Energy Sparing of the LEACH Communication Mechanism in Heterogeneous WSN

Zoltan Gal, PhD, Faculty of Informatics, University of Debrecen, Kassai Str. 26, H-4028 Debrecen, Hungary

ABSTRACT

One of the most efficient energy management of the wireless sensor network nodes is provided by the LEACH (Low Energy Adaptive Clustering Hierarchy) mechanism. This hierarchical protocol use cluster head function of the nodes to aggregate and forward the messages from the cluster members to the Sink node. Each node decides to become cluster head stochastically in each epoch time intervals. Nodes have enough energy to reach directly through radio channel any of the nearest cluster nodes, even the Sink node, too. Optimization algorithm is required to provide efficient energy consumption on the WSN clusters level. A node being cluster head in the actual epoch time can repeat this function just with probability p in time. Classical nodes find the nearest cluster head to minimize the transmission energy consumption and use time division multiple access method to send the radio frames in each epoch time. In this paper we use own developed simulation method and software to analyse the remaining energy process in time of the whole WSN network and the number forwarded frames in function on the probability p for a set of nodes having heterogeneous level of initial energy.

KEYWORDS

Internet of Things (IoT), Wireless Sensor Network (WSN), Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, radio communication, energy sparing, stochastic process.

Implementation of Machine Learning Spectrum Sensing for Cognitive Radio Applications

Mohamed El-Tarhuni¹, Khaled Assaleh², and Firas Kiftaro¹, ¹American University of Sharjah, Sharjah, U.A.E and ²Ajman University, Ajman, U.A.E

ABSTRACT

In this paper, a cognitive radio system is implemented using National Instruments (NI) Universal Software Radio Peripheral (USRP) devices. The implemented system provides a working prototype to compare the performance of spectrum sensing algorithms based on energy detection and polynomial classifier techniques. The experimental results show that the polynomial classifier has a better performance over the energy detector in terms of the misclassification rate.

KEYWORDS

Cognitive Radio, Spectrum Dynamic Access, Spectrum Sensing, Polynomial Classifier, Energy Detection, USRP

Development of Time Series Data collection mechanism for analysis Sea-level

Amila Shanaka*, FazlinaAhmatRuslan^#, ^#Faculty of Electrical Engineering,UniversitiTeknologiMARA, 40450 Shah Alam, Selangor, Malaysia

ABSTRACT

Tide monitoring has become significantly important to ocean hydrography survey as it provides guidance for safe ship navigation in shallow water ports and calculation of Mean Sea Level. The Tide level variation is volatile according to the geographical location. Sri Lanka is one of the topical countries near to equator typically tide variation is less than 1 meter where needs develop its own tide level database. Recording tide is not a simple task in order to achieve high data availability and the accuracy which demands a larger workforce for gathering manual data from different regions generally this process is the labour-intensive process and time-consuming. To minimize the efforts, time, and cost, automation of those activities has done using, cutting-edge web and Internet of things(IoT). The designed IOT based tide gauge capable for calculating tide height and send it to the central database where a user can retrieve data using any web browser for further analysis. The central database server keeps all record of connected tide gauges in a period of each 5 minutes. The database is dramatically growing with millions of data it is mandatory to collect data 10 to 15 years in order to calculate Mean Sea Level. Further the system capable to monitor real-time value for a selected location to identify and notify tsunami status real time.

KEYWORDS

Mean sea level; Tide gauge; Web Services Using ASP.NETWeb Service; IOT; Ultrasonic Sensor;Sensor data collection; Time series data; SQL injection; Real Time Tide Gauge (RTTG).

PROPOSAL FOR ENCRYPTION BY USING MODIFIED PLAY FAIR ALGORITHM AND BIOINFORMATICS TECHNIQUES

RashaS.Ali¹,RajaaKadhom Hassoun²,Inas Fadhil Jaleel³ and Noor Subhi Ali⁴ ,^1,ALNisour University College,²Ministry of Science and Technology,³Ministry of higher education,⁴Baghdad University.

ABSTRACT

This work is about securing text by using modified Play fair algorithm. The encryption and decryption processes are done by using secret key and message provided by the user. The encryption and decryption process depended on using same key. The proposed modified Play fair algorithm depended on using biomolecular computing. The size of the proposed modified Play fair matrix includes 4 columns and 16 rows instead of 5*5 matrix of conventional method. In this work the matrix of equal dimensions was not required; the amino acid codes(mRna codes) are used in this work. The amino acid codes are also converted to special symbols; this means instead of using triple replacement of amino acid codes the triple characters are converted to one symbol. The single replacement was used, by this the size of encrypted text was decreased to 1:3 of using triple replacement. Each three character of amino acid codes is become represented by one character and this is also leading to save memory storage 3 times of using original representation of amino acid codes. Also, in this work any type of characters or numbers and symbols can be encrypted by using the proposed method comparative to the previous works which is used to encrypt only English characters with some numbers. The program of this work executed by utilizing vb.net application.

KEYWORDS

Cryptography, Modified Play fair Algorithm, DNA Computing, Amino Acid Codes.

Energy prediction system of smart home

Brahim lejdel, University of EL-Oued ,EL-Oued, Algeria

ABSTRACT

Actually, the energy consumption prediction in a smart home is an important subject of research. In this paper, we will propose a model that based on genetic algorithm that can help inhabitants and decision-makers to make the best decision, in term of energy consumption. We will implement our system, which can help the inhabitants optimize their energy consumptions using many technics as NodeMCU, cloud computing, and genetic algorithm.

KEYWORDS

smart home; NodeMCA; Cloud computing; genetic algorithm.

AUTOMATED FAULT TOLERANT FIRE DETECTION AND WARNING COMMUNICATOR SYSTEM

Laith Khader, RashaAboushakra, WidadAlwashahi and Akbar Mohideen, College of Engineering, University of Buraimi, Alburaimi, Sultanate of Oman

ABSTRACT

The scope of our project is to detect any fire or gas leakage in the property using sensors and to notify the owner and the fire department immediately using IoT and SMS. The problem solved by our system is to have a quick automated warning alert to the fire department, the owner of the premises, and the neighbors to reduce the losses. In case of fire, a buzzer will be activated and a fast-automated alert notification will be sent via IoT and SMS to the owner and fire department. The fire department system will notify the buildings near to the fire location. The system will then wait for an acknowledgment from the firefighting personal and the owner. If no response, repeated notifications will be sent until the system receives an acknowledgment from the owner and the fire department to ensure the warning alert is noticed by them.

KEYWORDS

Fire alert system, communication system, acknowledgment mechanism, fire station system

DETECTING BOT NETWORKS BASED ON HTTP AND TLS TRAFFIC ANALYSIS

Zahra Nafarieh¹ ,Ebrahim Mahdipour² ,and Haj Hamid Haj Seyed Javadi³

¹Science and Research Branch, Islamic Azad University, Tehran, Iran. ²Department of Computer Engineering Science and Research Branch, Islamic Azad University, Tehran, Iran. ³Department of Computer Engineering Science and Research Branch, Shahed University, Tehran, Iran.

ABSTRACT

Bot networks are a serious threat to cyber security, whose destructive behavior affects network performance directly. Detecting of infected HTTP communications is a big challenge because infected HTTP connections are clearly merged with other types of HTTP traffic. Cybercriminals prefer to use the web as a communication environment to launch application layer attacks and secretly engage in forbidden activities, while TLS protocols allow encrypted communication between client and server in the context of Internet provides. Traffic behavior analysis methods do not depend on package shipments, which means they can work with encrypted network communication protocols. Hence, the analysis of TLS and HTTP traffic behavior has been considered for detecting malicious activities. Because the exchange of information in the network context is very high and the volume of information is very large, the storage and indexation of this massive data require a Big data platform.

KEYWORDS

Bot Networks, HTTP Traffic Analysis, TLS Traffic Analysis , Intrusion Detection, Network Security, Security Threats.

Dependencies Preserving Database Shuffling

Hatim Alsuwat¹, Emad Alsuwat¹, Tieming Geng¹, Csilla Farkas², and Chin-TserHuang², ¹University of South Carolina, Columbia SC 29208, USA ,²University of South Carolina, Columbia SC 29208, USA

ABSTRACT

Shuffling Algorithms have been used to protect the confidentiality of sensitive data.However, shuffling algorithms may not preserve functional dependencies and data driven associations. In this paper, we present two solutions for addressing these shortcomings: (1) Functional dependencies preserving shuffle; (2) Data-driven associations preserving shuffle. For preserving functional dependencies, we propose a method using Boyce-Codd Normal Form (BCNF) decomposition. Instead of shuffling the original relation, we recommend to shuffle each BCNF decomposition. The final shuffled relation is constructed by the natural join of the shuffled decompositions. We show that our approach is lossless and preserves functional dependencies if the BCNF decomposition is dependency preserving. For preserving data-driven association, we generate the transitive closure of the sets of attributes that are associated. Attributes of each set are bundled together during shuffling.

KEYWORDS

Secure Cryptographic Shuffling Algorithms, Functional Dependencies, Data-drive Association, Database Normalization, Data Confidentiality.

CONSTRUCTING MIXED FAILURE MODEL AND ESTIMATING ITS FUZZY HAZARD RATE FUNCTION BY DIFFERENT METHODS USING SIMULATION

Dr.Inaam Rikan Hassan ,Dr. Jane Jaleel Stephan and Alaa Hamza Omran University of information technology and communications,Iraq

ABSTRACT

In this paper we work on estimating parameters of compound three parameters (Burr-XII) which is one of time to failure model distribution with two shapes parameters (α,r) and one scale parameter (λ), the three parameters (α,r,λ) are estimated by methods of moments and method of maximum likelihood and also method of Least square.

KEYWORDS

fuzzy hazard, Burr-XII, failure model, maximum likelihood method, moment method.

THE SYSTEM DESIGN FOR FATIGUE DRIVING DETECTIONBY BRAINWAVES ANALYSIS IN SMARTPHONE

Yung Gi Wu, Jwu Jenq Chen and Rui Hsin Wang

Department of Computer Science & Information Engineering, Chang Jung Christian University, Taiwan.

ABSTRACT

The car accidents caused by fatigue driving have been reporting from the news. In order to avoid the accidents mentioned above, we survey some published methods that used to detect fatigue and find that every method has its own specified purposes. Therefore, this research design the system to detect fatigue and alert driver with light weight brainwave detector and smartphone that everyone has for most car drivers. In this system, the driver wear a light weight head mounted brainwave device and the signal is transmitted to the smartphone.Our algorithm calculates the driver’s fatigue index by focus, eyes blink frequency, α-wave, β-wave, δ-wave and θ-wave to judge the driver’s condition. The experimental results show that the system can not only remind driverwhen they are actually fatigued but also find the fatigue period by examine historical records. This research can reduce the occurrence of car accident caused by driver’s fatigue.

KEYWORDS

Brainwave Detecting,Fatigue Detecting, APP

A MULTI-AGENT SYSTEM FOR INTELLIGENT GENERATION OF UNIVERSITY TIME SCHEDULES

Abderrahim Siam¹ and Samir Safir ²

¹ ICOSI Lab, Abbes Laghrour University, Khenchela, Algeria.

² Department of Mathematics and Computer Engineering, Abbes Laghrour University, Khenchela, Algeria

ABSTRACT

This paper gives a presentation of an approach based on a multi agent system vision for the resolution of the problem of university timetable or time schedules. The proposed approach involves agents to find in a parallel way one or more timetables that constitute a compromise of a multitude of points of view.

KEYWORDS

Multi Agent Systems, University Timetable, &Resolution

EFFICIENT TECHNIQUE FOR COURSES SCHEDULING

Abdoul Rjoub¹ , Renad Haddad¹ and Rayeh Alghsoon²

¹ Department of Computer Engineering,Jordan University of Science and Technology, Irbid, Jordan.

² University of Jordan, Amman, Jordan

ABSTRACT

TIn addition to its monotony and time-consuming, manual school timetabling leads to have more than one class are assigned to the same instructor. Moreover, more than one instructor are assigned to the same class at the same time slot. In this paper school timetable generation process is developed and new technique is adopted to address the challenges of creating school timetable. Hill climbing algorithm has been used to transact hard and soft constraints. The implementation of this technique has been successfully experimented in different schools with various kinds of side constraints. Results show that the initial solution can be improved by 72% towards the optimal solution within the first iteration and by 50% from the second iteration. While, the optimal solution will be achieved after 15 iteration ensuring that more than 50% of scientific courses will take place in the early time slots.

KEYWORDS

Course Schedule, Hill Climbing Algorithm, School Timetable, Scheduling, Timetabling.

A DFG PROCESSOR IMPLEMENTATION FOR DIGITAL SIGNAL PROCESSING APPLICATIONS

Ali Shatnawi, Osama Al-Khaleel and Hala Alzoubi

Department of Computer Engineering, Jordan University of Science and Technology, Irbid, Jordan

ABSTRACT

This paper proposes a new scheduling technique for digital signal processing (DSP) applications represented by data flow graphs (DFGs). Hardware implementation in the form of a specialized embedded system, is proposed. The scheduling technique achieves the optimal schedule of a given DFG at design time. The optimality criterion targeted in the proposed algorithm is the maximum throughput than can be achieved by the available hardware resources. Each task is presented in a form of an instruction to be executed on the available hardware. The architecture is composed of one or multiple homogeneous pipelined processing elements, designed to achieve the maximum possible sampling rate for several DSP applications. In this paper, we present a processor implementation of the proposed architecture. It comprises one processing element where all tasks are processed sequentially. The hardware components are built on an FPGA chip using Verilog HDL. The architecture requires a very small area size, which is represented by the number of slice registers and the number of slice lookup tables (LUTs). The proposed scheduling technique is shown to outperform the retiming technique, which is proposed in the literature, by 19.3%.

KEYWORDS

Data Flow Graphs, Task Scheduling, Processor Design, Hardware Description Language

VISUAL CATEGORIZATION OF OBJECTS INTO ANIMAL AND PLANT GROUPS USING GLOBAL SHAPE DESCRIPTORS

Zahra Sadeghi,Department of Electrical and Computer Engineering, University of Tehran, Iran

ABSTRACT

How can humans distinguish between general categories of objects? Are the subcategories of living things visually distinctive? In a number of semantic-category deficits, patients are good at making broad categorization but are unable to remember fine and specific details. It has been well accepted that general information about concepts are more robust to damages related to semantic memory. Results from patients with semantic memory disorders demonstrate the loss of ability in subcategory recognition. While bottom-up feature construction has been studied in detail, little attention has been served to top-down approach and the type of features that could account for general categorization. In this paper, I show that broad categories of animal and plant are visually distinguishable without processing textural information. To this aim I utilize shape descriptors with an additional phase of feature learning. The results are evaluated with both supervised and unsupervised learning mechanisms. The obtained results confirmed that global encoding of visual appearance of objects accounts for high discrimination between animal and plant object categories.

KEYWORDS

general categorization; visual shape descriptors; object recognition.

AN EFFICIENT SINGLE-PLANE FLUOROSCOPY IMAGE REGISTRATION METHOD FOR 3D POST-OPERATIVE ANALYSIS OF TOTAL KNEE ARTHROPLASTY

Shabnam Saadat¹, Mark Pickering¹, Diana Perriman², Jennie M. Scarvell³ and Paul N. Smith⁴, ¹School of Engineering and Information Technology, UNSW Canberra, Canberra, Australia,²Trauma and Orthopaedic Research Unit, The Canberra Hospital, Canberra, Australia,³Faculty of Health, University of Canberra, Canberra, Australia,⁴School of Medicine, The Australian National University, Canberra, Australia

ABSTRACT

Image registration has applications in different areas of medical image analysis. It can be used to assist the investigation of joint kinematics in conditions such as ligament injury, osteoarthritis, and after joint replacement. Analysing the 3D movement of joints after total knee arthroplasty surgery is crucial as the correct position and relative movement of knee implants will significantly impact the success of the surgery. However, the evaluation of the movement of the implanted components has received limited attention and most studies on this aspect are still insufficient and developing. In this paper, we propose a non-invasive and robust 3D to 2D registration method which can be used for 3D evaluations of the status of knee implants. This method addresses several challenges with regard to the registration of the implants. The experimental results show that the proposed method is not only robust but also fast.

KEYWORDS

Model to image registration, total knee arthroplasty, medical image analysis, similarity measure, Edge Position Difference.

ACTIVE CONTOUR PRIOR SHAPE SEGMENTATION FOR DIABETIC PLANTAR FOOT THERMAL IMAGES

Asma Bougrine¹, Rachid Harba¹, Raphael Canals¹, Roger Ledee¹, Meryem Jabloun^{1 1} PRISME Laboratory - University of Orleans – France

ABSTRACT

The segmentation of diabetic plantar foot thermal images that are taken with no constraining setup is a challenging problem. The present paper is dedicated to the comparison of three active contour-based methods with prior shape that are well suited to the given problem. The first method was recently proposed in [1] by the present authors. It is based on the Kass et al. method and on a new extra term that minimizes the difference between the curve curvature of the active contour and the prior shape one. The second method is the Ahmed et al. one [2], a Fourier-based method with prior shape matching. The third one was suggested by Chen et al. [3] where a geodesic snake is associated to a prior shape energy function. Using a database of 50 plantar foot thermal images, results show that our proposed method outperforms the two others with a RMSE equal to 5.12 pixels and a DSC score of 93.9%. In addition, our method is robust to initial contour variations and fast, therefore suitable for smartphone application in the context of diabetic foot problem.

KEYWORDS

Prior shape-based segmentation, active contours, plantar foot thermal images, diabetic foot

A REVIEW OF LOCAL AND HOLISTIC APPROACHES IN FACIAL EXPRESSIONS RECOGNITION WITH SUPPORT VECTOR MACHINES

kennedychengeta, University of KwaZulu Natal,School of Computer Science and Mathematics, Westville Campus, Durban, South Africa

ABSTRACT

In facial expression identification, algorithms with higher classification rates and lower computational costs are preferred. To achieve that, feature extraction and classification should be accurate and efficient. Feature extraction optimization involves selecting the optimal feature escriptor.Various algorithms in computer vision involve holistic, local and deep learning algorithms. Holistic algorithms analyze the whole facial image and includes algorithms like Linear Discriminant Analysis or fisherfaces, eigenfaces (PCA), Histograms of Oriented Gradients and Gray Level Co-occurrence Matrix (GLCM). Local feature descriptors involve using local facial components separately then aggregating them into a combined histogram. Local binary patterns (LBP), local directional patterns (LDP) and scale-invariant feature transform (SIFT) feature extraction algorithms have been successfully used in local feature extraction. Deep learning involves using convolutional neural networks for image analysis. The most popular models are AlexNet, VGG-Face and GoogleNet. The study evaluates computational accuracy and efficiency of the three forms of facial expression recognition namely holistic, local and deep learning algorithms. The JAFFE and CK+ datasets are used for analysis. Gabor Filters are used for preprocessing filtering of the images whilst Viola Jones OpenCV toolset is used for image visualization. The study concludes that local algorithms compete very well with deep learning algorithms in terms of accuracy but use less processing power than convolutional networks. For real time facial expression analysis with minimal processing power and need for quick response times, LBP algorithms are recommended.