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Due to wide variety of applications, Wireless Sensor Networks have been extensively used and many researchers are engaged in research in this area including Mobile Wireless Sensor Networks (MWSN). The major issues in use of MWSN however are energy consumption, security, scalability, robustness etc.. It is generally observed that clustered WSN networks using hierarchical routing protocols, significantly reduce the overall energy consumed. However, some of these protocols do not address the security issues related to different attacks. A security enhanced Sec-LEACH protocol results in higher security using random key predistribution and TESLA.In this paper, an approach has been implemented to arrive at an energy aware security enhancing strategy for Power-Efficient GAthering in Sensor Information Systems (PEGASIS) and referred to as Sec-PEGASIS protocol. The performance evaluations have been done for three important metrics namely residual energy, packet delivery ratio and routing overheads. The performance comparison has been done with respect to LEACH, Sec-LEACH and normal PEGASIS protocol for different rounds of data collection. It is seen that energy aware security enhancing strategy proposed in this paper provides higher level of security, retaining the energy efficiency feature of PEGASIS protocol unaltered.

A new approach is proposed to quantitatively evaluate the binary detection performance of the biometric personal recognition systems. The importance of correlation between the overall detection performance and the area under the genuine acceptance rate (GAR) versus false acceptance rate (FAR) graph, commonly known as receiver operating characteristics (ROC) is recognized. Using the ROC curve, relation between GAR min and minimum recognition accuracy is derived, particularly for high security applications (HSA). Finally, effectiveness of any binary recognition system is predicted using three important parameters, namely GAR min , the time required for recognition and computational complexity of the computer processing system. The palm print (PP) modality is used to validate the theoretical basis. It is observed that by combining different useful feature-extraction techniques, it is possible to improve the system accuracy. An optimum algorithm to appropriately choose weights has been suggested, which iteratively enhances the system accuracy. This also improves the effectiveness of the system.