AN INTELLIGENT APPROACH ON SECURITY AND PRIVACY IN WSN

Security worries for WSNs there are an assortment of novel difficulties. Security change procedures have computational communication and capacity pre requisites which additionally force sensor hubs. Also it is unreasonable to have a main issue of control in sensor networks on account of their asset limitations and network elements. In this way the improvement of a decentralized security arrangement is essential for WSN ideal proficiency. In this thesis we propose a new approach for securing wireless communications for wearable and implantable healthcare devices using gait signal energy variations and an Artificial Neural Network (ANN) framework. By simultaneously extracting similar features from BSN sensors using our approach binary keys can be generated on demand without user intervention. Through an extensive analysis on our approach using a gait dataset the results have shown that the binary keys generated using our approach have high entropy for all subjects. Wearable sensors are currently the basis of monitoring and analyzing gait outside the clinical environment with among others tele-health and tele-care applications.