Nowadays wireless networks are the most popular way of communication. For example, internet services in companies, cafes, e-markets and in homes. Therefore, it must be protected against the spiteful users who try to harm the privacy, genuineness and privacy of it. Also there is need of traffic control of information sent over these wireless networks. In this research paper, a technique for controlling the congestion over the wireless networks is shown and to implement it, fuzzy logic and machine learning tools are used. Some of the parameters which are necessary to be considered for congestion control decision mechanism are:Transmission energy, queue size, distance from receiver, transmission rate, cost assigned. On evaluating these parameters using fuzzy logic, a desired output for congestion control can be determined and its efficiency is evaluated using machine learning tools.
In this paper Fuzzy clustering Neural network (FCNN) is proposed with its learning algorithm, which utilizes fuzzy sets as cluster of patterns. The performance of FCNN is found better than FMN, FMPCNN, FHLSCNN and MBCNN clustering algorithms when compared with moderate number of clusters created. The cluster prototypes calculated reduces the confusion by giving fair treatment to the dense populated patterns. The total number of clusters created can be controlled by grouping factor A. The recall time per pattern of FCNN is smaller than the FMN, FMPCNN, FHLSCNN and MBCNN. Hence it can be used for real time applications.
<p>As a mobile user travels between radio networks, a handover mechanism is required to vary its radio connection. The persistence of a call is one of the major quality measurements in wireless cellular networks. Handover mechanism permits a cellular network to offer such a facility by again allocating an ongoing call from one base station to another base station. To achieve handover neural network techniques can be used. In this paper, a handover decision mechanism is proposed using Radial Basis function (RBF) of neural networks.</p>
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