iosr-JCE   Volume-1 ~ Issue-3

ABSTRACT : Cost estimation of maintenance phase is necessary to predict the reliability, improve the productivity, project planning, controlling and adaptability of the software. Accurate estimation makes good understanding between the customer and user. Maintenance plays important role in various software developments like Iterative development, Agile development and Component Based Software Development (CBSD). In the recent year, software development turned into engineering through the introduction of Component Based Software Development. Though there are various models to estimate the maintenance cost of traditional software like COCOMO model, SLIM, Function Point (FP) model but till now there is no such model to estimate the maintenance cost of Component Based Software Development. In this kind of situation there is needed to develop a model to estimate the maintenance cost of Component-Based Software (CBS). This paper presents proposed basic maintenance cost estimation model for Component Based Software on the basis of COCOMO. This model is based on three parameters: Development cost of Component Based Software, ACT (Annual Change Traffic), Technical and Non-Technical factors which affect the maintenance cost of Component Based Software.
Keywords -ACT (Annual Change Traffic), Basic maintenance cost estimation model, Component-Based Software (CBS), Cost Estimation, Software Maintenance, Technical and Non-Technical Factors.

[1] Roger S. Pressman, Software Engineering: A Practitioner's Approach Seventh Edition, McGraw-Hill Higher Education, 2010.
[2] Vingder, Building Maintainable Component Based Systemhttp://www.sei.cmu.edu/icse99/papers/38/38.htm., 1990.
[3] Rodrigo B.M., Vergilio S.R., Software Effort Estimation Based on Use Cases , proceeding of 30th annual International Conference on ComputerSoftware and Application,2006,221-228.
[4] Jaun Carlos Granja Alvarez and Manvel Jose Barranco Garcia, A Method for Estimating Maintenance Cost in a Software Project : A Case Study, JSoftw Main Evol-9, 1997,161-175. [5] Hareton Leung and Zhang Fan, Software Cost Estimation,ftp://cs.pitt.edu/chang/handbook/42b.pdf.
[6] T. Wijayasiriwardhane, R. Lai, K. C. Kang, Effort estimation of component- based software development- a surveyIET Softw., vol. 5,2011, 216-228.
[7] K. Aggarwal, Yogesh Singh, Pravin Chandra and Manimala Puri, Measurement of software maintainability using a fuzzy Model,Journal of Computer Science, ISSN 1549-3636, 2005 Science Publications, 538-542.

ABSTRACT: The main objective of this paper is to detect an accident in which immediately help is required to driver and driver is not in position to inform any medical rescue team. In this kind of situation there is a need to develop such system which should inform automatically to medical rescue team that some accidents have taken place along with geo-graphical location. This paper proposed a method using various sensors for monitoring accidents and providing on spot medical facility.
Keywords – GPS, ITS, VANET, WSN

[1] http://en.wikipedia.org/wiki/Intelligent_transportation_system.
[2] Mishra, T., Garg, D., Gore, M.M., A Publish/Subscribe Communication Infrastructure for VANET Applications,Journal Of Computing, VOL-3, 2011, pp. 442 - 446
[3] Sok-Ian Sou, Tonguz, O.K, Enhancing VANET Connectivity Through Roadside Units on Highways, Vehicular Technology, IEEE Transaction, Volume 60 , 2011, pp. 3586 - 3602.
[4] QiyuanPeng,Design of Expressway emergency Rescue Management System Based on the GIS-T, proceedings of International Conference on Transportation Engineering, 2009.
[5] Wang Jun, Chen Hong, Analysis on Reliability of Emergency Rescue System on Highway, proceedings of National Conference on Power and Energy Engineering, 2011, pp.1 – 5.
[6] Moreira N., Venda M. , Silva C. , Marcelino L. , Pereira, A. @Sensor - Mobile Application to Monitor a WSN, proceedings of 6th Iberian Conference onInformation Systems and Technologies (CISTI), 2011, pp. 1-6.
[7] Yu Xiao, Xiaoyan Cui, Hang Li, Teng Xi , A protocol simplifying mechanism for a WSN module, proceedings of International Conference on Electronics and Information Engineering (ICEIE), Vol-2, 2010, pp. 474-477.

ABSTRACT:Localization is one of the most fundamental problems in wireless sensor networks, since the locations of the sensor nodes are critical to both network operations and most application level tasks. To solve the localization problem for find out the location of sensor nodes in wireless sensor network, a Anchor based localization scheme for wireless sensor networks with energy efficiency is presented in this thesis. First, we develop a standardized clustering-based approach for the local coordinate system formation wherein a multiplication factor is introduced to regulate the number of cluster head nodes and neighbor nodes and the degree of connectivity among cluster head nodes. The algorithm operates asynchronously without a centralized controller; and in this algorithm location of the cluster heads be known a priori which are called anchor nodes. In this thesis to calculate the distance between the nodes triangulation technique is used. A simulation analysis on a specific numerical example is conducted for specifying the energy efficiency in finding the location by using anchor nodes. Experiments show that our algorithm beneficiary for less energy consumption.

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[4] Wang, J.; Ghosh, R.K.; Das, S.K. A survey on sensor localization. J. Control Theory Appl 8 2010, 2–11.
[5] Shah, R. C., and Rabaey, J. M. Energy aware routing for low energy ad-hoc sensor networks. In Proceedings of the 3rd IEEE Wireless communications and Networking Conference, Orlando, FL, 2001, 151—165.
[6] Megeurdichian, S., Slijepcevic, S., Karayan, V., and Potkonjak, M. Localized algorithms in wireless ad-hoc networks:Location discovery and sensor exposure, MobiHOC, 106—116, 2001.
[7] Bordim, J. L., Nakano, K., and Shen, H. Sorting on a single channel wireless sensor networks. In Proceedings of the International Symposium on Parallel Architectures and Networks, 2002, 153—158.
[8] L. Doherty, K. S. Pister, and L. E. Ghaoui, Convex position estimation in wireless sensor networks in INFOCOM 2001, vol. 3, Anchorage,AK, USA, , April 2001 pp. 1655–1663.
[9] A. Savvides, C.-C. Han, and M. B. Strivastava, Dynamic fine-grained localization in ad-hoc networks of sensors in 7th ACM/IEEE International Conference on Mobile Computing and Networking, Rome, Italy, 2001 pp. 166–179.
[10] D. Niculescu and B. Nath, Ad hoc positioning system (aps) in IEEE Global Communications Conference (GlobeCom '01), no. 5, San Antonio, TX, USA, , November 2001pp. 2926–2931.

Abstract : Wireless sensor networks (WSNs) consist of a large number of limited capability (power and processing) Micro Electro Mechanical Systems (MEMS) capable of measuring and reporting physical variables related to their environment. In surveillance applications, sensors are deployed in a certain field to detect and report events like presence, movement, or intrusion in the monitored area. Minimizing energy dissipation and maximizing network lifetime are important issues in the design of applications and protocols for sensor networks. Energy-efficient sensor state planning consists in finding an optimal assignment of states to sensors in order to maximize network lifetime.
The existing scheme developed a centralized mechanism for near-optimal state assignment to sensors in large-scale cluster-based monitoring wireless sensor networks. The existing one was based on a tabu algorithm that computes a near-optimal network configuration in which each sensor can be activated, put in sleep mode or promoted as cluster head. The existing mechanism maximizes network lifetime while ensuring the full coverage of the monitored area and the connectivity of the obtained configuration. Connectivity is fulfilled through an optimally computed spanning tree connecting all the cluster heads. Due to abnormal node distribution in case of land surveillance, the existing tabu based optimal energy setting become complex. In addition the tabu algorithm keeps the probabilistic event detection independent for the respective node.
To overcome the abnormal node Distribution event detection triviality, distributed energy efficient algorithm is proposed in this work. The proposed work of this thesis, develop a more sophisticated heuristic to improve the network lifetime. The proposed scheme handles distance-dependent probabilistic event detection. The distance based probability is a function of the distance of the corresponding sensor from the event. The proposed system develop distributed algorithm which addresses the energy-efficient clustering under the joint coverage and routing constraint. The experimental simulations are carried for the proposed model using Network Simulator 2 (NS-2) for multiple simulation times, routing topology and energy coverage area.

[1] C.Y. Chong and S.P. Kumar, "Sensor Networks: Evolution, Opportunities, and Challenges," Proc. IEEE, vol. 91, no. 8, pp. 1247-1256, Aug. 2003.
[2] M. Cardei, M.T. Thai, Y. Li, and W. Wu, "Energy-Efficient Target Coverage in Wireless Sensor Networks," Proc. IEEE INFOCOM, vol. 3, pp. 1976-1984, 2005.
[3] V. Raghunathan, C. Schurgers, S. Park, and M.B. Srivastava, "Energy-Aware Wireless Microsensor Networks," IEEE Signal Processing Magazine, vol. 19, pp. 40-50, 2002.
[4] K. Akkaya and M. Younis, "A Survey of Routing Protocols in Wireless Sensor Networks," Ad Hoc Network J., vol. 3, no. 3, pp. 325-349, 2005.
[5] L.M.C. Arboleda and N. Nasser, "Comparison of Clustering Algorithms and Protocols for Wireless Sensor Networks," Proc. Canadian Conf. Electrical and Computer Eng., pp. 1787-1792, May 2006.
[6] M. Younis, P. Munshi, G. Gupta, and S.M. Elsharkawy, "On Efficient Clustering of Wireless Sensor Networks," Proc. Second IEEE Workshop Dependability and Security in Sensor Networks and Systems, pp. 78-91, 2006.
[7] H. Karl and A. Wiling, Protocols and Architectures for Wireless Sensor Networks. Wiley, 2005.
[8] Y. Yao and G.B. Giannakis, "Energy-Efficient Scheduling for Wireless Sensor Networks," IEEE Trans. Comm., vol. 53, no. 8, pp. 1333-1342, 2005.
[9] S.F. Hwang, Y.Y. Su, Y.Y. Lin, and C.R. Dow, "A Cluster-Based Coverage-Preserved Node Scheduling Scheme in Wireless Sensor Networks," Proc. Third Ann. Int'l Conf. Mobile and Ubiquitous Systems: Networking and Services, pp. 1-7, July 2006.
[10] W.R. Heizelman, A. Chandrakasan, and H. Balakrishnan, "Energy-Efficient Communication Protocol for Wireless Micro Sensor Networks," Proc. IEEE Hawaii Int'l Conf. System Sciences, Jan. 2000.

Abstract : Cloud computing is a technique to deliver software, storage and processing. It increases system's capability without changing the existing infrastructure, educating new people or taking license for the softwares. It improves the existing software capabilities and extends the Information Technology resources. In recent years, cloud computing has grown up rapidly and boosted the business concept in IT industry. Despite of all the achievements in cloud computing, security is still a critical challenge in cloud computing paradigm. These challenges include user's secret data (like health and financial data) loss, leakage and disclosing of privacy. We have studied literature and discussed various model in cloud computing, it shows that privacy/protection in cloud is still immature.
Keywords: Cloud computing, Data privacy, Data protection, Security issues.

[1] T. Mather, S. Kumaraswamy, and S. Litif, Cloud Security and Privacy: An enterprise perspectives on Risks and Compliance (Theory in Practice ). O' Reilly, 2009.
[2] IEEE International Conference on Cloud Computing. 2009.
[3] P.T.Jaeger, J.Lin, and M. grimes, Cloud computing and information policy: Computing in a policy cloud? Journal of Information Technology and politics, 2009. 5(3).
[4] Cloud Computing: Clash of the clouds. the economist., 2009.
[5] B.P.Rimal, E.Choi, and I.Lumb. A taxonomy and survey of Cloud Computing Systems. in Networked Computing and Advanced Information Management, International Conference. 2009.
[6] B.R. Kandukuri, R.P.V., and A. Rakshit. Cloud security issues. in IEEE International Conference on Services Computing (SCC). 2009.
[7] L.M.Kaufman, Data security in the World of Cloud Computing. IEEE Security and Privacy, 2009. 7(4):: p. 61-64.
[8] M.Peter and G. T, The NIST definition of Cloud Computing. 2009.
[9] Security Guidance for Critical Area of Focus in Cloud Computing,. 2009.
[10] R.Chow, et al. Controlling Computation without Outsourcing Control. in CCSW'09, ACM workshop on Cloud computing security. 2009.

Abstract-This paper contains concept of ontologies on the basis of user behaviour being analysed. Ontologies are the structural frameworks for organizing information and are used in artificial intelligence, the Semantic Web, systems engineering, software engineering, biomedical informatics, library science, enterprise bookmarking, and information architecture as a form of knowledge representation about the world or some part of it. The creation of domain ontologies is also fundamental to the definition and use of an enterprise architecture framework. This paper also describes creation of ontological user profiles for web information gathering and How the ontology is useful in Web Personalization.
Keywords - ontologies, framework, semantic web, enterprise bookmarking ,web information gathering.

[1] http://www.unicist.org/what-is-an-ontology.pdf
[2] http://en.wikipedia.org/wiki/Ontology_%28information_science%29
[3] http://en.wikipedia.org/wiki/Ontology
[4] http://www.caftextension.org.in/CAFT2012Lectures/16.pdf
[5] http://hem.hj.se/~blev/HandbookChapter_ODPs.pdf
[6] http://web.mit.edu/smadnick/www/wp/2004-11.pdf
[7] Matteo Cristani (Universita di Verona, Italy) and Roberta Cuel (Universita di Verona, Italy)," A Survey on Ontology Creation Methodologies", Volume 1, Issue 2. Copyright © 2005.
[8] Vijayan Sugumaran, Veda C. Storey"Ontologies for conceptual modeling: their creation, use, and management", Data & Knowledge Engineering, Volume 42, Issue 3, September 2002, Pages 251-271.
[9] Antonio Paredes-Moreno, Francisco J-Martinez, David G-Schwartz,"A methodology for semi-automatic creation of data driven detailed business ontologies", Information Systems,Volume 35,Issue 7,November 20110.pages 758-773.
[10] Xiaohui Tao, Yuefeng Li, and Ning Zhong, Senior Member, IEEE. "A Personalized Ontology Model for
Web Information Gathering", IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 23, NO. 4, APRIL 2011.

Abstract:Since the early eighties, an active temporal database research community has sought new insight into the management of time-referenced, or temporal, data and has developed concepts, tools and techniques that better support the management of such data. Much of this activity has been motivated by the observations that most databases contain substantial amounts of temporal data and that conventional database technology offers precious little support for temporal data management. Temporal data stored in a temporal database is different from the data stored in non-temporal database in that a time period attached to the data expresses when it was valid or stored in the database. In this paper we are going to design an algorithm for simulating temporal database using Exponential Distribution.
Keywords - Exponential Distribution, Interval based Temporal Database Model, Temporal Database, Transaction Time, Valid Time.

[1] Christian S. Jensen, Richard T, and Michael D. Soo. Extending Existing Dependency Theory to Temporal Databases. IEEE Transactions on Knowledge and Data Engineering, August 1996.
[2] Claudio Bettini, Member, IEEE,X.Sean Wang, Member, IEEE Computer Society,and Sushil Jajod Senior Member, IEEE. Temporal Semantic Assumptions and Their Use in Databases. IEEE transactions on knowledge and data engineering, March/April 1998.
[3] C.S. Jensen, J. Clifford, S.K.Gadia, A.Segev, R.T.Snotgrass. A Glossary of Database Concepts. Sigmod Record, September 1992.
[4] Mohamad H. Saraee and Babis Theodoulidis. Knowledge Discovery in Temporal Databases. Department of Computation, at UMIST Manchester, UK, 1995.
[5] Seo-Young Noh and Shashi K. Gadia. Efficient Self- Join algo in Interval- based Temporal Data Models. Department of Computer Science, Iowa State University, Ames, Iowa, USA, Sept 2005.