Control of call admission and management of bandwidth are the two important functionalities to achieve higher call handling capacity in heterogeneous wireless networks (HWN). This work addresses the problem of supporting differential qualities of services (QoS) with adherence to multicriteria factors in addition to reducing call-dropping probability in HWN. Toward this end, learning-assisted admission and bandwidth management are proposed. The decision to control the call acceptance ratio and bandwidth allocation level is learned continuously based on current network dynamics and the differential QoS requirements of the current calls. This learning reduces the call drop probability and slippage in QoS for calls. The parameters employed for evaluation in the suggested approach for call admission control include call priority, service type, service delivery mode, bandwidth availability for scalable and nonscalable calls, QoS distortion rate, and call ratio.

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In the past recent, identification of a person in an effective manner is a foremost concern for any security authentication in numerous applications such as, banking, e-commerce, communications etc. One of the best identification technology for person identification and authentication compared with the existing password based authentication is the multimodal biometric technology. Multimodal can be defined as, a system which uses two or more biometrics for identification of person. In the paper we propose a multimodal bio-metric system with a unique methodology and features extraction method incorporated in system for a secure authentication. The two modalities used in the system are face and thumb. We use Harris based image feature extraction for both and choose the best unique features from both and fused using concatenation. The extracted unique features are embedded in a cover image using modulo operator based steganography technique. This encrypted data is shared as an image file to the receiver for authentication. At the receiver end the hidden features are decrypted and separated into face and thumb features. These decrypted features are compared with the pre-trained authorized person feature, based on the multi-svm classifier result the person is decided as authorized or unauthorized. The accuracy of the system is been calculated and was resulted in a good accuracy. The system can be made much more secure by adding an additional secret key for encryption and decryption.