Search results for: heterogeneous networks
3327 Normalizing Scientometric Indicators of Individual Publications Using Local Cluster Detection Methods on Citation Networks
Authors: Levente Varga, Dávid Deritei, Mária Ercsey-Ravasz, Răzvan Florian, Zsolt I. Lázár, István Papp, Ferenc Járai-Szabó
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One of the major shortcomings of widely used scientometric indicators is that different disciplines cannot be compared with each other. The issue of cross-disciplinary normalization has been long discussed, but even the classification of publications into scientific domains poses problems. Structural properties of citation networks offer new possibilities, however, the large size and constant growth of these networks asks for precaution. Here we present a new tool that in order to perform cross-field normalization of scientometric indicators of individual publications relays on the structural properties of citation networks. Due to the large size of the networks, a systematic procedure for identifying scientific domains based on a local community detection algorithm is proposed. The algorithm is tested with different benchmark and real-world networks. Then, by the use of this algorithm, the mechanism of the scientometric indicator normalization process is shown for a few indicators like the citation number, P-index and a local version of the PageRank indicator. The fat-tail trend of the article indicator distribution enables us to successfully perform the indicator normalization process.Keywords: citation networks, cross-field normalization, local cluster detection, scientometric indicators
Procedia PDF Downloads 2053326 Communication of Sensors in Clustering for Wireless Sensor Networks
Authors: Kashish Sareen, Jatinder Singh Bal
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The use of wireless sensor networks (WSNs) has grown vastly in the last era, pointing out the crucial need for scalable and energy-efficient routing and data gathering and aggregation protocols in corresponding large-scale environments. Wireless Sensor Networks have now recently emerged as a most important computing platform and continue to grow in diverse areas to provide new opportunities for networking and services. However, the energy constrained and limited computing resources of the sensor nodes present major challenges in gathering data. The sensors collect data about their surrounding and forward it to a command centre through a base station. The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) as they are very useful in target detecting and other applications. However, hierarchical clustering protocols have maximum been used in to overall system lifetime, scalability and energy efficiency. In this paper, the state of the art in corresponding hierarchical clustering approaches for large-scale WSN environments is shown.Keywords: clustering, DLCC, MLCC, wireless sensor networks
Procedia PDF Downloads 4833325 Analysis of Multilayer Neural Network Modeling and Long Short-Term Memory
Authors: Danilo López, Nelson Vera, Luis Pedraza
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This paper analyzes fundamental ideas and concepts related to neural networks, which provide the reader a theoretical explanation of Long Short-Term Memory (LSTM) networks operation classified as Deep Learning Systems, and to explicitly present the mathematical development of Backward Pass equations of the LSTM network model. This mathematical modeling associated with software development will provide the necessary tools to develop an intelligent system capable of predicting the behavior of licensed users in wireless cognitive radio networks.Keywords: neural networks, multilayer perceptron, long short-term memory, recurrent neuronal network, mathematical analysis
Procedia PDF Downloads 4213324 Value Proposition and Value Creation in Network Environments: An Experimental Study of Academic Productivity via the Application of Bibliometrics
Authors: R. Oleko, A. Saraceni
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The aim of this research is to provide a rigorous evaluation of the existing academic productivity in relation to value proposition and creation in networked environments. Bibliometrics is a vigorous approach used to structure existing literature in an objective and reliable manner. To that aim, a thorough bibliometric analysis was performed in order to assess the large volume of the information encountered in a structured and reliable manner. A clear distinction between networks and service networks was considered indispensable in order to capture the effects of each network’s type properties on value creation processes. Via the use of bibliometric parameters, this review was able to capture the state-of-the-art in both value proposition and value creation consecutively. The results provide a rigorous assessment of the annual scientific production, the most influential journals, and the leading corresponding author countries. By means of citation analysis, the most frequently cited manuscripts and countries for each network type were identified. Moreover, by means of co-citation analysis, existing collaborative patterns were detected through the creation of reference co-citation networks and country collaboration networks. Co-word analysis was also performed in order to provide an overview of the conceptual structure in both networks and service networks. The acquired results provide a rigorous and systematic assessment of the existing scientific output in networked settings. As such, they positively contribute to a better understanding of the distinct impact of service networks on value proposition and value creation when compared to regular networks. The implications derived can serve as a guide for informed decision-making by practitioners during network formation and provide a structured evaluation that can stand as a basis for future research in the field.Keywords: bibliometrics, co-citation analysis, networks, service networks, value creation, value proposition
Procedia PDF Downloads 2053323 Review on Application of DVR in Compensation of Voltage Harmonics in Power Systems
Authors: S. Sudhharani
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Energy distribution networks are the main link between the energy industry and consumers and are subject to the most scrutiny and testing of any category. As a result, it is important to monitor energy levels during the distribution phase. Power distribution networks, on the other hand, remain subject to common problems, including voltage breakdown, power outages, harmonics, and capacitor switching, all of which disrupt sinusoidal waveforms and reduce the quality and power of the network. Using power appliances in the form of custom power appliances is one way to deal with energy quality issues. Dynamic Voltage Restorer (DVR), integrated with network and distribution networks, is one of these devices. At the same time, by injecting voltage into the system, it can adjust the voltage amplitude and phase in the network. In the form of injections and three-phase syncing, it is used to compensate for the difficulty of energy quality. This article examines the recent use of DVR for power compensation and provides data on the control of each DVR in distribution networks.Keywords: dynamic voltage restorer (DVR), power quality, distribution networks, control systems(PWM)
Procedia PDF Downloads 1383322 A Comparative Study on Supercritical C02 and Water as Working Fluids in a Heterogeneous Geothermal Reservoir
Authors: Musa D. Aliyu, Ouahid Harireche, Colin D. Hills
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The incapability of supercritical C02 to transport and dissolve mineral species from the geothermal reservoir to the fracture apertures and other important parameters in heat mining makes it an attractive substance for Heat extraction from hot dry rock. In other words, the thermodynamic efficiency of hot dry rock (HDR) reservoirs also increases if supercritical C02 is circulated at excess temperatures of 3740C without the drawbacks connected with silica dissolution. Studies have shown that circulation of supercritical C02 in homogenous geothermal reservoirs is quite encouraging; in comparison to that of the water. This paper aims at investigating the aforementioned processes in the case of the heterogeneous geothermal reservoir located at the Soultz site (France). The MultiPhysics finite element package COMSOL with an interface of coupling different processes encountered in the geothermal reservoir stimulation is used. A fully coupled numerical model is developed to study the thermal and hydraulic processes in order to predict the long-term operation of the basic reservoir parameters that give optimum energy production. The results reveal that the temperature of the SCC02 at the production outlet is higher than that of water in long-term stimulation; as the temperature is an essential ingredient in rating the energy production. It is also observed that the mass flow rate of the SCC02 is far more favourable compared to that of water.Keywords: FEM, HDR, heterogeneous reservoir, stimulation, supercritical C02
Procedia PDF Downloads 3863321 Suitable Models and Methods for the Steady-State Analysis of Multi-Energy Networks
Authors: Juan José Mesas, Luis Sainz
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The motivation for the development of this paper lies in the need for energy networks to reduce losses, improve performance, optimize their operation and try to benefit from the interconnection capacity with other networks enabled for other energy carriers. These interconnections generate interdependencies between some energy networks and others, which requires suitable models and methods for their analysis. Traditionally, the modeling and study of energy networks have been carried out independently for each energy carrier. Thus, there are well-established models and methods for the steady-state analysis of electrical networks, gas networks, and thermal networks separately. What is intended is to extend and combine them adequately to be able to face in an integrated way the steady-state analysis of networks with multiple energy carriers. Firstly, the added value of multi-energy networks, their operation, and the basic principles that characterize them are explained. In addition, two current aspects of great relevance are exposed: the storage technologies and the coupling elements used to interconnect one energy network with another. Secondly, the characteristic equations of the different energy networks necessary to carry out the steady-state analysis are detailed. The electrical network, the natural gas network, and the thermal network of heat and cold are considered in this paper. After the presentation of the equations, a particular case of the steady-state analysis of a specific multi-energy network is studied. This network is represented graphically, the interconnections between the different energy carriers are described, their technical data are exposed and the equations that have previously been presented theoretically are formulated and developed. Finally, the two iterative numerical resolution methods considered in this paper are presented, as well as the resolution procedure and the results obtained. The pros and cons of the application of both methods are explained. It is verified that the results obtained for the electrical network (voltages in modulus and angle), the natural gas network (pressures), and the thermal network (mass flows and temperatures) are correct since they comply with the distribution, operation, consumption and technical characteristics of the multi-energy network under study.Keywords: coupling elements, energy carriers, multi-energy networks, steady-state analysis
Procedia PDF Downloads 813320 An Energy Efficient Clustering Approach for Underwater Wireless Sensor Networks
Authors: Mohammad Reza Taherkhani
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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make a connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.Keywords: underwater sensor networks, clustering, learning automata, energy consumption
Procedia PDF Downloads 3623319 Flow Conservation Framework for Monitoring Software Defined Networks
Authors: Jesús Antonio Puente Fernández, Luis Javier Garcia Villalba
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New trends on streaming videos such as series or films require a high demand of network resources. This fact results in a huge problem within traditional IP networks due to the rigidity of its architecture. In this way, Software Defined Networks (SDN) is a new concept of network architecture that intends to be more flexible and it simplifies the management in networks with respect to the existing ones. These aspects are possible due to the separation of control plane (controller) and data plane (switches). Taking the advantage of this separated control, it is easy to deploy a monitoring tool independent of device vendors since the existing ones are dependent on the installation of specialized and expensive hardware. In this paper, we propose a framework that optimizes the traffic monitoring in SDN networks that decreases the number of monitoring queries to improve the network traffic and also reduces the overload. The performed experiments (with and without the optimization) using a video streaming delivery between two hosts demonstrate the feasibility of our monitoring proposal.Keywords: optimization, monitoring, software defined networking, statistics, query
Procedia PDF Downloads 3333318 Using Dynamic Bayesian Networks to Characterize and Predict Job Placement
Authors: Xupin Zhang, Maria Caterina Bramati, Enrest Fokoue
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Understanding the career placement of graduates from the university is crucial for both the qualities of education and ultimate satisfaction of students. In this research, we adapt the capabilities of dynamic Bayesian networks to characterize and predict students’ job placement using data from various universities. We also provide elements of the estimation of the indicator (score) of the strength of the network. The research focuses on overall findings as well as specific student groups including international and STEM students and their insight on the career path and what changes need to be made. The derived Bayesian network has the potential to be used as a tool for simulating the career path for students and ultimately helps universities in both academic advising and career counseling.Keywords: dynamic bayesian networks, indicator estimation, job placement, social networks
Procedia PDF Downloads 3803317 A Tutorial on Network Security: Attacks and Controls
Authors: Belbahi Ahlam
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With the phenomenal growth in the Internet, network security has become an integral part of computer and information security. In order to come up with measures that make networks more secure, it is important to learn about the vulnerabilities that could exist in a computer network and then have an understanding of the typical attacks that have been carried out in such networks. The first half of this paper will expose the readers to the classical network attacks that have exploited the typical vulnerabilities of computer networks in the past and solutions that have been adopted since then to prevent or reduce the chances of some of these attacks. The second half of the paper will expose the readers to the different network security controls including the network architecture, protocols, standards and software/ hardware tools that have been adopted in modern day computer networks.Keywords: network security, attacks and controls, computer and information, solutions
Procedia PDF Downloads 4573316 Bounded Rational Heterogeneous Agents in Artificial Stock Markets: Literature Review and Research Direction
Authors: Talal Alsulaiman, Khaldoun Khashanah
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In this paper, we provided a literature survey on the artificial stock problem (ASM). The paper began by exploring the complexity of the stock market and the needs for ASM. ASM aims to investigate the link between individual behaviors (micro level) and financial market dynamics (macro level). The variety of patterns at the macro level is a function of the AFM complexity. The financial market system is a complex system where the relationship between the micro and macro level cannot be captured analytically. Computational approaches, such as simulation, are expected to comprehend this connection. Agent-based simulation is a simulation technique commonly used to build AFMs. The paper proceeds by discussing the components of the ASM. We consider the roles of behavioral finance (BF) alongside the traditionally risk-averse assumption in the construction of agent's attributes. Also, the influence of social networks in the developing of agents’ interactions is addressed. Network topologies such as a small world, distance-based, and scale-free networks may be utilized to outline economic collaborations. In addition, the primary methods for developing agents learning and adaptive abilities have been summarized. These incorporated approach such as Genetic Algorithm, Genetic Programming, Artificial neural network and Reinforcement Learning. In addition, the most common statistical properties (the stylized facts) of stock that are used for calibration and validation of ASM are discussed. Besides, we have reviewed the major related previous studies and categorize the utilized approaches as a part of these studies. Finally, research directions and potential research questions are argued. The research directions of ASM may focus on the macro level by analyzing the market dynamic or on the micro level by investigating the wealth distributions of the agents.Keywords: artificial stock markets, market dynamics, bounded rationality, agent based simulation, learning, interaction, social networks
Procedia PDF Downloads 3553315 Dimensioning of Circuit Switched Networks by Using Simulation Code Based On Erlang (B) Formula
Authors: Ali Mustafa Elshawesh, Mohamed Abdulali
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The paper presents an approach to dimension circuit switched networks and find the relationship between the parameters of the circuit switched networks on the condition of specific probability of call blocking. Our work is creating a Simulation code based on Erlang (B) formula to draw graphs which show two curves for each graph; one of simulation and the other of calculated. These curves represent the relationships between average number of calls and average call duration with the probability of call blocking. This simulation code facilitates to select the appropriate parameters for circuit switched networks.Keywords: Erlang B formula, call blocking, telephone system dimension, Markov model, link capacity
Procedia PDF Downloads 6143314 Entropy Analysis in a Bubble Column Based on Ultrafast X-Ray Tomography Data
Authors: Stoyan Nedeltchev, Markus Schubert
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By means of the ultrafast X-ray tomography facility, data were obtained at different superficial gas velocities UG in a bubble column (0.1 m in ID) operated with an air-deionized water system at ambient conditions. Raw reconstructed images were treated by both the information entropy (IE) and the reconstruction entropy (RE) algorithms in order to identify the main transition velocities in a bubble column. The IE values exhibited two well-pronounced minima at UG=0.025 m/s and UG=0.085 m/s identifying the boundaries of the homogeneous, transition and heterogeneous regimes. The RE extracted from the central region of the column’s cross-section exhibited only one characteristic peak at UG=0.03 m/s, which was attributed to the transition from the homogeneous to the heterogeneous flow regime. This result implies that the transition regime is non-existent in the core of the column.Keywords: bubble column, ultrafast X-ray tomography, information entropy, reconstruction entropy
Procedia PDF Downloads 3923313 Estimation and Comparison of Delay at Signalized Intersections Based on Existing Methods
Authors: Arpita Saha, Satish Chandra, Indrajit Ghosh
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Delay implicates the time loss of a traveler while crossing an intersection. Efficiency of traffic operation at signalized intersections is assessed in terms of delay caused to an individual vehicle. Highway Capacity Manual (HCM) method and Webster’s method are the most widely used in India for delay estimation purpose. However, in India, traffic is highly heterogeneous in nature with extremely poor lane discipline. Therefore, to explore best delay estimation technique for Indian condition, a comparison was made. In this study, seven signalized intersections from three different cities where chosen. Data was collected for both during morning and evening peak hours. Only under saturated cycles were considered for this study. Delay was estimated based on the field data. With the help of Simpson’s 1/3 rd rule, delay of under saturated cycles was estimated by measuring the area under the curve of queue length and cycle time. Moreover, the field observed delay was compared with the delay estimated using HCM, Webster, Probabilistic, Taylor’s expansion and Regression methods. The drawbacks of the existing delay estimation methods to be use in Indian heterogeneous traffic conditions were figured out, and best method was proposed. It was observed that direct estimation of delay using field measured data is more accurate than existing conventional and modified methods.Keywords: delay estimation technique, field delay, heterogeneous traffic, signalised intersection
Procedia PDF Downloads 3023312 The Role of Online Social Networks in Social Movements: Social Polarization and Violations against Social Unity and Privacy of Individuals in Turkey
Authors: Tolga Yazıcı
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As a matter of the fact that online social networks like Twitter, Facebook and MySpace have experienced an extensive growth in recent years. Social media offers individuals with a tool for communicating and interacting with one another. These social networks enable people to stay in touch with other people and express themselves. This process makes the users of online social networks active creators of content rather than being only consumers of traditional media. That’s why millions of people show strong desire to learn the methods and tools of digital content production and necessary communication skills. However, the booming interest in communication and interaction through online social networks and high level of eagerness to invent and implement the ways to participate in content production raise some privacy and security concerns. This presentation aims to open the assumed revolutionary, democratic and liberating nature of the online social media up for discussion by reviewing some recent political developments in Turkey. Firstly, the role of Internet and online social networks in mobilizing collective movements through social interactions and communications will be questioned. Secondly, some cases from Gezi and Okmeydanı Protests and also December 17-25 period will be presented in order to illustrate misinformation and manipulation in social media and violation of individual privacy through online social networks in order to damage social unity and stability contradictory to democratic nature of online social networking.Keywords: online social media networks, democratic participation, social movements, social polarization, privacy of individuals, Turkey
Procedia PDF Downloads 3433311 Heterogeneous and Homogeneous Photocatalytic Degradation of Acid Orange 10 in Aqueous Solution
Authors: Merouani Djilali Redha, F. Abdelmalek, A. A. Addou
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Advanced oxidation processes (AOPs) utilizing Homogenous photocatalysis (Fenton and photo-Fenton reactions), and Heterogeneous photocatalyse (TiO2 and ZnO) were investigated for the degradation of commercial azo dye ‘Orange G’ wastewater. Fenton and photo-Fenton experimental conditions were: Hydrogen peroxide concentration (10-2 M), Ferrous ions concentration (5.10-4 M), pH (2.8 – 3), UV lamp power (6 watt). Adding more ferrous ions enhanced the oxidation rate for the H2O2/Fe2+ and UV/H2O2/Fe2+ processes. The optimum catalyst loading was found 2.0 g.L-1 in our case for both catalysts TiO2 and ZnO. A comparative study of the photocatalytic degradation showed that these two catalysts have a comparable reactivity; it follows a pseudo-first-order kinetics. The degradation trends followed the order: UV365/Fenton > UV365/TiO2 > Solar Fenton > Solar TiO2 > Fenton ~UV365/ZnO. Among AOPs, processes using Fenton type reagent are relatively cheap and easy to operate and maintain. Moreover, UV365/Fenton process has been shown as effective in the treatment of OG dye. Dye was degraded following second-order kinetics. The rate constants was 0,041 .10+6 L.M-1.min-1. The degradation was followed by spectrophotometric method, chemical oxygen demand (COD) measures and high performance liquid chromatography analyses (HPLC). Some aromatic and aliphatic degradation compounds were identified. Degradation of Orange G by UV Fenton mechanism was also proposed.Keywords: AOPs, homogeneous catalysis, heterogeneous catalysis, acid orange 10, hydroxyl radical
Procedia PDF Downloads 4103310 Algorithm and Software Based on Multilayer Perceptron Neural Networks for Estimating Channel Use in the Spectral Decision Stage in Cognitive Radio Networks
Authors: Danilo López, Johana Hernández, Edwin Rivas
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The use of the Multilayer Perceptron Neural Networks (MLPNN) technique is presented to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision stage in cognitive radio networks (CRN) to determine approximately in which time instants of future may secondary users (SUs) opportunistically use the spectral bandwidth to send data through the primary wireless network. To validate the results, sequences of occupancy data of channel were generated by simulation. The results show that the prediction percentage is greater than 60% in some of the tests carried out.Keywords: cognitive radio, neural network, prediction, primary user
Procedia PDF Downloads 3723309 Oxidation of Alcohols Types Using Nano-Graphene Oxide (NGO) as Heterogeneous Catalyst
Authors: Ali Gharib, Leila Vojdanifard, Nader Noroozi Pesyan, Mina Roshani
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We describe an efficient method for oxidation of alcohols to related aldehydes and ketones by hydrogen peroxide as oxidizing agent, under reflux conditions. Nano-graphene oxide (NGO) as a heterogeneous catalyst was used and had their activity compared with other various catalysts. This catalyst was found to be an excellent catalyst for oxidation of alcohols. The effects of various parameters, including catalyst type, nature of the substituent in the alcohols and temperature, on the yield of the carboxylic acids were studied. Nano-graphene oxide was synthesized by the oxidation of graphite powders. This nanocatalyst was found to be highly efficient in this reaction and products were obtained in good to excellent yields. The recovered nano-catalyst was successfully reused for several runs without significant loss in its catalytic activity.Keywords: nano-graphene oxide, oxidation, aldehyde, ketone, catalyst
Procedia PDF Downloads 4263308 Experimental Networks Synchronization of Chua’s Circuit in Different Topologies
Authors: Manuel Meranza-Castillon, Rolando Diaz-Castillo, Adrian Arellano-Delgado, Cesar Cruz-Hernandez, Rosa Martha Lopez-Gutierrez
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In this work, we deal with experimental network synchronization of chaotic nodes with different topologies. Our approach is based on complex system theory, and we use a master-slave configuration to couple the nodes in the networks. In particular, we design and implement electronically complex dynamical networks composed by nine coupled chaotic Chua’s circuits with topologies: in nearest-neighbor, small-world, open ring, star, and global. Also, network synchronization is evaluated according to a particular coupling strength for each topology. This study is important by the possible applications to private transmission of information in a chaotic communication network of multiple users.Keywords: complex networks, Chua's circuit, experimental synchronization, multiple users
Procedia PDF Downloads 3503307 Multiple Query Optimization in Wireless Sensor Networks Using Data Correlation
Authors: Elaheh Vaezpour
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Data sensing in wireless sensor networks is done by query deceleration the network by the users. In many applications of the wireless sensor networks, many users send queries to the network simultaneously. If the queries are processed separately, the network’s energy consumption will increase significantly. Therefore, it is very important to aggregate the queries before sending them to the network. In this paper, we propose a multiple query optimization framework based on sensors physical and temporal correlation. In the proposed method, queries are merged and sent to network by considering correlation among the sensors in order to reduce the communication cost between the sensors and the base station.Keywords: wireless sensor networks, multiple query optimization, data correlation, reducing energy consumption
Procedia PDF Downloads 3363306 Prediction of Wind Speed by Artificial Neural Networks for Energy Application
Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui
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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed
Procedia PDF Downloads 6943305 Uplink Throughput Prediction in Cellular Mobile Networks
Authors: Engin Eyceyurt, Josko Zec
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The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.Keywords: drive test, LTE, machine learning, uplink throughput prediction
Procedia PDF Downloads 1583304 A Learning Automata Based Clustering Approach for Underwater Sensor Networks to Reduce Energy Consumption
Authors: Motahareh Fadaei
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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.Keywords: clustering, energy consumption, learning automata, underwater sensor networks
Procedia PDF Downloads 3173303 Detecting Black Hole Attacks in Body Sensor Networks
Authors: Sara Alshehri, Bayan Alenzi, Atheer Alshehri, Samia Chelloug, Zainab Almry, Hussah Albugmai
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This paper concerns body area networks sensor that collect signals around a human body. The black hole attacks are the main security challenging problem because the data traffic can be dropped at any node. The focus of our proposed solution is to efficiently route data packets while detecting black hole nodes.Keywords: body sensor networks, security, black hole, routing, broadcasting, OMNeT++
Procedia PDF Downloads 6473302 Design and Implementation of 2D Mesh Network on Chip Using VHDL
Authors: Boudjedra Abderrahim, Toumi Salah, Boutalbi Mostefa, Frihi Mohammed
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Nowadays, using the advancement of technology in semiconductor device fabrication, many transistors can be integrated to a single chip (VLSI). Although the growth chip density potentially eases systems-on-chip (SoCs) integrating thousands of processing element (PE) such as memory, processor, interfaces cores, system complexity, high-performance interconnect and scalable on-chip communication architecture become most challenges for many digital and embedded system designers. Networks-on-chip (NoCs) becomes a new paradigm that makes possible integrating heterogeneous devices and allows many communication constraints and performances. In this paper, we are interested for good performance and low area for implementation and a behavioral modeling of network on chip mesh topology design using VHDL hardware description language with performance evaluation and FPGA implementation results.Keywords: design, implementation, communication system, network on chip, VHDL
Procedia PDF Downloads 3803301 Detecting and Secluding Route Modifiers by Neural Network Approach in Wireless Sensor Networks
Authors: C. N. Vanitha, M. Usha
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In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.Keywords: neural networks, pattern learning, security, wireless sensor networks
Procedia PDF Downloads 4053300 Adequacy of Museums' Internet Resources to Infantile and Young Public
Authors: Myriam Ferreira
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Websites and social networks allow museums to divulge their works by new and attractive means. Besides, these technologies provide tools to generate a new history of art’s contents and promote visits to their installations. At the same time, museums are proposing more and more activities to families, children and young people. However, these activities usually take place in the museum’s physical installations, while websites and social networks seem to be mainly targeted to adults. The problem is that being children and young people digital natives, they feel apart from museums, so they need a presence of museums in digital means to feel attracted to them. Some institutions are making efforts to fill this vacuum. In this paper, resources designed specifically for children and teenagers have been selected from websites and social networks of five Spanish Museums: Prado Museum, Thyssen Museum, Guggenheim Museum, America Museum and Cerralbo Museum. After that, we have carried out an investigation in a school with children and teenagers between 11 and 15 years old. Those young people have been asked about their valuation of those web pages and social networks, with quantitative-qualitative questions. The results show that the least rated resources were videos and social networks because they were considered ‘too serious’, while the most rated were games and augmented reality. These ratings confirm theoretical papers that affirm that the future of technologies applied to museums is edutainment and interaction.Keywords: children, museums, social networks, teenagers, websites
Procedia PDF Downloads 1533299 A Fast Community Detection Algorithm
Authors: Chung-Yuan Huang, Yu-Hsiang Fu, Chuen-Tsai Sun
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Community detection represents an important data-mining tool for analyzing and understanding real-world complex network structures and functions. We believe that at least four criteria determine the appropriateness of a community detection algorithm: (a) it produces useable normalized mutual information (NMI) and modularity results for social networks, (b) it overcomes resolution limitation problems associated with synthetic networks, (c) it produces good NMI results and performance efficiency for Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks, and (d) it produces good modularity and performance efficiency for large-scale real-world complex networks. To our knowledge, no existing community detection algorithm meets all four criteria. In this paper, we describe a simple hierarchical arc-merging (HAM) algorithm that uses network topologies and rule-based arc-merging strategies to identify community structures that satisfy the criteria. We used five well-studied social network datasets and eight sets of LFR benchmark networks to validate the ground-truth community correctness of HAM, eight large-scale real-world complex networks to measure its performance efficiency, and two synthetic networks to determine its susceptibility to resolution limitation problems. Our results indicate that the proposed HAM algorithm is capable of providing satisfactory performance efficiency and that HAM-identified communities were close to ground-truth communities in social and LFR benchmark networks while overcoming resolution limitation problems.Keywords: complex network, social network, community detection, network hierarchy
Procedia PDF Downloads 2293298 Production of Biodiesel Using Brine Waste as a Heterogeneous Catalyst
Authors: Hilary Rutto, Linda Sibali
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In these modern times, we constantly search for new and innovative technologies to lift the burden of our extreme energy demand. The overall purpose of biofuel production research is to source an alternative energy source to replace the normal use of fossil fuel as liquid petroleum products. This experiment looks at the basis of biodiesel production with regards to alternative catalysts that can be used to produce biodiesel. The key factors that will be addressed during the experiments will focus on temperature variation, catalyst additions to the overall reaction, methanol to oil ratio, and the impact of agitation on the reaction. Brine samples sources from nearby plants will be evaluated and tested thoroughly and the key characteristics of these brine samples analysed for the verification of its use as a possible catalyst in biodiesel production. The one factor at a time experimental approach was used in this experiment, and the recycle and reuse characteristics of the heterogeneous catalyst was evaluated.Keywords: brine sludge, heterogenous catalyst, biodiesel, one factor
Procedia PDF Downloads 172