Showing papers in "International Journal of Computing and Digital Systems in 2020"

Data Aggregation Mechanisms in the Internet of Things: A Study, Qualitative and Quantitative Analysis

Abstract: Internet of Things (IoT), a paradigm added to the ever-growing technological arena in recent times acts like a bridge between the things in the physical world and their representation within the digital world. The basic “things” in the IoT are sensor devices, which gather as well as monitor all types of data on physical machines and human social life. IoT enables data sending and receiving for each “thing” through the communication network. The purpose of Data Aggregation is to decrease the number of communications/transmissions among the objects/things in the Internet of Things framework. The effectiveness of the data aggregation technique employed is a key factor in the success of IoT systems in terms of data freshness and efficiency. Different data aggregation techniques have been proposed in the recent past, which include – Tree-Based, Cluster-Based and Centralized data aggregation techniques. The paper aims at a detailed study and analysis of data aggregation schemes employed in the Internet of Things in terms of working and time complexity. Lowest Common Ancestor (LCA) aided Tree-Based Data Aggregation algorithm is designed. In addition, the Cluster-Based data aggregation algorithm incorporated with the β-dominating set and Centralized Data Aggregation algorithm incorporated with the SUM() aggregation function are proposed. The algorithms are supported by wellformed flowcharts describing the flow and working of the data aggregation mechanisms designed. The results are obtained on a system consisting of 60 nodes with all the three aggregation algorithms being evaluated against each other. The centralized data aggregation algorithm is better when the number of nodes in the network is lesser. However, as the number of nodes increases, the cluster-based and tree-based algorithms produce better results as compared to the centralized data aggregation algorithm.

A State-of-Art on Cloud Load Balancing Algorithms

Abstract: The cloud computing is alarmingly getting into mainstream for the booming companies and the research organizations as; they seek to gain benefits from its on-demand access, service models and deployment models. It provides unique features like ondemand access to shared pool of resources over internet in a self-accessible, dynamically scalable and metered manner. It is widely accepted because of its “pay-as-you-go” model. These features make this paradigm a buzzword in the area of high-performance distributed computing (HPDC). Though, this domain is widely accepted still it demands enhancements to bring out the optimized performance. The load balancing among the virtual machines (VMs) belongs to NP-hard problem as far as the equilibrium load distribution is concerned. The hardness of this problem can be defined by considering two factors such as: large solution space and polynomial bounded computation. One of the major issues in cloud computing which, needs serious attention is load balancing for its efficient performance. In the present work, a deep literature study has been carried out by considering the state of art algorithms for cloud load balancing. The algorithm includes traditional methods, heuristic, meta-heuristic, and hybrid approach. From the analysis and study of the methods presented in the deep literature survey, it has been observed that the existing heuristic algorithms are not generating near to optimal solution within polynomial time. The amalgamation of meta-heuristics, and hybrid-heuristics techniques have been proved to produce suboptimal solutions within reasonable time. This paper provides an extensive historical survey and comparative analysis on various existing load balancing (LB) literature. The presented work will be a help hand tool for researchers to design new efficient load balancing algorithms in the Cloud computing domain.

An Automatic Arabic Sign Language Recognition System based on Deep CNN: An Assistive System for the Deaf and Hard of Hearing

Abstract: People with disabilities have long been ignored. With the advancement of recent technologies, so many tools and software are designed for disabled people to improve their lives. In this research, the Arabic Sign Language (ArSL) recognition system is developed using the proposed architecture of the Deep Convolutional Neural Network (CNN). The aim is to help people with hearing problems to communicate with normal people. The proposed system recognizes the signs of the Arabic alphabet based on real-time user input. The Deep CNN architectures were trained and tested using a database of more than 50000 Arabic sign images collected from random participants of different age groups. Several experiments are performed with changing CNN architectural design parameters in order to get the best recognition rates. The experimental results show that the proposed Deep CNN architecture achieves an excellent accuracy of 97.6%, which is higher than the accuracy achieved by similar other studies.

Development of an IoT-based Intensive Aquaculture Monitoring System with Automatic Water Correction

Abstract: Due to the depleting stocks of fish in the market, there have been an increased interest in aquaculture. However, raising fishes in an Intensive Aquaculture System results on a low-quality fish or even fish kills as fishes are being cultured in artificial tanks and cage systems, not on their natural habit. This paper presents a water quality monitoring system with automatic correction to monitor and maintain vital water quality parameters essential for fish growth, such as temperature, potential hydrogen (pH) level, oxidationreduction potential, turbidity, salinity, and dissolved oxygen to achieve optimum yield using Arduino and Raspberry Pi 3B+ through LoRaWAN IoT Protocol. The system uses sensors, microcontrollers, and a web application for acquiring and monitoring data of six different water quality parameters and are maintained in a desired level optimal for fish growth using aquarium heater, motor for sodium bicarbonate distribution, solenoid valve and water pump that serves as correcting devices. The proponents measured the system’s efficiency and reliability through monitoring two intensive aquaculture setups – controlled and conventional setup. From the data gathered, the controlled setup greatly increased efficiency, reduced the work of fish farmers, avoided fish kills, and surpassed yield quality of the conventional setup.

Tracking and Controlling High-Speed Vehicles Via CQI in LTE-A Systems

Abstract: One of the most serious problems facing the community around the world is car accidents. These accidents occur mainly due to the high-speed of vehicles. Thus, the paper aims to capture, track, and control high-speed vehicles using LTE-A mobile networks to avoid high-speed situations as well as decrease the number of accidents. The paper assumes that all vehicle drivers are now days carrying their mobiles that can be considered as mobile network user equipment (UE). This paper presents an innovative tracking and controlling high-speed vehicles in the LTE-A system that taking the advantages of Channel Quality Indicator (CQI) value mapped to the UE speed. The method can be accomplished by uploading the CQI index to the base station (BS), at the uplink, then the evolved node base station (eNB) sends an extra warning message at the downlink to initiate the radio frequency identifier (RFID) component fixed on the vehicle. The proposed scheme design assumes that the LTE networks have the all traffic speed for the covered area and to be activated when the speed is beyond the maximum speed. In that case, the RFID is activated and an alarm is switched on. Under now response, the RFID will activate the vehicle's traction control (TC), Engine Control Unit (ECU) and automatic brake system (ABS) to decrease the speed gradually. The proposed scheme was simulated using the system level-simulator (SLS) and the performance is depicted. The evaluations show that the CQI values are decreased meaningfully to 2 when the UE movement in the high-way increases to 150 km/h. Consequently, with the obtainability of CQI values at the LTE-A system, an immediate activity is completed to control the vehicle speed and warn the driver.

A Comprehensive Real-Time Road-Lanes Tracking Technique for Autonomous Driving

Abstract: In this paper, an advanced-and-reliable road-lanes detection and tracking solution is proposed and implemented. The proposed solution is well suited for use in Advanced Driving Assistance Systems (ADAS) or Self-Driving Cars (SDC). The main emphasis of the proposed solution is the precision and the predictability in identifying the driving-lane boundaries (linear or curved) and tracking it throughout the drive. Moreover, the solution provides fast enough computation to be embedded in affordable CPUs that are employed by ADAS systems. The proposed solution is mainly a pipeline of reliable computer-vision algorithms that augment each other and take in raw RGB images to produce the required lane boundaries that represent the front driving space for the car. The main contribution of this paper is the precise fusion of the employed algorithms where some of them work in parallel to strengthen each other in order to produce a sophisticated real-time output. Each used algorithm is described in detail, implemented and its performance is evaluated using actual road images and videos captured by the front-mounted camera of the car. The whole pipeline performance is also tested and evaluated on real videos. The evaluation of the proposed solution shows that it reliably detects and tracks road boundaries under various conditions.

The Direct and Indirect Effects of Personality on Data Breach in Education Through the Task-Related Compulsive Technology use: M-Learning Perspective

Abstract: Literature is lack of understanding the negative outcome of compulsive technology use (CTU) and security/data breach in virtual learning environment. This research employs task-related CTU with consideration of university students’ use of mobile devices in m-learning environment and investigates the impact of personality diversity on task-related CTU. It is also tested whether task-related CTU increases the likelihood of data breach. Medium of access (mobile devices) is tested as moderating variable. Agreeableness, conscientiousness, openness, extraversion, and external locus of control significantly influences task-related CTU, while agreeableness and conscientiousness also impact the likelihood of data breach. Task-related CTU significantly leads to likelihood of data breach and medium of access moderates this relationship. This research is one of the few studies addressing data breach in education and role of excessive technology use on risky cyber-security behavior of students, which can guide m-learning system designers to take safety measures against data and privacy theft.

A new system for massive RDF data management using Big Data query languages Pig, Hive, and Spark

Abstract: The era of big data has emerged. The volume of generated data has never been greater. Massive quantities of data are stored on a huge number of servers that are inter-connected and share their storage space. Computation methods have been developed to perform computation operations directly on these machines, previously used mainly for storage. Tools such as Hive, Pig, and Spark provide the means for data query and analysis but are not suitable for Semantic Data. For this kind of data, a specialized tool called SPARQL is dedicated to query semantic data represented by the Resource Description Framework or RDF. The aim of our work is to transform a given SPARQL query into a Hive program, a Pig program or a Spark script according to the user's choice. To achieve this goal, we propose a Model-Driven Approach which consists of creating a metamodel for each of these tools, to define a mapping between SPARQL metamodel on one hand and each of the previous Big Data query languages (Pig, Hive, and Spark). The transformation is then performed using Atlas Transformation Language or ATL. We conducted that an experiment on three datasets containing a large volume of distributed RDF data on a powerful server cluster to validate our approach.

Service-oriented Broker for Effective Provisioning of Cloud Services – a Survey

Abstract: Services computing plays a vital role in the field of information technology and enables users to perform web and cloud services in more efficient and effective manner. In the life cycle of services computing, phases such as service discovery, composition, and delivery of services and managing services as per the Service Level Agreements (SLA) have been received significant attention by the research community. Though web services and cloud services are two instances of services computing, their user communities significantly differ in various aspects, which include service specification, consumption, and adherence to service agreements. This paper approaches services computing from the perspective of two architectural paradigms namely Service-oriented Architecture (SOA) and Cloud computing. The existing research attempts performed in the phases of service discovery, composition and provisioning of services as per the SLA has been extensively reviewed from the perspective of SOA and Cloud. Based on the literature review, a number of research issues are also summarized towards achieving service excellence further. At the end, the paper emphasizes the need for service-oriented broker to enhance the discovery and provisioning process of cloud services.

Design and Analysis of Dual band Microstrip Antenna for Millimeter Wave Communication Applications

Abstract: Millimeter wave (MMW) communication is a key technology to enable the seamless connectivity among the various devices in the next generation wireless network (5G and beyond). However, the antenna design at MMW frequencies is a critical challenge. In this context, this paper presents a compact dual band Rectangular Microstrip Antenna (RMSA) for MMW communication applications. The design has been carried out at 33.5 GHz (Ka band) and two symmetrical slots are integrated at the optimum position to achieve the higher resonance at 62.5 GHz in the V band of MMW spectrum. The simulation results show that the antenna performs quite well and achieves the return loss of -16.6 dB and -15.03 dB and peak gain of 4.93 and 3.67 dB at 33.5 GHz and 62.53 GHz respectively. The proposed antenna design has been further extended to a dual element antenna array and analysed for improvement of different antenna parameters like gain, directivity, bandwidth and VSWR. The feasibility of the proposed antenna has been demonstrated by experimental results. The paper also presents the parametric and equivalent circuit analysis of the designed antenna.

Robust Adaptive Sliding Mode Control Design for Quadrotor Unmanned Aerial Vehicle Trajectory Tracking

Abstract: The quadrotor unmanned aerial vehicles (UAV) systems have been getting more focus recently from researchers and engineers due to their outstanding impact and wide range of applications either in civilian or military. In this article, the sliding mode controller has been designed to control the attitude of the quadrotor as the inner loop controller. The major aim in this research is to reduce the chattering associated with the conventional sliding mode control (SMC), by implementing the adaptive fuzzy gain scheduling SMC technique (AFGS-SMC). Meanwhile, the performance of the proposed control has been evaluated in the presence of the model parameters uncertainty. The PD controller has been implemented as an outer loop controller to control the quadrotor position and supply the inner loop proposed controller (AFGS-SMC) with the desired generated quadrotor's attitude. Finally, the performance of the proposed AFGS-SMC controller has been evaluated by simulation in Matlab/Simulink platform, and compared with the classical SMC, in terms of chattering attenuation and robust trajectory tracking in the presence of the parameter uncertainty in the mass of the quadrotor UAV.

Cyber-attacks on medical implants: A case study of Cardiac Pacemaker vulnerability

Abstract: This paper describes the vulnerability of the medical implants due to cyber-attacks, which can result in unexpected behavior of these devices thus causing severe damage to human safety. Although, it seems hard to believe that someone’s implantable medical device (IMD), e.g. pacemaker or insulin pump can be hacked by an eavesdropper, in reality, researchers have demonstrated that these embedded medical devices can turn into assassination weapons by modifying the operation through remote access. It is therefore important to address these issues to ensure safety and security in medical cyber physical systems. Model based control is implemented in MATLAB/Simulink to demonstrate the control of pacemaker device. Moreover, certain attack models are used to visualize the effects of cyber-attacks on cardiac pacemaker.

Machine Learning model to predict the number of cases contaminated by COVID-19

Abstract: This paper presents a dedicated machine learning model to predict the number of cases infected by the Corona Virus; the case of Morocco was chosen to validate this study. Completely realized in Spark ML with the 'Scala' language and tested for a certain number of algorithms generated on datasets coming from dedicated sources to gather Covid19 data in the world. The results show the possibility of achieving better scores prediction after using the proposed method. We tested our model on the case of China and the results were relevant. The proposed Machine Learning model can be applied to data from any country in the world. We have applied it in this paper to the case of Morocco and China. We are sending this work to the world to help them fight this 2019 Corona Virus pandemic.

Genetic algorithm optimization analysis for temperature control system using cascade control loop model

Abstract: This research presented a holistic approach in determining the trade-off optimized Proportional-Integral-Derivative (PID) tunings for both servo and regulatory controls of the cascade control loop by using Genetic Algorithm (GA). Performance of GA-based PID tunings was significantly compared with the IMC-based single loop tunings and conventional cascade control tunings. GA-based PID tunings eliminated the complicated mathematic calculations in obtaining the correlation PID tuning values and also reduce the dependency on engineering knowledge, experience, and skills. The performance of transient and steady-state responses was compared through time domain specification, performance index, and process response curve. It is concluded that the GA-based PID tunings for the cascade control loop had produced the best result for both servo and regulatory control objectives, which is eventually determined.

Harmonic Analysis of Photovoltaic Generation in Distribution Network and Design of Adaptive Filter

Abstract: The aim of this paper is to investigate the effect of increased penetration level of Photovoltaic (PV) generation on the distribution network. Harmonic distortion is the main factor studied in this paper and a typical three-bus distribution network is built in MATLAB/Simulink to understand the harmonics problem. The obtained results show that current harmonics are more susceptible to fluctuate compared to voltage harmonics. Based on existing IEEE harmonic standards, total demand distortion of current (TDDi) is evaluated to estimate maximum PV penetration level at Point of Common Coupling (PCC), and the maximum acceptable TDDi at each bus differs according to specific loading and short-circuit levels. Meanwhile, total harmonic distortion of current (THDi) at inverter outputs represents inverter performance. Instead of assessing at standard test conditions (STC), the impact of irradiance variations is studied. Low irradiance results in an increased THDi of the inverter whilst doesn’t explicitly affect TDDi at PCC. A simple and low-cost solution is proposed to dynamically vary the settings of inverter’s filter elements against irradiance, and harmonic distortion at low irradiance of the inverter is successfully mitigated.

Key Factors Increasing Trust in Cloud Computing Applications in the Kingdom of Bahrain

Abstract: During recent years, cloud-computing applications have been increasing. However, with this increase there are many concerns that affect the adoption of these applications. One of these is the perceived trust of users. This research investigates the factors that influence perceived trust in cloud storage based applications in the Kingdom of Bahrain. Toward this aim, this research followed a quantitative research approach where the main research strategy is based on the results of a questionnaire. Through the questionnaire, a proposed model was tested with 178 cloud storage application users in order to identify factors affecting their trust of cloud storage applications. The results revealed that security and reliability both directly affect users’ trust in cloud storage applications. At the same time, backup and recovery, availability and cloud transparency all affect trust indirectly through security. The contribution of this research resides in proposing a new model of users’ trust in cloud computing applications that can be added to other researchers’ models in the field. In addition, the results of this research provide insights for the developers of cloud computing applications toward better perceived trust by users.

Traceability of Cryptocurrency Transactions using Blockchain Analytics

Abstract: Cryptocurrencies are increasingly used as a medium to conduct illicit activity. The fundamental concept empowering Bitcoin and other cryptocurrencies is the blockchain, which is a distributed ledger technology that transactions are stored on. The entire ledger, which is widely distributed among the peer-to-peer network, is susceptible to the temporal analysis of the transaction to be performed. This type of blockchain analytics threatens the expected privacy of transactions and has resulted in the growing number of privacy-oriented solutions that reduce the traceability and increase the anonymity of crypto transactions. Despite the evolving technological advancements of privacy-oriented features for cryptocurrencies, traceability of transactions is still possible. In this paper, the core features of blockchain are reviewed as well as their resistance to traceability. Existing countermeasures that attempt to obfuscate user activity are also considered. Also, a prototype software solution is proposed that could be used in the incidence response of criminal activity involving cryptocurrencies.

Data Summarization for Sensor Data Management: Towards Computational-Intelligence-Based Approaches

Abstract: The rapid and significant increase in the amount of sensor data to be processed requires the use of techniques to reduce the size of data in order to efficiently extract the relevant knowledge. In this paper, we present two approaches used to derive data summaries. The first one relies on linguistic quantifiers in the sense of Yager. The second one leverages the notion of the typical value of a data set. Then, we present the implementation of these two methods with some experiments conducted on different databases (realflight data collected from the ADSB project and real data for smart city collected from neOCampus project). Finally, a comparative study is discussed to show the best approach w.r.t. execution time.

Evaluating the contributions of the different knowledge management components towards organizational performances

Abstract: This paper looks at examining the extent to which organizations in Oman incorporate the different key knowledge management components namely Knowledge Creation, Knowledge Application & Dissemination, and knowledge capture & sharing within their daily operations with respect to achieving performance improvement. It also examines how well the individual knowledge management component can explain the variation in operational performances and their respective impact on operational performances. Quantitative empirical approach is used with Microsoft Excel data analysis tool pack as the investigative tool to perform descriptive statistical analysis, analyze the relationship between the knowledge management components and operational performance, and to develop a regression model for explaining the associated operational performance. The findings confirmed that organizations perceive a positive association between the knowledge management components and operational performances. However, the developed regression model was deemed statistically reliable through the use of only two out of the three key knowledge management components namely knowledge application & dissemination, and knowledge capture & sharing, and so could only partly support such perception. Anyhow, knowledge application & dissemination, and knowledge capture & sharing proved to be reliable and effective explanatory variables for explain the variation in operational performances. The findings also indicated that knowledge application & dissemination is the most impactful knowledge management component with respect to operational performances.

Biometric Information Based On Distribution of Arabic Letters According To Their Outlet

Abstract: In this research, the correlation and homogeneity properties of the presence matrix of the speech signal for the Arabic letters were tested and evaluate the possibility of distinguishing between them was achieved by extracting characteristic properties. The speech signal of the letters (acquired through the Recorder) with a binary matrix for its configuration and calculations of two properties of presence matrix, correlation, homogeneity studied. The values of these properties and the extent of their variation from one person to another and how close they are within the same group represent the exits which they belong. The results in this paper illustrate the correlation and homogeneity properties of the Arabic letters to persons in alphabetical order provide a distinctive description for the person. The important of Arabic language or any other language how they affected by an significant factors as the economic situation of their users and civilization, as well as their scientific future. This study is a good and authentic attempt in terms of using two types of relationships (correlation and homogeneity properties) , whose results will lead to important conclusions in the field of extracting the properties of sound and its difference from one person to another, depending on the outlets of speech. The results of the practical application for the proposed algorithm show that correlation gave distinct results for the adoption of characters as a characteristic of the voice of the speaker, whereas homogeneity was a weak indicator that varied greatly for the same character with the same person.

Towards Author Recognition of Ancient Arabic Manuscripts Using Deep Learning: A Transfer Learning Approach

Abstract: Due to the significance of ancient Arabic manuscripts and their role in enriching valuable historical information, this study aims to collect Arabic manuscripts in a dataset and classify its images to predict their authors. We accomplished this study through two main phases. First is the data collection phase. Arabic manuscripts gathered, including 52 Arabic Authors. Second is the models’ development phase to extract the visual features from the images and train the networks on them. We built four deep learning models named: MobileNetV1, DenseNet201, ResNet50, and VGG19. We configured the models by tuning their learning hyperparameters toward optimizing their recognition process. Afterward, we performed a comparative analysis between all the models to measure their performance. Eventually, we reached that minimizing the learning rate, combining “Sigmoid” with “Softmax”, and increasing the number of neurons on the final classification dense layer improved the networks’ recognition performance significantly since all utilized deep learning models reached above 95% validation accuracy.

An Intelligent Camera Surveillance System with Effective Notification Features

Abstract: The focal point of this work is to develop an intelligent camera surveillance system which englobes the key functionalities of existing surveillance systems. Other than regular functionalities such as motion detection, object detection, face recognition and counting people, it also integrates a novel and advanced object displacement detection feature to provide more security by determining if an object has been displaced by an intruder. When people are detected, a counting module displays the number of persons present in the surveillance area. A face recognition module distinguishes between authorised and unauthorised users. This biometric functionality reduces false alarms which makes the system more robust. An object detection module detects certain valuable objects such as handbags, laptops and smartphones. Also, images and short video recordings are stored on the cloud. Furthermore, the system introduces innovative real-time notification approaches for surveillance systems such as WhatsApp messages and phone calls, in addition to SMS and emails. Thus, this system is reliable and meets the aim of a modern intelligent surveillance system by combining multiple approaches to detect intrusions and to inform users effectively.

System Identification and Conditional Control for an Optimal Operation of a Pilot Plant Binary Distillation Column

Abstract: In this paper a new control structure has been proposed that minimizes the actuator energy by optimally reducing the error at steady state of closed loop response. A dynamic switching phenomena is employed such that effective actuator input manipulates the process variable. Parameter identification using PRBS input signal is carried out for MISO and MIMO processes. Pilot scale binary distillation column is considered for the study with tray temperature as variable of interest, heater voltage and reflux flow rate as its manipulated variables. The usage of proposed control structure with existing controller improves the system performance and reduces the operational cost. To depict the efficacy of the control structure, system is subjected to uncertainty by perturbing plant parameters of 30% from their actual values. Result shows significant improvement in actuator energy consumption. Control methodology has been experimentally validated on both MISO and MIMO schemes.

PerLCol: A Framework for Personalized e-Learning with Social Collaboration Support

Abstract: The emergence of Web 2.0 technologies like social media has enhanced the interaction and social collaboration support in the educational field. On the one hand, the integration of such sources equips learners with channels to share information, resources, ideas, as well as expressing opinions and comments during the interactions. On the other hand, such integration raises some issues related to personalization by filtering the collaboration support and provide a tailored pedagogical intervention to provide adequate hints and feedback as a means of adaptivity. Existing learning management systems provide restricted collaboration features and are not tailored to students’ characteristics like knowledge level and individual traits. In order to overcome these issues, we propose a framework to upgrade the virtual learning environment to provide flexible collaboration and be adaptive to learners’ needs. The framework integrates social media tools for seamless collaboration. In addition, it utilizes the generated content during collaboration to identify discussed concepts and learners’ characteristics to provide a personalized learning package accordingly. Descriptions of the notions required to develop the framework and an overview of its components and functionality are provided in this paper.

An Approach to Reduce Cloud Spot Instances Cost

Abstract: The cost reduction is one of the attractive features offered by the cloud. Spot leasing is one way to reduce the cost. Spot leasing is done by leasing the unused excess instances with low price. On the other hand, spot instances are facing risks that minimize their reliability and desirability. Risks including instances reclaiming and dynamic price changing. Minimizing the risks associated with the spot leasing is going to help to increase the utilization of the spot instances, which in turn is going to attract more users. In this paper, a framework has been proposed to mitigate the instances reclaiming risk while reducing the leasing cost as possible. This is done by monitoring many markets and hopping between instances. The proposed framework has been evaluated through simulating using randomly generated data and actual data collected from Amazon web services. The proposed framework recorded 9% to 42% of cost reduction compared with the actual cost.