As the number of Internet of Things (IoT) devices proliferates, the magnitude and velocity of data continues to increase rapidly. IoT systems rely primarily on using messaging protocols for exchanging IoT data and there exists several protocols or frameworks that support distinct types of messaging patterns. Given that IoT devices typically have limited computational resources and processing power, choosing a lightweight, reliable, scalable, interoperable, extensible and secure messaging protocol becomes a very challenging task. As a result, it is not uncommon that IoT systems may employ multiple messaging protocols for supporting device heterogeneity and different message exchange patterns. In addition, basic similarities among existing several messaging protocols or frameworks that exist today for exchanging IoT data within IoT systems suggest the potential of interoperability. Given that IoT systems help facilitate the interconnectivity among distributed, heterogeneous entities, interoperability among existing messaging protocols will play an increasingly important role in simplifying the development and deployment of IoT systems. In this paper, we present a comprehensive review of the existing messaging protocols that can be used in deploying IoT systems. Throughout this paper, we highlight the protocols' distinctive approaches and applicability of using them across various IoT environments. In addition, we highlight challenges, strengths and weaknesses of these messaging protocols in the context of IoT.

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Application with sequential algorithm can no longer rely on technology scaling to improve performance. Image processing applications exhibits high degree of parallelism and are excellent source for multi-core platform. Major challenge of parallel processing is not only aim to high performance but is to give solution in less time and better utilization of resources. Medical imaging require more computing power than a traditional sequential computer can do and we also know that for medical imaging, it is necessary that the image is clear and be obtained as quickly as possible. We can achieve through the process of parallelizing. Parallelizing optimizes the speed at which the image is produced.This paper presents the different types of parallelism in image processing i.e., data, task and pipeline parallelism. This paper also discusses three types of operators; point operators, neighborhood operators and global operators used for image processing. Different algorithms used for parallel image processing are discussed and the application of medical imaging is discussed using work flow engine Taverna for scientific processing.

Fruit disease detection is critical at early stage since it will affect the farming industry. Farming industry is critical for the growth of the economic conditions of India. To this end, proposed system uses universal filter for the enhancement of image captured from source. This filter eliminates the noise if any from the image. This filter is not only tackle’s salt and pepper noise but also Gaussian noise from the image. Feature extraction operation is applied to extract colour and texture features. Segmented image so obtained is applied with Convolution neural network and k mean clustering for classification. CNN layers are applied to obtain optimised result in terms of classification accuracy. Clustering operation increases the speed with which classification operation is performed. The clusters contain the information about the disease information. Since clusters are formed so entire feature set is not required to be searched. Labelling information is compared against the appropriate clusters only. Results are improved by significant margin proving worth of the study.