In Wireless Body Area Networks (WBAN), the key factors to be considered for transmission of confidential data are security and privacy as it is mostly having applications in emergency medical response systems. The lack of security may lead to loss of data privacy resulting in an adversary to bring in bogus data or altering the legal ones. Hence in this study, a secure key management technique for WBAN is proposed. The proposed architecture consists of a set of WBANs connected to the master server via backend server using authentication channel. Initially, backend server and master server use a shared symmetric key. When a node wants to join a network, it forwards a request message protected by the Message Authentication Code (MAC) to the master server via the backend server. The master server verifies the MAC and generates message key and master key for the node and sends it to backend server. The backend server encrypts the message key with the master key and sends it to the node that initiates the joining process. After all nodes receive key information from the master server, the Base Server (BS) schedules a re-keying period to refresh the master key. By simulation results, it is shown that the proposed technique is more authenticated. The proposed approach offers data confidentiality and integrity in WBANs
The trend of researches in cognitive reading has become so popular in programming area in the field of computer science from late 1990s when scientists and researchers show more interests in computational approaches are complex in nature to derive from a known algorithm of solution. For instance, in the research areas of biology, medicine and human management sciences there are various problems where we need cognitive reading to deliver a complex and in-exact solution when there is no polynomial time to arrive at an exact solution. This article explains some of the methods in cognitive reading in image processing for character recognition and briefly discusses the steps involved in the process of character recognition in image processing.
There are many techniques involved in Handwritten character recognition and many of the methods uses common process like pre-processing, segmentation, stroke identification and character interpretation. But the technique applied for these steps differ from different implementation. Building the training data is a tedious task but it is more important as the success of entire recognition system lies on the amount of training the neural network and building the knowledge base. Hence this is an important task and it is costly due to amount of time and resource used for training the dataset. This article explains some of the methods in Cognitive reading in image processing for character recognition and introduces a self-learning based training system which provides improved method in less time/resource consuming in automatically training the knowledge base.
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