iosr-JCE   Next Generation Computing Technologies

Abstract: The purpose of this work is to develop an auditing scheme which is at ease, efficient to apply and own the skills which include privacy preserving, public auditing, keeping the statistics integrity alongside confidentiality. Thus the new auditing scheme has been developed consisting of three entities. Data proprietor, TPA and cloud server. The data proprietor performs diverse operations consisting of splitting the document to blocks, encrypting those using AES. It verifies the integrity of records on demand of the users in cloud. The cloud server is used handiest to save the encrypted blocks of facts. This proposed auditing scheme uses AES algorithm for encryption.

Keywords - Cloud Computing, TPA methods, AES Algorithm encryption and decryption

[1]. Zissis, Dimitrios, and Dimitrios Lekkas. Addressing cloud computing security issues. Future Generation computer systems 28.3 (2012): 583-592.
[2]. Cong Wang, Sherman SM Chow, Qian Wang, Kui Ren, and Wenjing Lou. Privacy Preserving Public Auditing for Secure Cloud Storage. http://eprint.iacr.org/2009/579.pdf
[3]. Mell, Peter, and Tim Grance. The NIST definition of cloud computing. (2011).
[4]. Balakrishnan S, Saranya G, et al. (2011). Introducing Effective Third Party Auditing (TPA) for Data Storage Security in Cloud, International Journal of Computer Science and Technology, vol 2(2), 397–400.
[5]. Qian Wang, Cong Wang, Kui Ren, Wenjing Lou, and Jin Li. Enabling Public Auditability and Data Dynamics for Storage Security in Cloud Computing. Parallel and Distributed Systems, IEEE Transactions on, 22(5):847–859, 2011.

Abstract: Wireless sensor networks (WSN) are spatially distributed autonomous sensors to monitor the physical or environmental conditions such as temperature, sound, pressure, etc. It is the collection of large number of sensor nodes in sensor fields. The major application in WSN is like remote environmental monitoring, detections of forest fire and target tracking. This environment is particularly in sensors for recent years that are smaller, cheaper and intelligent. The sensors are associated with wireless interfaces with which the communication takes place with one another to form a network.This paper discuss about wireless sensor networksin addition to that it includes some of the fields in radio networks and also provides new applications for sensing and transferring of information from various environments.

Keywords: Wireless Sensor Networks Advantages, Applications, Architecture, Characteristics, Design.

[1]. Application and characteristics of wireless sensor networks https://en.wikipedia.org/wiki/Wireless_sensor_network
[2]. WSN definition and its applications https://www.google.co.in/search?q=wsn&oq=wsn&aqs=chrome..69i57j69i60j69i61.1142j0j8&sourceid=chrome&ie=UTF-8
[3]. Wireless Sensor Networks and its Application https://link.springer.com/chapter/10.1007%2F978-3-540-69033-7_10 http://microcontrollerslab.com/wireless-sensor-networks-wsn-applications/
[4]. Architecture of wireless sensor networks https://www.elprocus.com/architecture-of-wireless-sensor-network-and-applications/

[5]. Fred Stann, John Heidemann, -RMST: Reliable Data Transport in sensor networks||.

Abstract: With the plethora of schemes available in the Indian markets, an investors needs to evaluate and consider various factors before making an investment decision. The present investigation is aimed to examine the performance of safest investment instrument in the security market in the eyes of investors i.e., mutual funds by specially focusing on systematic investment plan in mutual fund schemes. The study of research helps the people and financial analysts to analyze various securities or funds while selecting the best investment alternative out of the galaxy of investment alternatives. Time series analysis in data mining helps to find the market movement in order to find out the comparative analysis report of various funds.

Keywords- Data mining, Forecasting, Portfolio, Time series, Artificial Neural Network, Feature selection

[1]. Deepika Sharma, Poonam Loothra, Ashish Sharma "Comparitive study of selected equity diversified mutual fund schemes" on International Journal of Computer Science & Management Studies, Vol. 11, Issue 01, May 2011.
[2]. Boris Kovalerchuk and Evgenii Vityaev "Data Mining For Financial Applications" pp1-22 Agrawal V. P. Financial Markets Operations; Sahitya Bhawan Publications; Ed. 2005, p. 239-240.
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Abstract: Swarm intelligence (SI) is the collective behaviour of decentralized, self-organized systems, natural or artificial. SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behaviour, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling..............

Keywords: Artificial Bee Colony, Bird flocking, Particle Swarm Optimization, Swarm Intelligence, structural optimization

[1]. B. K. Panigrahi, Y. Shi, and M.-H. Lim (eds.): Handbook of Swarm Intelligence. Series: Adaptation, Learning, and Optimization, Vol 7, Springer-Verlag Berlin Heidelberg, 2011. ISBN 978-3-642-17389-9.
[2]. M. Dorigo, E. Bonabeau, and G. Theraulaz, Ant algorithms and stigmergy, Future Gener. Comput. Syst., Vol. 16, No. 8, pp. 851–871, 2000.
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[4]. D. Karaboga, An Idea Based On Honey Bee Swarm for Numerical Optimization, Technical Report-TR06,Erciyes University, Engineering Faculty, Computer Engineering Department, 2005
[5]. M. Dorigo and T. Stützle, Ant Colony Optimization. MIT Press, Cambridge, 2004. ISBN: 978-0-262-04219-2

Abstract: In this paper LBS systems employ pull model or user-initiated model, where a user issues a query to a server which responds with location aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in next-generation location-based services In addition, thesis on Location-Based Service (LBS) have been rising in recent years due to a wide range of potential applications. One of the active topics is the mining and prediction of mobile movements and associated transactions. Most of existing studies focus on discovering mobile patterns from the whole logs. However, this kind of patterns may not be precise enough for predictions since the differentiated mobile behaviour among users and temporal periods are not considered. The paper proposes a novel algorithm, namely, Cluster-based Temporal Mode Sequential Pattern Mine (CTMSP-Mine), to discover the Cluster-based Temporal Mode Sequential Patterns (CTMSPs). Moreover.........

Keyword: Web Dataset, Filtering, Prediction Strategy, R- Tree, ALBS.

[1] X. Cao, G. Cong, and C. S. Jensen, "Retrieving top-k prestige-based relevant spatial web objects," Proc. VLDB Endowment, vol. 3, no. 1, pp. 373–384, 2010.
[2] X. Cao, G. Cong, C. S. Jensen, and B. C. Ooi, "Collective spatial keyword querying", in Proc. ACM SIGMOD Int. Conf. Manage.Data, 2011, pp. 373–384.
[3] J. Fan, G. Li, L. Zhou, S. Chen, and J. Hu, "Seal: Spatio-textual similarity search," Proc. VLDB Endowment, vol. 5, no. 9, pp. 824–835, 2012.
[4] J. Lu, Y. Lu, and G. Cong, "Reverse spatial and textual k nearest neighbor search," in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2011, pp. 349–360
[5] D. Wu, M. L. Yiu, C. S. Jensen, and G. Cong, "Efficient continuously moving top-k spatial keyword query processing," in Proc. Int. Conf. Data Eng., 2011, pp. 541–552.

Abstract: Now-a-day's agriculture is one of the most important field in the emerging real world and it is the main occupation and backbone of our country. Agriculture is in poor condition since before comparing previous years. The main reasons for this is without a well formed pattern about farming and proper guidance to the farmers. Due to these problems, farming affects the yield of crop and unawareness about the crop cultivation methodologies. And also season to cultivate the crop and choosing which soil is the best to cultivate the particular crop based on the weather condition and also when to harvest the crop for the best yield. If the farmer is aware about the crop cultivation methodologies and harvesting it will more helpful for the people in the real world and also to maximize the crop productivity. Data mining is the process of finding new template from large data sets.........

Keywords: Crop yield, Data mining, K-Means, K-Nearest Neighbor(KNN), Artificial Neural Networks(ANN).

[1]. G. Nasrin Fathima, R.Geetha, "Agriculture Crop Pattern Using Data Mining Techniques" , "International Journal of Advanced Research in Computer Science and Software Engineering , Volume 4, Issue 5, May 2014 ISSN: 2277 128X".
[2]. Hetal Patel, Dharmendra Patel, "A Brief survey of Data Mining Techniques Applied to Agricultural Data", "International Journal of Computer Applications (0975 – 8887)Volume 95– No. 9, June 2014".
[3]. Ms.Kalpana,Dr.Shanthi,Dr.Arumugam, "A Survey on Data Mining Techniques in Agriculture", "International Journal of Advances in Computer Science and Technology, 3(8), August 2014, 426 – 431 426".
[4]. Ramesh A, Vijay. S. Rajpurohit, " A survey on Data Mining Techniques for Crop Yield Prediction", "International Journal of Advance Research in Volume 2, Issue 9, September 2014".
[5]. Surabhi Chouhan ,Divakar Singh, Anju Singh, "A Survey and Analysis of Various Agricultural Crops Classification Techniques", "International Journal of Computer Applications (0975 – 8887) Volume 136 – No.11, February 2016 25.

Abstract: Crime prevention and detection become an important trend in crime and a very challenging to solve crimes. The crime data previously stored from various sources have a tendency to increase steadily. To solve the problems, data mining techniques employ many learning algorithms to extort hidden knowledge from huge volume of data. Data mining is data analyzing techniques to find patterns and trends in crimes. In this propose paper clustering is a data analyzing technique in unsupervised type. This technique is used to divide the same data into the same group and the different data into the other group. For the simple and effective clustering techniques, there are several algorithms such...........

Keywords— Data Mining, KNN Classification, Crime Pattern Analysis, E-Knn Classification

[1]. J. Han and M. Kamber, Data Mining: Concepts and Techniques, second ed. Morgan Kaufmann, 2006.
[2]. C.C. Aggarwal and P.S. Yu, "Finding Generalized Projected Clusters in High Dimensional Spaces," Proc. 26th ACM SIGMOD Int'l Conf. Management of Data, pp. 70-81, 2000.
[3]. K. Kailing, H.-P. Kriegel, P. Kro ¨ger, and S. Wanka, "Ranking Interesting Subspaces for Clustering High Dimensional Data," Proc. Seventh European Conf. Principles and Practice of Knowledge Discovery in Databases (PKDD), pp. 241-252, 2003.
[4]. K. Kailing, H.-P. Kriegel, and P. Kro ¨ger, "Density-Connected Subspace Clustering for High-Dimensional Data," Proc. Fourth SIAM Int'l Conf. Data Mining (SDM), pp. 246-257, 2004.
[5]. E. Mu ¨ ller, S. Gu ¨nnemann, I. Assent, and T. Seidl, "Evaluating Clustering in Subspace Projections of High Dimensional Data," Proc. VLDB Endowment, vol. 2, pp. 1270-1281, 2009.

Abstract: In this paper describe a specific type of summaries that Query facet the main topic of given text. Existing summarization algorithms are classified into different categories in terms of their summary construction methods (abstractive or extractive), the number of sources for the summary (single document or multiple documents), types of information in the summary (indicative or informative), and the relationship between review and query (generic or query-based. In this project, adding these lists may develop both accuracy and recall of query facets. Part-of-speech information can be used to check the homogeneity of lists and improve the quality of query facets. In this paper related topics to finding query facets. Good descriptions of query facets may be helpful for users............

[1] W. Kong and J. Allan, "Extending faceted search to the general web," in Proc. ACM Int. Conf. Inf. Knowl. Manage., 2014, pp.
839–848.
[2] K. Balog, E. Meij, and M. de Rijke, "Entity search: Building bridges between two worlds," in Proc. 3rd Int. Semantic Search
Workshop, 2010, pp. 9:1–9:5.
[3] C. Li, N. Yan, S. B. Roy, L. Lisham, and G. Das, "Facetedpedia: Dynamic generation of query-dependent faceted interfaces for
wikipedia," in Proc. 19th Int. Conf. World Wide Web, 2010, pp. 651–660.
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Data Eng., 2008, pp. 466–475.
[5] A. Herdagdelen, M. Ciaramita, D. Mahler, M. Holmqvist, K. Hall, S. Riezler, and E. Alfonseca, "Generalized syntactic and
semantic models of query reformulation," in Proc. 33rd Int. ACM SIGIR Conf. Res. Develop. Inf. retrieval, 2010, pp. 283–290.

Abstract: In several application domains, giant scale device networks area unit being deployed to gather device knowledge that may be utilized in deciding for important infrastructures. A malicious opponent might introduce a malicious node into the network or might compromise the prevailing legitimate node inside the network. Hence, making certain trustiness of knowledge is critical for effective deciding. Knowledge root could be a key think about evaluating trustiness of knowledge in device network. But, root management in device network faces many challenges like low energy, storage and information measure consumption, restricted resources, and opponent attack throughout transmission. During this paper, a completely unique light-weight theme is projected to firmly transmit root knowledge............

Keywords: Wireless sensor network, Provenance data, Bloom filter, Security

[1]. Jamal N. Alkaraki "Wireless Sensor Networks: Security Issues, Challenges and Solutions," International Journal of Information and Computing Technology, ISSN 0974-2239 Volume 4, Number 8 (2014), pp. 859-868.
[2]. Gergel Aces, Levente Butty ́an "Secure Data Aggregation in Wireless Sensor Network: A Survey," Information Security Institute, Qeensland University of Technology, PO Box 2434, Brisbane, Queensland 4001.
[3]. Shio Kumar Singh, M P Singh, "Issues in Wireless Sensor Networks," Proceedings of the World Congress on Engineering 2008 Vol I, WCE 2008, July 2- 4, 2008, London, U.K.
[4]. DaWei Xu, Jing GAO, "A Provenance Based mechanism to Identify Malicious Packet Dropping Adversaries in Sensor Networks," Proceedings of the 2011 International Conferrence
[5]. V.Chandrasekaran, Dr.A.Shanmugam, "Provenance-Based Trustworthiness Assessment in Sensor Networks," Proc. Seventh Int'l Workshop Data Management for Sensor Networks, pp. 2-7, 2010

Abstract: Data gathering is one of most important functions provided by WSNs, where sensor readings have to be collected from sensor nodes to one or few data collection sinks node. Due to the fact that there may exist high correlations among these sensor readings, it is inefficient to directly deliver raw data to the destination(s).In this study the application of CS with random walks for data gathering in WSNs. In this paper adopt the standard random walk algorithm to collect random measurements along multiple random paths. However, such associate approach can result in the non-uniform selection of measurements, totally different from uniform sampling in the traditional CS theory. It is still unknown whether such an approach can be used to recover sparse signals in a WSN scenario. To the best of our knowledge, the problem of data gathering with CS based on random walk algorithm has not been significantly investigate and they focus on designing rateless code to exploit the correlation of the signals and use belief propagation..........

Keywords : Data gathering, Compression sensing, random walk, Wireless sensor network..

[1]. W. Wang, M. Garofalakis, and K. Ramachandran, "Distributed Sparse Random Projections for Refinable Approximation,"Proc. ACM/IEEE 6th Int. Conf. Inf. Process. Sensor Network. (IPSN ‟07), Apr. 2007.
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Abstract: Online social networks (OSNs) such as Facebook, Google+ and Twitter are inherently designed to alter individuals to share personal and public data and build social connections with friends, coworkers, colleagues, family and even with strangers. In recent years, it has seen unprecedented growth in the application of OSNs. An interesting phenomenon of user shared images is observed from the intensive measurements, and this is formulated with a proposed method for a system to discover and recommend user connections in follower/followed relationships using user shared images directly.In this paper keep the information of social graphs (SGs) available to their related business services. Some users also hide or border the information of their connections from the public in social media...............

Keywords:- Online Social Networks, Recommendation, On-line image sharing, social graphs, connection.

[1]. T. Ristenpart, E. Tromer, H. Shacham, and S. Savage, "Hey, You, Get Off of My Cloud: Exploring Information Leakage in Third-Party Compute Clouds," Proc. 16th ACM Conf. Computer and Comm. Security (CCS ‟09), pp. 199-212, 2009.
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[3]. J. Somorovsky, M. Heiderich, M. Jensen, J. Schwenk, N. Gruschka, and L. Lo Iacono, "All Your Clouds Are Belong to Us: Security Analysis of Cloud Management Interfaces," Proc. Third ACM Workshop Cloud Computing Security Workshop (CCSW ‟11), pp. 3-14, 2011.
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Abstract: Land surface temperature (LST) is a solution variable in climatological and ecological studies. However, accurate measurements of LST over continents are not yet available for the whole globe. In this paper describes first reviews the state of the science of land surface temperature (LST) estimates from remote sensing platforms, models, and in situ approaches. Considering the suspicions, we review the current LST justification and estimate method. Then the requirements for LST products are specified, from the different user communities. Finally to identify the gaps between state of the science and the user community requirements, and discuss solutions to bridge these gaps. In this paper analysis a physics-based method to retrieve LST from the MODIS daytime MIR data in channels 22 (centered at 3.97 μm) and 23 (centered at 4.06 μm). On the basis of radiative transfer theory in the MIR region, a.........

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Abstract: In this paper, propose a simple Energy Efficient framework for Social-based Routing (EE-SR) in mobile social sensing networks to balance the load of nodes while maintaining the delivery ratio within an acceptable range by limiting the chances of forwarding in traditional social-based routing. Furthermore, propose an improved version of EE-SR to dynamically adjust the controlling parameter. Simulation results on real-life mobile traces demonstrate the efficiency of proposed framework. In this thesis, the problem of routing in intermittently connected wireless networks comprising multiple classes of nodes is addressed. We show that proposed solution, which perform well in homogeneous scenario, are not as competent in this setting. To this end, propose a class of routing schemes that can identify the nodes of "highest utility" for routing, improving the delay and delivery ratio. Additionally, proposed an analytical framework based on fluid models that can be used to analyze the performance of various opportunistic routing strategies, in heterogeneous settings.

Keywords: Social Routing, Social Metrics, MANET, Topology Routing, EE-SR

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Abstract: Different routing protocol perform completely different role within the underwater detector networks. All routing perform each and every specific task into underwater detector network that liable for networking issues that's why this is often the newest method of analysis. Routing term derived from "route" meaning a path some way that perform completely different terms in underwater detector network drawback connected issue. The most effective half is these days several routing protocol are within the underwater wireless detector network. Some completely different attributes comes underwater wireless detector network like high bit error rates, restricted band-width, 3D preparation and high propagation delay. This paper is bearing on as useful for giving transient summary concerning each and every protocol and liable for entire underwater wireless detector networks.

Keywords: Underwater detector networks, routing protocol

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