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Internet of things is a new technology that connects billions of smart devices through the Internet. This important technology is heavily affecting different aspects of human life and plays an important role in variety of domains. The number of smart devices connecting to IoT and the amount of data gathered or produced by these devices are increasing. This large volume of data requires a lot of effort for processing and analysis. In order to organize, secure and balance the load in IoT, the novel and creative ideas for IoT design and management are required. Software Defined Networking is a new and promising paradigm and its main idea is to decouple the control and management operations from physical devices and setting them inside a logically centralized control layer. Due to its unique capabilities, SDN can be a good choice for managing dynamic and complicated networks such as IoT. In this paper, a distributed approach is proposed for accelerating data management and balancing the load between IoT devices. The proposed framework makes it possible to apply different kinds of management, security and load balancing strategies using a distributed control plane. Experimental results indicate that our scheme exhibits a reduction in the average turnaround time and the average waiting time and it will improve the processing performance. The proposed approach will also distribute tasks between devices, so that there will be no under-loaded device when a part of network is under the processing pressure.

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Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively.