Multichannel Allocation in Data Schemes using Data Mining(DMM) based Methodology for Multi Moving Nodes in Adhoc Computing System

In this paper, we present a new data mining algorithm which involves Data mining for multi moving nodes in a adhoc computing environment and quest the mining results to develop multichannel data allocation schemes so as to improve the overall performance of a adhoc system. First, we propose an algorithm to capture the frequent multichannel user moving nodes from a set of log data in a mobile(adhoc) environment. The algorithm proposed is enhanced with the Data mining capability and is able to discover new moving nodes efficiently without compromising the quality of results and enhanced output obtained. Mining results of node moving patterns and the properties of data objects & data set in data mining, we develop multichannel data allocation schemes that can utilize the knowledge of mobile nodes in adhoc pattern for proper allocation of both personal and shared data. By employing the data allocation schemes, the occurrences of cost for remote access scan be minimized and the performance of a mobile adhoc system is thus improved. For personal data allocation(Individual channel), two data allocation schemes, which explore different levels of mining results, are devised: one utilizes the set level of mobile nodes(moving patterns) and the other utilizes the path level of mobile nodes(i.e., OSPF- routing(moving patterns) Performance of these data allocation schemes is comparatively analysed. It is shown by our simulation results that the knowledge obtained from the mobile nodes is very important in devising effective in multichannel data allocation schemes which can lead to significant performance improvement in a mobile adhoc system(mobile computing).