Edge Computing in IoT: Vision and Challenges

The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction

New Error Model of Entropy Encoding for Image Compression

Entropy coding provides the lossless compression of data symbols and is a critical component in signal compression algorithm. In our case we have designed a new model which reduces the Interpixel redundancy and have better results as compared to other models like lossless predictive code (LPC) and Differential pulse code modulation (DPCM). Our new proposed Scheme for Huffman coding will achieve higher compression as we have also reduce the standard deviation in the error image tremendously as compare to LPC and DPCM

Image Contrast Enhancement with Brightness Preserving Using Feed Forward Network

Image improvement techniques are very useful in our daily routine.In the field of image enhancement Histogram Equalization is a very powerful, effective and simple method. Histogram Equalization (HE) is a popular, simple, fast and effective technique for improving the gray image quality. Contrast enhancement was very popular method but it was not able to preserve the brightness of image. Image Dependent Brightness Preserving Histogram Equalization (IDBPHE) technique improve the contrast as well as preserve the brightness of a gray image. Image features Peak Signal to Noise Ratio (PSNR) and Absolute Mean Brightness Error (AMBE) are the parameters to measure the improvement in a gray image after applying the algorithm. Unsupervised learning algorithm is an important method to extract the features of neural network. We propose an algorithm in which we extract the features of an image by unsupervised learning. After apply unsupervised algorithm on the image the PSNR and AMBE features are improved

Evaluating the Performance of Similarity Measures in Effective Web Information Retrieval

Information Retrieval (IR) manages recovering and showing data inside the WWW and online databases and furthermore looks through the web reports The quick development of site pages accessible on the Internet as of late, seeking applicable and coming data has turned into a pivotal issue. Data recovery is a standout amongst the most essential segments in web crawlers and their improvement would greatly affect enhancing the looking productivity because of dynamic nature of web it turns out to be much hard to discover applicable and late data. That is the reason an ever increasing number of individuals began to utilize centered crawler to get correct data in their uncommon fields today. The information retrieval field mainly deals with the grouping of similar documents to retrieve required information to the user from huge amount of data. The researchers proposed different types of similarity measures and models in information retrieval to determine the similarity between the texts and for document clustering. This research intends the study of genetic algorithm based information retrieval using similarity measures like cosine coefficient, jaccard coefficient, dice coefficient