iosr-JCE   Volume-12 ~ Issue-3

Paper Type	:	Research Paper
Title	:	Handoff Management protocols MIPV6 and HMIPV6 Comparative analysis in 4G wireless networks
Country	:	India
Authors	:	Sonali Chavan, Vanita Mane
	:	10.9790/0661-1230105

Abstract: With the increasing demands for new data and real-time services, wireless networks should support calls with different traffic characteristics and different Quality of Service (QoS) guarantees. Instead of developing a new uniform standard for all wireless communications systems, 4G communication networks strive to seamlessly integrate various existing wireless communication technologies. IP has been recognized to be the de facto protocol for next-generation integrated wireless. In this paper we discuss different types of protocol for handoff management in 4G. Mobile IPv6, Hierarchical Mobile IPv6 and their comparative study and analysis.

Keywords: 4G networks, Handoff management, Handoff latency, HMIPV6, MIPV6

[1] Ibrahim Al-Surmi, Mohamed Othman, Borhanuddin Mohd Ali, "Mobility management for IP-based next generation mobile networks: Review, challenge and perspective", ELSEVIER Journal of Network and Computer Applications 35, 2012.

[2] C. Makaya and S. Pierre, "An Analytical Framework for Performance Evaluation of IPv6-Based mobility Management Protocols", IEEE Transactions on Wireless Communications, vol. 7, no. 3, pp. 972–983, March 2008.

[3] Xavier Pérez Costa, Ralf Schmitz, Hannes Hartenstein, Marco Liebsch, "A MIPv6, FMIPv6 and HMIPv6 handover latency study: analytical approach"

[4] P Deepika H1, Nilakshee R2 and Bhavana A3," Handover in next generation networks - challenges and solution", World Journal of Science and Technology 2012, 2(4):89-92ISSN: 2231 – 2587.

[5] C. Makaya and S. Pierre, "An Analytical Framework for Performance Evaluation of IPv6-Based mobility Management Protocols", IEEE Transactions on Wireless Communications, vol. 7, no. 3, pp. 972–983, March 2008.

[6] Jong-Hyouk Lee, Jean-Marie Bonnin, Ilsun You, Tai-Myoung Chung, "Comparative Handover Performance Analysis of IPv6 Mobility Management Protocols", IEEE Transactions On Industrial Electronics, 2012.

[7] Nguyen Van Hanh , Soonghwan Ro, Jungkwan Ryu, "Simplified fast handover in mobile IPv6 networks", ELSEVIER Computer Communications 31, 2008.

[8] Xavier P´erez-Costaa Marc Torrent-Moreno, "A Performance Comparison of Mobile IPv6, Hierarchical Mobile IPv6, Fast Handovers for Mobile IPv6 and their Combination". Mobile Computing and Communications Review, Volume 7, Number 4

[9] Archan Misra, Subir Das, "IDMP-based FAST HANDOFFS AND PAGING INIP-BASED CELLULAR NETWORKS" 2001 Telcordia Technologies Inc. and the University of Texas at

[10] Arlington ROSLI SALLEH and XICHUN LI "HANDOFF TECHNIQUES FOR 4G MOBILE WIRELESS INTERNET "3rd International Conference: Sciences of Electronic, Technologies of Information and Telecommunications March 27-31, 2005 – TUNISIA

Abstract: Bones are calcified connective tissue forming the major portion of the skeleton of most vertebrates. There are about 206 bones in our body and contains more calcium. Bones begin to develop before birth. From the moment of birth until the time one has grown up, bones go through a set a characteristic changes. Therefore the skeletal maturity, or bone age, can be estimated from radiographs of specific bones in the human body. Children who grow exceptionally slow or fast are often examined by making a radiograph of their left hand and wrist. The aim of this work is to develop a system for skeletal bone age estimation using region of extraction. By analyzing left hand x-ray image, the feature extracted are CROI(Carpal ROI), EMROI (Ephiphysial/Metaphysial ROI),using discrete wavelet transformation, ISEF edge detector, energy based segmentation, Jacobi method, cell full and vertex full method. Extracted features are classified using k-mean classifier. Results obtained on a sample of 24 X-rays are discussed. The systems were studied and their performances were compared by various other methods.

Keywords - Bone age assessment, CROI, EMROI, Jacobi method, TW2 method.

[1] D. Giordano, R. Leonardi, F. Maiorana, G. Scarciofalo, and C. Spampinato: "Epiphysis and Metaphysis Extraction and Classification by Adaptive Thresholding and DoG Filtering for Automated Skeletal Bone Age Analysis" Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité Internationale, Lyon, France August 23-26, 2007.

[2] P. Thangam, K. Thanushkodi ,T. V. Mahendiran: "Comparative Study of Skeletal Bone Age Assessment Approaches using Partitioning Technique"International Journal of Computer Applications (0975 – 8887) Volume 45– No.18, May 2012.

[3] N. Olarte L, A. Rubiano F, A. Mejía F: "Comparison of Valuation Techniques for Bone Age Assessment" World Academy of Science, Engineering and Technology 68 2012.

[4] P.Thangam, T.V.Mahendiran and K. Thanushkodi: "Skeletal Bone Age Assessment – Research Directions" Review Article Journal of Engineering Science and Technology Review 5 (1) (2012) 90 – 96.

[5] P. Thangam,K. Thanushkodi,&T. V. Mahendiran: "Computerized Convex Hull Method of Skeletal Bone Age Assessment from Carpal Bones" European Journal of Scientific Research ISSN 1450-216X Vol.70 No.3 (2012), pp. 334-344.

Abstract: To present game theory approach to detect identity crimes. Improve the adaptability of identity crime detection systems to real time application. Time constraints on the reactive time of the detection and fraud events need to be minimized. Identity crime has major thrust in credit application. Existing work presented multilayered detection system based on two layers named as Communal detection and Spike detection. Dynamic Time Warping algorithm is applied to minimize the time constraints on detecting fraudulent identity usage and reaction time. Performance analysis is carried out on CD and SD with real credit applications. Experiment is conducted on real time credit card application using UCI repository data sets with synthetic and real data sets.

Keywords - Data mining-based fraud detection, security, and data stream mining, and anomaly detection.

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[4] R. Caruana and A. Niculescu-Mizil, "Data Mining in Metric Space: An empirical Analysis of Supervised
[5] Learning Performance Criteria," Proc. 10th ACM SIGKDD Int'l Conf. Knowledge discovery and Data Mining (KDD '04), 2004, doi: 10.1145/1014052.1014063.
[6] Exper Detect: Application Fraud Prevention System, Whitepaper, http://www.experian.com/products/pdf/ experian_detect.pdf, 2008.
[7] T. Fawcett, "An Introduction to ROC Analysis," Pattern Recognition Letters, vol. 27, pp. 861-874, 2006, Doi: 10.1016/j .patrec. 2005.10.010.