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Movement Prediction of Wireless Nodes in Mobile Ad Hoc Networks (MANETS)
Makhlouf, Nermin ; Šimák, Boris (oponent) ; Slavíček, Karel (oponent) ; Koton, Jaroslav (vedoucí práce)
The rapid evolution in the field of mobile computing has led to a new alternative way for mobile communication, in which mobile nodes form a self-organising wireless network, called a Mobile Ad hoc Network (MANET). The specific characteristics of MANETs impose many challenges to network protocol designs on all layers of the protocol stack because of unpredictable topology changes and mobile nature. Mobility prediction is a tool to deal with the problems emerging from the nodes’ mobility by predicting future changes in the network topology. This is crucial for different tasks such as routing. In this doctoral thesis, two mobility prediction methods for MANET networks are developed. The first method supposes that each node can build its virtual map depending on its location over the time. This method is called mobility prediction using virtual map. In order to evaluate the developed prediction algorithm, it has been implemented in the network simulator NS-2. I have investigated existing mobility models, and how the prediction method can be applied to them. Simulations respectively realize performance improvement in terms of average end to end delay, packet delivery ratio and network throughput under different mobility model. The proposed prediction concept is implemented over AODV (Ad Hoc On-Demand Distance Vector) routing protocol. In the second method, I have developed an artificial neural network for movement prediction in MANETs. The prediction model for mobility has been done by the data collected from location patterns. The Bayesian technique was used for learning or training ANNs. It has been implemented in software for training Bayesian neural networks called Model Manager. The best way to evaluate the final model is done by making predictions and comparing predictions with target data. The predictions are made by using 50 patterns as input variables. The reached and in the thesis discussed results show that improvement in the most significant network parameters, i.e. delay, throughput and packet delivery ratio, are reached even by 30% compared to AODV routing protocol, where the proposed prediction model is not utilized.
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Movement Prediction of Wireless Nodes in Mobile Ad Hoc Networks (MANETS)
Makhlouf, Nermin ; Šimák, Boris (oponent) ; Slavíček, Karel (oponent) ; Koton, Jaroslav (vedoucí práce)
The rapid evolution in the field of mobile computing has led to a new alternative way for mobile communication, in which mobile nodes form a self-organising wireless network, called a Mobile Ad hoc Network (MANET). The specific characteristics of MANETs impose many challenges to network protocol designs on all layers of the protocol stack because of unpredictable topology changes and mobile nature. Mobility prediction is a tool to deal with the problems emerging from the nodes’ mobility by predicting future changes in the network topology. This is crucial for different tasks such as routing. In this doctoral thesis, two mobility prediction methods for MANET networks are developed. The first method supposes that each node can build its virtual map depending on its location over the time. This method is called mobility prediction using virtual map. In order to evaluate the developed prediction algorithm, it has been implemented in the network simulator NS-2. I have investigated existing mobility models, and how the prediction method can be applied to them. Simulations respectively realize performance improvement in terms of average end to end delay, packet delivery ratio and network throughput under different mobility model. The proposed prediction concept is implemented over AODV (Ad Hoc On-Demand Distance Vector) routing protocol. In the second method, I have developed an artificial neural network for movement prediction in MANETs. The prediction model for mobility has been done by the data collected from location patterns. The Bayesian technique was used for learning or training ANNs. It has been implemented in software for training Bayesian neural networks called Model Manager. The best way to evaluate the final model is done by making predictions and comparing predictions with target data. The predictions are made by using 50 patterns as input variables. The reached and in the thesis discussed results show that improvement in the most significant network parameters, i.e. delay, throughput and packet delivery ratio, are reached even by 30% compared to AODV routing protocol, where the proposed prediction model is not utilized.
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Chalenges of integration of smart antennas in Ad Hoc NETwork
Makhlouf, N.
Due to the need for increasing exchange and share data, users demand easy connectivity and fast network wherever they are. Recently, users are interested in interconnecting all their personal electronic devices together using Mobile Ad Hoc NETwork (MANET). The capacity of ad hoc network can be limited because of interference. When a smart antennas system is integrated in such network, we can achieve significant spatial reuse, decreasing the interference and thereby increasing the capacity of the network. In this paper we examine the challenges at medium access control caused by integration of smart antennas system in ad hoc networks.
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