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Intrusion recognition is the act of discovering undesirable visitors on a system or a system. An IDS can be a piece of set up software or a physical equipment that watches system visitors in order to identify undesirable action and activities such as unlawful and harmful visitors, visitors that goes against security plan, and visitors that goes against appropriate use policies. Intruder detection system can be implemented using various data mining approaches. This paper summarizes intrusion motives and some of the methods used and implemented for intrusion detection system. This paper also reviewed about processing environment and type of data required for evaluation of Intruder detection system.

Intruder Detection System; Data Mining; Kddcup99

[1] Asmaa shaker,Ashroor 2011 International conference on Future Information Technology IPCSIT vol.13 (2011) � (2011) IACSIT Press, Singapore.
[2] Singh, S. and S. Kandula, �Argus - a distributed network-intrusion detection system,� Undergraduate Thesis, Indian Institute of Technology, May 2001.
[3] Jiawei Han and Micheline Kamber Data Mining Concepts and Techniques Second Edition Morgan Kauffman Publishers ,2006
[4] Shaik Akbar, Dr.K. Nageswara Rao, Dr.J.A. Chandulal IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.8, August 2011pp 138-144
[5] Mrutyunjaya Panda, Manas Ranjan Patra IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.12, December 2007 pp 258- 263
[6] P.Jenson, "Bayesian networks and decision graphs�, Springer, New-york, USA, 2001.
[7] Srinivas Mukkamala, Guadalupe Janoski, Andrew Sung 0-7803-7278-6/02 �2002 IEEE
[8] Nani Yasmin1, Anto Satriyo Nugroho2, Harya Widiputra3,� Optimized Sampling with Clustering Approach for Large Intrusion Detection Data�, International Conference on Rural Information and Communication Technology 2009 Pp.56-60
[9] Yu Guan and Ali A. Ghorbani, Nabil Belacel,�Y-Mean: A Clustering method For Intrusion Detection�, 1CCECE 2003, pp.1-4
[10] Fangfei Weng, Qingshan Jiang, Liang Shi, and Nannan Wu,�An Intrusion Detection System Based on the Clustering Ensemble�, IEEE International workshop on 16-18 April 2007,pp.121 � 124
[11] Kusum kumara Bharati,Sanyam Shukla, Swetha Jain Special Issue of IJCCT Vol.1 Issue 2, 3, 4; 2010 for International Conference [ACCTA-2010], 3-5 August 2010
[12] Wenkee Lee, Salvatore J. Stolfo, Kui W. Mok c 2000 Kluwer Academic Publishers. Printed in Netherlands.
[13] Rahimeh Rouhi , Farshid Keynia, Mehran Amiri Journal of Computer Sciences and Applications, 2013, Vol. 1, No. 3, 33-38
[14] Shengi YiJiang,Xiaoyu Song, Hui Wang, Jian-Jun Han,Qing-Hua Li Science direct � 2005 Elsevier pp 802-810

B. Kiranmai, A. Damodaram, "A Comprehensive Survey on Methods Implemented For Intruder Detection System," International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.70-73, 2014.

Orthogonal Frequency Division Multiplexing (OFDM) has proved it is ability to provide the required services and offer large data rates with sufficient strength to Electromagnetic wave channel destruction The major drawback concern in the OFDM data transmission is loss of orthogonality of signal due to Carrier Frequency Offset (CFO), caused either due to mismatching of frequency of oscillator at transmitter side and receiver side or due to Doppler shift. CFO leads to create problems like Inter carrier interference (ICI) and Signal to Noise plus Interference Ratio decrease (SINR). As a result, the overall capacity of the system is reduced. The estimation of CFO is difficult problem. Researchers have proposed various CFO estimation techniques to compensate the effect of CFO by now. This paper provides major CFO estimation algorithm and techniques in literature briefly along with the analysis of their advantages and disadvantages.

OFDM, orthogonality, Maximum likelihood (ML), Cyclic Prefix (CP), Inter Carrier Interference (ICI), Signal to Noise plus Interference Ratio (SINR)

[1] A. Goldsmith, Wireless Communication. Cambridge University Press, 2005.
[2] J.J van de Beek and M.Sandell,�ML Estimation of Time and Frequency Offset in OFDM Systems�,IEEE Trans. On Signal pocessing, Vol. 45, No. 7, July. 1997.
[3] M. Morelli and U. Mengali, �An improved Frequency offset Estimator for OFDM Applications�, IEEE Commun. Letters, Vol. 3, No. 3, March 1999.
[4] Mingqui Li and W. Zhang,� A Novel Method of Carrier Frequency Offset Estimation for OFDM Systems�, IEEE Trans. On Consumer Electronics, Vol. 49, No. 4, Nov. 2003.
[5] P. H. Moose, �A technique for orthogonal frequency division multiplexing frequency offset correction,� IEEE Trans. Commun., vol. 42, pp. 2908-2914, Oct. 1994.
[6] Richard van Nee and Ramjee Prasad,�OFDM for Wireless Multimedia Communications�.
[7] T. Schmidl and D. Cox, �Robust frequency and timing synchronization for OFDM�, IEEE Trans. Commun., vol. 45, no. 12, pp. 1613-1621, Dec. 1997
[8] U. Tureli, H. Liu, and M.D. Zoltowski , �OFDM blind carrier offset estimation: ESPRIT,�IEEE tans. On Signal Processing, vol. 48,pp. 1459-1461, Sept.2000
[9] Xiaoli Ma, Mi-Kyung Oh, G.B. Giannakis, �Hopping Pilots for Estimation of Frequency Offset and Mulitantenna Channels in MIMO-OFDM �, IEEE Trans. On Communication, Vol. 53, No. 1, Jan. 2005.
[10] Y. Yao and G.B. Giannakis, �Blind Carrier Frequency Offset Estimation in SISO, MIMO, and Multiuser OFDM Systems �, IEEE Trans. On Communication, Vol. 53, No. 1, Jan. 2005.

S. Singh, A.S. Buttar, "Carrier Frequency Offset Estimation Techniques in OFDM System: A Survey," International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.74-77, 2014.

Routing protocol design is an important research area in wireless sensor networks, reliability, low-cost and easy to maintain are design goals of WSN routing protocol, hop based routing protocol has been receiving extensive attention for its simple and effective design ideas. HBRRP (Hop Based Robust Routing Protocol for WSN) is proposed. In data transmission phase, HBRRP makes parents and siblings as forward selection; relying on a formula for evaluating the routing quality, routing mechanism has a comprehensive consideration of the forward selection trigger update mechanism is used to maintain dynamic network topology. Exploiting the intuition that a less dynamic route lasts longer, we propose a new metric, the Route Fragility Coefficient (RFC), to compare routes. RFC estimates the rate at which a given route expands or contracts. Expansion refers to adjacent nodes moving apart, while contraction refers to their moving closer. RFC combines the individual link contraction or expansion behavior to present a unified picture of the route dynamics.

Hop Based Robust Routing Protocol, Route Fragility Coefficient, And Wireless Sensor Network

[1] R. Aquino-Santos, L.A. Villasenor-Gonzalez, V. Rangel Licea, O. Alvarez Cardenas, and A. Edwards Block.�Performance analysis of routing strategies for wireless sensor networks�

[2] K.H. Han, Y.B. Ko, and J.H. Kim. �A Novel Gradient approach for efficient data dissemination in wireless sensor networks�, IEEE 2004 International Conference on Vehicular Technology Conference (VTC), pp. 2979-2983, (2004).
[3] C. Intanagonwiwat, R. Govindan, and D. Estrin.�Directed diffusion:A scalable and robust communication paradigm for sensor networks�,Proceedings of the 6th annual international conference on Mobile computing and networking, pp. 56-67,(2000).
[4] W.F.Duan, J.D.Qi, Y.D.Zhao, and Q.H.Xu. �A Research on Minimum Hop Count Routing Protocol in Wireless Sensor Network�, Computer Engineering and Applications, in press.
[5] A. Ahmed and N. Fisal. �A real-time routing protocol with load distribution in wireless sensor networks�, Computer Communications, vol. 31, pp. 3190-3203,(2008).
[6] Shao-Shan Chiang, Chih-Hung Huang, and Kuang-Chiung Chang. �A Minimum Hop Routing Protocol for Home Security Systems Using Wireless Sensor Networks�, Consumer Electronics, IEEE Transactions on, vol. 53, pp. 1483-1489, (2007).
[7] O. Powell, A. Jarry, P. Leone, and J. Rolim. �Gradient based routing in wireless sensor networks: a mixed strategy�, Arxiv preprint cs/0511083, (2005).
[8] M.C. Zheng, D.F. Zhang, and J. Luo. �Minimum Hop Routing Wireless Sensor Networks Based on Ensuring of Data Link Reliability�, 2009 Fifth International Conference on Mobile Ad-hoc and Sensor Networks, pp. 212-217, (2009).

D. Suresh, K. Selvakumar, "A Hop Based Robust Routing Protocol in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.78-81, 2014.

In today�s communications, data transmission play a vital role, but sending data in secure is crucial and important and it should free from unauthorized access. There are various possible techniques like Cryptography, Steganography, and Quantum Cryptography are available for secure transmission of data .This paper discusses some of the mechanisms for secure data transmission securely.

Cryptograph; Steganography; Quantum cryptography

[1]. Prof. Pooja Shah , Dr. (Prof.) Subhash Desai, Prof. Amita Shah EEE: Efficiency Evaluation of Encryption Algorithms in Data Security.
[2]. MohitKumar, Abhishek Gupta, Kinjal Shah, Atul Saurabh, Pravesh Saxena, Vikas Kumar Tiwari Jaypee� Data Security Using Stegnography and Quantum Cryptography�.Network and Complex Systems, ISSN 2224-610X (Paper) ISSN 2225-0603 (Online) Vol 2, No.2, 2012
[3].GillesVanAssche quantum cryptography and secure key distillation , Cambridge university press.
[4].http://voices.yahoo.com/comparing-symmetricasymmetric-key-encryption-6329400.html
[5].http://www.omnisecu.com/security/public-key-infrastructure/asymmetric-encryption-algorithms.php
[6].Network Security and cryptography Principles and practices by William Stallings 2nd Edition.

B. Jyoshna, "Mechanisms for Secure Data Transmission: A Survey," International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.82-83, 2014.

Cloud computing security challenges and it�s additionally an issue to numerous analysts; first necessity was to concentrate on security which is the greatest concern of associations that are considering a move to the cloud. The points of interest of Cloud computing incorporate decreased expenses, simple upkeep and re-provisioning of assets, and consequently expanded benefits. Yet the appropriation and the section to the Cloud computing applies just if the security is guaranteed. Instructions to surety a finer information security furthermore in what manner would we be able to keep the customer private data secret? There are two real inquiries that present a test to Cloud computing suppliers. At the point when the information exchanged to the Cloud we utilize standard encryption routines to secure the operations and the stockpiling of the information. Anyhow to process information placed on a remote server, the Cloud suppliers need to get to the basic information. In this paper we are proposing Homomorphic encryption algorithm to execute operations on encoded information without decoding them which will give us the same comes about after computations as though we have worked straightforwardly on the basic information.

Homomorphic Encryption, Cloud Computing, Cryptography, Information Security

[1] Vic (J.R.) Winkler, �Securing the Cloud, Cloud Computer Security, Techniques and Tactics�, Elsevier, 2011.
[2] Pascal Paillier. Public-key cryptosystems based on composite degree residuosity classes. In 18th Annual Eurocrypt Conference (EUROCRYPT`99), Prague, Czech Republic, volume 1592, 1999
[3] Julien Bringe and al. An Application of the Goldwasser-Micali Cryptosystem to Biometric Authentication, Springer-Verlag, 2007.
[4] R. Rivest, A. Shamir, and L. Adleman. A method for obtaining digital signatures and public key cryptosystems. Communications of the ACM, 21(2) :120-126, 1978. Computer Science, pages 223-238. Springer, 1999.
[5] Taher ElGamal. A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Transactions on Information Theory, 469-472, 1985.
[6] Craig Gentry, A Fully Homomorphic Encryption Scheme, 2009.

K.Revana Suresh, Ch. Shasikala, S.P. Kumar, "Augmentation of Information Security by Cryptography in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.84-87, 2014.

With a rise in usage of mobile devices it`s continuously expected that a mobile device perform the execution of all applications the approach a desktop device do. Mobile devices became associate integral a part of somebody`s life. However, with restricted process power, memory & battery time period of mobile phones it becomes tough to execute computationally intensive applications like image process. Computation offloading provides a way to save lots of energy within which a number of the applying computer code parts are often offloaded from mobile device to run on a distant server. Offloading the computation from mobile devices into cloud can end in extended battery time period, improved knowledge storage capability and process power. Offloading the computation from mobile devices (low in resources) into cloud (resourceful machine) solves the matter. This analysis presents service primarily {based} offloading mechanism that permits execution of multiple computations and repair based access mechanism for higher purpose. Here to perform multiple computations, multiple services square measure run each at consumer aspect (mobile devices) and server aspect (Cloud). The cloud modules contains of Image clump, Image retrieval and image search service whereas robot module encompass Image storage and management, Login and registration ,Image search and image transfer service. The execution of multiple services can guarantee quicker execution and increase in battery time period.

Mobile applications, Energy saving, Cloud computing, computation offloading, Mobile Cloud Computing (MCC), Service based offloading mechanism

[1] Dejan Kovachev, Tian Yu and Ralf Klamma, �Adaptive Computation Offloading from Mobile Devices into the Cloud �, IEEE 2012.
[2] Karthik Kumar and Y.-H. Lu, �Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?� Computer, vol. 43, no. 4, pp. 51�56, April 2010.
[3] C. Hewitt, �Orgs for scalable, robust, privacy-friendly client cloud computing,� Internet Computing, IEEE, vol. 12, no. 5,pp. 96�99, 2008.
[4] R. Buyya, C. Yeo, and S. Venugopal, �Market-oriented services as computing utilities,� in High Performance Computing and Communications, 2008. HPCC�08. 10th IEEE International Conference on. IEEE, 2008, pp. 5�13.
[5] L. Youseff, M. Butrico, and D. Da Silva, �Toward a unified ontology of cloud computing,� in Grid Computing Environments Workshop, 2008. GCE�08. IEEE, 2008, pp. 1�10.
[6] P. Mell and T. Grance, "The NIST Definition of CloudComputing,"2009.[Online]. Available:http://csrc.nist.gov/groups/SNS/ cloud-computing/cloud-defv15.doc
[7] S. Kosta, C. Perta, J. Stefa, P. Hui, and A. Mei, �Clone2clone (c2c): Enable peer-to-peer networking of smartphones on the cloud,� T-Labs, Deutsche Telekom, Tech. Rep. TR-SK032012AM, 2012.
[8] S. Kosta, A. Aucinas, P. Hui, R. Mortier, and X. Zhang, �Thinkair: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading.� in Proc. of IEEE INFOCOM 2012, 2012.
[9] E. Cuervo, A. Balasubramanian, D. Cho, A. Wolman, S. Saroiu, R. Chandra, and P. Bahl, �Maui: making smartphones last longer with code offload,� in Proc. of MobiSys �10, 2010.
[10] Joe and Y. Lee, �Design of remote control system for data protection and backup in mobile devices,� in Proc. of ICIS 2011,2011.
[11] V. Ottaviani, A. Lentini, A. Grillo, S. D. Cesare, and G. Italiano, �Shared backup & restore: Save, recover and share personal information into closed groups of smartphones,� in Proc. of IFIP NTMS 2011, 2011.
[12] C. Ai, J. Liu, C. Fan, X. Zhang, and J. Zou, �Enhancing personal information security on android with a new synchronization scheme,� in Proc. of WiCOM 2011, 2011.

K. Venkateswarlu, M.S. Lakshmi, S.P. Kumar, "Energy Saving For Mobile Users Using Cloud Computing Via S3," International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.88-92, 2014.

Cloud computing is an emerging technology of business computing and it is becoming a development trend. The process of entering into the cloud is generally in the form of queue, so that each user needs to wait until the current user is being served. Cloud Computing User requests Cloud Computing Service Provider to use the resources, if Cloud Computing User finds that the server is busy then the user has to wait till the current user complete the job which leads to more queue length and increased of waiting time. So to solve this problem it is the work of Cloud Computing Service Providers to provide service to users with less waiting time otherwise there is a chance that the user might be leaving from queue. Cloud Computing Service Providers takes such factors into considerations as the amount of service, the workload of an application environment, the configuration of a multi-server system, the service-level agreement, the satisfaction of a consumer, the quality of a service, the quality of a service, the penalty of a low-quality service, the cost of renting and a service providers margin and profit. Cloud Computing Service Providers can use multiple servers for reducing queue length and waiting time. This project shows how the multiple servers can reduce the mean queue length and waiting time. The project approach is to treat a multi-server system as an M/M/m queuing model.

Cloud Computing;Multi-server System; Queuing Model; Waiting time; Service-level agreement

[1]http://en.wikipedia.org/wiki/Service_level_agreement, 2012.
[2] M. Armbrust et al., �Above the Clouds: A Berkeley View of Cloud Computing,� Technical Report No. UCB/EECS-2009-28, Feb. 2009.
[3] R. Buyya, C.S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, �Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the Fifth Utility,� Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2009.
[4] P. Mell and T. Grance, �The NIST Definition of Cloud Computing,� Nat�l Inst. of Standards and Technology, http://csrc.nist. gov/groups/SNS/cloud-computing/, 2009.
[5] D. Durkee, �Why Cloud Computing Will Never be Free,� Comm. ACM, vol. 53, no. 5, pp. 62-69, 2010.
[6] L. Kleinrock, Queueing Systems:Theory, vol. 1. John Wiley and Sons, 1975.
[7] Junwei Cao, Kai Hwang, Keqin Li, and Zomaya A.Y, �Optimal Multiserver Configuration for Profit Maximization in Cloud Computing,� IEEE Trans. Parallel and Distributed Systems, vol. 24, no. 6, pp. 1087-1096, Jun. 2013, doi: 10.1109/TPDS.2012.203.
[8] K. Li, �Optimal Load Distribution for Multiple Heterogeneous Blade Servers in a Cloud Computing Environment,� Proc. 25th IEEE Int�l Parallel and Distributed Processing Symp. Workshops, pp. 943- 952, May 2011.
[9] K. Li, �Optimal Configuration of a Multicore Server Processor for Managing the Power and Performance Tradeoff,� J. Supercomputing, vol. 61, no. 1, pp. 189-214, 2012.
[10] F.I. Popovici and J. Wilkes, �Profitable Services in an Uncertain World,� Proc. ACM/IEEE Conf. Supercomputing, 2005.

M.G. Madhusudhan, K.D. Babu, "Optimization of Multi-server Configuration for Profit Maximization using M/M/m Queuing Model," International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.93-98, 2014.

Distributed Environment (DE) aims at achieving higher execution speed than the one obtainable with uniprocessor system by exploiting the collaboration of multiple computing nodes interconnected in some fashion. The idea has been to partition a uniprocessor computing load into multiple units of execution and assigning them to the various processing nodes. The best possible speed up will obviously be obtained if the various partitions of the given computational task can run independently in parallel. The processing nodes of the system must be sharing the computational load of the system so as to be able to provide proper execution characteristics. All the processing nodes must be made busy as much as possible by receiving and executing multiple tasks. In such scenario the number of tasks are lesser than available number of processors in DE, the tasks will be assign to the processor without any concern, but incase the numbers of tasks are greater than the numbers of processors then the task allocation problem will introduce in to real life scenario. Task allocation problem for processing of �m� tasks to �n� processors (m>n) in a DE is presented here through a new modified tasks allocation scheme to allocate the task in DE. The allocation scheme, proposed in this paper allocates the tasks to the processor to increase the performance of the DE on the consideration of reliability of the task to the processors.

Allocation Scheme, Distributed Environment, Performance, Processing Reliability, Task allocation

[1] Andrey G. Bronevich, Wolfgang Meyer, �Load balancing algorithms based on gradient methods and their analysis through algebraic graph theory�. Journal of Parallel and Distributed Computing, Volume 68, Issue 2, February 2008, Pages 209-220
[2] Anurag Raii, Vikram Kapoor, �Reliable Clustering Model for Enhancing Processors Throughput in Distributed Computing System�, International Journal of Computer Applications, Volume 38, Issue 8, Pages 47-50, 2012.
[3] Bo Yang, Huajun Hu, Suchang Guo, �Cost-oriented task allocation and hardware redundancy policies in heterogeneous distributed computing systems considering software reliability�. Computers & Industrial Engineering, Volume 56, Issue 4, Pages 1687-1696
[4] Bruce Hendrickson, Karen Devine, �Dynamic load balancing in computational mechanics�. Computer Methods in Applied Mechanics and Engineering, Volume 184, Issues 2-4, Pages 485-500, 2010
[5] D. Coit, A. Smith, �Reliability optimization of series-parallel systems using genetic algorithm�, IEEE Tran on. Reliability, Volume 45, Issue 2, Pages. 254-266, 1996.
[6] Dorta, C. Le�n, C. Rodr�guez, �Performance analysis of Branch-and-Bound skeletons�.Mathematical and Computer Modelling, Volume 51, Issues 3-4, Pages 300-308, 2010
[7] Faizul Navi Khan, Kapil Govil, "A Static approach to optimize time cost and reiliability in Distributed Processing Environment". International Journal of Scientific & Engineering Research, Volume 05, Issue 5, Pages 1016-1021, 2014
[8] Faizul Navi Khan, Kapil Govil, "Cost Optimization Technique of Task Allocation in Heterogeneous Distributed Computing System". Int. J. Advanced Networking and Applications, Volume 05, Issue 3, Pages 1687-1696, 2013
[9] Faizul Navi Khan, Kapil Govil, "Static Approach for Efficient Task Allocation in Distributed Environment". International Journal of Computer Applications, Volume 81, Issue 15, Pages 19-22, 2013
[10] Gamal Attiya, Yskandar Hamam, �Task allocation for maximizing reliability of distributed systems: A simulated annealing approach�. Journal of Parallel and Distributed Computing, Volume 66, Issue 10, Pages 1259-1266, 2006
[11] Hsieh, Chung-Chi, Hsieh, Yi-Che, �Reliability and cost optimization in distributed computing systems�, journal of Computers & Operations Research, Volume 30, Issue 8, Pages 1103-1119, 2003.
[12] I. Kuban Altinel, Necati Aras, Evren G�ney, Cem Ersoy. �Binary integer programming formulation and heuristics for differentiated coverage in heterogeneous sensor networks�. Computer Networks, Volume 52, Issue 12, Pages 2419-2431, 2008
[13] Jeffery L. Kennington, Eli V. Olinick, Gheorghe Spiride. �Basic mathematical programming models for capacity allocation in mesh-based survivable networks�. Omega, Volume 35, Issue 6, Pages 629-644, 2007
[14] Kapil Govil, �Processing Reliability based a Clever Task Allocation Algorithm to Enhance the Performance of Distributed Computing Environment�, Int. J. Advanced Networking and Applications, Volume 03, Issue 01, Pages 1025-1030, 2011
[15] Manoj B.S, Sekhar Archana, Siva Ram Murthy C, �A state-space search approach for optimizing reliability and cost of execution in distributed sensor networks� , Journal of Parallel and Distributed Computing, Volume 69, Issue 1, Pages 12-19, 2009
[16] Maria Jo�o Alves, Jo�o Cl�maco, �A review of interactive methods for multiobjective integer and mixed-integer programming�. European Journal of Operational Research, Volume 180, Issue 1, Pages 99-115, 2007
[17] Nirmeen A. Bahnasawy, Fatma Omara, Magdy A. Koutb, Mervat Mosa , �A new algorithm for static task scheduling for heterogeneous distributed computing system�, International Journal of Information and Communication Technology Research , Volume 1, Issue 1, Pages 10-19, 2011.
[18] P. K. Yadav, M. P. Singh, Kuldeep Sharma, �An Optimal Task Allocation Model for System Cost Analysis in Heterogeneous Distributed Computing Systems: A Heuristic Approach�, International Journal of Computer Applications, Volume 28, Issue 4, Pages 30-37, 2011
[19] Pankaj Saxena, Kapil Govil. �An Optimized Algorithm for Enhancement of Performance of Distributed Computing System�, International Journal of Computer Applications, Volume. 64, No. 2, Pages 37-42, 2013
[20] Pankaj Saxena, Dr. Kapil Govil, Neha Agrawal, Saurabh Kumar, and Deep Narayan Mishra. "An approach for allocating tasks in optimized time in a distributed processing environment", International Journal of Innovative Research and Development, Volume 1, Issue 5, Pages. 431-437, 2012
[21] Pradeep Kumar Yadav, M.P. Singh, Kuldeep Sharma, �Task Allocation Model for Reliability and Cost optimization in Distributed Computing System�, International Journal of modeling, simulation and scientific computations, Volume 2, Issue 2, Pages. 1-19, 2011.
[22] Zubair khan, ravinder singh, Jahangir alam, �task allocation using fuzzy inference in parallel and distributed system�, Journal of Information and Operations Management, Volume 3, Issue 2, Pages-322-326, 2012

F.N. Khan, K. Govil, "Reliability Based Task Allocation Scheme to Enhance the Performance of Distributed Environment," International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.99-102, 2014.

This paper proposes the use of FBG sensor to measure the lateral and vertical forces at the flange contact point. The ratio of lateral and vertical force at the rail-wheel contact point is called Nadal limit for a certain critical value which decides the derailment. Simulation has been done for the measurement of lateral and vertical forces with the increasing velocity and taking the range of radius of curvature from 600m to 1000m.

Fiber Bragg Grating, Nadal�s limit, flange climb derailment, curve negotiation

[1] K. Y. Lee, K. K. Lee, S. L. Ho, �Exploration of Using FBG Sensor for Derailment Detector,�. WSEAS Issue 6, Vol. 3, pp 2433-2447, 2004
[2]C. Hung, Y. Shuda, M. Aki, T. Tsuji, M. Morikawa, T. Yamashita, T Kawanabe, T. Kuimi, �Study on early signs of derailment for railway vehicles�, vol.48, supplement, pp.451-466, 2010
[3] Takai, H.; Uchida, M.; Muramatsu, H.; Ishida, H. �Derailment safety evaluation by analytical equations.� Quarterly Report of RTRI . v. 43 no. 3 p. 119�124.
[4] Marquis, B., and Grief, R., 2011, �Application of Nadal Limit in the Prediction of Wheel Climb Derailment,� "Proceedings of the ASME/ASCE/IEEE 2011 Joint Rail Conference", Pueblo, CO, March 16�18, 2011, Paper No. JRC2011-56064.
[5] Hiroaki Ishida, Takefumi Miyamoto, Eiichi Maebashi, Hisayo Doi, Kouhei Iida, Atsushi Furukawa,�Safety Assessment for Flange Climb Derailment of Trains Running at Low Speeds on Sharp Curves� QR of RTRI, Vol.47,No.2,may.2006
[6] Chu-Liang Wei et.3al,�A Fiber Bragg Grating Sensor System for Train Axle Counting�, IEEE Sensors J. vol.10, No.12, pp.1905-1911, 2010
[7] Hiroaki Ishida et.al, �Safety Assessment for Flange Climb Derailment of Trains Running at Low Speeds on Sharp Curves, �QR of RTRI,vol.47,No.2,May2006,pp.65-70.
[9] Akira Matsumoto et.al. ,�Continuous observation of wheel/rail contact forces in curved track and theoretical consideration�, vehicle system dynamics, vol.50, supplement, pp.349-364, 2012
[10] Huimin Wu, Nicholas Wilson, �Railway Vehicle Derailment and Prevention�,taylor & Francis Group, pp.209- 233, 2002.
[11] 1.Raman Kashyap, �Fiber Bragg Gratings,� Academic Press, 1999.
[12] Turan Erdogan,� Fiber Grating Spectra�, journal of lightwave technology, vol. 15, no. 8, august 1997

A. Kumar, "Derailment Assessment using FBG," International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.103-107, 2014.

Now a days smartphones are providing much rich contents and social interactions like email, gaming, voice communication and web browsing etc. However there are some limitations to high quality of service such as limited battery lifetime and unstable wireless connectivity. The recent cloud computing provides an ideal platform to support the desired mobile services. In this paper, we propose the design of a Cloud-based, sOcial Mobile tV system that shares encoded video streams. The system effectively utilizes both PaaS (Platform-as-a-Service) and IaaS (Infrastructure-as a-Service) cloud services to offer the living-room experience of video watching to a group of disparate mobile users. The mobile users may be separated geographically but they can interact socially while sharing the video. To ensure that the streaming quality is good with time varying wireless connectivity, we employ an alternate for each user in IaaS cloud for social exchanges and video downloading on behalf of the user. The alternate performs efficient stream transcoding that matches the connectivity quality of the mobile user. As the battery lifetime is a bottleneck to high quality of service, we advocate the use of burst transmission from alternate to the mobile users. The burst size is decided carefully because it leads to high energy efficiency and streaming quality. The social interactions among the users are effectively achieved by efficient designs of data storage with BigTable and dynamic handling of large volumes of concurrent messages in a typical PaaS cloud.

encoded video stream sharing, social tv, GAE, Amazon EC2, PaaS and IaaS

[1] Z. Huang, C. Mei, L. E. Li, and T. Woo, �Cloudstream: Delivering high-quality streaming videos through a cloud-based svc proxy,� in INFOCOM�11, 2011, pp. 201�205.
[2] M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, �The case for vm-based cloudlets in mobile computing,� IEEE Pervasive Computing, vol. 8, pp. 14�23, 2009.
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[4] A. Carroll and G. Heiser, �An analysis of power consumption in as smartphone,� in Proc. of USENIXATC, 2010.
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P.B. Prakash, P.S. Basha, "Encoded Video Stream Sharing in Mobile Social TV using Clouds," International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.108-111, 2014.