Pub Date : 2014-12-01DOI: 10.1109/ICCES.2014.7030971
Esraa Elhariri, Nashwa El-Bendary, A. Hassanien
This paper presents a classification approach based on Random Forests (RF) and Linear Discriminant Analysis (LDA) algorithms for classifying the different types of plants. The proposed approach consists of three phases that are pre-processing, feature extraction, and classification phases. Since most types of plants have unique leaves, so the classification approach presented in this research depends on plants leave. Leaves are different from each other by characteristics such as the shape, color, texture and the margin. The used dataset for this experiments is a database of different plant species with total of only 340 leaf images, was downloaded from UCI- Machine Learning Repository. It was used for both training and testing datasets with 10-fold cross-validation. Experimental results showed that LDA achieved classification accuracy of (92.65%) against the RF that achieved accuracy of (88.82%) with combination of shape, first order texture, Gray Level Co-occurrence Matrix (GLCM), HSV color moments, and vein features.
{"title":"Plant classification system based on leaf features","authors":"Esraa Elhariri, Nashwa El-Bendary, A. Hassanien","doi":"10.1109/ICCES.2014.7030971","DOIUrl":"https://doi.org/10.1109/ICCES.2014.7030971","url":null,"abstract":"This paper presents a classification approach based on Random Forests (RF) and Linear Discriminant Analysis (LDA) algorithms for classifying the different types of plants. The proposed approach consists of three phases that are pre-processing, feature extraction, and classification phases. Since most types of plants have unique leaves, so the classification approach presented in this research depends on plants leave. Leaves are different from each other by characteristics such as the shape, color, texture and the margin. The used dataset for this experiments is a database of different plant species with total of only 340 leaf images, was downloaded from UCI- Machine Learning Repository. It was used for both training and testing datasets with 10-fold cross-validation. Experimental results showed that LDA achieved classification accuracy of (92.65%) against the RF that achieved accuracy of (88.82%) with combination of shape, first order texture, Gray Level Co-occurrence Matrix (GLCM), HSV color moments, and vein features.","PeriodicalId":339697,"journal":{"name":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122802710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/ICCES.2014.7030996
Eman A. Maghawry, R. Ismail, N. Badr, M. Tolba
Due to the existence of the “database as a service” (DaaS) model on a cloud computing environment, several challenges have been made, such as query scheduling. Using an efficient query scheduler can improve the queries response time submitted from various clients in a DaaS model. Scheduling the queries in a cost aware way has an economic impact on the service provider by meeting the clients' service level agreements (SLAs). In this paper, we proposed an enhanced scheduler technique in order to schedule the queries effectively to minimize the overall query response time. The experimental results show that using our enhanced scheduler improves significantly the query response time.
{"title":"An Enhanced Queries Scheduler for query processing over a cloud environment","authors":"Eman A. Maghawry, R. Ismail, N. Badr, M. Tolba","doi":"10.1109/ICCES.2014.7030996","DOIUrl":"https://doi.org/10.1109/ICCES.2014.7030996","url":null,"abstract":"Due to the existence of the “database as a service” (DaaS) model on a cloud computing environment, several challenges have been made, such as query scheduling. Using an efficient query scheduler can improve the queries response time submitted from various clients in a DaaS model. Scheduling the queries in a cost aware way has an economic impact on the service provider by meeting the clients' service level agreements (SLAs). In this paper, we proposed an enhanced scheduler technique in order to schedule the queries effectively to minimize the overall query response time. The experimental results show that using our enhanced scheduler improves significantly the query response time.","PeriodicalId":339697,"journal":{"name":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114372990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/ICCES.2014.7030929
N. Semary, Ahmed F. Gad
Human face is the most representative part of body that can be used to differentiate one person among others. Accurate face identification system is still a challenge to Image Processing and Pattern Recognition researchers. In this paper, a complete framework for face-based personal identification system is proposed. The proposed frame work is composite of three basic stages; face skin detection (FSD), facial features positioning (FFP), representative features extraction (RFE) and face matching (FM). For FSD stage, RGB-H-CbCr color model is used after a comparative study between different color models. Enhanced Haar-like features are utilized for FFP stage. After accurate features positioning, the representative features are calculated using the centers of eyes, nose and mouth organs. The experimental results of this paper depict that the proposed frame work accurately identify persons of The Center for Vital Longevity Face Database. The proposed system could Identify the correct person with 40 saved image with accuracy 98%, while it could reject wrong persons with accuracy 98.17%. The overall accuracy of correct identification reaches 98.14%.
{"title":"A proposed framework for robust face identification system","authors":"N. Semary, Ahmed F. Gad","doi":"10.1109/ICCES.2014.7030929","DOIUrl":"https://doi.org/10.1109/ICCES.2014.7030929","url":null,"abstract":"Human face is the most representative part of body that can be used to differentiate one person among others. Accurate face identification system is still a challenge to Image Processing and Pattern Recognition researchers. In this paper, a complete framework for face-based personal identification system is proposed. The proposed frame work is composite of three basic stages; face skin detection (FSD), facial features positioning (FFP), representative features extraction (RFE) and face matching (FM). For FSD stage, RGB-H-CbCr color model is used after a comparative study between different color models. Enhanced Haar-like features are utilized for FFP stage. After accurate features positioning, the representative features are calculated using the centers of eyes, nose and mouth organs. The experimental results of this paper depict that the proposed frame work accurately identify persons of The Center for Vital Longevity Face Database. The proposed system could Identify the correct person with 40 saved image with accuracy 98%, while it could reject wrong persons with accuracy 98.17%. The overall accuracy of correct identification reaches 98.14%.","PeriodicalId":339697,"journal":{"name":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116090557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/ICCES.2014.7030926
S. Aly, Loay Hassan, A. Sagheer
Pedestrian detection is an important area in computer vision with key applications in intelligent vehicle and surveillance systems. One of the main challenges in pedestrian detection is occlusion. In this paper, we propose a novel pedestrian detection approach capable of handling partial occlusion. Three stage cascaded classifier is used in the proposed approach. Global classifier based on HOG features and linear-SVM is first employed to classify the whole scanning window. For ambiguous patterns, a set of part-based classifiers trained on features derived from non-occluded dataset are employed on the second stage. Several fusion methods including average, maximum, linear and non-linear SVM classifiers are examined to combine the obtained part scores. The linear/non-linear fusion coefficients are estimated by learning an additional third stage SVM classifier. The training data in the third stage classifier is augmented by generating a set of artificially occluded samples which simulate real occlusion conditions commonly occurred in pedestrians. Experimental results using Daimler and INRIA data sets show the effectiveness of the proposed approach.
{"title":"Partially Occluded Pedestrian Classification using Three Stage Cascaded Classifier","authors":"S. Aly, Loay Hassan, A. Sagheer","doi":"10.1109/ICCES.2014.7030926","DOIUrl":"https://doi.org/10.1109/ICCES.2014.7030926","url":null,"abstract":"Pedestrian detection is an important area in computer vision with key applications in intelligent vehicle and surveillance systems. One of the main challenges in pedestrian detection is occlusion. In this paper, we propose a novel pedestrian detection approach capable of handling partial occlusion. Three stage cascaded classifier is used in the proposed approach. Global classifier based on HOG features and linear-SVM is first employed to classify the whole scanning window. For ambiguous patterns, a set of part-based classifiers trained on features derived from non-occluded dataset are employed on the second stage. Several fusion methods including average, maximum, linear and non-linear SVM classifiers are examined to combine the obtained part scores. The linear/non-linear fusion coefficients are estimated by learning an additional third stage SVM classifier. The training data in the third stage classifier is augmented by generating a set of artificially occluded samples which simulate real occlusion conditions commonly occurred in pedestrians. Experimental results using Daimler and INRIA data sets show the effectiveness of the proposed approach.","PeriodicalId":339697,"journal":{"name":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131668565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/ICCES.2014.7030917
Federico Cristina, Sebastián H. Dapoto, Pablo J. Thomas, Patricia Pesado
The need for sharing information among mobile devices exists in many applications, and almost every data exchange between these devices involve the same requirements: a means for discovering other mobile devices in a wireless network, establishing logical connections, communicating application data, and gathering information related to the physical connection. This paper presents a multiplatform open-source developer-oriented framework that acts as a support layer for host discovery, data communication among devices, and quality of service monitoring. Its purpose is to simplify the issues related to networking for mobile application developers. Currently, the framework is implemented for different platforms, such as Android, J2SE, and J2ME.
{"title":"A simplified multiplatform communication framework for mobile applications","authors":"Federico Cristina, Sebastián H. Dapoto, Pablo J. Thomas, Patricia Pesado","doi":"10.1109/ICCES.2014.7030917","DOIUrl":"https://doi.org/10.1109/ICCES.2014.7030917","url":null,"abstract":"The need for sharing information among mobile devices exists in many applications, and almost every data exchange between these devices involve the same requirements: a means for discovering other mobile devices in a wireless network, establishing logical connections, communicating application data, and gathering information related to the physical connection. This paper presents a multiplatform open-source developer-oriented framework that acts as a support layer for host discovery, data communication among devices, and quality of service monitoring. Its purpose is to simplify the issues related to networking for mobile application developers. Currently, the framework is implemented for different platforms, such as Android, J2SE, and J2ME.","PeriodicalId":339697,"journal":{"name":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124456096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/ICCES.2014.7030984
E. Emary, Waleed Yamany, A. Hassanien
This paper presents a new feature selection technique based on rough sets and bat algorithm (BA). BA is attractive for feature selection in that bats will discover best feature combinations as they fly within the feature subset space. Compared with GAs, BA does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime. A fitness function based on rough-sets is designed as a target for the optimization. The used fitness function incorporates both the classification accuracy and number of selected features and hence balances the classification performance and reduction size. This paper make use of four initialisation strategies for starting the optimization and studies its effect on bat performance. The used initialization reflects forward and backward feature selection and combination of both. Experimentation is carried out using UCI data sets which compares the proposed algorithm with a GA-based and PSO approaches for feature reduction based on rough-set algorithms. The results on different data sets shows that bat algorithm is efficient for rough set-based feature selection. The used rough-set based fitness function ensures better classification result keeping also minor feature size.
{"title":"New approach for feature selection based on rough set and bat algorithm","authors":"E. Emary, Waleed Yamany, A. Hassanien","doi":"10.1109/ICCES.2014.7030984","DOIUrl":"https://doi.org/10.1109/ICCES.2014.7030984","url":null,"abstract":"This paper presents a new feature selection technique based on rough sets and bat algorithm (BA). BA is attractive for feature selection in that bats will discover best feature combinations as they fly within the feature subset space. Compared with GAs, BA does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime. A fitness function based on rough-sets is designed as a target for the optimization. The used fitness function incorporates both the classification accuracy and number of selected features and hence balances the classification performance and reduction size. This paper make use of four initialisation strategies for starting the optimization and studies its effect on bat performance. The used initialization reflects forward and backward feature selection and combination of both. Experimentation is carried out using UCI data sets which compares the proposed algorithm with a GA-based and PSO approaches for feature reduction based on rough-set algorithms. The results on different data sets shows that bat algorithm is efficient for rough set-based feature selection. The used rough-set based fitness function ensures better classification result keeping also minor feature size.","PeriodicalId":339697,"journal":{"name":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129462602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/ICCES.2014.7030992
Mohamed A. Mohamed, Abdelhameed Ibrahim, E. A. Othman
Recovering a complete, detailed, accurate and realistic 3D model from images is still a difficult task. This paper proposes an improved 3D modeling using multistage feature extraction and matching. A 3D modeling technique is applied based on the features extracted from images of the same scene taken from different directions. Matching of the images are used to construct the 3D model. Using multistage features extraction and matching algorithms, multiple 3D models can be obtained. This paper will focus on modeling from reality rather than computer graphics creation of artificial world models. Experimental results are illustrated to explore the effectiveness of the proposed method.
{"title":"Improved 3D modeling using multistage feature extraction and matching","authors":"Mohamed A. Mohamed, Abdelhameed Ibrahim, E. A. Othman","doi":"10.1109/ICCES.2014.7030992","DOIUrl":"https://doi.org/10.1109/ICCES.2014.7030992","url":null,"abstract":"Recovering a complete, detailed, accurate and realistic 3D model from images is still a difficult task. This paper proposes an improved 3D modeling using multistage feature extraction and matching. A 3D modeling technique is applied based on the features extracted from images of the same scene taken from different directions. Matching of the images are used to construct the 3D model. Using multistage features extraction and matching algorithms, multiple 3D models can be obtained. This paper will focus on modeling from reality rather than computer graphics creation of artificial world models. Experimental results are illustrated to explore the effectiveness of the proposed method.","PeriodicalId":339697,"journal":{"name":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122004407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/ICCES.2014.7030948
Zhendong Wu, Kai Lu, Xiaoping Wang, Xu Zhou, Chen Chen
Races hidden in concurrent programs can lead to harmful bugs. These bugs are difficult to detect due to their non-deterministic characteristics. Previous work has tried to dynamically verify races in actual executions to check whether they would lead to failures. However, it is inefficient to verify all the races to find the harmful bugs if there are a large number of races. To improve the efficiency, PFinder is the first technique that uses a parallel method to verify multiple races on multiple machines simultaneously. We have implemented PFinder as a prototype tool and have experimented on a number of real-world concurrent programs. All the known bugs in known benchmarks are detected. Also, PFinder could scale well as the number of machines increases. Additionally, the speedup of PFinder can be increased linearly with the number of machines.
{"title":"PFinder: Efficiently detecting bugs in concurrent programs through parallelizing race verification","authors":"Zhendong Wu, Kai Lu, Xiaoping Wang, Xu Zhou, Chen Chen","doi":"10.1109/ICCES.2014.7030948","DOIUrl":"https://doi.org/10.1109/ICCES.2014.7030948","url":null,"abstract":"Races hidden in concurrent programs can lead to harmful bugs. These bugs are difficult to detect due to their non-deterministic characteristics. Previous work has tried to dynamically verify races in actual executions to check whether they would lead to failures. However, it is inefficient to verify all the races to find the harmful bugs if there are a large number of races. To improve the efficiency, PFinder is the first technique that uses a parallel method to verify multiple races on multiple machines simultaneously. We have implemented PFinder as a prototype tool and have experimented on a number of real-world concurrent programs. All the known bugs in known benchmarks are detected. Also, PFinder could scale well as the number of machines increases. Additionally, the speedup of PFinder can be increased linearly with the number of machines.","PeriodicalId":339697,"journal":{"name":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128592529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/ICCES.2014.7030985
H. Elsayed
This paper presents a lossless audio coding using Burrows-Wheeler Transform (BWT) and a combination of a Move-To-Front coding (MTF) and Run Length Encoding (RLE). Audio signals used are assumed to be of floating point values. The BWT is applied to this floating point values to get the transformed coefficients; and then these resulting coefficients are converted using the Move-to-Front coding to coefficients can be better compressed and then these resulting coefficients are compressed using a combination of the Run Length Encoding, and entropy coding. Two entropy coding are used which are Arithmetic and Huffman coding. Simulation results show that the proposed lossless audio coding method outperforms other lossless audio coding methods; using only Burrows-Wheeler Transform method, using combined Burrows-Wheeler Transform and Move-to-Front coding method, and using combined Burrows-Wheeler Transform and Run Length Encoding method.
{"title":"Burrows-Wheeler Transform and combination of Move-to-Front coding and Run Length Encoding for lossless audio coding","authors":"H. Elsayed","doi":"10.1109/ICCES.2014.7030985","DOIUrl":"https://doi.org/10.1109/ICCES.2014.7030985","url":null,"abstract":"This paper presents a lossless audio coding using Burrows-Wheeler Transform (BWT) and a combination of a Move-To-Front coding (MTF) and Run Length Encoding (RLE). Audio signals used are assumed to be of floating point values. The BWT is applied to this floating point values to get the transformed coefficients; and then these resulting coefficients are converted using the Move-to-Front coding to coefficients can be better compressed and then these resulting coefficients are compressed using a combination of the Run Length Encoding, and entropy coding. Two entropy coding are used which are Arithmetic and Huffman coding. Simulation results show that the proposed lossless audio coding method outperforms other lossless audio coding methods; using only Burrows-Wheeler Transform method, using combined Burrows-Wheeler Transform and Move-to-Front coding method, and using combined Burrows-Wheeler Transform and Run Length Encoding method.","PeriodicalId":339697,"journal":{"name":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128074598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/ICCES.2014.7030921
Dina M. Ibrahim, E. Sallam, T. Eltobely, M. Fahmy
Modelling is a general method used throughout the development of systems. Numerous modelling languages were proposed for analyzing and building systems. Petri Nets language is considered as one of the formal modelling and analysis techniques. These techniques allow users to do both the performance evaluation and model checking. Coloured Petri Nets (CPN) is one of the modelling languages especially for discrete-event systems. In this paper, we use Coloured Petri Nets to model and analyze the behavior of the Clustering Vector-Based Forwarding (CVBF) routing protocol in Underwater Wireless Sensor Networks (UWSNs). Our proposed model is tested and verified by the state space statistics analysis which results that the proposed CPN model is liveness, responsiveness and free from deadlocks. The results of the performance evaluation of the proposed model demonstrate the proposed model capability to increase both the packet delivery ratio and the average end-to-end delay.
{"title":"Modelling of CVBF algorithm using Coloured Petri Nets","authors":"Dina M. Ibrahim, E. Sallam, T. Eltobely, M. Fahmy","doi":"10.1109/ICCES.2014.7030921","DOIUrl":"https://doi.org/10.1109/ICCES.2014.7030921","url":null,"abstract":"Modelling is a general method used throughout the development of systems. Numerous modelling languages were proposed for analyzing and building systems. Petri Nets language is considered as one of the formal modelling and analysis techniques. These techniques allow users to do both the performance evaluation and model checking. Coloured Petri Nets (CPN) is one of the modelling languages especially for discrete-event systems. In this paper, we use Coloured Petri Nets to model and analyze the behavior of the Clustering Vector-Based Forwarding (CVBF) routing protocol in Underwater Wireless Sensor Networks (UWSNs). Our proposed model is tested and verified by the state space statistics analysis which results that the proposed CPN model is liveness, responsiveness and free from deadlocks. The results of the performance evaluation of the proposed model demonstrate the proposed model capability to increase both the packet delivery ratio and the average end-to-end delay.","PeriodicalId":339697,"journal":{"name":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130168604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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