Abstract:In this paper, a new secured data model for cloud computing is proposed which uses partial resource description framework (RDF) encryption and token based access control system in which sensitive data in an RDF graph is encrypted and all other non-sensitive data are publicly lucid. The security process, the decryption process, and query process are the three essential procedures in this framework. The consequence of the security process is the encrypted data, encrypted metadata, and plain text fragments. The proposed technique permits the token based access control system for the decryption procedure. The query process incorporates the map reduce framework is for lessening the immense measure of employments. At long last, the query answer is sent to the user in light of the access token list (AT-list) of the system administrator. Our test comes about demonstrate that, the performance of the proposed technique is assessed in view of the precision, recall and execution time of the framework. Our proposed approach is actualized using Java and keep running on Windows XP framework and the Lehigh University Benchmark (LUBM) datasets are used for our examination. In the paper this new secured RDF data model is deployed and tested using AWS elastic beanstalk.
In day to day life, the process of projecting the correct information to the authorized person is more difficult, which makes complexity to the decision making process. Web Page Recommendation Systems (WPRS) used in various fields to identify the customer needs and to help the users to take appropriate decisions over the service or product according to his/her preference. The group of users with similar preference will be identified by using Possibilistic Fuzzy C-Means (PFCM) algorithm with an S3I Similarity Measure (SM). The proposed method will determine the gain and loss of the web users based on the web directories which can be modified by using Relevance Feedback Bayesian Network (RFBN) technique. The experimental results are conducted on the MNSBC dataset and the outcomes are compared with the existing methods like Singular Value Decomposition (SVD) methods. The method predicts the accuracy up to 85% when compared with the existing methods and the outcome results proved the effectiveness of the PFCM -RFBN method.
Cluster head selection enacts a prominent role in Wireless Sensor Network to optimize the energy usage during the data collection. Few research works have been designed to choose the best cluster head in wireless network using different optimization techniques. However, cluster head selection performance of conventional algorithms was lower to extend the lifetime of network. Therefore, a Deep Neural Glowworm Swarm Optimized Soft C-Means Clustering (DNGSOSCC) model is proposed. Initially, DNGSOSCC model obtains number of sensor nodes as input at the input layer. After taking input, soft clustering process is carried in DNGSOSCC model at the first hidden layer where it groups the sensor nodes in WSN into a different cluster. Then, the glow worm population is initialized in DNGSOSCC model with the support of number of clusters formed at the second hidden layer. Next, Luciferin value is assessed in DNGSOSCC model for all sensor nodes according to their residual energy level in the third hidden layer. Followed by, the sensor node with higher luciferin value within cluster is selected as optimal cluster head at the fourth hidden layer to perform energy efficient data gathering in WSN. Finally, the output layer provides the selection result of optimal cluster heads in WSN. By using the above process, DNGSOSCC model significantly gathers the data from its cluster member through selected optimal cluster heads with minimal amount of energy. From that, DNGSOSCC model upsurges the lifespan of network through performing efficient route discovery and data collection in WSN. The DNGSOSCC model conducts the simulation work using metrics such as data deliverance ratio, data loss ratio, data transmission delay and network lifetime with reverence to number of data packets and sensor nodes. From the experiments conducted, DNGSOSCC model improves the data deliverance ratio and network lifetime by 19% and 10% as well as minimizes the data loss ratio and data transmission delay by 76% and 34% respectively than existing methods.
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