The authors proposed a fusion protocol using multilevel clustering (FPMC), which is completely dependent on the increased coverage and wireless sensor network (WSN) reliability-clustering framework to eliminate duplication. Therefore, a static sensor for a point of interest is allocated where possible so that the particular area can be protected. Furthermore, FPMC ensures that only one sensor node is allocated to cover a specific point of interest. The network coverage, energy usage, mean movement of WSNs nodes and sensor nodes are active in each loop due to which efficiency of the proposed protocol was assessed under various densities, and its performance was also analysed. Evaluations have shown that with restricted active nodes and sensor nodes, the FPMC can increase the networking coverage. The FPMC should be stressed as reducing the overlap level. The proposed protocol is based on the fusion protocol using multi-level clustering for improving coverage and connectivity WSNs.

Internet user has caused a tremendous growth in the information quantity, information accesses, information classification and complexity of Internet topologies handling information. Multidimensional data (mobile apps usage data (mix data set (log data))) contains a huge number of irrelevant redundant information. Users of a web application find it difficult to get the required information quickly and effectively due to enormous size of data (huge data generated per second). Numerous machine learning algorithms are useful to an amount of dataset to find the efficiency and accuracy of the classifiers. One of the potent solutions for this problem is web personalization. Providing personalized recommendations to users for improving credibility depend on the web application usage of the useful information in web application. It is very difficult to predict the behavior of such personalization systems. Our proposed intelligent map reducer model is based on machine learning concept. The results of this research are significant for training and testing of big datasets for Map Reduce Fusion Deep Learning Based on Back Propagation Neural Network algorithm established classification problems. The proposed algorithm is implemented using the HADOOP framework and enhanced the performance of existing map reducer model by improving the accuracy and reduce error rate.