In a dense environment, wireless sensor network (WSN) requires more energy to work in an effective and efficient manner. Hence, energy conservation is the main objective. In the paper, we have proposed a methodology to construct a backbone using modular antennas in combination with spanning tree protocol (STP), graph sampling, and Connecting Dominating Set (CDS) strategy. The backbone construction is based upon the modular antenna based WSNs, where the dominating sets can avoid the intermediate connection in order to reduce the hop count and energy consumption. The dominating sets have been connected using the modular transmission range of the wireless sensor networks to construct the backbone. The dominating set selection procedure to construct the WSN backbone is based upon the degree of connections of the nodes, which enables the locally centralized behavior of the connected dominating sets. The proposed methodology has been proved effective resulting in the construction of an energy efficient backbone.

Applications based on Fuzzy logic use mathematical reasoning to find the solution to the minutest possible fuzzy set that can range anywhere between 0 and 1. In this study, the practical implementation of a fuzzy logic controller for automated lighting was presented as a real case of an Indian university to detect the occupancy in the classroom and maintain the luminance level by sensing the daylight in the room. The result of the experiment indicates that the fuzzy logic control method could reduce wasted hours of lighting in unoccupied classrooms.

Nowadays, the volume of the data is increasing with time which generates a problem in storage and transfer. To overcome this problem, the data compression is the only solution. Data compression is the science (or an art) of representing information in compact form. This is an active research area. Compression is to save the hardware storage space and transmission bandwidth by reducing the redundant bits. Basically, lossless & lossy are two types of data compression technique. In lossless data compression, original data is similar to decompressed or decoded data, but in lossy technique is not same. In this paper, Study lossless image compression technique. The purpose of image compression is to maximum bandwidth utilization and reduces storage capacity. This technique is beneficial to image storage and transfer. At the present time, Mostly image compression research have focused on the wavelet transform due to better performance over another transform. The performance is evaluated by using MSE & PSNR. DWT, quantization, Arithmetic, Huffman coding and DCT techniques are briefly introduced. After decompression, the quality of image is evaluated using PSNR parameter between original & decoded image. Compression ratio (CR) parameter is calculated to measure how many times image compressed.