The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform
Leonard Barolli - One of the best experts on this subject based on the ideXlab platform.
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a neural network based user identification for tor networks comparison analysis of different activation functions using Friedman Test
Network-Based Information Systems, 2016Co-Authors: Tetsuya Oda, Ryoichiro Obukata, Masahiro Hiyama, Masafumi Yamada, Leonard Barolli, Makoto TakizawaAbstract:In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP) metric and activation function. We analyze the data using Friedman Test. From the results, we adopt null hypothesis H0 since p
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NBiS - A Neural Network Based User Identification for Tor Networks: Comparison Analysis of Different Activation Functions Using Friedman Test
2016 19th International Conference on Network-Based Information Systems (NBiS), 2016Co-Authors: Ryoichiro Obukata, Masahiro Hiyama, Masafumi Yamada, Leonard Barolli, Makoto TakizawaAbstract:In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP) metric and activation function. We analyze the data using Friedman Test. From the results, we adopt null hypothesis H0 since p
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a neural network based user identification for tor networks comparison analysis of activation function using Friedman Test
Complex Intelligent and Software Intensive Systems, 2016Co-Authors: Tetsuya Oda, Ryoichiro Obukata, Masahiro Hiyama, Masafumi Yamada, Taro Ishitaki, Leonard BarolliAbstract:Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect or identify the bad behavior users in Tor networks. In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP) metric and activation function. We present many simulation results considering Tor client. We analyze the data using Friedman Test. From the results, we see that by using softplus activation function the system can identify Tor client.
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a neural network based user identification for tor networks data analysis using Friedman Test
Advanced Information Networking and Applications, 2016Co-Authors: Taro Ishitaki, Tetsuya Oda, Leonard BarolliAbstract:Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect or identify the bad behavior users in Tor networks. In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP), Round Trip Time (RTT), jitter, packet loss and throughput metrics. We present many simulation results considering Tor client. We analyze the data using Friedman Test. From the results, we see that for 15 hidden units the system can identify Tor client.
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AINA Workshops - A Neural Network Based User Identification for Tor Networks: Data Analysis Using Friedman Test
2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2016Co-Authors: Taro Ishitaki, Tetsuya Oda, Leonard BarolliAbstract:Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect or identify the bad behavior users in Tor networks. In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP), Round Trip Time (RTT), jitter, packet loss and throughput metrics. We present many simulation results considering Tor client. We analyze the data using Friedman Test. From the results, we see that for 15 hidden units the system can identify Tor client.
Tetsuya Oda - One of the best experts on this subject based on the ideXlab platform.
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a neural network based user identification for tor networks comparison analysis of different activation functions using Friedman Test
Network-Based Information Systems, 2016Co-Authors: Tetsuya Oda, Ryoichiro Obukata, Masahiro Hiyama, Masafumi Yamada, Leonard Barolli, Makoto TakizawaAbstract:In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP) metric and activation function. We analyze the data using Friedman Test. From the results, we adopt null hypothesis H0 since p
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a neural network based user identification for tor networks comparison analysis of activation function using Friedman Test
Complex Intelligent and Software Intensive Systems, 2016Co-Authors: Tetsuya Oda, Ryoichiro Obukata, Masahiro Hiyama, Masafumi Yamada, Taro Ishitaki, Leonard BarolliAbstract:Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect or identify the bad behavior users in Tor networks. In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP) metric and activation function. We present many simulation results considering Tor client. We analyze the data using Friedman Test. From the results, we see that by using softplus activation function the system can identify Tor client.
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a neural network based user identification for tor networks data analysis using Friedman Test
Advanced Information Networking and Applications, 2016Co-Authors: Taro Ishitaki, Tetsuya Oda, Leonard BarolliAbstract:Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect or identify the bad behavior users in Tor networks. In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP), Round Trip Time (RTT), jitter, packet loss and throughput metrics. We present many simulation results considering Tor client. We analyze the data using Friedman Test. From the results, we see that for 15 hidden units the system can identify Tor client.
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AINA Workshops - A Neural Network Based User Identification for Tor Networks: Data Analysis Using Friedman Test
2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2016Co-Authors: Taro Ishitaki, Tetsuya Oda, Leonard BarolliAbstract:Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect or identify the bad behavior users in Tor networks. In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP), Round Trip Time (RTT), jitter, packet loss and throughput metrics. We present many simulation results considering Tor client. We analyze the data using Friedman Test. From the results, we see that for 15 hidden units the system can identify Tor client.
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CISIS - A Neural Network Based User Identification for Tor Networks: Comparison Analysis of Activation Function Using Friedman Test
2016 10th International Conference on Complex Intelligent and Software Intensive Systems (CISIS), 2016Co-Authors: Tetsuya Oda, Ryoichiro Obukata, Masahiro Hiyama, Masafumi Yamada, Taro Ishitaki, Leonard BarolliAbstract:Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect or identify the bad behavior users in Tor networks. In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP) metric and activation function. We present many simulation results considering Tor client. We analyze the data using Friedman Test. From the results, we see that by using softplus activation function the system can identify Tor client.
Fatos Xhafa - One of the best experts on this subject based on the ideXlab platform.
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analysis of wmn hc simulation system data using Friedman Test
Complex Intelligent and Software Intensive Systems, 2015Co-Authors: Shinji Sakamoto, Leonard Barolli, Algenti Lala, Tetsuya Oda, Vladi Kolici, Fatos XhafaAbstract:With the fast development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In this paper, we used our proposed and implemented system based on Hill Climbing algorithm, called WMN-HC for mesh router node placement in WMNs. We analyze WMN-HC simulation system data for different number of nodes using Friedman Test. We took into consideration 8, 16, 32, 64, 128 mesh routers and 24, 48, 96, 192, 384 mesh clients. We use Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC) as metrics. From the analysis, for all cases, the p-value of SGC is more than 0.05. Thus, we adopt H0. On the other hand, for NCMC, we adopt H1 because the p-value is less than 0.05. Friedman Test results show that there is not difference for SGC parameter. However, there is difference for NCMC parameter.
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Friedman Test for analysing wmns a comparison study for genetic algorithms and simulated annealing
Innovative Mobile and Internet Services in Ubiquitous Computing, 2015Co-Authors: Donald Elmazi, Leonard Barolli, Shinji Sakamoto, Tetsuya Oda, Evjola Spaho, Fatos XhafaAbstract:In this paper, we deal with connectivity and coverage problem in Wireless Mesh Networks (WMNs). We used Friedman Test to check if we can compare Genetic Algorithm (GA) and Simulated Annealing (SA). We found out that GA and SA have differences in their performance. Then, we used the implemented systems WMN-GA and WMN-SA to evaluate and compare the performance of the systems for different distributions of mesh clients in terms of Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC). The simulation results show that for Uniform distribution the WMN-SA performs better than WMN-GA. For Normal distribution, for big radius of communication distance, the WMN-GA has the best performance. For Exponential distribution, the WMN-SA performs better than WMN-GA for all communication distances. For Weibull distribution, the WMN-SA has better performance than WMN-GA.
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analysis of node placement in wireless mesh networks using Friedman Test a comparison study for genetic algorithms and hill climbing
Complex Intelligent and Software Intensive Systems, 2015Co-Authors: Donald Elmazi, Leonard Barolli, Algenti Lala, Tetsuya Oda, Vladi Kolici, Fatos XhafaAbstract:In this paper, we deal with connectivity and coverage problem in Wireless Mesh Networks (WMNs). We used Friedman Test to check if we can compare Genetic Algorithm (GA) and Hill Climbing (HC). We found out that GA and HC have differences in their performance. Then, we used the implemented systems WMN-GA and WMN-HC to evaluate and compare the performance of the systems for different distributions of mesh clients in terms of Size of Giant Component (SGC) and Number of Covered Mesh Clients(NCMC). The simulation results show that for Uniform distribution the WMN-HC performs better than WMN-GA. For Normal distribution, for big radius of communication distance, the WMN-GA has the best performance. For Exponential distribution, the WMN-HC performs better than WMN-GA for all communication distances. For Weibull distribution, the WMN-HC has better performance than WMN-GA.
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analysis of node placement in wireless mesh networks using Friedman Test a comparison study for tabu search and hill climbing
Innovative Mobile and Internet Services in Ubiquitous Computing, 2015Co-Authors: Tetsuya Oda, Leonard Barolli, Algenti Lala, Vladi Kolici, Donald Elmazi, Fatos XhafaAbstract:In this paper, we deal with connectivity and coverage problem in Wireless Mesh Networks (WMNs). We used Friedman Test to check if we can compare Tabu Search (TS) and Hill Climbing (HC). We found out that TS and HC have differences in their performance. Then, we used the implemented systems WMN-TS and WMN-HC to evaluate and compare the performance of the systems for different distributions of mesh clients in terms of Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC). The simulation results show that for Uniform distribution, for small radius of communication distance, the SGC of WMN-TS is better than WMN-HC. For Normal distribution, the WMN-HC performs better than WMN-TS. For Exponential distribution, the WMN-HC performs better than WMN-TS for all radius of communication distances. For Weibull distribution, the WMN-HC has a good performance for big radius of communication distance.
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CISIS - Analysis of WMN-HC Simulation System Data Using Friedman Test
2015 Ninth International Conference on Complex Intelligent and Software Intensive Systems, 2015Co-Authors: Shinji Sakamoto, Leonard Barolli, Algenti Lala, Tetsuya Oda, Vladi Kolici, Fatos XhafaAbstract:With the fast development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In this paper, we used our proposed and implemented system based on Hill Climbing algorithm, called WMN-HC for mesh router node placement in WMNs. We analyze WMN-HC simulation system data for different number of nodes using Friedman Test. We took into consideration 8, 16, 32, 64, 128 mesh routers and 24, 48, 96, 192, 384 mesh clients. We use Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC) as metrics. From the analysis, for all cases, the p-value of SGC is more than 0.05. Thus, we adopt H0. On the other hand, for NCMC, we adopt H1 because the p-value is less than 0.05. Friedman Test results show that there is not difference for SGC parameter. However, there is difference for NCMC parameter.
Taro Ishitaki - One of the best experts on this subject based on the ideXlab platform.
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a neural network based user identification for tor networks comparison analysis of activation function using Friedman Test
Complex Intelligent and Software Intensive Systems, 2016Co-Authors: Tetsuya Oda, Ryoichiro Obukata, Masahiro Hiyama, Masafumi Yamada, Taro Ishitaki, Leonard BarolliAbstract:Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect or identify the bad behavior users in Tor networks. In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP) metric and activation function. We present many simulation results considering Tor client. We analyze the data using Friedman Test. From the results, we see that by using softplus activation function the system can identify Tor client.
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a neural network based user identification for tor networks data analysis using Friedman Test
Advanced Information Networking and Applications, 2016Co-Authors: Taro Ishitaki, Tetsuya Oda, Leonard BarolliAbstract:Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect or identify the bad behavior users in Tor networks. In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP), Round Trip Time (RTT), jitter, packet loss and throughput metrics. We present many simulation results considering Tor client. We analyze the data using Friedman Test. From the results, we see that for 15 hidden units the system can identify Tor client.
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AINA Workshops - A Neural Network Based User Identification for Tor Networks: Data Analysis Using Friedman Test
2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2016Co-Authors: Taro Ishitaki, Tetsuya Oda, Leonard BarolliAbstract:Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect or identify the bad behavior users in Tor networks. In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP), Round Trip Time (RTT), jitter, packet loss and throughput metrics. We present many simulation results considering Tor client. We analyze the data using Friedman Test. From the results, we see that for 15 hidden units the system can identify Tor client.
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CISIS - A Neural Network Based User Identification for Tor Networks: Comparison Analysis of Activation Function Using Friedman Test
2016 10th International Conference on Complex Intelligent and Software Intensive Systems (CISIS), 2016Co-Authors: Tetsuya Oda, Ryoichiro Obukata, Masahiro Hiyama, Masafumi Yamada, Taro Ishitaki, Leonard BarolliAbstract:Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect or identify the bad behavior users in Tor networks. In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP) metric and activation function. We present many simulation results considering Tor client. We analyze the data using Friedman Test. From the results, we see that by using softplus activation function the system can identify Tor client.
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application of neural networks and Friedman Test for user identification in tor networks
Broadband and Wireless Computing Communication and Applications, 2015Co-Authors: Taro Ishitaki, Tetsuya Oda, Leonard BarolliAbstract:Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect or identify the bad behavior users in Tor networks. In this paper, we present the application of Neural Networks (NNs) and Friedman Test for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. We present many simulation results for different number of hidden units considering Tor client and Surface Web client. The simulation results show that our simulation system has a good approximation and can be used for user identification in Tor networks.
Donald Elmazi - One of the best experts on this subject based on the ideXlab platform.
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Friedman Test for analysing wmns a comparison study for genetic algorithms and simulated annealing
Innovative Mobile and Internet Services in Ubiquitous Computing, 2015Co-Authors: Donald Elmazi, Leonard Barolli, Shinji Sakamoto, Tetsuya Oda, Evjola Spaho, Fatos XhafaAbstract:In this paper, we deal with connectivity and coverage problem in Wireless Mesh Networks (WMNs). We used Friedman Test to check if we can compare Genetic Algorithm (GA) and Simulated Annealing (SA). We found out that GA and SA have differences in their performance. Then, we used the implemented systems WMN-GA and WMN-SA to evaluate and compare the performance of the systems for different distributions of mesh clients in terms of Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC). The simulation results show that for Uniform distribution the WMN-SA performs better than WMN-GA. For Normal distribution, for big radius of communication distance, the WMN-GA has the best performance. For Exponential distribution, the WMN-SA performs better than WMN-GA for all communication distances. For Weibull distribution, the WMN-SA has better performance than WMN-GA.
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analysis of node placement in wireless mesh networks using Friedman Test a comparison study for genetic algorithms and hill climbing
Complex Intelligent and Software Intensive Systems, 2015Co-Authors: Donald Elmazi, Leonard Barolli, Algenti Lala, Tetsuya Oda, Vladi Kolici, Fatos XhafaAbstract:In this paper, we deal with connectivity and coverage problem in Wireless Mesh Networks (WMNs). We used Friedman Test to check if we can compare Genetic Algorithm (GA) and Hill Climbing (HC). We found out that GA and HC have differences in their performance. Then, we used the implemented systems WMN-GA and WMN-HC to evaluate and compare the performance of the systems for different distributions of mesh clients in terms of Size of Giant Component (SGC) and Number of Covered Mesh Clients(NCMC). The simulation results show that for Uniform distribution the WMN-HC performs better than WMN-GA. For Normal distribution, for big radius of communication distance, the WMN-GA has the best performance. For Exponential distribution, the WMN-HC performs better than WMN-GA for all communication distances. For Weibull distribution, the WMN-HC has better performance than WMN-GA.
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analysis of node placement in wireless mesh networks using Friedman Test a comparison study for tabu search and hill climbing
Innovative Mobile and Internet Services in Ubiquitous Computing, 2015Co-Authors: Tetsuya Oda, Leonard Barolli, Algenti Lala, Vladi Kolici, Donald Elmazi, Fatos XhafaAbstract:In this paper, we deal with connectivity and coverage problem in Wireless Mesh Networks (WMNs). We used Friedman Test to check if we can compare Tabu Search (TS) and Hill Climbing (HC). We found out that TS and HC have differences in their performance. Then, we used the implemented systems WMN-TS and WMN-HC to evaluate and compare the performance of the systems for different distributions of mesh clients in terms of Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC). The simulation results show that for Uniform distribution, for small radius of communication distance, the SGC of WMN-TS is better than WMN-HC. For Normal distribution, the WMN-HC performs better than WMN-TS. For Exponential distribution, the WMN-HC performs better than WMN-TS for all radius of communication distances. For Weibull distribution, the WMN-HC has a good performance for big radius of communication distance.
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IMIS - Friedman Test for Analysing WMNs: A Comparison Study for Genetic Algorithms and Simulated Annealing
2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 2015Co-Authors: Donald Elmazi, Leonard Barolli, Shinji Sakamoto, Tetsuya Oda, Evjola Spaho, Fatos XhafaAbstract:In this paper, we deal with connectivity and coverage problem in Wireless Mesh Networks (WMNs). We used Friedman Test to check if we can compare Genetic Algorithm (GA) and Simulated Annealing (SA). We found out that GA and SA have differences in their performance. Then, we used the implemented systems WMN-GA and WMN-SA to evaluate and compare the performance of the systems for different distributions of mesh clients in terms of Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC). The simulation results show that for Uniform distribution the WMN-SA performs better than WMN-GA. For Normal distribution, for big radius of communication distance, the WMN-GA has the best performance. For Exponential distribution, the WMN-SA performs better than WMN-GA for all communication distances. For Weibull distribution, the WMN-SA has better performance than WMN-GA.
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Analysis of mesh router placement in wireless mesh networks using Friedman Test considering different meta-heuristics
International Journal of Communication Networks and Distributed Systems, 2015Co-Authors: Tetsuya Oda, Leonard Barolli, Shinji Sakamoto, Donald Elmazi, Yi Liu, Fatos XhafaAbstract:In this paper, we deal with connectivity and coverage problem in wireless mesh networks WMNs. We used Friedman Test to compare genetic algorithm GA, tabu search TS, hill climbing HC and simulated annealing SA. We found out that GA, TS, HC and SA have differences in their performance. Then, we used the implemented systems WMN-GA, WMN-TS, WMN-HC and WMN-SA to evaluate and compare the performance of the systems for different distributions of mesh clients in terms of size of giant component SGC and number of covered mesh clients NCMC. The simulation results show that for uniform distribution the WMN-HC and WMN-SA perform better than WMNGA and WMN-TS. However, for small radius of communication distance, the SGC of WMN-TS is better than other systems. For normal distribution, for big radius of communication distance, the WMN-GA has the best performance. For exponential distribution, the WMN-HC and WMN-SA perform better than WMN-GA for all communication distances. For Weibull distribution, the WMN-TS has a good performance for small radius of communication distance, but for big radius of communication distances the WMN-GA, WMN-HC and WMN-SA perform better.
