Secure Multicast Routing Protocol in Manets Using Efficient ECGDH Algorithm

An Ad-hoc Network covers a set of autonomous mobile nodes that communicates through wireless communication in an infrastructure-less environment. Mostly MANETs are used in group communication mechanisms like military applications, emergency search, rescue operations, vehicular ad-hoc communications and mining operations etc. In such type of networks, group communication is takes place by multicasting technique. Communication and collaboration is necessary among the nodes in the groups in multicast protocols. PUMA has the best multicast routing protocol compared to tree and mesh based multicast protocols although it suffers from security issues. PUMA mainly suffers from Man In The middle attack. MITM attack generates traffic flow, drop the packets and miscommunicate the neighbor nodes with false hop count. So defending from MITM attack we designed a new mechanism called Elliptic Curve Group Diffie-Hellman (ECGDH). This paper compares results of PUMA [1] routing protocol with legitimate, under attack and after providing security against attack. Finally we observed ECGDH [2] gives efficient results even attack has happened

Deep convolutional neural network to predict ground water level

In contrast to the atmosphere and fresh surface water, which can only briefly store water, the natural water cycle may use groundwater as a “reservoir” that stores water for extended periods. Even though there is a considerable degree of variation and complexity in the subsurface environment, there is a minimal availability of data from the field. Both of these challenges were faced by those who used models that were based on actual reality. Statistical modelling gradually improved the accuracy of the model’s calibration. Groundwater has become an increasingly important resource for supplying the water requirements of a rising global population. The fact that there is such a large stockpile allows it to be used once again, even during dry seasons or droughts. This article presents a deep convolutional neural network-based model for predicting groundwater levels. As part of the experimental setup, 174 satellite pictures of groundwater are included in the input data set. Images are preprocessed using the CLAHE method. The CNN, SVM, and AdaBoost methods make up the classification model. The results have shown that CNN can classify things correctly 98.5 per cent of the time. Precision and Recall rate of Deep CNN is also better for ground water image classification