This paper will try to find out the way to identify or to select a best and optimised head to communicate among the clusters with education in data redundancy and also will help better band width utilisation. Wireless Ad-hoc network is formed by the set of wireless devices, which move randomly as well communicate with other nodes through radio signal. Ad-hoc networks logically depicted as a group of clusters by assemble together on the basis of different criteria like as a-hop and bhop that are in close division with one another. Clusters are instituted by diffusing node specifications along the wireless links. Various heuristics employ have several policies to select cluster heads. Various policies of these are biased in approbation of some nodes. As a result at the end, these nodes should have greater authority and may deplete their energy speedily, resulting them to drop out from the network. So that, there is a requirement for process called load-balancing among selected cluster-heads to give all nodes the opportunity to present as a cluster-head.

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The World Wide Web is a source of knowledge; the knowledge is extracted from the web data. Web data is available in direct from normal web as contents to user and/or in direct forms to as the web access logs. For the web usage pattern analysis the web access logs are analysed. Web usage data used in various applications of web masters, user data recommendations, web pre-fetching and caching. In this paper using the web access log analysis, web next page recommendation system is introduced. The presented technique involves data personalization, user behavioural analysis and access patterns for recommendations. The proposed web page recommendation system contains the K-means algorithm for finding similar access patterns of the user sessions. Additionally for classification and prediction the KNN algorithm is implemented. The model also incorporate the similar user access pattern data which is belongs from the other user therefore the proposed model also predicts the rarely accessed patterns. Thus to make the recommendations web usages data is personalized, based on URL frequencies, user navigational frequencies, session based data analysis and time based data analysis. Additionally to combine these parameters a weighted technique is used. The proposed recommendation system is implemented using JAVA technology. And their performance in terms of accuracy, error rate, space complexity and time complexity is estimated. The experimentation with increasing amount of data provides more accurate results and also consumes less computational resources. Therefore the proposed data model is adoptable for accuracy and efficiency both.

Internet and cloud application is getting faster day by day. It increases the data exchange rate over internet. During this heavy data transmission security is considered as major issues in communication. Encryption method used as a primary technique for providing the security to information systems. Among all the encryption techniques attribute based encryption (ABE) is getting popularity among the users. For secure data access the client must be sure about the process used for this type of encryption but in cloud platform everything is provided by cloud. Thus the satisfaction of security at user level is not provided by any cloud. Thus this work proposes a novel Client end trust based security service mechanism (TBSSM) using behaviour based encryption for achieving the better results. This work focuses on the application area of cloud storage platform for user satisfaction. This model gives a unique stack based solution for achieving the end user security. In this methodology the attribute can be identified from the user attribute table.