This document summarizes an article from the International Journal of Next-Generation Networks that proposes an improved leader election algorithm for distributed systems. The algorithm modifies the existing ring election algorithm to minimize the number of messages exchanged during leader election. Simulation results showed that the proposed algorithm reduces message complexity compared to the original ring algorithm. The document provides background on leader election algorithms and discusses related work that has aimed to improve leader election in distributed systems through approaches like failure detectors and modifying the bully algorithm.
Hybrid ga svm for efficient feature selection in e-mail classificationAlexander Decker
1. The document discusses using a hybrid genetic algorithm-support vector machine (GA-SVM) approach for feature selection in email classification to improve SVM performance.
2. SVM has been shown to be inefficient and consume a lot of computational resources when classifying large email datasets with many features.
3. The hybrid GA-SVM approach uses a genetic algorithm to optimize feature selection for SVM in order to improve classification accuracy and reduce computation time for email spam detection.
11.hybrid ga svm for efficient feature selection in e-mail classificationAlexander Decker
The document summarizes a study that develops a hybrid genetic algorithm-support vector machine (GA-SVM) technique for feature selection in email classification. The technique uses a genetic algorithm to optimize the feature selection and parameters of an SVM classifier. The goal is to improve the SVM's classification accuracy and reduce computation time for large email datasets. The study tests the hybrid GA-SVM approach on a spam email dataset. The results show improvements in classification accuracy and computation time over using SVM alone.
An Extensive Literature Review of Various Routing Protocols in Delay Tolerant...IRJET Journal
This document summarizes an extensive literature review on routing protocols in delay tolerant networks (DTNs). It begins by defining DTNs as wireless networks with intermittent connectivity where nodes use a store-carry-forward mechanism. Common routing protocols for DTNs like epidemic, spray and wait, and prophet are described. The document then reviews several papers that propose and evaluate new routing algorithms or improvements for DTNs, analyzing metrics like delivery ratio, overhead, and latency. Key factors considered include node contact histories, social characteristics, energy constraints, and message prioritization. Finally, it suggests the contact duration between nodes could be an important parameter to further optimize routing in DTNs.
Community Detection in Networks Using Page Rank Vectors ijbbjournal
Nodes in the real world networks organize in the form of network communities. A community (also referred
to as module or cluster)is defined as where the links are denser inside the nodes and sparser outside the
nodes in the network. Communities in the networks also overlap because the nodes may belong to different
clusters at once. The task of detecting communities in networks becomes an open problem because of lack
of reliable algorithms. In practice all the existing community detection methods work good for nonoverlapping
communities and fail to detect communities with dense overlaps. We developed a novel method
for detecting communities by considering a single seed node. This method successfully captures the
overlapping networks ranging from social to information and from biological to citation networks. We
believe that the proposed system works well for the overlapping communities.
Minkowski Distance based Feature Selection Algorithm for Effective Intrusion ...IJMER
Intrusion Detection System (IDS) plays a major role in the provision of effective security to various types of networks. Moreover, Intrusion Detection System for networks need appropriate rule set for classifying network bench mark data into normal or attack patterns. Generally, each dataset is characterized by a large set of features. However, all these features will not be relevant or fully contribute in identifying an attack. Since different attacks need various subsets to provide better detection accuracy. In this paper an improved feature selection algorithm is proposed to identify the most appropriate subset of features for detecting a certain attacks. This proposed method is based on Minkowski distance feature ranking and an improved exhaustive search that selects a better combination of features. This system has been evaluated using the KDD CUP 1999 dataset and also with EMSVM [1] classifier. The experimental results show that the proposed system provides high classification accuracy and low false alarm rate when applied on the reduced feature subsets
network mining and representation learningsun peiyuan
This document discusses two papers related to network embedding and ranking over multilayer networks.
The first paper proposes metapath2vec, a network embedding technique for heterogeneous networks. It extends word2vec to learn latent representations of nodes in a heterogeneous network by considering metapath-guided random walks.
The second paper proposes CrossRank and CrossQuery algorithms for ranking and querying over a network of networks (NoN). CrossRank learns global ranking vectors for each domain network in the NoN by optimizing for within-network smoothness, query preference, and cross-network consistency. CrossQuery efficiently finds the top-k most relevant nodes in a target network for a query node in a source network. Both methods are evaluated on
Scalable Local Community Detection with Mapreduce for Large NetworksIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
A Heuristic Approach for Network Data Clusteringidescitation
In this growing world of technology there are lots of security threats received by
each and every area of computer networks. Most of the time the network security threats
produce high false positive and negative ratios, this creates an obstacle for any security
system to work improperly. The overwhelming threats make it challenging to understand
and manage the network data.
To address this problem we present a novel approach which eventually understand the
network data by clustering them without background knowledge of any threats according to
various parameters like source IP, Destination IP etc. And this approach saves
administrator’s time and energy in processing of large amount threats.
Hybrid ga svm for efficient feature selection in e-mail classificationAlexander Decker
1. The document discusses using a hybrid genetic algorithm-support vector machine (GA-SVM) approach for feature selection in email classification to improve SVM performance.
2. SVM has been shown to be inefficient and consume a lot of computational resources when classifying large email datasets with many features.
3. The hybrid GA-SVM approach uses a genetic algorithm to optimize feature selection for SVM in order to improve classification accuracy and reduce computation time for email spam detection.
11.hybrid ga svm for efficient feature selection in e-mail classificationAlexander Decker
The document summarizes a study that develops a hybrid genetic algorithm-support vector machine (GA-SVM) technique for feature selection in email classification. The technique uses a genetic algorithm to optimize the feature selection and parameters of an SVM classifier. The goal is to improve the SVM's classification accuracy and reduce computation time for large email datasets. The study tests the hybrid GA-SVM approach on a spam email dataset. The results show improvements in classification accuracy and computation time over using SVM alone.
An Extensive Literature Review of Various Routing Protocols in Delay Tolerant...IRJET Journal
This document summarizes an extensive literature review on routing protocols in delay tolerant networks (DTNs). It begins by defining DTNs as wireless networks with intermittent connectivity where nodes use a store-carry-forward mechanism. Common routing protocols for DTNs like epidemic, spray and wait, and prophet are described. The document then reviews several papers that propose and evaluate new routing algorithms or improvements for DTNs, analyzing metrics like delivery ratio, overhead, and latency. Key factors considered include node contact histories, social characteristics, energy constraints, and message prioritization. Finally, it suggests the contact duration between nodes could be an important parameter to further optimize routing in DTNs.
Community Detection in Networks Using Page Rank Vectors ijbbjournal
Nodes in the real world networks organize in the form of network communities. A community (also referred
to as module or cluster)is defined as where the links are denser inside the nodes and sparser outside the
nodes in the network. Communities in the networks also overlap because the nodes may belong to different
clusters at once. The task of detecting communities in networks becomes an open problem because of lack
of reliable algorithms. In practice all the existing community detection methods work good for nonoverlapping
communities and fail to detect communities with dense overlaps. We developed a novel method
for detecting communities by considering a single seed node. This method successfully captures the
overlapping networks ranging from social to information and from biological to citation networks. We
believe that the proposed system works well for the overlapping communities.
Minkowski Distance based Feature Selection Algorithm for Effective Intrusion ...IJMER
Intrusion Detection System (IDS) plays a major role in the provision of effective security to various types of networks. Moreover, Intrusion Detection System for networks need appropriate rule set for classifying network bench mark data into normal or attack patterns. Generally, each dataset is characterized by a large set of features. However, all these features will not be relevant or fully contribute in identifying an attack. Since different attacks need various subsets to provide better detection accuracy. In this paper an improved feature selection algorithm is proposed to identify the most appropriate subset of features for detecting a certain attacks. This proposed method is based on Minkowski distance feature ranking and an improved exhaustive search that selects a better combination of features. This system has been evaluated using the KDD CUP 1999 dataset and also with EMSVM [1] classifier. The experimental results show that the proposed system provides high classification accuracy and low false alarm rate when applied on the reduced feature subsets
network mining and representation learningsun peiyuan
This document discusses two papers related to network embedding and ranking over multilayer networks.
The first paper proposes metapath2vec, a network embedding technique for heterogeneous networks. It extends word2vec to learn latent representations of nodes in a heterogeneous network by considering metapath-guided random walks.
The second paper proposes CrossRank and CrossQuery algorithms for ranking and querying over a network of networks (NoN). CrossRank learns global ranking vectors for each domain network in the NoN by optimizing for within-network smoothness, query preference, and cross-network consistency. CrossQuery efficiently finds the top-k most relevant nodes in a target network for a query node in a source network. Both methods are evaluated on
Scalable Local Community Detection with Mapreduce for Large NetworksIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
A Heuristic Approach for Network Data Clusteringidescitation
In this growing world of technology there are lots of security threats received by
each and every area of computer networks. Most of the time the network security threats
produce high false positive and negative ratios, this creates an obstacle for any security
system to work improperly. The overwhelming threats make it challenging to understand
and manage the network data.
To address this problem we present a novel approach which eventually understand the
network data by clustering them without background knowledge of any threats according to
various parameters like source IP, Destination IP etc. And this approach saves
administrator’s time and energy in processing of large amount threats.
A NOVEL APPROACH TOWARDS COST EFFECTIVE REGION-BASED GROUP KEY AGREEMENT PROT...ijp2p
Peer-to-peer systems have gained a lot of attention as information sharing systems for the widespread exchange of resources and voluminous information that is easily accessible among thousands of
users. However, current peer-to-peer information sharing systems work mostly on wired networks. With
the growing number of communication-equipped mobile devices that can self-organize into
infrastructure-less communication platform, namely mobile ad hoc networks (MANETs), peer-to-peer
information sharing over MANETs becomes a promising research area. In this paper, we propose a
Region-Based structure that enables efficient and secure peer-to-peer information sharing over MANETs.
The implementation shows that the proposed scheme is Secure, scalable, efficient, and adaptive to node
mobility and provides Reliable information sharing.
03 fauzi indonesian 9456 11nov17 edit septianIAESIJEECS
Since the rise of WWW, information available online is growing rapidly. One of the example is Indonesian online news. Therefore, automatic text classification became very important task for information filtering. One of the major issue in text classification is its high dimensionality of feature space. Most of the features are irrelevant, noisy, and redundant, which may decline the accuracy of the system. Hence, feature selection is needed. Maximal Marginal Relevance for Feature Selection (MMR-FS) has been proven to be a good feature selection for text with many redundant features, but it has high computational complexity. In this paper, we propose a two-phased feature selection method. In the first phase, to lower the complexity of MMR-FS we utilize Information Gain first to reduce features. This reduced feature will be selected using MMR-FS in the second phase. The experiment result showed that our new method can reach the best accuracy by 86%. This new method could lower the complexity of MMR-FS but still retain its accuracy.
ISSUES RELATED TO SAMPLING TECHNIQUES FOR NETWORK TRAFFIC DATASETijmnct
The document discusses issues related to sampling techniques for network traffic datasets. It analyzes various sampling techniques for their ability to capture information while sampling imbalanced network traffic data. The key points are:
1) Network traffic data is huge, varying, and imbalanced with some classes distributed unequally. Sampling is needed to reduce training time for machine learning algorithms used to analyze the data, but sampling can lose important information.
2) The document evaluates random sampling, systematic sampling, stratified sampling, and re-sampling techniques using a dataset collected from Panjab University's network. It finds that random sampling can miss some protocol classes entirely, losing important information.
3) Careful sampling is needed to handle the
The document discusses feature selection and classification methods for detecting denial-of-service (DoS) attacks in intrusion detection systems. It proposes using Random Forests for feature selection and k-Nearest Neighbors for classification on the KDD99 dataset. Experimental results show that the proposed approach of using Random Forests to select important features before classifying with k-Nearest Neighbors increases detection accuracy while decreasing false positives compared to other algorithms.
SAMPLING BASED APPROACHES TO HANDLE IMBALANCES IN NETWORK TRAFFIC DATASET FOR...cscpconf
Network traffic data is huge, varying and imbalanced because various classes are not equally distributed. Machine learning (ML) algorithms for traffic analysis uses the samples from this
data to recommend the actions to be taken by the network administrators as well as training. Due to imbalances in dataset, it is difficult to train machine learning algorithms for traffic
analysis and these may give biased or false results leading to serious degradation in performance of these algorithms. Various techniques can be applied during sampling to minimize the effect of imbalanced instances. In this paper various sampling techniques have been analysed in order to compare the decrease in variation in imbalances of network traffic
datasets sampled for these algorithms. Various parameters like missing classes in samples probability of sampling of the different instances have been considered for comparison
A adaptive neighbor analysis approach to detect cooperative selfish node in m...Jyoti Parashar
Mobile network is a set of wireless device called wireless nodes(mobile, Laptop) which are dynamically connect and transfer the information. In MANET nodes can be source, destination and intermediate node of data transmission.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Handwriting identification using deep convolutional neural network methodTELKOMNIKA JOURNAL
Handwriting is a unique thing that produced differently for each person. Handwriting has a characteristic that remain the same with single writer, so a handwriting can be used as a variable in biometric systems. Each person have a different form of handwriting style but with a small possibility that same characters have something commons. We propose a handwriting identification method using sentence segmented handwriting forms. Sentence form is used to get more complete handwriting characteristics than using a single characters or words. Dataset used is divided into three categories of images, binary, grayscale, and inverted binary. All datasets have same image with different in color and consist of 100 class. Transfer learning used in this paper are pre-trained model VGG19. Training was conducted in 100 epochs. Highest result is grayscale images with genuince acceptance rate of 92.3% and equal error rate of 7.7%.
Probability Density Functions of the Packet Length for Computer Networks With...IJCNCJournal
The research on Internet traffic classification and identification, with application on prevention of attacks
and intrusions, increased considerably in the past years. Strategies based on statistical characteristics of
the Internet traffic, that use parameters such as packet length (size) and inter-arrival time and their
probability density functions, are popular. This paper presents a new statistical modeling for packet length,
which shows that it can be modeled using a probability density function that involves a normal or a beta
distribution, according to the traffic generated by the users. The proposed functions has parameters that
depend on the type of traffic and can be used as part of an Internet traffic classification and identification
strategy. The models can be used to compare, simulate and estimate the computer network traffic, as well
as to generate synthetic traffic and estimate the packets processing capacity of Internet routers
This document discusses a system for detecting suspicious emails using machine learning algorithms. It presents a model that filters emails containing images and PDFs to improve performance over existing text-based spam filtering systems. The model uses data collection, preprocessing, machine learning classification algorithms like Naive Bayes, Support Vector Machines, and Decision Trees. It aims to achieve acceptable recall and precision values. The document also reviews related work on email spam detection using machine learning and discusses the future scope of applying the algorithm to embedded images/PDFs and larger datasets.
Study, analysis and formulation of a new method for integrity protection of d...ijsrd.com
This document discusses a text-based fuzzy clustering algorithm to filter spam emails. It begins with an introduction discussing how most classification approaches are for structured data but large amounts of unstructured data are transmitted online. It then discusses spam emails being a major problem and filtering being an important approach. The paper aims to use a fuzzy clustering approach called Fuzzy C-Means to classify emails. It describes the training and testing modules, which extract features from emails to create vector space models and then applies the fuzzy clustering algorithm to determine if emails are spam or not spam. Evaluation results show the precision and accuracy of the approach on different datasets, with the author concluding the vector space model with fuzzy C-Means works well for both small and large datasets.
IDS IN TELECOMMUNICATION NETWORK USING PCAIJCNCJournal
This document summarizes a research paper that proposes using principal component analysis (PCA) as a dimension reduction technique for intrusion detection systems (IDS). The paper applies PCA to reduce the number of features from 41 to either 6 or 10 features for the NSL-KDD dataset. One reduced feature set is used to develop a network IDS with high detection success and rate, while the other is used for a host IDS also with good detection success and very high detection rate. The paper outlines the process of applying PCA for IDS, including performing PCA on training data to identify principal components, then using those components to map new online data and detect intrusions based on deviation thresholds.
This document summarizes and evaluates various rule extraction algorithms from trained artificial neural networks. It begins with an introduction explaining the importance of explanation capabilities for neural networks. It then provides a taxonomy for classifying rule extraction approaches based on the expressiveness of the extracted rules, whether the approach takes an open-box or black-box view of the neural network, any specialized training regimes used, the quality of explanations generated, and computational complexity. The document discusses sensitivity analysis as a basic method for understanding neural network relationships before focusing on decompositional and pedagogical rule extraction approaches.
This document summarizes a research paper that proposes a new email spam detection framework using feature selection and similarity coefficients. The framework first applies feature selection to identify the most important features for distinguishing spam and non-spam emails. It then calculates similarity coefficients between email pairs to quantify their similarity based on the selected features. The researchers claim this approach improves spam detection accuracy and rate by removing irrelevant and redundant features before classification. They test their framework on a standard email dataset and find that detection performance is better after applying feature selection compared to using all original features.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
A New Function-based Framework for Classification and Evaluation of Mutual Ex...CSCJournals
This paper presents a new function-based framework for mutual exclusion algorithms in distributed systems. In the traditional classification mutual exclusion algorithms were divided in to two groups: Token-based and Permission-based. Recently, some new algorithms are proposed in order to increase fault tolerance, minimize message complexity and decrease synchronization delay. Although the studies in this field up to now can compare and evaluate the algorithms, this paper takes a step further and proposes a new function-based framework as a brief introduction to the algorithms in the four groups as follows: Token-based, Permission-based, Hybrid and K-mutual exclusion. In addition, because of being dispersal and obscure performance criteria, introduces four parameters which can be used to compare various distributed mutual exclusion algorithms such as message complexity, synchronization delay, decision theory and nodes configuration. Hope the proposed framework provides a suitable context for technical and clear evaluation of existing and future methods.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
The document presents a new approach for secure routing of IoT devices based on a probabilistic polling system. It aims to address security issues in IoT by taking into account the opinions of different stakeholders. The proposed approach uses a polling system with multiple queues to represent stakeholders, and assigns them different priorities through probabilistic routing between queues. This allows votes from stakeholders in higher priority queues to be weighted more in determining the most secure routing path. The approach is described as providing a user-centric solution compared to previous works by incorporating stakeholder opinions.
The document presents a new approach for secure routing of IoT devices based on a probabilistic polling system. It aims to address security issues in IoT by taking into account the opinions of different stakeholders. The proposed approach uses a polling system with multiple queues to represent stakeholders, and assigns them different priorities through probabilistic routing between queues. This allows votes from stakeholders in higher priority queues to be weighted more in determining the most secure routing path. The approach is described as providing a user-centric solution compared to previous works by incorporating stakeholder opinions.
Grid computing can involve lot of computational tasks which requires trustworthy computational nodes. Load balancing in grid computing is a technique which overall optimizes the whole process of assigning computational tasks to processing nodes. Grid computing is a form of distributed computing but different from conventional distributed computing in a manner that it tends to be heterogeneous, more loosely coupled and dispersed geographically. Optimization of this process must contains the overall maximization of resources utilization with balance load on each processing unit and also by decreasing the overall time or output. Evolutionary algorithms like genetic algorithms have studied so far for the implementation of load balancing across the grid networks. But problem with these genetic algorithm is that they are quite slow in cases where large number of tasks needs to be processed. In this paper we give a novel approach of parallel genetic algorithms for enhancing the overall performance and optimization of managing the whole process of load balancing across the grid nodes.
Parallel and distributed genetic algorithm with multiple objectives to impro...khalil IBRAHIM
we argue that the timetabling problem reflects the problem of scheduling university courses, So you must specify the range of time periods and a group of instructors for a range of lectures to check a set of constraints and reduce the cost of other constraints ,this is the problem called NP-hard, it is a class of problems that are informally, it’s mean that necessary operations to solve the problem will increase exponentially and directly proportional to the size of the problem, The construction of timetable is the most complicated problem that was facing many universities, and increased by size of the university data and overlapping disciplines between colleges, and when a traditional algorithm (EA) is unable to provide satisfactory results, a distributed EA (dEA), which deploys the population on distributed systems, it also offers an opportunity to solve extremely high dimensional problems through distributed coevolution using a divide-and-conquer mechanism, Further, the distributed environment allows a dEA to maintain population diversity, thereby avoiding local optima and also facilitating multi-objective search, by employing different distribution models to parallelize the processing of EAs, we designed a genetic algorithm suitable for Universities environment and the constraints facing it when building timetable for lectures.
A NOVEL APPROACH TOWARDS COST EFFECTIVE REGION-BASED GROUP KEY AGREEMENT PROT...ijp2p
Peer-to-peer systems have gained a lot of attention as information sharing systems for the widespread exchange of resources and voluminous information that is easily accessible among thousands of
users. However, current peer-to-peer information sharing systems work mostly on wired networks. With
the growing number of communication-equipped mobile devices that can self-organize into
infrastructure-less communication platform, namely mobile ad hoc networks (MANETs), peer-to-peer
information sharing over MANETs becomes a promising research area. In this paper, we propose a
Region-Based structure that enables efficient and secure peer-to-peer information sharing over MANETs.
The implementation shows that the proposed scheme is Secure, scalable, efficient, and adaptive to node
mobility and provides Reliable information sharing.
03 fauzi indonesian 9456 11nov17 edit septianIAESIJEECS
Since the rise of WWW, information available online is growing rapidly. One of the example is Indonesian online news. Therefore, automatic text classification became very important task for information filtering. One of the major issue in text classification is its high dimensionality of feature space. Most of the features are irrelevant, noisy, and redundant, which may decline the accuracy of the system. Hence, feature selection is needed. Maximal Marginal Relevance for Feature Selection (MMR-FS) has been proven to be a good feature selection for text with many redundant features, but it has high computational complexity. In this paper, we propose a two-phased feature selection method. In the first phase, to lower the complexity of MMR-FS we utilize Information Gain first to reduce features. This reduced feature will be selected using MMR-FS in the second phase. The experiment result showed that our new method can reach the best accuracy by 86%. This new method could lower the complexity of MMR-FS but still retain its accuracy.
ISSUES RELATED TO SAMPLING TECHNIQUES FOR NETWORK TRAFFIC DATASETijmnct
The document discusses issues related to sampling techniques for network traffic datasets. It analyzes various sampling techniques for their ability to capture information while sampling imbalanced network traffic data. The key points are:
1) Network traffic data is huge, varying, and imbalanced with some classes distributed unequally. Sampling is needed to reduce training time for machine learning algorithms used to analyze the data, but sampling can lose important information.
2) The document evaluates random sampling, systematic sampling, stratified sampling, and re-sampling techniques using a dataset collected from Panjab University's network. It finds that random sampling can miss some protocol classes entirely, losing important information.
3) Careful sampling is needed to handle the
The document discusses feature selection and classification methods for detecting denial-of-service (DoS) attacks in intrusion detection systems. It proposes using Random Forests for feature selection and k-Nearest Neighbors for classification on the KDD99 dataset. Experimental results show that the proposed approach of using Random Forests to select important features before classifying with k-Nearest Neighbors increases detection accuracy while decreasing false positives compared to other algorithms.
SAMPLING BASED APPROACHES TO HANDLE IMBALANCES IN NETWORK TRAFFIC DATASET FOR...cscpconf
Network traffic data is huge, varying and imbalanced because various classes are not equally distributed. Machine learning (ML) algorithms for traffic analysis uses the samples from this
data to recommend the actions to be taken by the network administrators as well as training. Due to imbalances in dataset, it is difficult to train machine learning algorithms for traffic
analysis and these may give biased or false results leading to serious degradation in performance of these algorithms. Various techniques can be applied during sampling to minimize the effect of imbalanced instances. In this paper various sampling techniques have been analysed in order to compare the decrease in variation in imbalances of network traffic
datasets sampled for these algorithms. Various parameters like missing classes in samples probability of sampling of the different instances have been considered for comparison
A adaptive neighbor analysis approach to detect cooperative selfish node in m...Jyoti Parashar
Mobile network is a set of wireless device called wireless nodes(mobile, Laptop) which are dynamically connect and transfer the information. In MANET nodes can be source, destination and intermediate node of data transmission.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Handwriting identification using deep convolutional neural network methodTELKOMNIKA JOURNAL
Handwriting is a unique thing that produced differently for each person. Handwriting has a characteristic that remain the same with single writer, so a handwriting can be used as a variable in biometric systems. Each person have a different form of handwriting style but with a small possibility that same characters have something commons. We propose a handwriting identification method using sentence segmented handwriting forms. Sentence form is used to get more complete handwriting characteristics than using a single characters or words. Dataset used is divided into three categories of images, binary, grayscale, and inverted binary. All datasets have same image with different in color and consist of 100 class. Transfer learning used in this paper are pre-trained model VGG19. Training was conducted in 100 epochs. Highest result is grayscale images with genuince acceptance rate of 92.3% and equal error rate of 7.7%.
Probability Density Functions of the Packet Length for Computer Networks With...IJCNCJournal
The research on Internet traffic classification and identification, with application on prevention of attacks
and intrusions, increased considerably in the past years. Strategies based on statistical characteristics of
the Internet traffic, that use parameters such as packet length (size) and inter-arrival time and their
probability density functions, are popular. This paper presents a new statistical modeling for packet length,
which shows that it can be modeled using a probability density function that involves a normal or a beta
distribution, according to the traffic generated by the users. The proposed functions has parameters that
depend on the type of traffic and can be used as part of an Internet traffic classification and identification
strategy. The models can be used to compare, simulate and estimate the computer network traffic, as well
as to generate synthetic traffic and estimate the packets processing capacity of Internet routers
This document discusses a system for detecting suspicious emails using machine learning algorithms. It presents a model that filters emails containing images and PDFs to improve performance over existing text-based spam filtering systems. The model uses data collection, preprocessing, machine learning classification algorithms like Naive Bayes, Support Vector Machines, and Decision Trees. It aims to achieve acceptable recall and precision values. The document also reviews related work on email spam detection using machine learning and discusses the future scope of applying the algorithm to embedded images/PDFs and larger datasets.
Study, analysis and formulation of a new method for integrity protection of d...ijsrd.com
This document discusses a text-based fuzzy clustering algorithm to filter spam emails. It begins with an introduction discussing how most classification approaches are for structured data but large amounts of unstructured data are transmitted online. It then discusses spam emails being a major problem and filtering being an important approach. The paper aims to use a fuzzy clustering approach called Fuzzy C-Means to classify emails. It describes the training and testing modules, which extract features from emails to create vector space models and then applies the fuzzy clustering algorithm to determine if emails are spam or not spam. Evaluation results show the precision and accuracy of the approach on different datasets, with the author concluding the vector space model with fuzzy C-Means works well for both small and large datasets.
IDS IN TELECOMMUNICATION NETWORK USING PCAIJCNCJournal
This document summarizes a research paper that proposes using principal component analysis (PCA) as a dimension reduction technique for intrusion detection systems (IDS). The paper applies PCA to reduce the number of features from 41 to either 6 or 10 features for the NSL-KDD dataset. One reduced feature set is used to develop a network IDS with high detection success and rate, while the other is used for a host IDS also with good detection success and very high detection rate. The paper outlines the process of applying PCA for IDS, including performing PCA on training data to identify principal components, then using those components to map new online data and detect intrusions based on deviation thresholds.
This document summarizes and evaluates various rule extraction algorithms from trained artificial neural networks. It begins with an introduction explaining the importance of explanation capabilities for neural networks. It then provides a taxonomy for classifying rule extraction approaches based on the expressiveness of the extracted rules, whether the approach takes an open-box or black-box view of the neural network, any specialized training regimes used, the quality of explanations generated, and computational complexity. The document discusses sensitivity analysis as a basic method for understanding neural network relationships before focusing on decompositional and pedagogical rule extraction approaches.
This document summarizes a research paper that proposes a new email spam detection framework using feature selection and similarity coefficients. The framework first applies feature selection to identify the most important features for distinguishing spam and non-spam emails. It then calculates similarity coefficients between email pairs to quantify their similarity based on the selected features. The researchers claim this approach improves spam detection accuracy and rate by removing irrelevant and redundant features before classification. They test their framework on a standard email dataset and find that detection performance is better after applying feature selection compared to using all original features.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
A New Function-based Framework for Classification and Evaluation of Mutual Ex...CSCJournals
This paper presents a new function-based framework for mutual exclusion algorithms in distributed systems. In the traditional classification mutual exclusion algorithms were divided in to two groups: Token-based and Permission-based. Recently, some new algorithms are proposed in order to increase fault tolerance, minimize message complexity and decrease synchronization delay. Although the studies in this field up to now can compare and evaluate the algorithms, this paper takes a step further and proposes a new function-based framework as a brief introduction to the algorithms in the four groups as follows: Token-based, Permission-based, Hybrid and K-mutual exclusion. In addition, because of being dispersal and obscure performance criteria, introduces four parameters which can be used to compare various distributed mutual exclusion algorithms such as message complexity, synchronization delay, decision theory and nodes configuration. Hope the proposed framework provides a suitable context for technical and clear evaluation of existing and future methods.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
The document presents a new approach for secure routing of IoT devices based on a probabilistic polling system. It aims to address security issues in IoT by taking into account the opinions of different stakeholders. The proposed approach uses a polling system with multiple queues to represent stakeholders, and assigns them different priorities through probabilistic routing between queues. This allows votes from stakeholders in higher priority queues to be weighted more in determining the most secure routing path. The approach is described as providing a user-centric solution compared to previous works by incorporating stakeholder opinions.
The document presents a new approach for secure routing of IoT devices based on a probabilistic polling system. It aims to address security issues in IoT by taking into account the opinions of different stakeholders. The proposed approach uses a polling system with multiple queues to represent stakeholders, and assigns them different priorities through probabilistic routing between queues. This allows votes from stakeholders in higher priority queues to be weighted more in determining the most secure routing path. The approach is described as providing a user-centric solution compared to previous works by incorporating stakeholder opinions.
Grid computing can involve lot of computational tasks which requires trustworthy computational nodes. Load balancing in grid computing is a technique which overall optimizes the whole process of assigning computational tasks to processing nodes. Grid computing is a form of distributed computing but different from conventional distributed computing in a manner that it tends to be heterogeneous, more loosely coupled and dispersed geographically. Optimization of this process must contains the overall maximization of resources utilization with balance load on each processing unit and also by decreasing the overall time or output. Evolutionary algorithms like genetic algorithms have studied so far for the implementation of load balancing across the grid networks. But problem with these genetic algorithm is that they are quite slow in cases where large number of tasks needs to be processed. In this paper we give a novel approach of parallel genetic algorithms for enhancing the overall performance and optimization of managing the whole process of load balancing across the grid nodes.
Parallel and distributed genetic algorithm with multiple objectives to impro...khalil IBRAHIM
we argue that the timetabling problem reflects the problem of scheduling university courses, So you must specify the range of time periods and a group of instructors for a range of lectures to check a set of constraints and reduce the cost of other constraints ,this is the problem called NP-hard, it is a class of problems that are informally, it’s mean that necessary operations to solve the problem will increase exponentially and directly proportional to the size of the problem, The construction of timetable is the most complicated problem that was facing many universities, and increased by size of the university data and overlapping disciplines between colleges, and when a traditional algorithm (EA) is unable to provide satisfactory results, a distributed EA (dEA), which deploys the population on distributed systems, it also offers an opportunity to solve extremely high dimensional problems through distributed coevolution using a divide-and-conquer mechanism, Further, the distributed environment allows a dEA to maintain population diversity, thereby avoiding local optima and also facilitating multi-objective search, by employing different distribution models to parallelize the processing of EAs, we designed a genetic algorithm suitable for Universities environment and the constraints facing it when building timetable for lectures.
Scalable Rough C-Means clustering using Firefly algorithm..................................................................1
Abhilash Namdev and B.K. Tripathy
Significance of Embedded Systems to IoT................................................................................................. 15
P. R. S. M. Lakshmi, P. Lakshmi Narayanamma and K. Santhi Sri
Cognitive Abilities, Information Literacy Knowledge and Retrieval Skills of Undergraduates: A
Comparison of Public and Private Universities in Nigeria ........................................................................ 24
Janet O. Adekannbi and Testimony Morenike Oluwayinka
Risk Assessment in Constructing Horseshoe Vault Tunnels using Fuzzy Technique................................ 48
Erfan Shafaghat and Mostafa Yousefi Rad
Evaluating the Adoption of Deductive Database Technology in Augmenting Criminal Intelligence in
Zimbabwe: Case of Zimbabwe Republic Police......................................................................................... 68
Mahlangu Gilbert, Furusa Samuel Simbarashe, Chikonye Musafare and Mugoniwa Beauty
Analysis of Petrol Pumps Reachability in Anand District of Gujarat ....................................................... 77
Nidhi Arora
SECURING BGP BY HANDLING DYNAMIC NETWORK BEHAVIOR AND UNBALANCED DATASETSIJCNCJournal
The Border Gateway Protocol (BGP) provides crucial routing information for the Internet infrastructure. A problem with abnormal routing behavior affects the stability and connectivity of the global Internet. The biggest hurdles in detecting BGP attacks are extremely unbalanced data set category distribution and the dynamic nature of the network. This unbalanced class distribution and dynamic nature of the network results in the classifier's inferior performance. In this paper we proposed an efficient approach to properly managing these problems, the proposed approach tackles the unbalanced classification of datasets by turning the problem of binary classification into a problem of multiclass classification. This is achieved by splitting the majority-class samples evenly into multiple segments using Affinity Propagation, where the number of segments is chosen so that the number of samples in any segment closely matches the minority-class samples. Such sections of the dataset together with the minor class are then viewed as different classes and used to train the Extreme Learning Machine (ELM). The RIPE and BCNET datasets are used to evaluate the performance of the proposed technique. When no feature selection is used, the proposed technique improves the F1 score by 1.9% compared to state-of-the-art techniques. With the Fischer feature selection algorithm, the proposed algorithm achieved the highest F1 score of 76.3%, which was a 1.7% improvement over the compared ones. Additionally, the MIQ feature selection technique improves the accuracy by 3.5%. For the BCNET dataset, the proposed technique improves the F1 score by 1.8% for the Fisher feature selection technique. The experimental findings support the substantial improvement in performance from previous approaches by the new technique.
Securing BGP by Handling Dynamic Network Behavior and Unbalanced DatasetsIJCNCJournal
The Border Gateway Protocol (BGP) provides crucial routing information for the Internet infrastructure. A problem with abnormal routing behavior affects the stability and connectivity of the global Internet. The biggest hurdles in detecting BGP attacks are extremely unbalanced data set category distribution and the dynamic nature of the network. This unbalanced class distribution and dynamic nature of the network results in the classifier's inferior performance. In this paper we proposed an efficient approach to properly managing these problems, the proposed approach tackles the unbalanced classification of datasets by turning the problem of binary classification into a problem of multiclass classification. This is achieved by splitting the majority-class samples evenly into multiple segments using Affinity Propagation, where the number of segments is chosen so that the number of samples in any segment closely matches the minority-class samples. Such sections of the dataset together with the minor class are then viewed as different classes and used to train the Extreme Learning Machine (ELM). The RIPE and BCNET datasets are used to evaluate the performance of the proposed technique. When no feature selection is used, the proposed technique improves the F1 score by 1.9% compared to state-of-the-art techniques. With the Fischer feature selection algorithm, the proposed algorithm achieved the highest F1 score of 76.3%, which was a 1.7% improvement over the compared ones. Additionally, the MIQ feature selection technique improves the accuracy by 3.5%. For the BCNET dataset, the proposed technique improves the F1 score by 1.8% for the Fisher feature selection technique. The experimental findings support the substantial improvement in performance from previous approaches by the new technique.
This document summarizes an article about using an artificial bee colony (ABC) algorithm to extract knowledge from numerical data to generate fuzzy rules. The ABC algorithm is an optimization technique inspired by honeybee behavior that can be used for data-driven modeling when domain experts are unavailable. The article describes fuzzy systems and their components, defines the problem of generating fuzzy rules from data as a minimization problem, and provides an example of applying the ABC algorithm to generate rules for a rapid battery charger system based on temperature and charging rate data.
Multi-objective NSGA-II based community detection using dynamical evolution s...IJECEIAES
Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for community’s detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations.
A Survey on Wireless Network SimulatorsjournalBEEI
The Network simulator helps the developer to create and simulate new models on an arbitrary network by specifying both the behavior of the network nodes and the communication channels. It provides a virtual environment for an assortment of desirable features such as modeling a network based on a specific criteria and analyzing its performance under different scenarios. This saves cost and time required for testing the functionality and the execution of network. This paper has surveyed various Wireless Network Simulators and compared them.
Congestion Control in Wireless Sensor Networks Using Genetic AlgorithmEditor IJCATR
Sensor network consists of a large number of small nods, strongly interacting with the physical environment, takes
environmental data through sensors, and reacts after processing on information. Wireless network technologies are widely used in most
applications. As wireless sensor networks have many activities in the field of information transmission, network congestion cannot be
thus avoided. So it seems necessary that some new methods can control congestion and use existing resources for providing better traffic
demands. Congestion increases packet loss and retransmission of removed packets and also wastes of energy. In this paper, a novel
method is presented for congestion control in wireless sensor networks using genetic algorithm. The results of simulation show that the
proposed method, in comparison with the algorithm LEACH, can significantly improve congestion control at high speeds.
Dynamic analysis of agent network in self organisation using service level ag...inventionjournals
ABSTRACT: Self-organisation mechanism synthesizes the three principles: cloning, resource exchange and relation adaptation to achieve the better agent network structure. Self organization is usually defined as “the mechanism or the process enabling the system to change its organization without explicit external command during its execution time”. By the mechanism, an agent selected by task that particular agent having resource to complete it. When task given to the agent is overloaded then generate clone agents using famous method, Hybrid Model of Q-learning and also provide the guarantee of the agent network using SLA(Service Level Agreement) technique. The mechanism is evaluated through a comparison with the three approaches are the benefit of individual agent and the entire agent network, the load balancing among the agents and the time consumption to finish the task execution.
This document proposes a fuzzy rule-based system to classify user behavior in a computer network based on user logs. It involves collecting web, network, and machine logs from servers and extracting frequencies of activities. The frequencies are normalized and fed into a fuzzy rule-based system. The system uses if-then rules to classify a user's tendency to attempt restricted tasks based on their log data. Five behavior classes are defined based on the tendency output from very good to very bad. The system is demonstrated on sample user log data from an institution to classify users' behavior in different months.
Bra a bidirectional routing abstraction for asymmetric mobile ad hoc networks...Mumbai Academisc
This document summarizes a paper that presents a framework called BRA that provides a bidirectional abstraction of asymmetric mobile ad hoc networks to enable off-the-shelf routing protocols to work. BRA maintains multi-hop reverse routes for unidirectional links, improves connectivity by using unidirectional links, enables reverse route forwarding of control packets, and detects packet loss on unidirectional links. Simulations show packet delivery increases substantially when AODV is layered on BRA in asymmetric networks compared to regular AODV.
Clonal Selection Algorithm Parallelization with MPJExpressAyi Purbasari
This paper exploits the parallelism potential on a Clonal Selection Algorithm (CSA) as a parallel metaheuristic algorithm, due the lack of explanation detail of the stages of designing parallel algorithms. To parallelise population-based algorithms, we need to exploit and define their granularity for each stage; do data or functional partition; and choose the communication model. Using a library for a message-passing model, such as MPJExpress, we define appropriate methods to implement process communication. This research results pseudo-code for the two communication message-passing models, using MPJExpress. We implemented this pseudo-codes using Java Language with a dataset from the Travelling Salesman Problem (TSP). The experiments showed that multicommunication model using alltogether method gained better performance that master-slave model that using send-and receive method.
This document summarizes a research paper that analyzes the performance of a parallel merge sort algorithm implemented using MPI (Message Passing Interface). It first provides background on merge sort and parallel computing. It then describes the methodology used, which divides the sorting problem across processes in a tree structure. The processes communicate by sending sub-problems and sorted data to each other. Finally, it evaluates the communication and computation costs and presents results on the performance of the parallel merge sort approach.
Similar to An Improved Leader Election Algorithm for Distributed Systems (20)
Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional N...ijngnjournal
This document describes a system that uses big data telemetry from networks to enable trend-based networking decisions in SDN and traditional networks. It presents the design of the system, including functional block diagrams and descriptions of the design solution and its levels. It also discusses the implementation including data collection, processing, storage and analysis, and how trends are identified and used to make network configuration changes to balance traffic load. Future work areas are identified such as using machine learning on historical data and connecting traditional and SDN network topologies in hybrid network configurations.
TREND-BASED NETWORKING DRIVEN BY BIG DATA TELEMETRY FOR SDN AND TRADITIONAL N...ijngnjournal
Organizations face a challenge of accurately analyzing network data and providing automated action
based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and
improve the performance of the network services, but organizations use different network management
tools to understand and visualize the network traffic with limited abilities to dynamically optimize the
network. This research focuses on the development of an intelligent system that leverages big data
telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trendbased networking decisions. The results include a graphical user interface (GUI) done via a web
application for effortless management of all subsystems, and the system and application developed in
this research demonstrate the true potential for a scalable system capable of effectively benchmarking
the network to set the expected behavior for comparison and trend analysis. Moreover, this research
provides a proof of concept of how trend analysis results are actioned in both a traditional network and
a software-defined network (SDN) to achieve dynamic, automated load balancing.
PERFORMANCE PREDICTION OF 5G: THE NEXT GENERATION OF MOBILE COMMUNICATIONijngnjournal
The 5G standard is a mobile communication of the 5th generation, which presupposes an increase of the information exchange speed up to 10 Gbit/s. It is 30 times quicker than the speed of 4G network. It is a new stage in the development of technologies connecting society. This standard will provide an unlimited access to the network for individual users and devices. When developing the 5G standard, the advanced opportunities of LTE and HSPA, as well as other technologies of a radio access focused on the solution of specific objectives are considered. The main advantage of the mass introduction of the 5G communication development represents the so-called Internet of Things (IoT). There the devices and not people will be the main consumers of traffic. The functional requirements of5G networks, their speed, and its traffic parameters for HD video services and massifs of M2M-devices are analyzed in the paper. They will have been the most demandedones by 2020.
PERFORMANCE EVALUATION OF VERTICAL HARD HANDOVERS IN CELLULAR MOBILE SYSTEMSijngnjournal
With the rapid increase of new and diverse cellular mobile services, the overlapping of cells has become typical in the majority of the coverage area of the network. Vertical handovers occur between two layers of cells when a user is switched from one layer to the other. In this paper we investigate the influence of network parameters on vertical hard handover performance in a cell environment. The work considers two layers of cells: a layer of macrocells and a layer of microcells. Handover requests enter the macrocell from neighbor macrocells and from microcells that belong to a different layer. Using Markov chain analysis and simulation we calculate network performance parameters such as mean queue delay, handover dropping probability and channel utilization. We also compare the handover performance for the macrocell and macrocell traffic separately. Our results show the influence of total channels, maximum queue size and handover request arrival rate on handover performance. They also show that when the traffic from each layer is treated with equal priority in the system, the performance of each layer is comparable.
PERFORMANCE EVALUATION OF VERTICAL HARD HANDOVERS IN CELLULAR MOBILE SYSTEMSijngnjournal
With the rapid increase of new and diverse cellular mobile services, the overlapping of cells has become typical in the majority of the coverage area of the network. Vertical handovers occur between two layers of cells when a user is switched from one layer to the other. In this paper we investigate the influence of network parameters on vertical hard handover performance in a cell environment. The work considers two layers of cells: a layer of macrocells and a layer of microcells. Handover requests enter the macrocell from neighbor macrocells and from microcells that belong to a different layer. Using Markov chain analysis and simulation we calculate network performance parameters such as mean queue delay, handover dropping probability and channel utilization. We also compare the handover performance for the macrocell and macrocell traffic separately. Our results show the influence of total channels, maximum queue size and handover request arrival rate on handover performance. They also show that when the traffic from each layer is treated with equal priority in the system, the performance of each layer is comparable.
COMPARISON OF RADIO PROPAGATION MODELS FOR LONG TERM EVOLUTION (LTE) NETWORKijngnjournal
This paper concerns about the radio propagation models used for the upcoming 4th Generation (4G) of cellular networks known as Long Term Evolution (LTE). The radio wave propagation model or path loss model plays a very significant role in planning of any wireless communication systems. In this paper, a comparison is made between different proposed radio propagation models that would be used for LTE, like Stanford University Interim (SUI) model, Okumura model, Hata COST 231 model, COST Walfisch-Ikegami & Ericsson 9999 model. The comparison is made using different terrains e.g. urban, suburban and rural area.SUI model shows the lowest path lost in all the terrains while COST 231 Hata model illustrates highest path loss in urban area and COST Walfisch-Ikegami model has highest path loss for suburban and rural environments.
IMPLEMENTATION AND COMPARISION OF DATA LINK QUALITY SCHEME ON ODMRP AND ADMR ...ijngnjournal
An ad hoc network is a collection of wireless mobile nodes dynamically forming a temporary network without the use of any fixed network infrastructure or centralized administration. In order to enable communication within the network, a routing protocol is needed to discover routes between nodes. The primary goal of ad hoc network routing protocols is to establish routes between node pairs so that messages may be delivered reliably and in a timely manner. The objective of any routing protocol is to have packet delivered with least possible cost in terms of receiving power, transmission power, battery energy consumption and distance. All these factors basically effect the establishment of link between the mobile nodes and liability and stability of these links. In this paper, we implement a data link quality scheme on two protocols ODMRP and ADMR and compare them on the bases link quality and link stability.
The Performance of a Cylindrical Microstrip Printed Antenna for TM10 Mode as...ijngnjournal
A temperature is one of the parameters that have a great effect on the performance of microstrip antennas for TM10 mode at 2.4 GHz frequency range. The effect of temperature on a resonance frequency, input impedance, voltage standing wave ratio, and return loss on the performance of a cylindrical microstrip printed antenna is studied in this paper. The effect of temperature on electric and magnetic fields are also studied. Three different substrate materials RT/duroid-5880 PTFE, K-6098 Teflon/Glass, and Epsilam-10 ceramic-filled Teflon are used for verifying the new model.
Optimization of Quality of Service Parameters for Dynamic Channel Allocation ...ijngnjournal
This document summarizes a research paper that proposes optimizing quality of service parameters for dynamic channel allocation in cellular networks using a genetic algorithm. It discusses fixed and dynamic channel allocation schemes and describes three quality of service parameters - call duration, number of users, and residual bandwidth - that are considered for optimization. It then provides an overview of genetic algorithms and describes how one was implemented to optimize the three parameters. The genetic algorithm encoded the parameters as bit strings, calculated a fitness function, selected individuals for reproduction probabilistically based on fitness, and applied crossover and mutation over generations to arrive at an optimized allocation scheme. The optimized scheme was then compared to a non-optimized one to evaluate the genetic algorithm's effectiveness.
PURGING OF UNTRUSTWORTHY RECOMMENDATIONS FROM A GRIDijngnjournal
In grid computing, trust has massive significance. There is lot of research to propose various models in providing trusted resource sharing mechanisms. The trust is a belief or perception that various researchers have tried to correlate with some computational model. Trust on any entity can be direct or indirect. Direct trust is the impact of either first impression over the entity or acquired during some direct interaction. Indirect trust is the trust may be due to either reputation gained or recommendations received from various recommenders of a particular domain in a grid or any other domain outside that grid or outside that grid itself. Unfortunately, malicious indirect trust leads to the misuse of valuable resources of the grid. This paper proposes the mechanism of identifying and purging the untrustworthy recommendations in the grid environment. Through the obtained results, we show the way of purging of untrustworthy entities.
A SURVEY ON DYNAMIC SPECTRUM ACCESS TECHNIQUES FOR COGNITIVE RADIOijngnjournal
Cognitive radio (CR) is a new paradigm that utilizes the available spectrum band. The key characteristic of CR system is to sense the electromagnetic environment to adapt their operation and dynamically vary its radio operating parameters. The technique of dynamically accessing the unused spectrum band is known as Dynamic Spectrum Access (DSA). The dynamic spectrum access technology helps to minimize unused spectrum bands. In this paper, main functions of Cognitive Radio (CR) i.e. spectrum sensing, spectrum management, spectrum mobility and spectrum sharing are discussed. Then DSA models are discussed along with different methods of DSA such as Command and Control, Exclusive-Use, Shared Use of Primary Licensed User and Commons method. Game-theoretic approach using Bertrand game model, Markovian Queuing Model for spectrum allocation in centralized architecture and Fuzzy logic based method are also discussed and result are shown.
HYBRID LS-LMMSE CHANNEL ESTIMATION Technique for LTE Downlink Systemsijngnjournal
- The document proposes a hybrid LS-LMMSE channel estimation technique for LTE downlink systems that is robust to the effect of channel length.
- The technique chooses between LS and LMMSE estimation depending on whether the cyclic prefix is longer than or shorter than the channel length, and on the SNR value.
- When the cyclic prefix is longer than the channel length, LMMSE is used directly. When it is shorter, LMMSE is used for low SNR and LS is used for high SNR.
- Simulation results show the hybrid technique performs better than LMMSE alone, especially at high SNR values when the cyclic prefix is shorter than the channel length.
SERVICES AS PARAMETER TO PROVIDE BEST QOS : AN ANALYSIS OVER WIMAXijngnjournal
In this paper it is proposed to provide the QoS to the user by using the degradation of service under hostile environment being itself be a parameter to improve the QoS. Here the relation between the service and environment of its best performance drawn on the basis of simulation and analysis .The service then taken as a parameter to decide present environment of the user and to take measurable steps to improve the QoS either doing handover to nearby station or increasing power or to provide some marginal bandwidth etc.All analysis done over a WiMax network i.e. being designed and simulated using the Qualnet wireless simulator.
ENSURING QOS GUARANTEES IN A HYBRID OCS/OBS NETWORKijngnjournal
The bursting aggregation assembly in edge nodes is one of the key technologies in OBS (Optical Burst Switching) network, which has a direct impact on flow characteristics and packet loss rate. An optical burst assembly technique supporting QoS is presented through this paper, which can automatically adjust the threshold along with the increasing and decreasing volume of business, reduce the operational burst, and generate corresponding BDP (Burst Data Packet) and BCP (Burst Control Packet). In addition to the burst aggregation technique a packet recovery technique by restoration method is also described. The data packet loss due to the physical optical link failure is not currently included in the QoS descriptions. This link failure is also a severe problem which reduces the data throughput of the transmitter node. A mechanism for data recovery from this link failure is vital for guaranteeing the QoS demanded by each user. So this paper will also discusses a specific protocol for reducing the packet loss by utilizing the
features of both optical circuit switching (OCS) and Optical Burst switching (OBS) techniques
SECURITY ANALYSIS AND DELAY EVALUATION FOR SIP-BASED MOBILE MASS EXAMINATION ...ijngnjournal
IP Multimedia Subsystem (IMS) is considered to be one of the important features in Mobile Next Generation Networks (MNGN). It adds value to the mobile services and applications by integrating mobile network resources, such as location, billing and authentication. This is achieved by enabling a third party access to network resources. In previous work [1] we have presented a testbed to be used as platform for testing mobile application prior to actual deployment. We have chosen a novel IMS based MObile Mass EXamination (MOMEX) system to showcase the benefit of designing an IMS based mobile application. We identify two aspects essential to of the application namely security threats and delay analysis. In this paper we identify MOMEX security threats and suggest strategies to mitigate system vulnerabilities. We then
evaluate the performance of MOMEX system in terms of delay and security threats and vulnerabilities. The results presented show system performance limitation and tradeoffs.
OPTIMIZATION OF QOS PARAMETERS IN COGNITIVE RADIO USING ADAPTIVE GENETIC ALGO...ijngnjournal
Genetic algorithm based optimization rely on explicit relationships between parameters, observations and criteria. GA based optimization when done in cognitive radio can provide a criteria to accommodate the secondary users in best possible space in the spectrum by interacting with the dynamic radio environment at real time. In this paper we have proposed adaptive genetic algorithm with adapting crossover and mutation parameters for the reasoning engine in cognitive radio to obtain the optimum radio configurations. This method ensure better controlling of the algorithm parameters and hence the increasing the performance. The main advantage of genetic algorithm over other soft computing techniques is its multi – objective handling capability. We focus on spectrum management with a hypothesis that inputs are provided by either sensing information from the radio environment or the secondary user. Also the QoS requirements condition is also specified in the hypothesis. The cognitive radio will sense the radio frequency parameter from the environment and the reasoning engine in the cognitive radio will take the required decisions in order to provide new spectrum allocation as demanded by the user. The transmission parameters which can be taken into consideration are modulation method, bandwidth, data rate, symbol rate, power consumption etc. We simulated cognitive radio engine which is driven by genetic algorithm to determine the optimal set of radio transmission parameters. We have fitness objectives to guide one system to an optimal state. These objectives are combined to one multi – objective fitness function using weighted sum approach so that each objective can be represented by a rank which represents the importance of each objective. We have transmission parameters as decision variables and environmental parameters are used as inputs to the objective function. We have compared the proposed adaptive genetic algorithm (AGA) with conventional genetic algorithm (CGA) with same set of conditions. MATLAB simulations were used to analyze the scenarios
HIGH PERFORMANCE ETHERNET PACKET PROCESSOR CORE FOR NEXT GENERATION NETWORKSijngnjournal
As the demand for high speed Internet significantly increasing to meet the requirement of large data transfers, real-time communication and High Definition ( HD) multimedia transfer over IP, the IP based network products architecture must evolve and change. Application specific processors require high
performance, low power and high degree of programmability is the limitation in many general processor based applications. This paper describes the design of Ethernet packet processor for system-on-chip (SoC) which performs all core packet processing functions, including segmentation and reassembly, packetization classification, route and queue management which will speedup switching/routing performance making it
more suitable for Next Generation Networks (NGN). Ethernet packet processor design can be configured for use with multiple projects targeted to a FPGA device the system is designed to support 1/10/20/40/100 Gigabit links with a speed and performance advantage. VHDL has been used to implement and simulated the required functions in FPGA
ESTIMATION AND COMPENSATION OF INTER CARRIER INTERFERENCE IN WIMAX PHYSICAL L...ijngnjournal
WiMAX is Wireless Interoperability for Microwave Access has emerged as a promising solution for transmission of higher data rates for fixed and mobile applications. IEEE 802.16d and e are the standards proposed by WiMAX group for fixed and mobile. As the wireless channel have so many limitation Such as Multipath, Doppler spread, Delay spread and Line Of Sight (LOS)/Non Line Of Sight (NLOS) components. To attain higher data rates the Multi Carrier System with Multiple Input and Multiple Output (MIMO) is incorporated in the WiMAX. The Orthogonal Frequency Division Multiplexing (OFDM) is a multi carrier technique used with the WiMAX systems. In OFDM the available spectrum is split into numerous narrow band channels of dissimilar frequencies to achieve high data rate in a multi path fading environment. And all these sub carriers are considered to be orthogonal to each other. As the number of sub carriers is increased there is no guarantee of sustained orthogonality, i.e. at some point the carriers are not
independent to each other, and hence where the orthogonality can be loosed which leads to interference and also owing to the synchronization between transmitter and receiver local oscillator, it causes interference known as Inter Carrier Interference (ICI). The systems uses MIMO-OFDM will suffer with the effects of ICI and Carrier Frequency Offset (CFO) “ε”. However these affect the power leakage in the midst of sub carriers, consequently degrading the system performance. In this paper a new approach is proposed in order to reduce the ICI caused in WiMAX and improve the system performance. In this scheme at the transmitter side the modulated data and a few predefined pilot symbols are mapped onto the non
neighboring sub carriers with weighting coefficients of +1 and -1. With the aid of pilot symbols the frequency offset is exactly estimated by using Maximum Likelihood Estimation (MLE) and hence can be minimized. At demodulation stage the received signals are linearly combined along with their weighted
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An Improved Leader Election Algorithm for Distributed Systems
1. International Journal of Next-Generation Networks (IJNGN) Vol.5, No.1, March 2013
AN IMPROVED LEADER ELECTION ALGORITHM
FOR DISTRIBUTED SYSTEMS
P BeaulahSoundarabai∗, Thriveni J∗∗, K R Venugopal∗∗, L M Patnaik∗∗∗
*DepartmentofComputerScience,ChristUniversity,Bangalore
beaulah.s@christuniversity.in
**DepartmentofComputerScienceandEngineering
∗∗∗ Honarary Professor, IISc., Bangalore
UniversityVisvesvarayaCollegeofEngineering,BangaloreUniversity,Bangalore
ABSTRACT
Leader Election Algorithm , not only in distributed systems but in any communication network, is an
essential matter for discussion. Tremendous amount of work are happening in the research community on
this Election, because many network protocols are in need of a coordinator process for the smooth running
of the system. These socalled Leader or Coordinator processes are responsible for the synchronization of
the system. If there is no synchronization, then the entire system would become inconsistent which intern
makes the system to lose its reliability. Since all the processes need to interact with the leader process, they
all must agree upon who the present leader is. Furthermore, if the leader process crashes, the new leader
process should take the charge as early as possible. New leader is one among the currently running
processes with the highest process id. In this paper we have presented a modified version of ring algorithm.
Our work involves substantial modifications of the existing ring election algorithm and the comparison of
message complexity with the original algorithm. Simulation results show that our algorithmminimizes the
number of messages being exchanged in electing the coordinator..
KEYWORDS
Election, Coordinator,Message Complexity, Ring Algorithm,Distributed System.
1. INTRODUCTION
Leader Election is a vital and fundamental problem in distributed systems and in any
communication network. Distributed Systems is a collection of heterogeneous systems which
interact with each other through messages. The main objective of Distributed System is , though
there are heterogeneous systems in the network, it creates a single system image or uniprocessor
image to the user, through various transparency metrics. The communication between the
processes is achieved by exchanging messages. The software of the Distributed System is tightly
coupled and the processes of the system coordinate with each other. They have lots of resources
in common and so mutual exclusion algorithms are used to take care of the critical regions. While
they wait for the common resources, they might end up in a deadlock. Deadlock detection and
prevention algorithms should keep an eye on the resources and if there is a deadlock wound wait
or waitdie algorithms are used to kill the eldest or the youngest process to remove the deadlock.
The replicated data management, group communication, atomic commit protocols, etc needs the
process coordination. All the above stated protocols need a particular process among the group, to
be the leader to have the control over the situation. In general, the process with the highest
processid is the coordinator or the leader. Any process, which satisfies the rule can become the
DOI : 10.5121/ijngn.2013.5102 21
2. International Journal of Next-Generation Networks (IJNGN) Vol.5, No.1, March 2013
leader and the only issue in distributed system is that at any point of time there should be a unique
process available as the leader to do the coordination and all the other processes should agree up
on the present leader, without any confusion.
Motivation: If the processes of the distributed system never fail, then the leader process can be
decided at the time of the process group gets generated. But, there are systems where processes
keep coming and leaving in the group and processes do crash. Specially, in Wireless networking
like Wireless LAN, Satellite oriented Services cellular phones, the mobile systems are subject to
loss of messages or the data and the mobile host can crash or can be down for some time. Electing
a leader process is a basic operation which happens in the system very often. Many researchers
have contributed a lot of paradigms to simply this task. Different kinds of leader election
algorithms have been proposed and most of them are widely in usage.
Contribution: In this paper, we have proposed a modified ring algorithm with minimized number
of messages .The main objective of the algorithm is the fault tolerance. In mobile ad hoc network,
we always face node failure or the process crash. Even during the time of leader process crash,
there should be a new leader process available, without much wasting of time and the number of
messages which are exchanged.
Organization: The remainder of this paper is organized as follows: Section II reviews the related
work; Background of Election and Ring Election Algorithms is available in Section III. The
Efficient Ring Election Algorithm is proposed in Section IV; Section V deals with the
Performance Evaluation; Conclusions are presented in Section VI.
2. RELATED WORK
Sung Hoon Park [1] has proposed a concept called Failure Detector which works as an
independent module with a function that detects crash and recovery of a node in a system. This
report can be given to any process at request.
The author modified the bully algorithm using the failure detector. The performance of the system
goes down because of the overhead of Failure Detector. And as the Failure Detector is the
centralized component, it has the problem of single source failure and also creates bottleneck
situation to access the module.
Sandipan Basu [2] has addressed the issue of bully algorithm and proposed his modified
algorithm. In the original bully algorithm, when the leader process is crashed, immediately the
new leader is elected. But, if the old leader process comes back, it once again initiates the
election. The author suggests that there need not be another election, instead, the old leader
process can accept the new leader process by sending the new request of who the leader is?, to its
neighbour. In the next round of election, it can try becoming the leader.
Muhammad Mahbubur Rahman et al., [3] have also proposed a modified bully election algorithm.
In their paper , they say that the bully algorithm has O(n2) messages which increases the network
traffic. In the worst case, there will be n number of elections can occur in the system which again
in tern will yield in a heavy network traffic. They have proposed the same algorithm but with
Failure Detector, Helper processes to have unique election with the Election Commission.
ChangYoung Kim et al., [4] have proposed the election protocol for reconfigurable distributed
systems which agai n was based on bully election algorithm. The actual election is run by the
base stations making the protocol, energy efficien t. The protocol is also independent from the
overall number of mobile hosts and the data structures required by the algorithm are managed at
the base station, making the protocol scalable as well.
22
3. International Journal of Next-Generation Networks (IJNGN) Vol.5, No.1, March 2013
M S Kordafshari et al., [5] discussed the drawback of synchronous bully algorithm and modified
it with an optimal message algorithm. The authors have tried to reduce the number of elections
happening in the classical bully algorithm. The proposed algorithm has only one election at any
point of time, which brings down the number of messages being exchanged drastically. Sepehri
M et al., [6] have dealt with the distributed leader election algorithm for a set of processes
connected by a tree network. The authors have proposed a linear time algorithm using heap
structure using reheap up and reheap down algorithms. They also have analysed the algorithm and
reached a logarithmic number of message complexity
Cuibo Yu et al., [7] have proposed a different idea on SN(Super Node) election algorithm based
upon district partition which divided the whole overlay into k small districts and using distributed
and parallel computing in these small units was brought forward. This algorithm would decrease
the message complexity to O(n2 k) and increase the electing speed. At the same time, the elected
SN would be evenly distributed in the whole overlay.
Mehdi EffatParvar et al., [8] have proposed a new approach with fault tolerant mechanisms base
on heap for coordinator finding in wireless environment. The authors created the new algorithm
called Heap tree algorithm , by modifying the bully and the ring election algorithms. They have
also compared the algorithm's running time and message complexity with the existing algorithms.
3. BACKGROUND
A. Election Algorithm
Election algorithm is a special purpose algorithm, which is run for selecting the coordinator
process among N number of processes. These coordinator or leader process plays an important
role in the distributed system to maintain the consistency through synchronization. For example,
in a system of client server, the mutual exclusion algorithm is preserved by the server process Ps,
which is chosen from among the processes Pi where i=1,2,...,N that are the group of processes
which would use the critical region. Election Algorithm is needed in this situation to choose the
server process among the existing processes. Eventually all the processes must agree upon the
leader process. If the coordinator process fails due to various reasons, then immediately the
election should happen to choose a new leader process to take up the job of the failed leader.
Any process can initiate the election algorithm whenever it encounters that the failure of leader
process. There can be situations that all N processes could call N concurrent elections. At any
time, process Pi is one among the following two states, when the election happens: Participant
refers to the process is directly or indirectly involved in election algorithm, Nonparticipant refers
to the process in not engaged with the election algorithm currently. The goal of Election
Algorithm is to choose and declare one and only process as the leader even if all processes
participate in the election. And at the end of the election, all the processes should agree upon the
new leader process without any confusion. Without loss of generality, the elected process should
be the process with the largest process identifier. This may be any number representing the order /
birth/ priority/ energy of the process. Each process has a variable called LEAD, which contains
the process id of the current leader. When the process participates in the election, it sets this lead
to NULL.
Any Election Algorithm should satisfy the following two properties [9].
1) Saf ety : Any process P, has LEAD = NULL if it is participating in the election, or its
LEAD =P, where P is the highest PID and it is alive at present.
2) Likeness : All the processes should agree on the chosen leader P after the election. That
is, LEAD = PID Pi where i=1,2,...,N.
23
4. International Journal of Next-Generation Networks (IJNGN) Vol.5, No.1, March 2013
The Bully Election Algorithm [10] of Garcia Molina in 1982, elects the leader process uniquely
which satisfies the safety and likeness requirements. Depending on a network topology, many
algorithms have been presented for electing leader in distributed systems.
Depending on a network topology, many algorithms have been presented for electing leader in
distributed systems. The numbers may be allocated in simply numerical order of the Ethernet
address or some other numbers such as priority, the mere process id, etc. The Ring Election
Algorithm [11] is based on the ring topology with the processes ordered logically and each
process knows its successor in an unidirectional way, either clockwise or anticlockwise.
When any process notices that the coordinator process has crashed, it creates an EL MSG by
inserting its own PID and sends the message to its successor. If the successor is also down, the
message would skip that process and goes to the next process of the successor or to the next etc.,
till it reaches a process which is not dead, along the ring network. When the EL MSG is received
by any process, it adds its PID to the list in the message. Like this, all the available processes in
the ring would insert their respective PID in the list. Finally, the EL MSG comes back to the
process which initiated the message and the process too would recognize that it only had intiated
that message, by identifying its own PID in the list.gh
The Election initiator process analyses and finds the highest PID among the available processes,
converts the ELMSG into COMSG and removes all the PIDs from the list but the highest PID.
This COMSG message is circulated along the ring for one circulation to inform the running
processes about who the new COORDINATOR is. When this message is circulated once, it is
discarded and the Election Algorithm ends here.
When the message comes back to the process that started it:
(i) TheprocessseesitsIDinthelist.
(ii) It checks all the PIDs and decides the coordinator (the process with the hightest ID).
(iii) It changes the message type COMSG and enters the LEAD process in the message.
(iv) COMSGiscirculatedagain.
(v) When it comes back to the process that started it, and it gets discarded there.
Limitation of Ring Election Algorithm: Multiple election messages may happen in parallel when
more than one process detects the failure of the coordinator process. This creates a lot of overhead
of creating and servicing each and every election message. This causes heavy traffic and
sometimes congestion in the network. In the best case, when a single process detects the crashed
leader, Ni is obtained with O(n) as follows:
Ni =ne+nc. (1)
Where ne refers to the number of EL MSG and nc refers to the number of CO MSG. In the
average and worst case, when all the N processes start the election message, , Ni is obtained with
O(n2) from the following equation.
Ni =n(ne+nc ). (2)
This message complexity will drastically bring down the entire systems performance, as all the
processes spend quite a lot of amount of time in servicing these messages or processing these
messages. In order to bring up the performance even during election, we need to have exactly
only one complete election happening instead of simultaneous redundant elec-tions. All the other
redundant election messages need to be killed. For solving this problem an improved election
algorithm is proposed in the following section.
24
5. International Journal of Next-Generation Networks (IJNGN) Vol.5, No.1, March 2013
4. MESSAGE EFFICIENT RING ELECTION ALGORITHM (MEREA)
In the previous section we have seen that in the average and the worst case scenarios, the number
of messages that are exchanged between processes is high in the original Ring Algorithm.
Therefore it imposes heavy traffic on the network . The proposed algorithm tries to intensively
decrease the redundant Election messages.
Assumptions
1. All the processes in the distributed group should have their clocks synchronized to each other.
We have logical clock and physical clock synchronization algorithms namely Lamports algorithm
for the logical clock synchronization and Cristians and Bekeley algorithms for the real time
clocks or the logical clocks.
2. The Network is perfect. (i.e.) when any message is sent, it wont be lost/modified . It would
reach the destination. If the destination process is alive, it can see the message which was sent to
it. Here too, we have reliable primitives to keep the network perfect.
Table 1. MEREA Algorithm
begin
Step1:call Build EL_MSG
Step2: for k: = 1 to n - 1
call update EL_MSG
Send the EL_MSG to SUCCi
endfor
Step3: Build COMSG
Step4: Circulate COMSG
end
The proposed Message Efficient Ring Election Algorithm is given in Table 1 through 4 . When
process P realizes that the coordinator has crashed, it initiates the Message Efficient Election
Algorithm by creating the EL MSG. It inserts its ID, and the time of creation of the and circulates
the message in the ring by throwing it to its immediate neighbour.
According to our assumption, all the processes in the group have their clocks synchronized, and
so all the alive processes have the same time in their clock.When any process Q receives the EL
MSG n the ring, it reads its log, to check whether it has created any EL MSG recently. Any one of
the following 3 scenarios is possible.
(i) Q would have initiated the ELMSG before P.
(ii) Q would have initiated the ELMSG just after P..
(iii) Q did not create any ELMSG at all.
25
6. International Journal of Next-Generation Networks (IJNGN) Vol.5, No.1, March 2013
Figure 1. Passing and Destruction of Election Message
5. MATHEMATICAL ANALYSIS
A. Best Case Analysis
Let N be the number of processes, In the Best case, only one process detects that the the
coordinator has crashed. Then, altogether, there will be 2n messages sent, one full circulation EL
MSG for a maximum of N-1 processes and one full circulation CO MSG for a maximum of N-1
processes.
ne + nc = 2n (3)
leading to O(n) message complexity. The Ring Election Algo-rithm and the our proposed Ring
Election Algorithm have the same time complexity as in equation (1).
B. Worst Case Analysis
Ring Algorithms worst case message complexity is O(n 2) from equation (2). In our Algorithm,
the worst case of the modified Election Algorithm is further divided into three more cases of Best,
Average and Worst case.
1) Best Case: The best case is when, the processes initiate the Election in the anticlockwise
direction according to time when the ring network is of clockwise direction. (ie) when the
Election Algorithm gets invoked in the opposite direction of the ring according to time. The
Election Messages get deleted in the very first go itself, that is when Process i send s election
message EMi to its immediate neighbour Process j, and since the time of Election Message of
Process j, EMj ¡ Emi. Emi is killed by process j. Like this, except the very first or very earlier
election message, all the other election messages would be killed by the immediate successor
processes. The number of EL MSG and CO MSG = 1 + 1+ ...+ 1 (n-1 times) and EL MSG
destroyed by SUCC ≤ n − 1T herefore, T otal Messages = ne + nc + n-1y e Where ye represents
the number of young election messages. Here, the time complexity is O(n).
2) Average and Worst Case: It is when the processes initiate the Election in the clockwise
direction according to time when the ring network is of clockwise direction. (i.e.) when the
Election Algorithm gets invoked along the direction of the ring according to time. Let Process a
creates the first election message and passes it on along the ring. Then let its successor creates the
Election message and passes to its successor and so on. In this case, only Process (a) can kill the
election messages created by all the other processes. The duplicate election messages would take
n-1, n-2, n-3, ...(3,2,1 hops to reach Process a right from the successor to the immediate
predecessor). The number of messages are,
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7. International Journal of Next-Generation Networks (IJNGN) Vol.5, No.1, March 2013
T otalmessages = 1 + 2 + ... + n − 1 + n + n
N
∑ k = (1/2)n2 + (1/2)n +n
K=1
= n + n + n (n-1) /2
= n2 - n/2 + 2n (4)
Bringing the time complexity to O(n2) But in average case, the EL MSG would be destroyed
somewhere in the middle and most of the times by the SUCC itself. When the processes receive
the EL MSG by other processes, they wont initiate the Election at all. And so, which would be
O(n). The probability of having O(n2)time complexity is very rare.
Table 2. Function: Build EL _MSG(T, PID)
begin
Create EL MSG
Set T = Current Time
Set F = TRUE
Append PID
Return
end
Table 3. Function: Update EL MSG
begin
if (my F = TRUE)
then if (my T ¡ Ti )
then Destroy EL MSG
Return
endif
else
Append PID in the list
endif
end
Table 4. Function: Build CO MSG
begin
LEAD = highest PID from the list
Delete all the PID except LEAD
Change EL MSG to CO MSG
Return
end
We have focused more on the worst case as processes in the real world keep communicating with
each other and try sharing the common resources, many processes would identify the Leader's
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8. International Journal of Next-Generation Networks (IJNGN) Vol.5, No.1, March 2013
crash and intern initiate the Election. Therefore, best case would be very rare and the average case
to worst case are only possible. Our algorithm tries to reduce the number of messages in the worst
case to a great extent.
6. EXPERIMENTS AND SIMULATION RESULTS
We simulated Ring election algorithm and MEREA in Java. We created number of processes
such as 100, 200 and so on. The clock time of each process was synchronized. We kept 3
seconds message propogation time to reach from one process to another and 1 second to process
the message. We also made the process with the highest PID to be down which in turn invoked
the Election activity automatically. We focused only on the worst case scenario because in the
real time there is no possibility of having best case. Processes in the network with critical
applications keep on sharing the common resources and exchange messages and this needs to be
monitored and controlled by the coordinator. Worst case only is possible during these scenarios.
In MEREA, the Worst case is again classified into two sub cases like Worst-Worst case and
Worst-Best case. In Worst-Worst case, the message complexity is still O(n2) but it drastically
reduces the number of messages into one forth of the messages as in Ring Algorithm. Our
algorithm’s Worst-Best case’s performance is O(n). Figure 5 represents the number of messages
being shared by all these processes duing the simulation. The MEREA Worst-Worst case has
shown little lesser number of messages comparing to its mathematical analysis. That is because
when we destroy messages, it depends on the time of Election Message and the time it takes to
propogate. Figure 1 gives the comparison of the two algorithms namely the existing Ring Election
Algorithm and our Proposed Algorithm MEREA, in terms of messages being passed during the
best and the worst case.
Figure 1. Comparison of Ring and MEREA
7. CONCLUSIONS
The duplicate election messages are destroyed and so the respective COORDINATOR messages
are avoided. There is only one election message which completes its circulation and only one
COORDINATOR message. This scenario is applicable for all the best, average and worst cases.
N number of COORDINATOR messages are circulated in worst case of existing Ring Algorithm
and N-1 duplicate election messages were also present in the existing algorithm, but our
algorithm destroys all of them as early as possible by the help of synchronized clock time. These
two factors gives better performance in terms of time and messages. The idea can be applied in
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9. International Journal of Next-Generation Networks (IJNGN) Vol.5, No.1, March 2013
Bully’s Election algorithm. In future we would work on choosing the right cluster leaders in
distributed sensor network.
REFERENCES
[1] Sung-Hoon Park, “A Stable Election Protocol based on an Unreliable Failure Detector in Distributed
Systems”, Proceedings of IEEE Eighth International Conference on Information Technology: New
Generations, pp. 976-984, 2011.
[2] Sandipan Basu, “An Efficient Approach of Election Algorithm in Distributed Systems”, Indian Journal
of Computer Science and Engineering (IJCSE), vol. 2, No. 1, pp. 16-21.March 2011.
[3] Muhammad Mahbubur Rahman, Afroza Nahar , “Modified Bully Algorithm using Election
Commission”, MASAUM Journal of Computing(MJC),Vol.1 No.3,pp.439-446,October 2009, ISSN
2076-0833.
[4] Chang-Young Kim, Sung-Hoon Bauk, “ The Election Protocol for Reconfigurable Distributed Systems”
, ICWN, pp. 295-301, 2006.
[5] M. S. Kordafshari, M. Gholipour, M.Jahanshahi, A.T. Haghighat, “Modified Bully Election Algorithm
In Distributed System”, Wseas Conferences, Cancun, Mexico, May 11-14, 2005.
[6] Sepehri M , Goodarzi M , “Leader Election Algorithm Using Heap Structure”, Proceedings Of The
12th Wseas International Conference On Computers(Iccomp'08), 2008.
[7] Cuibo Yu, Xuerong Gou, Chunhong Zhang, Yang Ji, “Supernode Election Algorithm In P2p Network
Based Upon District Partition”, Dcta: International Journal Of Digital Content Technology And Its
Applications, Vol. 5, No. 1, Pp. 186 -194, 2011.
[8] Ben Ari, “Principles Of Concurrent And Distributed Programming”, Pearson Education, 2nd Edition,
2006.
[9] H. Garcia-Molina, “Elections in Distributed Computing System”, IEEE Transaction Computer, Vol. C-
310, pp. 48- 59, 1982.
[10] Andrew S and Tanenbaum, “Distributed Systems Principles and Paradigms”, Beijing: Tsinghua
University Press, pp.190–192, 2008.
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