, but are readily being transformed to data/service providing network-side tools. In essence, to offload some of the workload burden from the cloud, data can be either fully or partially replicated to edge and mobile devices for faster and more efficient data access in such dense environments. Moreover, densely crowded environments provide an opportunity to deliver, in a timely manner, through node collaboration, enriched user-specific services using the replicated data and device-specific capabilities. In this article, we first highlight the challenges that arise in densely crowded environments in terms of data/service management and delivery. Then we show how data replication and service composition are considered promising solutions for data and service management in densely crowded environments. Specifically, we describe how to replicate data from the cloud to the edge, and then to mobile devices to provide faster data access for users. We also discuss how services can be composed in crowded environments using service-specific overlays. We conclude the article with most of the open research areas that remain to be investigated. © 2019 IEEE.
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    Data imbalance in classification : experimental evaluation
    (Elsevier Inc., 2020-03) Thabtah, Fadi ; Hammoud, Suhel ; Kamalov, Firuz ; Gonsalves, Amanda
    The advent of Big Data has ushered a new era of scientific breakthroughs. One of the common issues that affects raw data is class imbalance problem which refers to imbalanced distribution of values of the response variable. This issue is present in fraud detection, network intrusion detection, medical diagnostics, and a number of other fields where negatively labeled instances significantly outnumber positively labeled instances. Modern machine learning techniques struggle to deal with imbalanced data by focusing on minimizing the error rate for the majority class while ignoring the minority class. The goal of our paper is demonstrate the effects of class imbalance on classification models. Concretely, we study the impact of varying class imbalance ratios on classifier accuracy. By highlighting the precise nature of the relationship between the degree of class imbalance and the corresponding effects on classifier performance we hope to help researchers to better tackle the problem. To this end, we carry out extensive experiments using 10-fold cross validation on a large number of datasets. In particular, we determine that the relationship between the class imbalance ratio and the accuracy is convex. © 2019 Elsevier Inc.
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    A Data-based Guiding Framework for Digital Transformation
    (CEUR-WS, 2021) Maamar, Zakaria ; Cheikhrouhou, Saoussen ; Elnaffar, Said
    This paper presents a framework for guiding organizations initiate and sustain digital transformation initiatives. Digital transformation is a long-term journey that an organization embarks on when it decides to question its practices in light of management, operation, and technology challenges. The guiding framework stresses out the importance of data in any digital transformation initiative by suggesting 4 stages referred to as collection, processing, storage, and dissemination. Because digital transformation could impact different areas of an organization for instance, business processes and business models, each stage suggests techniques to expose data. 2 case studies are adopted in the paper to illustrate how the guiding framework is put into action. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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    Deep learning regularization in imbalanced data
    (Institute of Electrical and Electronics Engineers Inc., 2020-11-03) Kamalov, Firuz ; Leung, Ho Hon
    Deep neural networks are known to have a large number of parameters which can lead to overfitting. As a result various regularization methods designed to mitigate the model overfitting have become an indispensable part of many neural network architectures. However, it remains unclear which regularization methods are the most effective. In this paper, we examine the impact of regularization on neural network performance in the context of imbalanced data. We consider three main regularization approaches: L{1}, L{2}, and dropout regularization. Numerical experiments reveal that the L{1} regularization method can be an effective tool to prevent overfitting in neural network models for imbalanced data. Index Terms-regularization, neural networks, imbalanced data. © 2020 IEEE.
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    Design and Implementation of Cadastral Geo-spatial IoT Network Gateway Analyzer for Urban Scale Infrastructure Health Monitoring
    (Institute of Electrical and Electronics Engineers Inc., 2020-01) Tariq, Hasan ; Abdaoui, Abderrazak ; Touati, Farid ; Al-Hitmi, Mohammed Abdulla E ; Crescini, Damiano ; Mnaouer, Adel Ben
    Urban and global scale health monitoring systems have gained significance in digital ecosystems. Every stakeholder is striving for efficiency through performance analysis of digital infrastructure health monitoring (IHM) solutions. In this work, a geospatial network analyzer (GNA) is designed and implemented in python using the synergic strengths of Plotly, NetworkX, Scipy, Numpy, MatplotLib, Network2tikz, Pysocks, and PyPing. The GNA uses as a case study a utility computing model (UCM) to make structural health monitoring (SHM) that is based on analysis of geographical area network (GAN). A geo-distributed SHM deployment is assessed from a network performance perspective and verified from geo-spatial packet processing in GNA. The results have shown that this work can lead to standardizing the future of global-scale IoT networks analytics. © 2020 IEEE.
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    Design and implementation of information centered protocol for long haul SHM monitoring
    (Institute of Electrical and Electronics Engineers Inc., 2019) Tariq, Hasan ; Touati, Farid ; Al-Hitmi, Mohammed Abdulla E. ; Crescini, Damiano ; Mnaouer, Adel Ben
    In structural health monitoring systems (SHM), robust data transmission is the fundamental constraint. In this work, an information centered protocol is being proposed for multi-sensor and multi-variable communication channels in (SHM). The core objective is communication traffic optimization, data streams compression, bottleneck compensation for seamless information system. A novel SHM hierarchical information model has been designed and implemented using addressing taxonomy and domain definitions accumulated with data segments, beacons and flags-handshaking. On both ends of an SHM channel, a SQLite based encoding and decoding preprocessor is implemented, which requires the use of serial protocols such as CANopen, UART, 12C and SPI. Results have shown that the proposed system optimizes traffic monitoring in handling critical situations of dynamic baud rate switching. © 2019 IEEE.
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    Design and Implementation of Multi-Protocol Data Networks Interface Detector in Heterogeneous IoTs
    (Institute of Electrical and Electronics Engineers Inc., 2020-02) Tariq, Hasan ; Abdaoui, Abderrazak ; Touati, Farid ; Al-Hitmi, Mohammed Abdulla E ; Crescini, Damiano ; Manouer, Adel Ben
    In Internet of Everything (IoE) or heterogeneous IoTs, there exist a plethora of protocols and inter-connected Internet of Things. For every network protocol, the complexity of communication and wiring at the physical interface becomes more and more challenging. In this work, an automated interface detector (or gateway) is proposed using a novel port, that scans the physical layer parameters, interpolates with Electronic Industry Association/Telecommunication Industry Association (EIA/TIA) standards specification parameters and compares packet format initiates communication. In this approach, line impedance, voltage, current, SNR, power, and network capacity are used as the coefficients of merit for physical layer detection by interpolation and averaging methods. Standard packet architectures are the key parameters for communication initiation and network fastening at both ends of the data line. The serial protocols such as CANopen, Ethernet, UART, I2C, and SPI are tested and verified. Results have shown that the proposed system can detect any physical layer interface and initiate a data network regardless of connector pinouts and interface wiring complexities. The implementation results exhibit a fertile resource for redundancy handling in-line parameters of data networks. © 2020 IEEE.
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    Design and implementation of programmable multi-parametric 4-degrees of freedom seismic waves ground motion simulation IoT platform
    (Institute of Electrical and Electronics Engineers Inc., 2019) Tariq, Hasan ; Touati, Farid ; Al-Hitmi, Mohammed Abdulla E. ; Crescini, Damiano ; Mnaouer, Adel Ben
    The early warning and disaster management agencies spend billions of dollars to counter and cater earthquakes but it has always been unique accident. In this work, a programmable four degrees of freedom electromechanical seismic wave events simulation platform design is being proposed to study and experiment seismic waves and earthquakes realization in form of ground motions. The platform can be programmed and interfaced through an IoT cloud-based Web application. The rig has been tested in the range of frequencies of extreme seismic waves from 0.1Hz to 178Hz and terrestrial inclinations from -5.000° to 5.000°, which is key contribution of this work. This would be an enabler for a variety of applications such as training self-balancing and calibrating seismic resistant designs and structures in addition to studying and testing seismic detection devices. Nevertheless, it serves as an adequate training colossus for machine learning algorithms and event management expert systems. © 2019 IEEE.
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    Design and simulation of a green bi-variable mono-parametric SHM node and early seismic warning algorithm for wave identification and scattering
    (Institute of Electrical and Electronics Engineers Inc., 2018) Touati, Farid ; Tariq, Hasan ; Crescini, Damiano ; Mnaouer, Adel Ben
    Early seismic warning systems are key for safe future scalable infrastructures. In this work, a dual variable i.e. vibration and line of sight (LOS) based structure health monitoring (SHM) node is designed to sense tilt angle for early seismic warning and wave scattering detection. The SHM node, consisting of high-precision five bi-axis tiltmeters and five Blue-Violet laser diodes transmitter/receiver/reflector(LDTRR) assembly, has been designed and simulated in Proteus 7ISIS, MATLAB 7 and drafted in AutoCAD. In AutoCAD, a four LDTRR assembly is oriented at the bottom of building and its four co-planer reflectors have been orthogonally placed at effective radii with respect to the characteristic wavelengths of P, S, and Rayleigh whilst Love seismic waves, and one reflector is placed at the bottom of building. PV umbrella with a Li-ion battery has been used for green ergonomic shape. The time plots from real tiltmeter sensor nodes and data acquired from the proposed SHM node show similar behavior and results. The derived parameters of wavelength S, i.e. seismic parameter F,varied linearly from safe to hazardous seismic conditions. The variation from safe seismic to hazardous seismic transition of randomly simulated environment, also varied network traffic in GPS module as per defined threshold of sensor variables in Proteus ISIS Electronics Design Automation (EDA) engine. As per early warning evaluation functions (EWEF), the proposed design for early seismic warning algorithm (ESWA) can be a cost-effective analytics resource for any scalable SHM solution for observation range within 5km+ radius at low cost and 20km at moderate/high cost. © 2018 IEEE.
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    Design of intelligent real-time safety supervision system for pension institutions based on LBS
    (Sciendo, 2021) Song, Caili ; Kateb, Faris ; Aouad, Marwan
    With the gradual ageing of China's population and the single-child family system becoming the backbone of society, the traditional pension way of relying on children at home not only does not guarantee the quality of life of the elderly, but also brings great pressure to the work and life of young people. The way of relying on pension institutions to provide for the aged is becoming gradually accepted by society. In elderly care institutions, safety supervision systems provide an important link to protect the safety of the elderly, thereby ensuring accurate analysis and supervision of the elderly persons' activity routes and activity rules, and thus allowing an early warning to be given in a dangerous situation. The system realises the real-time positioning and monitoring of the elderly activity track, and can give early warning and timely rescue to the elderly who enter into any dangerous area. The system includes a server monitoring program and mobile APP; ASP.NET Technology to develop the monitoring program; SQL Server 2012 for data storage; and model view controller (MVC) architecture for the APP to improve the efficiency of program development. The supervision program comprises the functions of personnel information management, fence management, positioning service, activity track record, danger warning and rapid rescue. The APP deploys the location-based service (LBS) technology of Android, uses Baidu map API to obtain location information and draw activity track, and stores the information in the mobile phone SQLite database. The APP is installed on mobile phones, bracelets and other devices carried by the elderly and staff, which can record the current position of the person in real-time and simultaneously transmit it to the computer. When the elderly enter into a dangerous area, the system can quickly select the nearest staff and inform them for rescue, so as to ensure the safety of the elderly. After simulation test, the system has now reached the preset function. © 2021 Caili Song et al., published by Sciendo 2021.