iosr-JCE   Volume-13 ~ Issue-4

Abstract: In recent years, FPGA's have become increasingly important and have found their way into system design. So, the desire emerges for a means that allows early area and performance estimation Understanding how a design maps to them and consumes various FPGA resources can be difficult to predict, so typically designers are forced to run full synthesis on each iteration of the design. For complex designs that involve many iterations and optimizations, the run-time of synthesis can be quite prohibitive .However, to achieve high performance; FPGA must be supported by efficient design methodology and optimization techniques. The motivation behind this work is to review different FPGA based design methodology and optimization techniques that can be employed to efficiently estimate hardware area utilized in terms of look up table (LUT'S) or configurable logic blocks (CLB'S).

Keyword: HW/SW Partitioning, Area Estimation, Latency Estimation

[1] Shi, C., Hwang, J., McMillan, S., Root, A., and Singh, V.,"A System Level Resource Estimation Tool for FPGAs",International Conference on Field Programmable Logic andApplications (FPL), 2004.

[2] C. Brandolese, W. Fornaciari, and F. Salice. "An AreaEstimation Methodology for FPGA Based Designs at SystemCLevel,"DAC'04, San Diego, California, USA, pp. 129 - 132, 2004.

[3] RoelMeeuws, "A Quantitative model for Hardware/Software partitioning," MSc thesis, Delft University of Technology, 2007.

[4] V. Srinivasan, S. Govindarajan, and R. Vemuri, "Fine-grained and coarse-grained behavioral partitioning with effective utilization of memory and design space exploration for multi-FPGA architectures," IEEE Transactions on Very Large Scale Integration(VLSI) Systems, Vol. 9, no. 1, pp. 140–158, 2001.

[5] F. Vahid and D. D. Gajski, "Incremental hardware estimation during hardware/software functional partitioning," in Readings in hardware/software co-design, G. De Micheli, R. Ernest, and W.Wolf (eds.), Morgan Kaufmann, pp. 516–521, 2002.

[6] L. Yan, T. Srikanthan, and N. Gang, "Area and Delay Estimation for FPGA Implementation of Coarse-Grained Reconfigurable Architectures," LCTES, Ottawa, Ontario, Canada,pp.182–188, 2006.

[7] R. Enzler, T. Jeger, D. Cottet, and G. Troster, "High level area and performance estimation of hardware building blocks on FPGAs," FPL 2000, Villach, Austria, pp. 525–534, 2000.

[8] D. Kulkarni, Walid A. Najjar, R. Rinker and F. J. Kurdahi,"Compile-Time Area Estimation for LUT-Based FPGAs," ACMTODAES, Vol. 11, No. 1, pp. 104–122, 2006.

[9] P Bjureus, M. Millberg, and A. Jantsch, "FPGA resource and timing estimation from MATLAB execution traces," CODES 2002,Estes Park, Colorado, USA, pp. 31–36, 2002.

[10] L. M. Reyneri, F. Cucinotta, A. Serra, and L. Lavagno, "A hardware/software co-design flow and IP library based on Simulink," DAC '01, Las Vegas, Nevada, USA, pp. 593 - 598,2001.

Abstract: The medial axis of an image pattern is the loci of all inscribed disks that touch two or more boundary points without crossing any of the boundaries. The medial axis transform (MAT) is a powerful representation for objects with inherent symmetry or near-symmetry. The medial axis of 2-D image patterns provides a conceptual design base, with transition to a detailed design occurring when the radius function is added to the medial axis or surface. To make such a design tool practicable, however, it is essential to be able to convert from an MAT format to a boundary representation of an object. In the proposed work, the medial axis transform has been extracted using the Euclidean distance transform based computation. The image pattern u prepared initially in binary form and then distance of each non-zero pixel to its closest zeroed pixel is computed. This process continues till the entire image pattern is scanned to its core.

Keywords: MAT -> Medial Axis Transform, EDT-> Euclidean Distance Transform, CCD-> Charge Coupled Device

[1]. Andrea Tagliasacchi, Hao Zhang, Daniel Cohen-Or, "Curve Skeleton Extraction from Incomplete Point Cloud", ACM Transaction.
[2]. G. Hamarneh is with the School of Computing Science, Simon Fraser,University, 8888 University Drive, Burnaby, BC V5A Canada.E-mail: hamarneh@cs.sfu.ca.
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[6]. C. Arcelli and G. Sanniti di Baja, "Ridge points in Euclideandistance maps," Pattern Recognition Letters, 13, 1992.
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[8]. Amenta, N. and Bern, M.W., Surface reconstruction by Voronoi filtering. Discrete & Computational Geometry. v22 i4. 481-504.

Abstract: The robust and complex real-time applications and dramatically increased sensor capabilities may play a vital role in enhancing the lifespan of WSNs. On the other hand majority of WSNs operate on battery powered infrastructure, therefore in order to enhance the lifespan maximization a robust and highly efficient protocols are required to be developed that can effectively minimize the battery utilization and the overall computational as well as communication complexity also could be minimized. Optimizations required to be adopted at the Routing Layer, MAC Layer and the Radio Layer of the wireless sensor node. In this paper in order to achieve a better network performance an scheme of elephant swarm optimization has been implemented which enables optimization of routing algorithm, adaptive radio link optimization and balanced TDMA MAC scheduling. The proposed Elephant Based Swarm Optimization scheme is analyzed and compared with the popular LEACH and PSO Protocols and results proves that the EBSO algorithm is the best among PSO and LEACH schemes.

Keywords: Active node ratio, Cross-layer design, Elephant Based Swarm Optimization (EBSO), LEACH, Network lifespan, Particle Swarm Optimization (PSO).

[1] Wilson, E.O. "Sociobiology: The New Synthesis". 25th Anniversary Editions. The Belknap Press of Harvard University Press Cambridge, Massachusetts and London, England, 2000.
[2] Krebs J. R., Davies N. B. "An Introduction to Behavioral Ecology". Third Edition. Blackwell Publishing, Oxford, UK, 1993.
[3] Elizabeth A. Archie, Cynthia J. Moss and Susan C. Alberts. "The ties that bind: genetic relatedness predicts the fission and fusion of social groups in wild African elephants." Proc. R. Soc. B 273, pp. 513–522, 2006.
[4] Elizabeth A., Archie, and Patrick I. Chiyo, "Elephant behavior and conservation: social relationships, the effects of poaching, and genetic tools for management", Molecular Ecology- 21, 765–778, (2012).
[5] Rachael Adams, "Social Behavior and Communication in Elephants- It's true! Elephants don't forget!" Available at: http://www.wildlifepictures-online.com/ elephant-communication.html, Accessed March 14th 2013.
[6] Wyatt, T.D. Pheromones and Animal behavior - Communication by Smell and Taste. Cambridge University Press. UK, 2003.
[7] I.F. Akyildiz; W. Su, Y; Sankara subramaniam; E. Cayirci, "Wireless Sensor Networks: a survey", Computer Networks-38, pp. 393–422, 2002.
[8] Supiya Ujjain, Peter J. Bentley, "Particle Swarm Optimization Recommender System", 2003.
[9] Berry, Randall A, Yeh, Edmund M. "Cross-layer wireless resource allocation", Signal Processing Magazine, IEEE Transactions, Volume: 21, Issue:5, pp.59 – 68, Sept. 2004.
[10] Jin, Lizhong, Jia, Jie, Chen, Dong; Li, Fengyun; Dong, Zhicao; Feng, Xue. "Research on Architecture, Cross-Layer MAC Protocol for Wireless Sensor Networks", Genetic and Evolutionary Computing (ICGEC), Fifth International Conference. pp. 291–294, Aug. 29 2011- Sept. 1-2011.

Abstract: Task Scheduling is Essential part for proper functioning of parallel processing system. Several approaches have been applied to solve this problem. Genetic algorithms have received much awareness as they are robust and guarantee for a good solution. In this paper a genetic algorithm is developed and implemented and performance of algorithm is measured under altering parameters. Adaptive parameter approach has been applied to enhance the performance of the genetic algorithm.

Keywords: Genetic Algorithm, Fitness Function, DAG, Crossover and Mutation.

[1] I. Ahmad, Y. Kwok, A new approach to scheduling parallel programs using task duplication, in: Proceeding of the 23rd International Conf. on Parallel Processing, August 1994, North Carolina State University, NC, USA, 1994.

[2] I. Ahmad, Y. Kwok, Benchmarking and comparison of the task graph scheduling algorithms, J. Parallel Distrib. Comput. 95 (1999) 381–422.

[3] Melanie Mitchell, "An Introduction to Genetic algorithms", The MIT Press, February 1998.

[4] Y. Kwok, I. Ahmad, Dynamic critical path scheduling: An effective technique for allocating task graphs to multi-processors, IEEE Trans. Parallel Distrib. Syst. 7 (1996) 506–521.

[5] D. E. Goldberg, "Genetic algorithms in search, optimization & machine learning", Addison Wesley, 1990.

[6] F.A. Omara, M. Arafa, Genetic algorithms for task scheduling problem,J. Parallel Distrib. Comput. 70 (2010) 13–22

[7] A.J. Page, M. Keane,Multi-heuristic Dynamic task allocation using genetic algorithms in a heterogeneous distributed system,J. Parallel Distrib. Comput. 70 (2010) 758-765.

[8.] Gurvinder Singh, J. Singh, Task Scheduling using Performance Effective Genetic Algorithm for Parallel Heterogeneous System, International Journal of Computer Science and Telecommunications [Volume 3, Issue 3, March 2012].

[9] M. Srinivas, L.M. Patnaik, Adaptive probabilities of crossover and mutation in genetic algorithm, IEEE Trans. Syst. Man Cybern. 24 (4) (1994) 656–667.

[10] Haluk Topcuoglu, Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing,IEEE transactions on parallel and distributed systems, vol. 13, no. 3, march 2002.

Abstract: In automated medical diagnosis, shape plays a key role in image processing and pattern matching. In particular, microscopic visual examination, as used in this paper, extensively uses shape to diagnose anemia using the shapes of Red Blood Cells (RBCs). This automated process depends entirely on the ability of the mammalian RBCs to change shape causing some types of anemia, which makes use of cell-discrimination on these varying RBC shapes an effective method of diagnosing anemia. In this automated diagnosis, image processing and pattern matching follows four steps: shape extraction, shape representation, shape size normalization and application of Fourier descriptors to obtain shapes of normal and abnormal RBCs. In testing the results, a client-server computer program applies invariant-moments to filter irrelevant shapes from the query, and aspect ratio of RBCs to improve on shape retrieval and verify results whereas increasing the geometrical number to reduces this accuracy by 10% to a promising 90%.

Keyword: Aspect Ratio, Fourier Descriptors, Invariant-Moments, Shape Extraction, Shape Representation, Image Processing, Red Blood Cells.

[1] Bacus, W., J., & Trobaugh, E., F. (1972). An Automated Classification of Peripheral Blood Cells by Means of Digital Image Processing. Sao Paulo, Brazil, 47(48).

[2] Davies, E., R. (1997). Machine Vision: Theory, Algorithms, Practicalities. Academic Press.

[3] Engstrom, G., & Lofvenberg, E. (2012). Treatment of Myeloproliferative Disorders with Hydroxyurea: Effects on Red Blood Cell Geometry and Deformability. American Society of Hematology

[4] Gonzalez, R., C., & Woods, R., E. (1992). Digital Image Processing. Addison-Wesley Idris, H., & Panchathan, T. (1997). Image and Video Indexing Techniques. Journal of Communication and Image Representation. 8(2), 146-166 International Association for Pattern Recognition. (2003). What is Pattern Recognition. IAPR Newsletter, 25(1), 1.

[5] Kauppinen, H., Seppanes, T., & Pietikainen. (1995). An Experiment Comaprison of Fourier-Based Descriptors in 2D Shape Classification. IEEE Trans, 17(2), 210-207.

[6] Lam, M., Disney, T., & Raicu, J. (2007). Content Based Image Retrieval: Medical Informatics and Imaging,65(16).

[7] Mauroy, B. (2010). Red Blood Cells in a Capillary. Labaratory MSC, University Paris 7/ CNRS – France [8] Niblack, W. et al. (1993). The QBIC Project: Querying Images by Content using Color, Texture and Shape. SPIE conference, 19(8), 173-187.

[9] Otterloo, D., J. (1991). A Contour-Based Approach to Shape Analysis. London: Prentice Hall

[10] Pavlidis, T. (1982). Algorithms for Image Processing and Pattern Matching. Computer Science Press.

Abstract: Function approximation is to find the underlying relationship from à given finite input-output data. It has numerous applications such as prediction, pattern recognition, data mining and classification etc. Multi-layered feed-forward neural networks (MLFNNs) with the use of back propagation algorithm have been extensively used for the purpose of function approximation recently. Another class of neural networks BAM has also been experimented for the same problem with lot of variations. In the present paper we have proposed the application of back propagation algorithm to MLFNN in such a way that it works like BAM and the result thus presented show greater and speedy approximation for the example function.

Keywords: Neural networks, Multilayered feed-forward neural network (MLFNN), Bidirectional Associative Memory (BAM), Function approximation

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[4] Pearlmutter B.A., Gradient calculations for dynamic recurrent neural networks: a survey. IEEE Trans. On Neural Networks, 6
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[6] Kwan H. K. and Yan J., Second-order recurrent neural network for word sequence learning, Proceedings of 2001 International
Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, May 2-4. 2001, pages 405-408.

Abstract: Windows 8, the most popular operating system by Microsoft launched in October 2012. It is developed for use of desktops, laptops, tablets, home theatre PC's. Windows 8 is more secure than previous versions. It has its built in Anti-malware protection system so no need to worry if an Anti-virus is not installed. Pentesting – It is a process to imitate all ways used by hackers to compromise a system. But with the diference its is purely ethical in deed so as to know in prior how a machine can suffer security breach attack. The main objective of this paper is to compromise a system with windows 8 OS. Generally penetration testing using metasploit framework proceed with the combo of a exploit and payload which in turn gives us a reverse connection to target system. But it is not that easy to do with windows 8 as it immediately detect and eve delete the payload file as soon it is loaded on to the windows 8 system. So we make use of syringe utility with special permissions of windows system

Keywords: Penetration Testing, Metasploit Framework , Exploit , Payload, FUD(fully undetectable).

[1] Bing Duan "An Easy-to-Deploy Penetration Testing Platform." Young Computer Scientists, 2008. ICYCS 2008. The 9th International Conference for Date : 18-21 Nov. 2008 Page(s): 2314 – 2318.
[2] Bechtsoudis, A. "Aiming at Higher Network Security through Extensive Penetration Tests". Latin America Transactions, IEEE (Revista IEEE America Latina) April 2012 Volume: 10 , Issue: 3 Page(s): 1752 - 1756

[3] Turpe, S. "Common Precautions in Penetration Testing." Academic and Industrial Conference - Practice and Research Techniques, 2009. TAIC PART '09. Date : 4-6 Sept. 2009. Page(s): 205 - 209

[4] "Metasploit: The Penetration Tester's Guide" By David Kennedy, Jim O'Gorman, Devon Kearns, Mati Aharon. ISBN-10: 1-59327-288-X.

[5] "Metasploit Toolkit for Penetration Testing, Exploit Development, and Vulnerability Research" By David Maynor, K.K.Mookhey, Jacopo Cervini, Fairuzan Roslan, Kevin Bewer. ISBN 13: 978-1-59749-074-0. By Syngress Publishing Inc.

[6] Sankalp Singh "Fast Model Based Penetration Testing". Simulation Conference, 2004. Proceedings of the 2004 Winter Date : 5-8 Dec. 2004. Volume 1 Page(s): 2314 – 2318.

[7] Bishop M. "About Penetration Testing". Security and privacy IEEE Nov-Dec 2007. Volume 5 Page(s): 84 – 87.

[8] Greenwald, L, Shanley, R.. "Automated Planning for Remote Penetration Testing". Military Communications Conference, 2009. MILCOM 2009. IEEE Date : 18-21 Oct. 2009. Volume 5 Page(s): 1 – 7.

[9]. Robinson, S. "Art of Penetration Testing" Security of Distributed Control Systems, 2005. The IEEE Seminar on Date : 2 Nov. 2005.

Abstract: The computer aided diagnosis (CAD) problems of detecting potentially diseased structures from medical images are typically distinguished by the following challenging characteristics: extremely unbalanced data between negative and positive classes; stringent real-time requirement of on- line execution; multiple positive candidates generated for the same malignant structure that are highly correlated and spatially close to each other. To address all these problems, we propose a novel learning formulation to combine cascade classification and multiple instance learning (MIL) in a unified min-max framework, leading to a joint optimization problem which can be converted to a tractable quadratic ally constrained quadratic program and efficiently solved by block-coordinate optimization algorithms. We apply the proposed approach to the CAD problems of detecting pulmonary embolism and colon cancer from computed tomography images. Experimental results show that our approach significantly reduces the computational cost while yielding comparable detection accuracy to the current state-of-the-art MIL or cascaded classifiers. Although not specifically designed for balanced MIL problems, the pro- posed method achieves superior performance on balanced MIL benchmark data such as MUSK and image data sets.

[1] S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-instance learning Advances in neural information processing systems, 15, 2002.

[2] J. Bi and J. Liang. Multiple instance learning of pulmonary embolism detection with geodesic distance along vascular structure. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2007.

[3] L. Bourdev and J. Brandt. Robust object detection via soft cascade. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2:236–243, 2005.

[4] T. G. Dietterich, R. H. Lathrop, and T. Lozano-Perez. Solv- ing the multiple instance problem with axis-parallel rectan- gles. Artificial Intelligence Journal, 89:31C71, 1997.

[5] M. Dundar and J. Bi. Joint optimization of cascaded clas- sifiers for computer aided detection. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2007, 2007.

[6] G. Fung, M. Dundar, B. Krishnapuram, and R. B. Rao. Mul- tiple instance learning for computer aided diagnosis. Ad- vances in neural information processing systems, 19:425– 432, 2007.

[7] D. Jemal, R. Tiwari, T. Murray, A. Ghafoor, A. Saumuels, E. Ward, E. Feuer, and M. Thun. Cancer statistics, 2004.

[8] J. Liang and J. Bi. Local characteristic features for com- puter aided detection of pulmonary embolism in ct angiogra- phy. Proceedings of Pulmonary Image Analysis at Annual Conference on Medical Image Computing and Computer- Assisted Intervention, 2008.

[9] O. Maron and T. Lozano-Perez. A framework for multiple- instance learning. Advances in neural information process- ing systems, 10, 1998.

[10] V. C. Raykar, B. Krishnapuram, J. Bi, M. Dundar, and B. Rao. Bayesian multiple instance learning: automatic fea- ture selection and inductive transfer. Proceedings of the 25th International Conference on Machine Learning, pages 808–815, 2008.

Abstract: The green computing is the technology which is based on the environmental use of computer related resources. The computer related resources includes processing units, storage units etc. In such type of technology the energy consumption is the main concern. Green computing wills leads to reduce in resource consumption and electronic waste. The new technology of green computing will also include cloud computing services, grid computing services. In the past decades, many techniques have been proposed for the energy conservation in green computing. In this paper, we are proposing new technique for energy conservation in green computing. This novel technique is based on neural networks. The neural network is having capability of learning from the past experiences. The dynamic clustering approach is used with the neural networks for the energy conservation. The proposed technique is implemented in NS2 and simulation results are shown in the graphical form.

Keywords: Green computing, neural networks, dynamic clustering, learning

[1] R.Yamini ―Power Management in Cloud Computing Using Green Algorithm‖ IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012) March 30, 31, 2012.

[2] Rana ―GREEN COMPUTING SAVES GREEN‖ International Journal of Advanced Computer and Mathematical Sciences. Vol 1, Issue 1, Dec, 2010, pp 45-51.

[3] S.V.S.S. Lakshmi ― A Study On Green Computing: The Future Computing And Eco-Friendly Technology‖ International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue4, July-August 2012, pp.1282-1285.

[4] Mujtaba Talebi and Thomas Way, ―Methods, Metrics and Motivation for a Green Computer Science Program‖ SIGCSE'09, March 3–7, 2009, Chattanooga, Tennessee, USA‖ Copyright 2009 CM 978-1-60558-183-5/09/03...$5.00

[5] Dr. Pardeep Mittal el al,‖ Study and Analyze the Factors Related to Green Computing‖, International Journal of Advanced Research in Computer Science and Software Engineering 3(4),April - 2013, pp. 366-369 [6] Karsen Schmid,‖ Green Compting A case for data caching and Flash Disks‖, University of Kaiserslautern, Gottlieb-Daimler-Str., 67663 Kaiserslautern, Germany

[7] Limin Meng, Kai Zhou, Jingyu Hua, Zhijiang Xu, ―A Dynamic Clustering-Based Algorithm for Wireless Sensor Networks‖, International Symposium on Computer Science and Computational Technology,2008

[8] Eric Lee Eekhoff , ―Wireless sensor networks and personal area networks for data integration in a virtual reality environment‖

Abstract: Evolutionary changes have occurred throughout the various elements of the data centre, starting with server and storage virtualization and also network virtualization. Motivations for server virtualization were initially associated with massive cost reduction and redundancy but have now evolved to focus on greater scalability and agility within the data centre. Data center focused LAN technologies have taken a similar path; with a goal of redundancy and then to create a more scalable fabric within and between data centres. The TCP's congestion control opens the network to denial of service attacks and performance interference.. We propose a natural evolution of data center transport from TCP to multipath TCP. We show that multipath TCP can effectively and seamlessly use available bandwidth, providing improved throughput and better fairness in these new topologies when compared to single path TCP and randomized flow-level load balancing.

[1] M. Al-Fares, A. Loukissas, and A. Vahdat. A scalable, commodity data center network architecture. In Proc. SIGCOMM 2010.

[2] M. Al-Fares, S. Radhakrishnan, B. Raghavan, N. Huang, and A. Vahdat. Hedera: Dynamic flow scheduling for data center networks. In Proc. Usenix NSDI 2010.

[3] C. Clos. A study of non-blocking switching networks. Bell System Technical Journal, 32(5):406–424, 1952. [4] A. Ford, C. Raiciu, M. Handley, and S. Barre. TCP Extensions for Multipath Operation with Multiple Addresses. Internet-draft, IETF, 2009.

[5] A. Greenberg el al. VL2: a scalable and flexible data center network. In Proc. ACM Sigcomm 2009.

[6] C. Guo, G. Lu, D. Li, H. Wu, X. Zhang, Y. Shi, C. Tian, Y. Zhang, and S. Lu. Bcube: a high performance, server-centric network architecture for modular data centers. In Proc. SIGCOMM 2009.

[7] M. Mitzenmacher. The power of two choices in randomized load balancing. IEEE Trans. Parallel Distrib. Syst., 12(10):1094–1104, 2001.

[8] J. Mudigonda, P. Yalagandula, M. Al-Fares, and J. C. Mogul. Spain: Cots data-center ethernet for multipathing over arbitrary topologies. In Proc. NSDI 2010.
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[10] V. Vasudevan et al. Safe and effective fine-grained tcp retransmissions for datacenter communication. In Proc. SIGCOMM 2009.

Abstract: Generalization and Bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi- identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than gen- eralization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an ef- ficient algorithm for computing the sliced data that obey the ℓ-diversity requirement.Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.

[1] C. Aggarwal. On k-anonymity and the curse of dimensionality. In VLDB, pages 901–909, 2005.

[2] A. Asuncion and D. Newman. UCI machine learning repository, 2007.

[3] A. Blum, C. Dwork, F. McSherry, and K. Nissim. Practical privacy: the sulq framework. In PODS, pages 128–138, 2005.

[4] J. Brickell and V. Shmatikov. The cost of privacy: destruction of data-mining utility in anonymized data publishing. In KDD, pages 70–78, 2008.

[5] B.-C. Chen, R. Ramakrishnan, and K. LeFevre.Privacy skyline: Privacy with multidimensional adversarial knowledge. In VLDB, pages 770–781, 2007.

[6] H. Cramt'er. Mathematical Methods of Statistics.Princeton, 1948.

Abstract: Wireless sensor networks are appealing to researchers due to their wide range of application in several fields such as military settings, critical infrastructure protection, target detection and tracking, environmental monitoring, industrial process monitoring etc. Communication among wireless sensor nodes is usually achieved by means of a unique channel. It is the characteristic of this channel that only a single node can transmit a message at any given time. Therefore, shared access of the channel requires the establishment of a MAC protocol among the sensor nodes. The objective of the MAC protocol is to regulate access to the shared wireless medium. For sensor nodes that are battery powered, it is sometimes difficulty or impractical to charge or replace exhausted battery. So the medium access protocol (MAC) for wireless sensor network must be energy efficient. In this paper, we outline the sensor network properties that are crucial for the design of MAC layer protocols. Then, describe four MAC protocols proposed for sensor networks emphasizing their strengths and weaknesses and compared those protocols. Analysis of power consumption and latency performance for these four MAC protocol is also shown in this report.

Keywords: Latency, Medium access control, power consumption.

[1] I. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci: A Survey on Sensor Networks, IEEE Communication Magazine, pp. 102-114, August 2002

. [2] Wei Ye, J.Heidemann and D. Estrin: An Energy-Efficient MAC Protocol for Wireless Sensor Networks, IEEE INFOCOM, New York, Vol. 2, pp. 1567-1576 ,June 2002

[3] Tijs van Dam, Koen Langendoen: An Adaptive Energy Efficient MAC Protocol for Wireless Networks, in Proceedings of the First ACM Conference on Embedded Networked Sensor Systems November 2003.

[4] J. Polastre, J. Hill, D. Culler: Versatile low Power Media Access for Wireless Sensor Networks, Proceedings of the 2nd ACM Conference on Embedded Networked Sensor Systems (SenSys'04), Baltimore, MD, November 2004.

[5] C.C. Enz, A. El-Hoiydi, J.-D. Decotignie, V. Peiris: WiseNET: An Ultralow-Power Wireless Sensor Network Solution, IEEE Computer, Vol. 37, Issue 8 , August 2004.

[6] A. El-Hoiydi: Spatial TDMA and CSMA with Preamble Sampling for Low Power Ad-hoc Wireless Sensor Network, Proceedings of ISCC'02, Seventh International Symposium on Computers and Communications, pp. 685-692 ,July 2002.

[7] A. El-Hoiydi and J. D. Decotignie, WiseMAC: An ultra low power MAC protocol for multihop wireless sensor networks, in First Int. Workshop on Algorithmic Aspects of Wireless Sensor Networks (ALGOSENSORS 2004), Lecture Notes in Computer Science, LNCS 3121, Springer-Verlag, pp. 18–31, July 2004.

[8] S. Coleri-Ergen and P. Varaiya, Pedamacs: Power efficient and delay aware medium access protocol for sensor networks, IEEE Trans. on Mobile Computing, pp. 920–930, May 2006.

[9] S.S. Kulkarni, "TDMA Services for Sensor Networks", proceedings of 24th International Conference on Distributed Computing Systems Workshops, Pages: 604-609, 23-24 March 2004

[10] M. Buettner, G. Yee, E. Anderson, and R. Han, X-MAC: A short preamble MAC protocol for duty-cycled wireless networks, in 4th ACM Conf. on Embedded Networked Sensor Systems (SenSys 2006), Boulder, CO, pp. 307–320,Nov 2006.

Abstract: Cryptography is the science of secret codes, enabling the confidentiality of communication through an insecure channel to make the system more complex and robust Chaos is applied in the various cryptographic algorithms. In this paper we use most commonly used algorithm AES, RC5, IDEA, RSA, ELGamal. In this paper firstly we implement all the algorithm in MATLAB then Chaos is applied on it. After applying Chaos in these algorithms we observe that both Security and Speed increases as compare to the conventional cryptographic algorithm.

Keywords: Chaos, Cryptography, AES, RSA, IDEA, RC5, ELGamal.

[1] B. Schneier, Applied Cryptography Practical Algorithms and SourceCodes in C, John Wiley, New York, 1996.

[2] Atul Kahate, "Cryptography and Network Security"2nd Edition , Tata McGraw Hill,2003

[3] El-Sayed Abdoul-Moaty ElBadawy et al. "A New Chaos Advanced Encryption Standard(AES) Algorithm for Data Security", IJSES 2010, Poland

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