Bücher von Ajaz Hussain Mir

Master's Thesis from the year 2013 in the subject Computer Science - Internet, New Technologies, grade: 8.12, , course: M. Tech, language: English, abstract: Wireless sensor network (WSN) is an emerging imperious leading technology of the current century that presents great promise for various pioneering applications. The use of sensor networks should keep developing, mainly in the fields of scientific, logistic, healthcare and military applications. Sensor networks interact and collect sensitive data and operate in unattended hostile environments, thus it is imperative to pay attention to the security of wireless sensor networks as they are highly prone to threats. For successful operation of the nodes, it is important to maintain the security and privacy of the transmitted data as well as the data stored at nodes. The different security protocols and models used in the wired and other wireless networks such as ad-hoc networks cannot be used in WSNs because the sensor size poses significant restrictions, mainly in terms of energy autonomy and node lifetime, as the batteries have to be too tiny. Also, the existing security protocols in these constrained networks are not sufficiently energy efficient, which is the real motivation behind this work. In this thesis, a light-weight dynamic security algorithm titled ¿An Energy Efficient and Dynamic Security Protocol (EEDSP)¿ for wireless sensor networks has been proposed and implemented at nodal level. We also implement an existing algorithm and then carry out the performance analysis of both the algorithms in terms of energy consumptions, node lifetime and memory requirements at source and intermediate nodes to verify the improvement in our protocol¿s results.

From security point of view, identity of a person is of critical importance. Increased threats to conventional security methods of personal identification has given rise to personal identification and verification systems based on biometrics. These systems establish human identity using individual¿s physical or behavioral attributes. These attributes besides being universal, unique and permanent need to be acceptable as well. One such biometric modality that has potential for further exploration is Ear. As a step in this direction, we have made an attempt to explore the potential of time series based Autoregressive (AR) modelling. Performance of AR model using ear biometrics motivated us to make an attempt for 100% RR of a person based on multimodal biometrics. After a study of state of art biometric modalities and potential of second order texture based methods having capability to extract information beyond visual perception, we found it reasonable to fuse AR based feature vector of Ear shape with second order texture based statistics of biometric modality Iris.