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Abstract-Wireless sensor networks (WSN) are being used for huge range of applications where the traditional infrastructure based network is mostly infeasible. The most challenging aspect of WSN is that they are energy resource-constrained and that energy cannot be replenish. the wireless sensor network of power limited sensing devices called sensor deployed in a region to sense various types physical information from the environment, when these sensors sense and transmit data to other sensors present in the network, even the cluster head is elected according to check their residual energy considerable amount of energy will drain automatically to overcome this drawback by considering the protocol a fuzzy logic approach is used to elect the cluster head based on three descriptors-energy, centrality & distance and second CH is elected according to TDMA to overcome the data lost during energy drain occur in the CH .NS-2 simulation shows that proposed protocol provides higher energy efficiency. This paper proposes the mechanism or device is capable of utilizing its own system of control simply called as self-configurable clustering mechanism to detect the disordered CHs and replace them with other nodes. And results have been derived from simulator ns-2 to show the better performance.

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allows the user to design a custom module which controls the home applications by connecting the android equipped device and its corresponding home applications to an MCU wherein it uses relay circuits to connect the entire applications using GPRS network to connect the application controller and the android device. These devices can be used to control industrial applications, home applications like light, fan etc., and thereby conserving electricity.

Brain tumor has the foremost distinguished etiology of high morality. Neoplasm, a categorization of brain tumors, is very operative in distinguishing and determining the tumor’s exact location in the brain. Magnetic resonance imaging (MRI) is an efficient noninvasive technique for the anatomical examination of brain tumors. Growth tissues have a distinguishable look in MRI pictures in order that they are unit-wide used for brain tumor feature extraction. The existing research algorithms for brain tumors have some limitations such as different qualities, low sensitivity, and diagnosing the tumor at its stages. In this particular piece of research, an innovative method of optimization known as the procedure for lightning attachment algorithm (PLA) is used, and for the purpose of classification, a CNN model known as DenseNet-169 is applied. PLA was used in order to optimize the growth, and a network model known as the DenseNet-169 model was utilized in order to extract the various growth-optimization choices. First, the MR images of the brain were preprocessed to remove any outliers. Next, the Dense Net-169 CNN model was used to extract network choices from the MR images. In addition, it is used to execute the function of a classifier in order to identify the growth as either an aberrant growth or a traditional growth. In addition, the publicly benchmarked datasets that are widely utilized have validated the algorithmic rule that was granted. The planned system demonstrates the satisfactory accuracy in getting ready to on the dataset and outperforms many of the notable current techniques.