iosr-JCE   Volume-1 ~ Issue-4

ABSTRACT : For advanced streaming applications over wired-wireless networks TCP-Friendly Rate Control (TFRC) has been widely adopted nowadays to give smooth sending rate and unceasing quality in streaming applications TFRC applies an equation-based rate control scheme. However, TFRC tends to fail in wireless environment if packet lost event was done by poor channel quality but network congestion. Therefore, TFRC not able to provide high quality-of-service for streaming applications over wired-wireless networks. In this paper, we proposed a delay based uni-directional delay jitter based TFRC with end-to-end semantic over wired-wireless networks. This scheme provide smooth sending rate and TCP friendly characteristics like standard TFRC, even it also increase the throughput by estimating the available bandwidth in wired-wireless networks with bursty nature of background traffic. Simulation results show performance improvement without intrusiveness issue and even if background traffic is bursty over wired-wireless networks.
Keywords – Bursty network Congestion Control Mechanism, Streaming applications, TCP Friendly Rate Control (TFRC), Uni Directional Delay Jitter

[1] H. Balakrishnan, V. N. Padmanabhan, S. Seshan, and R. H. Katz. A comparison of mechanisms for improving tcp performance over wireless links.IEEE/ACM Trans. Netw., 5:756–769, December 1997.
[2] H. Balakrishnan, S. Seshan, and R. H. Katz. improving reliable transport and handoff performance in cellular wireless networks. Wirel. Netw., 1:469–481, December 1995.
[3] S. Biaz and N. H. Vaidya. "de-randomizing" congestion losses to improve tcp performance over wired-wireless networks. IEEE/ACM Trans. Netw., 13:596–608, June 2005.
[4] S. Cen, P. C . Cosman, and G. M. Voelker. End-to-end differentiation of congestion and wireless losses. IEEE/ACM Trans. Netw., 11:703–717, October 2003.
[5] S. Floyd and K. Fall. Promoting the use of end-to-end congestion control in the internet. IEEE/ACM Trans Netw., 7:458–472, August 1999.
[6] R. Jain. The art of computer systems performance analysis. Wiley, New York, 1991.
[7] X. Liu, K. Ravindran, B. Liu, and D. Loguinov. Single-hop probing asymptotics in available bandwidth estimation: sample-path analysis. In Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, IMC '04, pages 300–313, New York, NY, USA, 2004. ACM.
[8] S. Mascolo, C. Casetti, M. Gerla, M. Y. Sanadidi, and R. Wang. Tcp westwood: Bandwidth estimation for enhanced transport over wireless links. In Proceedings of the 7th annual international conference on Mobile computing and networking, MobiCom '01, pages 287–297, New York, NY, USA, 2001. ACM.
[9] The network simulator ns-2. ttp://www.isi.edu/nsnam/ns/
[10] E.-K. Wu and M.-Z. Chen. Jtcp: jitter-based tcp for heterogeneous wireless networks. IEEE Journal on Selected Areas in Communications, 22(4):757 – 766, may 2004.

ABSTRACT: Mobile technology presents many inherent problems that lead to poor network connectivity, low bandwidth. To overcome poor connectivity, Mobile clients are forced to operate in disconnected and partially connected modes. This paper review a designed method which led to support data availability using data replication and data caching in mobile environment to improving the availability of data and enhancing the performance of data access for users operates in mobile environments.
Keywords: Data Caching, Data Replication, Disconnect Operation, Mobile Computing

[1] M. Tarafdar and M. S. Haghjoo, "Location Privacy in Processing Location Dependent Queries in Mobile Database Systems," Computer Engineering, pp. 181-186, 2010.
[2] A. A. Fadelelmoula, "Optimistic Replication in Mobile Traffic Control Environment," Information Sciences, pp. 543-548, 2007.
[3] N. Dimokas, D. Katsaros, L. Tassiulas, and Y. Manolopoulos, "High performance, low complexity cooperative caching for wireless sensor networks," Wireless Networks, vol. 17, no. 3, pp. 717-737, Dec. 2010.
[4] Y. Saito, "Optimistic replication," Science, vol. 37, no. 1, Publisher: ACM Press, Pages: 42-81, 2005.
[5] H. K. Chavan, "A Survey of Mobile Database Cache," Engineering.
[6] S. K. Madria, "Mobile data management," Ieee Potentials, no. November, pp. 11-15, 2001.
[7] L. Deboosere, B. Vankeirsbilck, P. Simoens, F. D. Turck, B. Dhoedt, and P. Demeester, "Cloud-based Desktop Services for Thin Clients," Ieee Internet Computing, no. ii, pp. 1-6, 2011.
[8] L. Y. and G. Cao, "Supporting Cooperative Caching in Ad Hoc Networks," IEEE Trans. Mobile Computing, vol. 5, pp. 77-89, 2006.
[9] C. D. S. Lim, W. Lee, G. Cao, "A Novel Caching Scheme for Internet Based Mobile Ad Hoc Networks Performance," Ad Hoc Networks, vol. 4, pp. 225-239, 2006.
[10] K. S. Du, Y., & Gupta, "COOP: A cooperative caching service in MANETs," . In Proceedings of ICAS-ICNS, pp. 58–63, 2005.

ABSTRACT:A number of Call Graph construction algorithms have been designed for construction of Call Graphs for object-oriented languages. Each of the Call Graph contraction algorithms were proposed to keep in mind the improvements over previous Call Graphs in terms of precision, cost and accuracy. In object oriented languages the Call Graphs are generally contracted to represent the calling relationship between the program's methods. The Call Graph forms the bases for deducing the information about the classes and the methods that are actually invoked, this information can be used to find call sites were virtual function calls can be replaced by direct calls and inline-expansions can be put into work where ever possible. In this paper we present an empirical comparison of various well known Call Graph construction algorithms. Here we used Scoot bytecode reader as front-end to implement various Call Graph construction algorisms. In the processes Scoot bytecode reader is used to read the bytecode of a specific java program then the reachable methods are found for each invoked method. For storing information about the classes, methods, fields and statements we created our own set of data structures. Finally we tested and evaluated the developed algorithms with a variety of java benchmark programs to gather the information for the comparison of various Call Graph algorithms which is the goal of this work. We have included most of the Call Graph algorithms of popularity in this work. The main aim of the work is to consider all the dimensions of the Call Graph construction algorithms like cost, precision, memory and time requirements for its construction. The previous works has either not included all the algorithms of fame or have left some of their construction constraints untouched. This work will bring an effective empirical comparison to the front and will help to reveal that which Call Graph construction algorithm is best and when. The results in the work are mainly considered valid for java and other statically typed object-oriented languages.

[1] Jeffrey Dean, David Grove, and Craig Chambers, Optimization of Object-oriented Programs Using Static Class Hierarchy Analysis ECOOP 95.
[2] David Grove and Craig Chambers. A Framework for Call Graph Construction Algorithms. ACM Transactions on Programming Languages and Systems (TOPLAS), November 2001.
[3] David Grove, Greg DeFouw, Jeffrey Dean and Craig Chambers. Call Graph Construction in Object-Oriented Languages. OOPSLA '97: Proceedings of the 12th ACM SIGPLAN conference on Object-oriented programming, systems, Languages, and applications, October 1997..
[4] Gail C. Murphy, David Notkin, William G. Griswold, and Erica S. lan. An Empirical Study of Static Call Graph Extractors. Transactions on Software Engineering and Methodology (TOSEM), April 1998.
[5] Damien Sereni.Termination Analysis and Call Graph Construction for Higher- Order Functional Programs. ICFP '07: Proceedings of the 12th ACM SIGPLAN International conference on Functional programming, October 2007.
[6] Ondrej Lhotak and David R. Cheriton. Comparing Call Graphs, Proceedings of the 7th ACM SIGPLAN-SIGSOFT workshop on Program analysis for Software tools and engineering, June 2007.
[7] Soot: A Java Optimization Framework,March2010,http://www.sable.mcgill.ca/soot/
[8] James Gosling, Bill Joy, Guy Steele and Gilad Bracha. The Java™ Language Specification Third Edition. Addison-Wesley May 2005.
[9] Raja Vallée-Rai, Phong Co, Etienne Gagnon, Laurie Hendren, Patrick Lam and Vijay Sundaresan. Soot-a Java Bytecode Optimization Framework. CASCON '99: Proceeding of the 1999 conference of the Centre for Advanced Studies on Collaborative research, November 1999.
[10] F. Tip and J. Palsberg. Scalable propagation-based call graph construction algorithms. In Proceedings of the Conference on Object-oriented Programming, Languages, Systems and Applications, pages 281–293, Oct.2000.

Abstract : Ensemble methods for different classifiers like Bagging and Boosting which combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity of the members of an ensemble is known to be an important factor in determining its generalization error. DECORATE (Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples), that directly constructs diverse hypotheses using additional artificially-constructed training examples. The technique is a simple, general meta-learner that can use any strong learner as a base classifier to build diverse committees. The diverse ensembles produced by DECORATE are very effective for reducing the amount of supervision required for building accurate models. DECORATE ensembles can also be used to reduce supervision through active learning, in which the learner selects the most informative examples from a pool of unlabeled examples, such that acquiring their labels will increase the accuracy of the classifier.
KEYWORDS: Data Classification, Ensemble of classifiers Accuracy of classifier, Diversity

[1] Krogh, A., & Vedelsby, J. (1995). Neural network ensembles, cross validation and active learning.(Krogh & Vedelsby, 1995).
[2] J. Basilico, D. Dunlavy, S. Verzi, T. Bauer, and W. Shaneyfelt. Yucca mountain LSN archive assistant. Technical Report SAND2008-1622, Sandia National Laboratories, 2008.
[3] Abe, N., & Mamitsuka, H. (1998). Query learning strategies using boosting and bagging.In Proceedings of the Fifteenth International Conference on Machine Learning (ICML-98), pp. 1–10.
[4] Zadrozny, B., & Elkan, C. (2002). Transforming classifier scores into accurate multiclass probability estimates. In Proceedings of the Eighth ACM SIGKDD International Conferenceon Knowledge Discovery and Data Mining (KDD-2002), Edmonton, Alberta.
[5] Kuncheva, L., & Whitaker, C. (2003). Measures of diversity in classifier ensembles and their relationship with ensemble accuracy. Machine Learning, 51(2), 181–207.
[6] Melville, P., & Mooney, R. J. (2003). Constructing diverse classifier ensembles using artificial training examples. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-2003), pp. 505–510, Acapulco, Mexico.
[7] R. Ban_eld, L. Hall, K. Bowyer, and W. P. Kegelmeyer.A comparison of decision tree ensemble creation techniques. IEEE Trans. Pat. Recog. Mach. Int., 29(1):173{180, 2007.

Abstract :Anonymity has received increasing attention in the literature due to the users' awareness of their privacy nowadays. Anonymity provides protection for users to enjoy network services without being traced. On the other hand, the network authority requires conditional anonymity such that misbehaving entities in the network remain traceable. In this paper, we propose a security architecture to ensure unconditional anonymity for honest users and traceability of misbehaving users for network authorities in WMNs. The proposed architecture strives to resolve the conflicts between the anonymity and traceability objectives, in addition to guaranteeing fundamental security requirements including authentication, confidentiality, data integrity, and nonrepudiation.
Index Terms: Anonymity, traceability, pseudonym, misbehavior, revocation, wireless mesh network (WMN).

[1] European Telecomm. Standards Inst. (ETSI), "GSM 2.09: Security Aspects," June 1993.
[2] P. Kyasanur and N.H. Vaidya, "Selfish MAC Layer Misbehavior in Wireless Networks," IEEE Trans. Mobile Computing, vol. 4, no. 5, pp. 502-516, Sept. 2005.
[3] A. Perrig, J. Stankovic, and D. Wagner, "Security in Wireless Sensor Networks," Comm. ACM, vol. 47, no. 6, pp. 53-57, 2004.
[4] W. Lou and Y. Fang, A Survey on Wireless Security in Mobile Ad Hoc Networks: Challenges and Possible Solutions, X. Chen, X. Huang, and D.-Z. Du, eds., Kluwer Academic Publishers/ Springer, 2004.
[5] L. Zhou and Z.J. Haas, "Securing Ad Hoc Networks," IEEE Network Magazine, vol. 13, no. 6, pp. 24-30, Dec. 1999.
[6] SAT: A Security Architecture Achieving Anonymity and Traceability in Wireless Mesh Networks. IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, VOL. 8, NO. 2, MARCH-APRIL 2011.

Abstract-Sequential dark and photo- fermentation is a rather new approach in biological hydrogen gas production. In the present work, two-stage fermentation process consisting of dark and photo-fermentation periods was carried out in a batch reactor. The study mainly emphasized on assessing the potential of biological conversion of different substrates to hydrogen by studying various parameters like temperature, pH and cell density to achieve maximum hydrogen production. In the first stage, substrate was fermented in the dark stage using Bacillus licheniformis, Enterobacter cloacae and Halobacterium salinarum to produce acetate, CO2 and H2. The acetate produced in the first stage is fermented to H2 and CO2 by Rhodobacter sphaeroides for further hydrogen production in the second, illuminated stage. The percentage yield for Bacillus licheniformis, Enterobacter cloacae and Halobacterium salinarum using Rhodobacter sphaeroides was found to be 35.7%, 32%, and 26% respectively and process proficiency was found to be 0.2, 0.35 and 0.16 moles/kg.
Key Words: Hydrogen; Dark fermentation; Photo- fermentation; Bacillus licheniformis, Enterobacter cloacae; Halobacterium salinarum; Rhodobacter sphaeroides

1. Yokoi H., Mori S., Hirose J., Hayashi S., and Takasaki Y, H2 production from starch by a mixed culture of Clostridium butyricum and Rhodobacter sp. M-19, Biotechnology Letter., 20, 890– 895, (1998)
2. Yokoi H., Maki R., Hirose, J., Hayashi S., Microbial production of hydrogen from starch manufacturing wastes., Biomass Bioengineering, 22, 89-39, (2002)
3. Yokoi H., Saitsu A., Uchida H., Hirose J., Hayashi S, Takasaki Y., Microbial hydrogen production form sweet potato starch residue, J. Bioscience Bioengineering., 91, 58 – 63, (2001)
4. Fascetti E., D. Addario, E. Todini, O. Robertiello, A.: Photosynthetic hydrogen evolution with volatile organic acid derived from the fermentation of source selected municipal wastes, International J. of Hydrogen Energy, 23, 753-760, (1998)
5. Kawaguchi H., Hashimoto K., Hirata K., Miyamoto K., H2 production from algal biomass by mixed culture of Rhodobium marinum A-501 and Lactobacillus amylovorus, J. Bioscience Bioengineering, 91, 277-282, (2001)
6. Ike A., Murakawa T., Kawaguchi H., Hirata, K., Miyamoto K., Photoproduction of hydrogen from raw starch using a halophilic bacterial community, J. Bioscience Bioengineering., 88, 72-77, (1999)
7. Das D. and Veziroglu TN., Hydrogen production by biological processes: a survey of literature. International J. Hydrogen Energy, 26, 13– 28, (2001)
8. Akkerman I., Janssen M, Rocha J. and Wijffels RH., Photobiological hydrogen production: photochemical efficiency and bioreactor design, International J. Hydrogen Energy, 27, 1195–1208, (2002)
9. Oh Y-K., Seol E-H., Yeol Lee E., and Park S., Fermentative hydrogen production by a new chemolithotrophic bacterium Rhodopseudomonas palustris P4. International J. Hydrogen Energy, 27, 1373–1379, (2002)
10. Oh Y-K., Seol E-H., Kim JR. and Park S., Fermentative biohydrogen production by a new chemoheterotrophic bacterium Citrobacter sp. Y19. International J. Hydrogen Energy 28, 1353–1359, (2003)