iosr-JCE   Volume-9 ~ Issue-2

Abstract: Voice over Internet Protocol (VoIP) is a technology that enables one to make and receive calls through the Internet instead of using the traditional analog PSTN (Public Switched Telephone Network) lines. Gtalk and Skype are frequently used for this purpose, which is a third party. So, we are utilizing their environment for conferencing. We are in need of a server to transfer the information from source to destination i.e. we are compromising our privacy at a certain level as they do not provide full security to our audio packets. Here, we do propose, novel flow analysis attacks that ensures both privacy and demonstrates the vulnerabilities in peer-to-peer VoIP networks. Solutions are proposed by quantifiable k-anonimity metrices.

Keywords: Flow analysis attacks, k-anonymity, mix networks, privacy, VoIP networks

1] Mudhakar Srivatsa, Arun Lyengar,Ling liu, Hongbo jiang ―Privacy in VoIP Networks: Flow Analysis Attacks and
Defense ―IEEE20ll.
[2] ―The Network Simulator NS-2,‖ http://www.isi.edu/nsnam/ns/,2010.
[3] ―The Network Simulator NS-2: Topology Generation,‖ http://www.isi.edu/nsnam/ns/ns-topogen.html , 2010'
[4] Phex Client,‖ http://www.phex.org, 2010
[5] ―Skype—The Global Internet Telephone Company,‖ http://www.skype.com, 2010.
[6] ―Telegeography Research,‖ http://www.telegeography.com, 2010.
[7] GT-ITM: Georgia, Tech Internetwork Topology Models,‖ http://www.cc.gatech.edu/projects/gtitm/ , 2010.
[8] M.J. Freedman and R. Morris, ―Tarzan: A Peer-to-Peer Anonymizing Network Layer,‖ Proc. Ninth ACM Conf. Computer and
Comm. Security (CCS), 2002.
[9] R. Dingledine, N. Mathewson, and P. Syverson, ―Tor: The Second Generation Onion Router,‖ Proc. 13th USENIX Security Symp., 2000.
[10] C# Network Programming book by Richard Blum.

Abstract: Location management refers to the problem of updating and searching the current locations of mobile nodes in a wireless network. The location administration basically is performed by using two registers, HLR (Home Location Register) and VLR (Visitor Location Register). The current location information has to be maintained efficiently in location registers for effective call delivery. In the system it is difficult to deliver a call to the users at an optimal time interval. This is used to manage and reduce the level of time of call forwarding from the caller to the callee. To make it efficient, the sum of update/lookup costs of a location database must be minimized. To reduce the cost of location database, this paper we are using the concept of time based location administration system. This paper proposed the CACHE scheme which register the user's frequent movement of repeated visit of same location and maintain the user's detail up-to-date. This optimizes the time-interval which minimize the location administration cortical factors to be established

1] Bjorn Landfeldt, "A Dynamic Location management on Scheme based on individual metrics and coordinates" IEEE Global
Telecommunications Conference, Vol. 3, pp. 1426–1430, 2006.
[2] Jie Li, Yi Pan and Yang Xiao, "A Dynamic HLR Location management Scheme for PCS", IEEE.
[3] Kwok, YK, "A quasi-static cluster-computing approach for dynamic channel assignment in cellular mobile communication systems"
IEEE 10.1109/VETECF.1999.797357 1999.
[4] Munadi, R.; Ismail, M.; Abdullah, M.; Misran, N.; Electr. Eng. Dept., Syiah Kuala Univ., Banda Aceh, Indonesia," Location
management cost reduction using fuzzy logic in cellular radio network", Space Science and Communication (IconSpace), 2011 IEEE
International Conference.
[5] Networks" IEEE Trans. 0-7803-8356-7/04/$20.00 IEEE INFOCOM 2004.
[6] Rodriguez-Dagnino, R.M.; Takagi, H.;
Monterrey Inst. of Technol. & Higher Educ. (ITESM), Monterrey," Movement-Based Location management for General Cell
Residence Times in Wireless Networks", IEEE Vehicular Technology Society, 2007.
[7] Travis Keshav "Location management in Wireless Cellular Networks" IEEE Trans. 2005: v.54, no.2 687-708.
[8] Vincent W S. Wong and Victor C. M. Leung, "Location management for Next Generation Personal Communication Networks"
Paper accepted by IEEE Network, Special Issue on Next Generation Wireless Broadband Networks.
[9] Yong Lee, "Optimal Time-Interval for Time-based Location Update in Mobile Communications"0-7803-7632-3/02/$17.000 2002
IEEE.
[10] S. Ratnasamy, B. Karp, L. Yin, F. Yu, D. Estrin, R. Govindan, and S. Shenker. GHT: a geographic hash table for data-centric
storage. In ACM Int'l workshop on Wireless Sensor Networks and Applications (WSNA), pages 78–87, 2002.

Abstract: Data cleaning is an automated process of detecting, removing and correcting incomplete, incorrect, inaccurate and irrelevant data from a record set. Our system works on simple text (*.txt) files using Extract, Transform and Load (ETL) model. In this paper we present a set of algorithms to correct errors such as alphanumeric errors, invalid gender, invalid ID pattern and redundant ID error. The text files are used as data storage which stores data in a tabular format and the algorithms are applied on each field value depending on its nature.

Keywords - ETL, Dirty Data, ID Validation, Alphabetic Validation, Numeric Validation

[1] R. Cody, "Data cleaning 101," Proceedings for the Twenty-Seventh SAS User Group International Conference. Cary, NC: SAS
Institute Inc,2000.
[2] Dr. Mortadha M. Hamad and Alaa Abdulkhar Jihad, "An Enhanced Technique to Clean Data in the Data Warehouse". Computer
Science Department. University of Anbar, Ramadi, Iraq.
[3] Hasimah Hj Mohamed, Tee Leong Kheng, Chee Collin and Ong Siong Lee, "E-Clean: A Data Cleaning Framework for Patient Data".
School of Computer Sciences. University Sains Malaysia Penang, Malaysia.
[4] Arindam Paul, Varuni Ganesan, Jagat Sesh Challa and Yashvardhan Sharma, "HADCLEAN: A Hybrid Approach to Data Cleaning in
Data Warehouses". Department of Computer Science & Information Systems . Birla Institute of Technology & Science, Pilani,
Rajasthan, India – 333031.
[5] Erhard Rahm and Hong Hai Do. "Data Cleaning: Problems and Current Approaches". University of Leipzig, Germany.
[6] Srivatsa Maddodi, Girija V. Attigeri and Dr. Karunakar A. K, "Data Deduplication Techniques and Analysis". Manipal Institute of
Technology, Manipal, India.
[7] R. Kimball and J. Caserta, "The Data Warehouse ETL Toolkit". Wiley,2004.
[8] V. Raman and J. M. Hellerstein, "Potter‟s Wheel: An Interactive Framework for Data Transformation and Cleaning.," in Proceedings
of the 27th VLDB Conference, Roma, Italy, 2001.
[9] K. Kukich, "Techniques for Automatically Correcting Words in Text", ACM Computing Surveys, vol. 24, no. 4, pp.377 -439, 1992.
[10] R. Bheemavaram, J. Zhang and W. N. Li, "Efficient Algorithms for Grouping Data to Improve Data Quality", roceedings of the 2006 International Conference on Information & Knowledge Engineering (IKE 2006), CSREA Press, Las Vegas, Nevada, USA, pp. 149-
154, 2006.

Abstract: Cloud computing is a model for enabling convenient, on demand network access to a shared pool of computing resources. The cloud storage system consists of a collection of storage servers and key servers. Storing data in a third party cloud system causes serious concern on data confidentiality, so a user divides the data into blocks, encrypts and stores them in various storage servers. The storage server encodes the data using erasure codeword symbol. When the sender wants to share his messages, he sends a re-encryption key to the storage server. The storage server re-encrypts the original codeword symbol into a re-encrypted codeword symbol. The re-encryption scheme is integrated with a secure decentralized erasure code so that a secure distributed system is designed. The proxy re-encryption scheme supports the encoding operations over encrypted messages as well as forwarding operations. The key server retrieves re-encrypted codeword symbols and performs partial decryption so that the receiver combines the blocks to retrieve the data. Keywords: Decentralized erasure code, proxy re-encryption, secure storage system, threshold cryptography

[1.] J. Kubiatowicz, D. Bindel, Y. Chen, P. Eaton, D. Geels, An Architecture for Global-Scale Persistent Storage," Proc. Ninth Int'l Conf. Architectural Support for Programming Languages and Operating Systems (ASPLOS), pp. 190-201, 2000.
[2.] P. Druschel and A. Rowstron, "PAST: A Large-Scale, Persistent Peer-to-Peer Storage Utility," Proc. Eighth Workshop Hot Topics in Operating System (HotOS VIII), pp. 75-80, 2001.
[3.] A. Adya, W.J. Bolosky, M. Castro, G. Cermak, R. Chaiken, J.R.Douceur, J. Howell, J.R. Lorch, M. Theimer, and R. Wattenhofer,
"Farsite: Federated, Available, and Reliable Storage for an Incompletely Trusted Environment," Proc. Fifth Symp. Operating System
Design and Implementation (OSDI), pp. 1-14, 2002.
[4.] Haeberlen, A. Mislove, and P. Druschel, "Glacier: Highly Durable, Decentralized Storage Despite Massive Corr elated Failures," Proc. Second Symp. Networked Systems Design and Implementation (NSDI), pp. 143-158, 2005.
[5.] A.G. Dimakis, V. Prabhakaran, and K. Ramchandran, "Decentralized Erasure Codes for Distributed Networked
Storage," IEEE Trans. Information Theory, vol. 52, no. 6 pp. 2809-2816, June 2006.
[6.] H.-Y. Lin and W.-G. Tzeng, "A Secure Decentralized Erasure Code for Distributed Network Storage," IEEE Trans.
Parallel and Distributed Systems, vol. 21, no. 11, pp. 1586-1594, Nov. 2010.

Abstract: This paper proposed for a new authenticated access control scheme for securing data in cloud system. In this scheme cloud system verifies the authenticity of user and provides access control only to applicable user to decrypt the stored data. The scheme prevents replay attacks and supports creation, modification, and reading data stored in the cloud. By impersonating legal users, intruders can use the copious resources of cloud computing environments. CIDS also provides a component to summarize the alerts and inform the cloud administrator. CIDS architecture is scalable and elastic with no central coordinator. This paper also describes the components, architecture, detection models, and advantages of CIDS.

Keywords - Authentication, Attribute-based signatures, Attribute-based encryption, intrusion detection, and masquerade attacks

[1] M Ozaki, Y. Adachi, Y. Iwahori, and N. Ishii, Application of fuzzy theory to writer recognition of Chinese characters, International
Journal of Modelling and Simulation, 18(2), 1998, 112-116. (8)
Note that the journal title, volume number and issue number are set in italics.
[2] R.E. Moore, Interval analysis (Englewood Cliffs, NJ: Prentice-Hall, 1966). (8)
Note that the title of the book is in lower case letters and italicized. There is no comma following the title. Place of publication and publisher are given.
[3] P.O. Bishop, Neurophysiology of binocular vision, in J.Houseman (Ed.), Handbook of physiology, 4 (New York: Springer-Verlag,
1970) 342-366. (8)
Note that the place of publication, publisher, and year of publication are enclosed in brackets. Editor of book is listed before book title.
[4] D.S. Chan, Theory and implementation of multidimensional discrete systems for signal processing, doctoral diss., Massachusetts
Institute of Technology, Cambridge, MA, 1978. (8)
Note that thesis title is set in italics and the university that granted the degree is listed along with location information
[5] W.J. Book, Modelling design and control of flexible manipulator arms: A tutorial review, Proc. 29th IEEE Conf. on Decision and
Control, San Francisco, CA, 1990, 500-506 (8)

Abstract: A number of new generation languages have been evolved for the past few years and have been adapted by the industries. As a result of which the project done under 3GL are becoming Legacy and also the information that is to be stored is increasing the complexity of the Legacy systems. These are issues because of which companies are migrating their legacy systems to SOA which provides loosely coupled and interoperable services. There are many approaches to migrate a legacy system to SOA. This paper presents a survey to migrate legacy system to SOA. We also propose a roadmap to migrate a legacy system to service oriented architecture.

Keywords - Legacy system, Migration, Reengineering, Replacement, Service oriented architecture, Wrapping

[1] Asil A. Almonaies, James R. Cordy, and Thomas R. Dean, Legacy System Evolution towards Service-Oriented Architecture,
International Workshop on SOA Migration and Evolution, Madrid, Spain, pp. 53–62 (March 2010).
[2] Maryam Razavian and Patricia Lago, Understanding SOA Migration Using a Conceptual Framework, Journal of Systems Integration
(MAR'10).
[3] K.Velmurugan, M.A. Maluk Mohamed, A Model driven Approach for Migrating from Legacy Software Systems to Web Services.
[4] Ravi Khadka, Service Identification Strategies in Legacy-to-SOA Migration, Paper presented at the Doctoral consortium of the 26th
International Conference on Software Maintenance (ICSM'11).
[5] CarlosMatos, ReikoHeckel, Migrating Legacy Systems to Service-Oriented Architectures, Electronic Communications of the EASST
Volume 16 (2009).
[6] Liam O'Brien, Dennis Smith, Grace Lewis, Supporting Migration to Services using Software Architecture Reconstruction,
Proceedings of the 13th IEEE International Workshop on Software Technology and Engineering Practice (STEP'05).
[7] Semih Cetin, N. Ilker Altintas, Halit Oguztuzun, Ali H. Dogru, Ozgur Tufekci and Selma Suloglu, A Mashup-Based Strategy for
Migration to Service-Oriented Computing, In pervasive Services, IEEE Inetnational Conference, pp. 169-172, (2001).
[8] Félix Cuadrado, Boni García, Juan C. Dueñas, Hugo A. Parada, A Case Study on Software Evolution towards Service-Oriented
Architecture, 22nd International Conference on Advanced Information Networking and Applications – Workshops (2008).

Abstract: Cognitive radio (CR) can significantly improve spectrum efficiency by allowing the secondary users to temporarily access the primary user's under-utilized licensed spectrum. Spectrum mobility issues arise when the primary user appears at the channels being occupied by the secondary users. The secondary users return to the occupied channel because the primary users have the preemptive priority to access channels. Spectrum handoff technique can help the interrupted secondary user to vacate the occupied licensed channel and find a suitable target channel to resume its unfinished data transmission. Preemptive resume priority (PRP) M/G/1 queuing network model characterizes the spectrum usage behaviors of the connection-based multiple channel spectrum handoffs. The proposed model derives the closed-form expression for the extended data delivery time of different proactive designed target channel sequences under various traffic arrival rates and service time distributions. The analytical method analyzes the latency performance of spectrum handoffs based on the target channel sequences specified in the IEEE 802.22 wireless regional area networks standards. We also suggest a traffic-adaptive target channel selection principle for spectrum handoffs under different traffic conditions.

Keywords- Cognitive radio, Spectrum handoff, PRP, M/G/1 queuing model, WRAN, Primary user, Secondary user.

[1] J. Mitola and G.Q. Maguire, "Cognitive Radio: Making Software Radios More Personal," IEEE Personal Comm., vol. 6, no. 4, pp.
13-18, Aug. 1999.
[2] S. Haykin, "Cognitive Radio: Brain-Empowered Wireless Communications," IEEE J. Selected Areas in Comm., vol. 23, no. 2, pp .
201-220, Feb. 2005.
[3] R.W. Thomas, L.A. DaSilva, and A.B. MacKenzie, "Cognitive Networks," Proc. IEEE Int'l Symp. Dynamic Spectrum Access
Networks (DySPAN), Nov. 2005.
[4] L.-C. Wang, C.-W. Wang and C.-J. Chang, "Optimal Target Channel Sequence for Multiple Spectrum Handoffs in Cognitive Radio
Networks," IEEE Trans. Comm., accepted, 2012.
[5] T.A. Weiss and F.K. Jondral, "Spectrum Pooling: An Innovative Strategy for the Enhancement of Spectrum Efficiency," IEEE
Radio Comm. Magazine, vol. 42, no. 3, pp. S8-S14, Mar. 2004.
[6] I.F. Akyildiz, W.-Y. Lee, M.C. Vuran, and S. Mohanty, "A Survey on Spectrum Management in Cognitive Radio Networks," IEEE
Comm. Magazine, vol. 46, no. 4, pp. 40-48, Apr. 2008.
[7] H.-J. Liu, Z.-X. Wang, S.-F. Li, and M. Yi, "Study on the Performance of Spectrum Mobility in Cognitive Wireless Network,"
Proc. IEEE Singapore Int'l Conf. Comm. Systems (ICCS), June
[8] 2008.
[9] L.-C. Wang, C.-W. Wang and K.-T. Feng, "A Queuing-Theoretical Framework for QoS-Enhanced Spectrum Management in
Cognitive Radio Networks," IEEE Wireless Comm. Magazine, vol. 18, no. 6, pp. 18-26, Dec. 2011.
[10] Q. Zhao, L. Tong, A. Swami, and Y. Chen, "Decentralized Cognitive MAC for Opportunistic Spectrum Access in Ad Hoc
Networks: A POMDP Framework," IEEE J. Selected Areas in

Abstract: Mobile phone in day-to-day life has become an important communication device, subjected from formal and informal talks to sharing important and secure information. These secure information falls from personal communication to large business deals. Hence, there is a necessity to ensure security to the applications that are used to transfer confidential information. In order to satisfy the users' requirement, many cryptographic protocols are chosen based on confidentiality, integrity and performance. Public Key Cryptography is one such solution that meets the above mentioned attributes. Systems that use public key cryptography weigh computing power, key size to measure the efficiency of the protocol. This paper mainly focuses on the performance attributes of the various public key cryptography algorithms. Here, the algorithms RSA and ECC are studied and compared. These algorithms implemented, provide the necessary basic security components like confidentiality, authentication, integration and non-repudiation of services for a mobile communication. In addition to the performance characteristics of the algorithms studied, we also extend our research in analyzing the power consumed by these efficient cryptographic protocols. The protocols' energy efficiencies are measured based on the consumption of current, power against time.

Keywords: ECC, Energy Profiler, NTRU, Public Key Cryptography, RSA

[1] Sameer Hasan Al-Bakri, M. L. Mat Kiah, A. A. Zaidan, B. B. Zaidan and Gazi Mahabubul Alam, "Securing peer-to-peer mobile
communications using public key cryptography: New security strategy", International Journal of the Physical Sciences Vol. 6(4), pp.
930-938, 18 February, 2011
[2] Vinod Namboodiri, Toolika Ghose, "To Cloud or Not To Cloud – A Mobile Device Perspective on Energy Consumption of
Applications", IEEE World of Wireless, Mobile and Multimedia Networks, pp. 1-9, 25-28 June, 2012
[3] A. Naresh Reddy,Rakesh Nayak,S. Baboo,"Analysis and Performance Characteristics of Cryptosystem using Image
Files",International Journal of Computer Applications (0975 – 8887) Volume 51– No.22, August 2012
[4] Goran Kalic, Iva Bojic and Mario Kusek, "Energy Consumption in Android Phones when using Wireless Communication
Technologies", MIPRO, 2012 Proceedings of the 35th International Convention, 21-25 May 2012
[5] K. Sathish Kumar,R. Sukumar, P. Asrin Banu, "An Experimental Study on Energy Consumption of Cryptographic Algorithms for
Mobile Hand-Held Devices", International Journal of Computer Applications (0975 – 8887) Volume 40– No.1, February 2012
[6] Helena Rif`a-Pous and Jordi Herrera-Joancomart, "Computational and Energy Costs of Cryptographic Algorithms on Handheld
Devices", Future Internet 2011, 3, 31-48, Published: 14 February 2011
[7] Lide Zhang, Birjodh Tiwana, Zhiyun Qian, Zhaoguang Wang, Robert P. Dick, Z. Morley Mao, Lei Yang, "Accurate Online Power
Estimation and Automatic Battery Behavior Based Power Model Generation for Smartphones", IEEE Hardware/Software Codesign
and System Synthesis, Pages 105-114, 24-29 October, 2010
[8] Andrew Rice and Simon Hay, "Measuring mobile phone energy consumption for 802.11 wireless networking", Elsevier, July 18,
2010
[9] Niranjan Balasubramanian,Aruna Balasubramanian,Arun Venkataramani, "Energy Consumption in Mobile Phones: A Measurement
Study and Implications for Network Applications" Association for Computing Machinery, November 4–6, 2009, Chicago, Illinois,
USA
[10] Vivek B. Kute, P. R. Paradhi and G. R. Bamnote, "A software comparison of RSA and ECC", International Journal Of Computer
Science And Applications Vol. 2, No. 1, April / May 2009

Abstract: As the data storage challenge continues to grow for insurers and everyone else, one of the obvious solutions is cloud technology. Storing data on remote servers rather than in-house is definitely a money-saver, but in insurance circles, the worry has been that having critical data reside outside the physical and virtual walls of the insurance enterprise is a risky situation. As the IT field is rapidly moving towards Cloud Computing, software industry's focus is shifting from developing applications for PCs to Data Centers and Clouds that enable millions of users to make use of software simultaneously. "Attempting computation on sensitive data stored on shared servers leaves that data exposed in ways that traditional encryption techniques can't protect against," the article notes. "The main problem is that to manipulate the data, it has to be decoded first". Now a new method, called fully homomorphic encryption (FHE) that performs computation with the encrypted data and send to the client and offers a realistic hope that such calculations can be performed securely in the cloud.

Keywords: Cloud computing, fully homomorphic encryption, security threats.

[1] Virtualization Overview. White Paper. Vmware. Retrieved April 6, 2011, available at:
http://www.vmware.com/pdf/virtualization.pdf
[2] Web Search For A Planet: The Google Cluster Architecture. Retrieved April 6, 2011, available at:
http://labs.google.com/papers/googlecluster-ieee.pdf
[3] What is Cloud. Retrieved April 6, 2011, available at: http://www.rackspace.co.uk/cloud-hosting/learn-more/whatis- cloud/
[4] What is Cloud Computing. Retrieved April 6, 2011, available at: http://www.microsoft.com/business/engb/
solutions/Pages/Cloud.aspx
[5] What is Cloud Computing. Retrieved April 6, 2011, available at: http://www.ibm.com/developerworks/cloud/newto.html#WHATIS
[6] Recession is good for cloud computing – Microsoft agrees - http://www.cloudave.com/2425/recession-is-goodfor- cloudcomputing-
microsoft-agrees/
[7] National Institute of Standards and Technology - Computer Security Divisionhttp://csrc.nist.gov/groups/SNS/cloud-computing/
[8] Bhaskar P., Admela J·, Dimitrios K·, Yves G.: Architectural Requirements for Cloud Computing Systems: An Enterprise Cloud
Approach. J. Grid Computing 9(1), 3-26 (2011)
[9] R. Rivest, L. Adleman, and M. Dertouzos. On data banks and privacy homomorphisms. In Foundations of Secure Computation, pp.
169–180, 1978.
[10] R. Rivest, A. Shamir, and L. Adleman. A method for obtaining digital signatures and public-key cryptosystems. In Comm. of the
ACM, 21:2, pages 120–126, 1978

Abstract: Frequent pattern mining is emerging as a powerful tool for many business applications such as ecommerce, recommender system and the group decision support system. Many techniques have been developed to mine the frequent patterns. However, it would be the centre of attraction if the degree of importance of each item is taken into consideration.. For this reason, weighted frequent pattern mining algorithms have been suggested. But these algorithms deal only with the static databases, whereas, in reality most databases are interactive and dynamic in nature. Incremental Weighted Frequent Pattern Mining based on Frequency Descending Order (IWFPFD) tree is used to deal with the dynamic nature of the databases while pushing the weight constraints into frequent pattern mining. Branch Sorting Method (BSM) with merge sort is used to restructure the IWFPFD tree. This makes it more convenient for mining the patterns from the tree. It also makes the IWFPFD highly compact to save memory space. This tree allows mining of frequent patterns through a single pass over the database.

Keywords - Frequent Pattern Mining, Incremental frequent pattern mining, Incremental mining, utility mining, Weighted Frequent Patterns

[1] Aditya Telang, Chengkai Li, and Sharma . Agrawal, T. Imielinski, and A. Swami, (1993), "Mining Association Rules between Sets of
Items in Large Databases," Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207-216.
[2] R. Agrawal, and R. Srikant,(March 1995), "Mining Sequential Patterns," Proceedings of the Eleventh International Conference on
Data Engineering, p 3-14.
[3] R. Agrawal, and R. Srikant,(1994), "Fast Algorithms for Mining Association Rules in Large Databases," Proceedings of the 20th
International Conference on Very Large Data Bases, (VLDB'94), pp. 487-499.
[4] C.F. Ahmed, S.K.Tanbeer, Y.K.Lee, (2012), "Single Pass Incremental and Interactive Mining For Weighted Patterns",Expert System
with applications.
[5] C. H. Cai, A. W. Chee Fu, C. H. Cheng, and W. W. Kwong, (July 1998), "Mining Association Rules with Weighted Items,"
Proceedings of the Sixth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2005).
[6] J. Han, J. Pei, and Y. Yin,(May 20000, "Mining Frequent Patterns without Candidate Generation," Proceedings of the 2000 ACM
SIGMOD International Conference on Management of Data, pp. 1-12.
[7] J. Han, J. Pei, Y. Yin, and R. Mao,(Jan 2004), "Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree
Approach," Data Mining and Knowledge Discovery, vol. 8, pp. 53-87.
[8] W. Cheung , "Frequent Pattern mining without candidate generation or support constraint." Master's thesis, University of Alberta,
SPRING '03, doi.ieeecomputersociety.org/10.1109/IDEAS.2003.1214917.
[9] D. W. Cheung, J. Han, V. T. Ng, and C. Y. Wong, (Feb 1996), "Maintenance of discovered association rules in large databases: an
incremental updating technique," In Proc. 12th Intl. Conf. on Data Engineering, New Orleans, LA, pp. 106-114.
[10] Carson Kai-Sang Leung, Quamrul I. Khan, Tariqul Hoque,(2005), "CanTree: a tree structure for efficient incremental mining of
frequent patterns", ICDM'05

Abstract: Wireless local area network (WLAN) has increased rapidly, providing flexibility and mobility to the device users. This made the technology popular amongst a wide range of users in the universe. But one of the common problems we are overcoming is fragment loss. So far we were following a scheme to retransmit the entire packet if any fragment is lost. Here we introduce a new scheme called Aggregation with Fragment Retransmission (AFR). In this method, multiple packets are aggregated and transmitted in a single large frame reducing the access overheads and hence increasing the data rate. In addition, to investigate the performance added value when each large frame is fragmented so in the case of error in data frame, only the erroneous fragments are to be retransmitted reducing the error control overheads and this decreases the frame error rate without requiring more overhead.

Keywords -802.11 wireless LAN, AES, weighted fair scheduling, Fragmentation, security overhead

[1] J. Tourrilhes, "Packet frame grouping: improving IP multimedia performance over CSMA/CA," in Proc. ICUPC, 1998, pp. 1345-
1349.
[2] B. Sadeghi, V. Kanodia, A. Sabharwal, and E. Knightly, "Opportunistic media access for multirate ad hoc networks," in Proc.
ACM MOBICOM, 2002, pp. 24-35.
[3] Y. Xiao and J. Rosdahl, "Throughput and delay limits of IEEE 802.11,"IEEE Commun. Lett., vol. 6, no. 8, Aug. 2002, pp. 355-357.
[4] Y. Xiao and J. Rosdahl, "Performance analysis and enhancement for the current and future IEEE 802.11 MAC protocols," ACM
SIGMOBILE Mobile Computing Commun. Review, vol. 7, no. 2, pp. 6-19, Apr. 2003.
[5] Q. Ni, I. Aad, C. Barakat, and T. Turletti, "Modeling and analysis of slow CW decrease for IEEE 802 .11 WLAN," in Proc. PIMRC,
2003, pp. 1717-1721.
[6] Q. Ni, T. Li, T. Turletti, and Y. Xiao, "AFR partial MAC proposal for IEEE 802.11n," IEEE 802.11-04-0950-00-000n, Aug. 2004.
[7] V. Vitsas, et al., "Enhancing performance of the IEEE 802.11 distributed coordination function via packet bursting," in Proc.
GLOBECOM, 2004, pp. 245-252.
[8] C. H. Foh and J. W. Tantra, "Comments on IEEE 802.11 saturation throughput analysis with freezing of backoff counters," IEEE
Commun. Lett., vol. 9, no. 2, pp. 130-132, Feb. 2005.
[9] J. Choi, J. Yoo, S. Choi, and C. Kim, "EBA: an enhancement of the IEEE 802.11 DCF via distributed reservation," IEEE Trans.
Mobile Comput., vol. 4, no. 4, pp. 378-390, July 2005.
[10] T. Li, Q. Ni, D. Malone, D. Leith, Y. Xiao, and T. Turletti,"A new MAC scheme for very high-speed WLANs," in Proc. IEEE
WOWMOM, 2006, pp. 171-180.

Abstract: ATCS is an Automated Toll Collection System used for collecting tax automatically. In this we do the identification with the help of radio frequency. A vehicle will hold an RFID tag. This tag is nothing but unique identification number assigned. This will be assigned by RTO or traffic governing authority. In accordance with this number we will store, all basic information as well as the amount he has paid in advance for the TOLL collection. Reader will be strategically placed at toll collection center. Whenever the vehicle passes the toll naka, the tax amount will be deducted from his prepaid balance. New balance will be updated. Incase if one has insufficient balance, his updated balance will be negative one. To tackle this problem, we are alarming a sound, which will alert the authority that this vehicle doesn't have sufficient balance and that particular vehicle can be trapped. As vehicles don't have to stop in a queue, it assures time saving, fuel conservation and also contributing in saving of money.

Keywords – ATCS, RFID Reader, RFID Tag, Toll Collection, prepaid account.

[1] The Time's of India paper April 20, 2012 "Now Road toll can be paid without stopping at Toll Plazas".
[2] The Time's of India paper May 28, 2012 "High-Tech number plates for 20 lakh vehicles soon".
[3] Tom Pettruzzelis,"TELEPHONE PROJECTS FOR THE EVIL GENIUS",BPB PUBLICATIONS
[4] Klaus Finkenzeller, "RFID Handbook: Radio-Frequency Identification Fundamentals and Applications". John Wiley & Sons, 2000.
[5] H. Vogt. Efficient Object identification with passive RFID tags. In F.Mattern and M. Naghsinesh, editors, International Conference
on Pervasive Computing. Volume 2414 of Lecture Notes in Computer Science, pages 98-113, Zurich, August 2002. Springer- Verleg.

Abstract: In this letter, a new satellite image contrast enhance- ment technique based on the discrete wavelet transform (DWT) and singular value decomposition has been proposed. The tech- nique decomposes the input image into the four frequency sub- bands by using DWT and estimates the singular value matrix of the low–low subband image, and, then, it reconstructs the en- hanced image by applying inverse DWT. The technique is com- pared with conventional image equalization techniques such as standard general histogram equalization and local histogram equalization, as well as state-of-the-art techniques such as bright- ness preserving dynamic histogram equalization and singular value equalization. The experimental results show the superiority of the proposed method over conventional and state-of-the-art techniques.

Keywords: Discrete wavelet transform, image equalization, satellite image contrast enhancement.

[1] K. Kinebuchi, D. D. Muresan, and T.W. Parks, "Image interpolation using wavelet based hidden Markov trees," in Proc. IEEE
ICASSP, 2001, vol. 3, pp. 7–11.
[2] M. S. Crouse, R. D. Nowak, and R. G. Baraniuk, "Wavelet-based statistical signal processing using hidden Markov models," IEEE
Trans. Signal Process., vol. 46, no. 4, pp. 886–902, Apr. 1998.
[3] S. Zhao, H. Han, and S. Peng, "Wavelet domain HMT -based image super resolution," in Proc. IEEE ICIP, Sep. 2003, vol. 2, pp. 933– 936.
[4] A. Temizel and T. Vlachos, "Image resolution upscaling in the wavelet domain using directional cycle spinning," J. Electron. Imaging,
vol. 14, no. 4, p. 040501, 2005.
[5] A. Gambardella andM.Migliaccio, "On the superresolution of microwave scanning radiometer measu rements," IEEE Geosci. Remote
Sens. Lett., vol. 5, no. 4, pp. 796–800, Oct. 2008.
[16] V. A. Tolpekin and A. Stein, "Quantification of the effects of land-coverclass spectral separability on the accuracy of Markov-randomfield- based superresolution mapping," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 9, pp. 3283–3297, Sep. 2009.
[7] A. Temizel and T. Vlachos, "Wavelet domain image resolution enhancement using cycle-spinning," Electron. Lett., vol. 41, no. 3, pp.
119–121,Feb. 3, 2005.
[8] L. A. Ray and R. R. Adhami, "Dual tree discrete wavelet transform with application to image fusion," in Proc. 38th Southeastern
Symp. Syst.Theory, Mar. 5–7, 2006, pp. 430–433.
[9] A. Temizel, "Image resolution