Volume-06 , Special Issue-02 , Mar - 2018 Go Back

Internet of things (IoT) is internetwork of objects, embedded with Hardware and computing software. The heterogeneity in IoT circumstance makes routing a challenging process. In IoT, some devices are connected directly to the internet and some devices connected to internet through gateway. The gateway acts as a bridge between IP Networks and Low Power Lossy Networks (LLN). RPL and LOADng are two major routing protocols used in 6LoWPAN for IoT applications. CALDUEL is a routing technique enhanced from loading protocol to minimize the Route Discovery Overhead. The proposed work is to enhance CALDUEL technique to increase throughput and packet delivery ratio. The proposed work is analyzed using different network scenarios.

IoT routing, 6LoWPAN, LOADng, QoS, Trust, Location validation

[1] Chen, D., Chang, G., Sun, D., Li, J., Jia, J., & Wang, X., TRM-IoT: A trust management model based on fuzzy reputation for internet of things. Computer Science and Information Systems, vol., 8, no., 4, 2011, pp. 1207-1228.
[2] S. Bozkurt1, G. Elibol, S. Gunal and U. Yayan “A Comparative Study on Machine Learning Algorithms for Indoor Positioning,” INISTA, International Symposium, 2015, pp. 1-8.
[3] D. Tran and Nguyen, “Localization in Wireless Sensor Networks Based on Support Vector Machines, IEEE Trans. on Parallel and Distributed Systems, vol. 19, no. 7, July 2008.
[4] S. Kumar et al., “Gaussian Process Regression for Fingerprinting based Localization,” Ad Hoc Networks (2016).
[5] H. Sallouha, A. Chiumento, and S. Pollin, Localization in Long-range Ultra Narrow Band IoT Networks using RSSI.” arXiv, March 2017.
[6] Z. Chen, F. Xia, T. Huang, F. Bu, and H. Wang, “A Localization Method for the Internet of Things.” The Journal of Super Computing, vol.63, pp.657-674, 2013.
[7] J. Ann Roseela, S. Ravi, and M. Anand, “RF based node location and mobility tracking in IoT,” Int. J. Appl. Eng. Res., vol. 11, no. 8, pp. 5714–5718, 2016.
[8] Kumar, A. Dalvin Vinoth, PD Sheba Kezia Malarchelvi, and L. Arockiam. "CALDUEL: Cost And Load overhead reDUction for routE discovery in LOAD ProtocoL." Advances in Computer and Computational Sciences. Springer, Singapore, 2017, pp. 229-237.
[9] Santiago S and Arockiam L, “E2TBR: Energy Efficient Transmission Based Routing for IoT Networks,” International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), vol. 7, no. 4, pp. 93–100, 2017.
[10] Yi, Jiazi, Thomas Clausen, and Antonin Bas. "Smart route request for on-demand route discovery in constrained environments." Wireless Information Technology and Systems, 2012, pp. 1-4.
[11] Kumar, A. Dalvin Vinoth, and L. Arockiam. "TOPQoS: TENSOR Based Optimum Path Selection in Internet of Things to Enhance Quality of Service.", vol. 2 no. 6, 2017, pp. 1-7.
[12] Kumar, V., & Tiwari, S. (2012). Routing in IPv6 over low-power wireless personal area networks (6LoWPAN): A survey. Journal of Computer Networks and Communications, 2012, pp. 1-5.
[13] Tsiftes, N., Eriksson, J. and Dunkels, “A Low-power wireless IPv6 routing with ContikiRPL”. In Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, 2010, pp. 406-407.
[14] Yi, J., Clausen, T., and Bas, A., “Smart route request for on-demand route discovery in constrained environments”. In Wireless Information Technology and Systems, 2012, pp. 1-4.
[15] Ee, G. K., Ng, C. K., Noordin, N. K., and Ali, B. M. “A review of 6LoWPAN routing protocols”. Proceedings of the Asia-Pacific Advanced Network, vol. 30, pp. 71-81.

A. Dalvin Vinoth Kumar, S. Santago L. Arockiam, "A Novel Technique to Enhance QoS using Location based Trust validation in 6LoWPAN for IoT Networks", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.1-5, 2018.

Our day to day life has always influenced by what people think. Opinion and ideas always precious our own opinions. Sentiment Analysis is machine learning approach in which machine analyzes and classifies the human’s sentiments, emotions, and opinions about some topic which are expressed in the form of either text or speech. Sentiment Analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. Till now, there are few different problems predominating in this research community, namely, sentiment classification, feature based classification and handling negations. In real world, public or consumer opinions about some product or brand are very important for its sales. Hence sentiment analysis is a very important research area for real life applications i.e. decision making. Hence this paper aims to cover different algorithms of opinion mining.

Sentiment Analysis, Opinion Mining, Machine Learning, Classification, Sentiment Polarity

[1] Dudhat Ankitkumar.M, Badra R.R, Mayura kinikar, “A Survey on sentiment analysis and opinion mining”, International Journal of Innovative Research in Computer and CommunicationEngineering,vol2,issue.11,Nov 2014.
[2] Vinodhini.G, Chandrasekaran.RM, “Sentiment analysis and opinion mining: A Survey”, International Journal of Advanced Research in Computer Science and Software Engineering, vol2,issue.6,june 2012.
[3] Bholane Savita Dattu, Deilpali V. Gore , “A Survey on sentiment analysis on twitter data using different techniques”, International Journal of Computer Science and Information Technologies, vol 6,issue.6,june 2015.
[4] Raisa Varghase, Jayasree M, “A Survey on sentiment analysis and opinion mining”, International Journal of Research in Engineering and Technology, vol2,issue.11,Nov 2013.
[5] Anu Sharma, Savleen kaur, “Techniques for sentiment analysis survey”, International Journal of Computer Technology and Application, Nov 2016. [6] M.Hu and B. Liu, “Mining and summarizing customer reviews,” in Proceedings of the Tenth ACMSIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’04), pp. 168–177, August 2004.
[7] B. Liu, M. Hu, and J. Cheng, “Opinion observer: analyzing and comparing opinions on the web,” in Proceedings of the International World Wide Web Conference Committee (IW3C2 ’05), Chiba, Japan, pp.10–14, May 2005.
[8] Krisztian Balog, Maarten de Rijke. 2006.Decomposing Bloggers` Moods - Towards a Time Series Analysis of Moods in the Blogosphere.
[9] Khairullah Khan, Baharum B. Baharudin, Aurangzeb Khan, Fazal-e-Malik, “Mining Opinion from Text Documents: A Survey”, 3rd IEEE International Conference on Digital Ecosystems and Technologies, 2009.
[10] K. Dave, S. Lawrence, and D. M. Pennock, "Mining the peanut gallery: opinion extraction and semantic classification of product reviews," presented at the Proceedings of the 12th international conference on World Wide Web, Budapest, Hungary, 2003.
[11] B. Liu. Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing, Second Edition, (editors: N. Indurkhya and F. J. Damerau), 2010.
[12]. B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis.” Foundations and Trends in Information Retrieval 2(1-2), pp. 1–135, 2008.
[13] G. Qiu, B. Liu, J. Bu, and C. Chen, "Opinion word expansion and target extraction through double propagation," Comput. Linguist., vol. 37, pp. 9-27, 2011.
[14] J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack, "Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques," presented at the Proceedings of the Third IEEE International Conference on Data Mining, 2003.
[14] N. Jakob and I. Gurevych, "Extracting opinion targets in a single- and cross-domain setting with conditional random fields," presented at the Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Cambridge, Massachusetts, 2010.
[15] W. Jin, H. H. Ho, and R. K. Srihari, "OpinionMiner: a novel machine learning system for web opinion mining and extraction," presented at the Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, Paris, France, 2009.

Angelpreethi, P. kiruthika, S. BrittoRameshKumar, "A Methodological Framework for Opinion Mining", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.6-9, 2018.

This Infrastructure Virtualization has been in place at industry for several years, and most recently new business drivers and rapid advances technology are taking virtualization a step further. While major drivers in the past have been around cost savings via server consolidation, Public cloud and software defined datacenter and business agility has become one of the key components in overall infrastructure. This paper provides a security architecture specification for infrastructure virtualization for private cloud which includes Threat analysis, vulnerabilities, Security architecture requirements and Security architecture specifications.

Security architecture; Infrastructure virtualization; Threat; Vulnerabilities

A. R. Riddle and S. M. Chung, "A Survey on the Security of Hypervisors in Cloud Computing," IEEE 35th International Conference on Distributed Computing Systems Workshops, Columbus, OH, pp.100-104, 2015.
[2] Arockiam, L. and Monikandan, S. and Parthasarathy G. “Cloud Computing: A Survey. International Journal of Internet Computing”, Volume 1, No. 2, , pp.26-33, 2011.
[3] A. Tolnai and S. H. von Solms, "Securing the Cloud`s Core Virtual Infrastructure" , IEEE International Conference on Broadband, Wireless Computing, Communication and Applications, Fukuoka, , pp. 447-452, 2010.
[4] K. M. Babu and P. S. Kiran, "A secure virtualized cloud environment with pseudo-hypervisor IP based technology", IEEE 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, pp. 626-630, 2016.
[5] Tony UcedaVelez, Marco M. Morana, “Risk Centric Threat Modeling: Process for Attack Simulation and Threat Analysis”, John Wiley & sons, pp. 547, 2015.
[6] S. N. Brohi, M. A. Bamiah, M. N. Brohi and R. Kamran, "Identifying and analyzing security threats to Virtualized Cloud Computing Infrastructures", IEEE International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM), Dubai, pp. 151-155, 2012.
[7] W. Yang and C. Fung, "A survey on security in network functions virtualization", IEEE NetSoft Conference and Workshops (NetSoft), Seoul, pp. 15-19, 2016.
[8] Arockiam, L. and Monikandan S. “Data Security and Privacy in Cloud Storage using Hybrid Symmetric Encryption Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering, Volume 2, No. 8, pp. 3064-3070, 2013.

S.S. Manikandasaran, K. Balaji, S. Raja, "Infrastructure Virtualization Security Architecture Specification for Private Cloud", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.10-14, 2018.

Big data is referred as a term that describes volume of data (terabytes to Exabyte’s), unstructured (include text and multimedia content), and complex in processing (from Medical data, Business transactions, Data capture by sensors, Social media/networks, Banking, Marketing, Government data, etc.). The traditional technologies are not sufficient to store, process and analyze the data. The unique technologies should be needed to analyze, manage the huge amount and unprocessed data. There are number of sources producing huge volume and variety of data. The number of sources produce amount of various descriptions for same object. This leads to data conflict and source conflict, when various sources generate various descriptions for same objects. Here it is the challenging one to identify which source produces quality information. The source could be identified by discovering the weight of the sources by using optimization method. Here optimization playing an important role to find highest achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. In comparison, maximization means trying to attain the highest or maximum result or outcome without regard to cost or expense.

Big data, optimization, Reliability, Accuracy, Consistency and Integrity

[1]. Amir Gandomi and Murtaza Haider “Beyond the hype: Big data Concepts, Methods and analytics”, International Journal of Information Management (IJIM) ELSEVIER, 2015, pp: 137-144.
[2]. Provost, F., & Fawcett, T. (2013).Data science and its relationship to big data and data driven decision making.Big Data, 1(1), 51–59.
[3]. Jerry Gao, Chunli Xie, Chuanqi Tao, “Big Data Validation and Quality Assurance –Issuses, Challenges, and Needs”, 2016 IEEE Symposium on Service-Oriented System Engineering, 978-1-5090-2253-3/16 $31.00 © 2016 IEEE , DOI 10.1109/SOSE.2016.63, 433-41.
[4]. Kushal Patel ,“Big Data, its Issues and Challenges”, 2017 IJEDR, Volume 5, Issue 3, ISSN: 2321-9939,123-27
[5].http://www.iso.org/iso/catalogue_detail?csnumber=45481)
[6]. Q. Li, Y. Li, J. Gao, B. Zhao, W. Fan, and J. Han, “Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation,” in Proceedings of the 2014 ACM SIGMOD international conference on Management of data. ACM, 2014, pp. 1187–1198.
[7]. Cai, L and Zhu, Y 2015 The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14: 2, pp. 1-10, DOI: http://dx.doi.org/10.5334/dsj-2015-002.
[8]. S. Boyd and L. Vandenberghe. Convex optimization. Cambridge University Press, 2004
[9]. Fan Zhang, Li Yu, Xiangrui Cai, Ying Zhang, Haiwei Zhang,“Truth Finding from Multiple Data Sources by Source Confidence Estimation”, 978-1-4673-9372-0/15 $31.00 © 2015 IEEE DOI 10.1109/WISA.2015.45, 153-56.
[10]. A. Banerjee, S. Merugu, I. S. Dhillon, and J. Ghosh. Clustering with bregman divergences. JMLR, 6:1705–1749, 2005.
[11]. Arunima Kumari, Dr. Dinesh Singh, Reviewing Truth Discovery Approaches And Methods For Big Data Integration. International Journal of Science, Engineering and Technology Research (IJSETR), Volume 5, Issue 8, Pages: 2766-2774, August 2016.
[12]. J. Bleiholder and F. Naumann. Conflict handling strategies in an integrated information system. In Proc. of IIWeb, 2006.
[13]. Steven Ji-fan Ren, Samuel Fosso Wamba, Shahriar Akter, Rameshwar Dubey
& Stephen J. Childe (2016): Modelling quality dynamics, business value and firm performance
in a big data analytics environment, International Journal of Production Research, DOI:10.1080 /00207543.2016.1154209

P. Bastin Thiyagaraj, A. Aloysius, "Quality Enhancement by Resolving Conflict using Optimization in Big data", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.15-17, 2018.

Cloud computing is an Internet-based computing service provided by the third party allowing share of resources and data among devices. It is widely used in many institutions and organizations. Now a days and becoming more popular because it changes the way of how the Information Technology of an organization is organized and managed. It provides lots of benefits such as simplicity and lower costs, almost unlimited storage, least maintenance, easy utilization, backup and recovery, continuous availability, quality of service, automated software integration, scalability, flexibility and reliability, easy access to information, elasticity, quick deployment and lower barrier to entry. This paper begins with defining cloud computing, its key characteristics, deployment and service models, relationship between them. Then paper describes the role of cloud computing in higher education.

Cloud computing, Characteristics of Cloud computing, Models of Cloud computing, Higher education.

[1]. K. Kurelovi, S. Rako and J. Tomljanovi, ”Cloud Computing in Education and Student’s Needs,” MIPRO, 2013, pp.856-861.
[2]. Rania Mohammedameen Almajalid “A Survey on the Adoption of Cloud Computing in Education Sector”
[3]. Sangeetha Rajesh, "Analysis of Security in Cloud-Learning Systems", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.36-40, 2017
[4]. Anjali Jain U.S Pandey “Role of Cloud Computing in Higher Education” Volume 3, Issue 7, July 2013
[5]. P.K. Paul, P.S. Aithal, A. Bhuimali, "Enhancing Cloud and Big Data Systems for healthy Food and Nutrition Information Systems Practice: A Conceptual Study", International Journal of Scientific Research in Biological Sciences, Vol.4, Issue.5, pp.18-22, 2017.
[6]. Saju Mathew “Implementation of cloud computing in Education –A Revolution, Vol. 4, No. 3, June 2012
[7]. Kiran Yadav “Role of cloud computing in Education, Vol. 2, Issue 2, February 2014.
[8]. Dan Morrill , “Cloud Computing in Education”, September 12, 2011,
[9]. https://www.cloudave.com/14857/cloud- computing-in-education/
[10]. Anand More, Priyesh Kanungo, "Use of Cloud Computing for Implementation of e-Governance Services", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.115-118, 2017

R. Josephine Therese , T. Semalatha, "Cloud Computing Applications in Higher Education", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.18-20, 2018.

MANET system is a sort of remote system, is self-designing frameworks less system gadgets are associated by remote. The gadgets of MANET organize is allowed to move freely toward any path that is the reason connecting with whatever other gadgets is effectively done. Each must forward movement random to its own particular utilize, and consequently be a switch. The essential objective of Mobile specially appointed system is every gadget to consistently keep up the data required to appropriately course movement. This paper discuss about the path loss in ad-hoc environment to compute path to destination. Current location and identified location to identify attacker node to ensure QoS. The packet loss is reduced and results of RREQ utilization is calculated using network scenarios.

MANET, Energy efficiency, Link Failure, AODV, Dalwi-AODV

[1] Shenbagapriya, R., and Kumar Narayanan. "An efficient proactive source routing protocol for controlling the overhead in mobile ad-hoc Networks." Indian Journal of Science and Technology 8.30 (2015): 1.
[2] A. Dalvin Vinoth kumar and L. Arockiam, Route Discovery Overhead Aware Routing Protocol for IoT to Enhance QoS, I J C T A, 9(27), 2016, pp. 249-254.
[3] Kumar, A. Dalvin Vinoth, PD Sheba Kezia Malarchelvi, and L. Arockiam. "CALDUEL: Cost And Load overhead reDUction for routE discovery in LOAD ProtocoL." Advances in Computer and Computational Sciences. Springer, Singapore, 2017. 229-237.
[4] Chen, Byron H., Maria E. Palamara, and Charles Varvaro. "Local positioning system." U.S. Patent No. 6,748,224. 8 Jun. 2004.
[5] Foster, Ian, et al. "End-to-end quality of service for high-end applications." Computer Communications 27.14 (2004): 1375-1388.
[6] Rastegar, Mohammad, Mahmud Fotuhi-Firuzabad, and Farrokh Aminifar. "Load commitment in a smart home." Applied Energy 96 (2012): 45-54.
[7] Kikinis, Dan. "System for controlling processing of data passing through network gateway between two disparate communications network." U.S. Patent No. 6,603,762. 5 Aug. 2003.
[8] Kumar, A. Dalvin Vinoth, A. Vithya Vijayalakshmi, and L. Arockiam. "TENSOR: A Technique to Enhance IoT Data Security using Bluetooth Low Energy Network for Smart Home Environment." (2017).
[9] Ko, JeongGil, et al. "Evaluating the Performance of RPL and 6LoWPAN in TinyOS." Workshop on Extending the Internet to Low Power and Lossy Networks Vol. 80. 2011 pp. 85-90
[10] Liu, C., Wu, K., & He, T. (2004, October). Sensor localization with ring overlapping based on comparison of received signal strength indicator. In Mobile Ad-hoc and Sensor Systems, 2004 IEEE International Conference on (pp. 516-518).
[11] Parssinen, Aarno, Jussi Vepsalainen, and Pauli Seppinen. "Method and apparatus for continuously controlling the dynamic range from an analog-to-digital converter." U.S. Patent No. 6,993,291. 31 Jan. 2006.
[12] Shanthi, P. M., A. Dalvin Vinoth Kumar, and L. Arockiam. "A Technique to Enhance Quality of Service using weighted Path Mechanism." International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol 4, no. 2, (2017), pp. 413-417.
[13] Unde, Mahadev G., and Bansidhar E. Kushare. "Analysis of Electromagnetic Fields of 1200kV UHV-AC Transmission Lines." Computational Intelligence and Communication Networks (CICN), 2013 5th International Conference on. IEEE, 2013.
[14] Loehr, Jim, James E. Loehr, and Tony Schwartz. The power of full engagement: Managing energy, not time, is the key to high performance and personal renewal. Simon and Schuster, 2005.
[15] Hu, Ningning, and Peter Steenkiste. "Evaluation and characterization of available bandwidth probing techniques." IEEE journal on Selected Areas in Communications 21.6 (2003): 879-894.

PM. Shanthi, A. Dalvin Vinoth Kumar, M. Edison, A. Aloysius, "TNLIQ: Trust Validation in Ad-Hoc Networks using Dynamic Location Identification to ensure QoS", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.21-24, 2018.

The drastic growth in cloud computing model has led to establishment of large scale virtualized data centers. Data centers consume enormous amount of electrical energy resulting in high operating costs and carbon-di-oxide emissions. The energy efficiency issues of data centers are of major importance as costs of power and cooling make up a significant part of their operational costs. Energy efficiency is an important issue. It is needful to reach a green solution to address all trends that affects Cloud energy consumption. There are number of ways of reducing power usage in data centers. There are four approaches of increasing energy efficiency, hardware level energy optimization, energy aware scheduling in grid systems, server consolidation by means of virtualization and power minimization. Among these techniques server consolidation is one of the main applications of virtualization technology in data centers. In this research work introduces Power Aware Load Prediction algorithm (PALP) for server consolidation providing efficient energy usage in Cloud computing making it greener is proposed. The virtualization has benefits of reducing total cost, increasing availability and agility to use this feature in Cloud computing environment. The PALP algorithm predicts the load in the host and act accordingly to minimize resource usage. The load prediction method is proposed which performs the classification of host overloading and under loading. This system improves energy efficiency rate and reduces power usage using PALP algorithm. The average energy efficiency rate is 87.93 kWh and time complexity is O (n) MHz which is considerably appreciable compared of existing analyzed algorithms.

PALP algorithm, Green cloud, virtualization, dynamic provisioning, energy efficiency

[1] “Green Cloud computing and Environmental Sustainability” Saurabh Kumar Garg and Rajkumar Buyya IEEE Xplore,
[2] “Performance Evaluation of a green Scheduling algorithm for energy savings in cloud computing” Troung Vinh Troung Duy,Yukinori Sato,Yashushi Inoguchi IEEE Xplore, March 2010.
[3] Sakshi kathuria, "A Survey on Security Provided by Multi-Clouds in Cloud Computing", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.1, pp.23-27, 2018.
[4] Yi Lua,Qiaomin Xiea,Gabriel Kliatb,Gellerb,James R.Larusb and Albert Greenberge “Join-Idle Queue- A novel Load Balancing Algorithm for Dynamically Scalable Web Services”, ELSEVIER, 2011.
[5]Gayathri.B,”Green Cloud Computing”-IET Conference Publication,IEEE Xplore Jan 2014
[6]Gayathri.B and Dr.R.Anbuselvi,”Holistic Approach for Green Cloud Computing and Environmental Sustainability approach”,IJETCSE Vol 12 Issue 4 Feb 2015
[7] Gayathri.B and Dr.R.Anbuselvi,”Holistic Approach for Green Cloud computing and Environmental Sustainability”IJARCSSE Mar 2015
[8] Gayathri.B and Dr.R.Anbuselvi,”Effects of Greem Cloud computing and Environmental Sustainability”IJERT Mar 2015
[9]Gayathri.B and Dr.R.Anbuselvi,”Hybrid Approach for enhancing the metrics in Green Cloud Computing” IJARCSSE Vol 7 Issue 11 Nov 2017

Gayathri. B, R. Anbuselvi, "PALP-Power Aware Load Prediction Algorithm to Enhance Energy Efficiency in Green Cloud Computing", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.25-28, 2018.

Cloud computing is an emerging internet based computing technology whereby the services are provided on-demand. The cloud environment basically comprises of the cloud user, broker and the Cloud Service Provider (CSP). Cloud computing basically offers platform, software and infrastructure as its services to its user. Multiple users can access resources in cloud from different devices. The actual resources are stored in physical storage devises and are accessed by the virtual machines (VM) serving a request. These user requests are to be scheduled and provisioned in an optimum time and cost with the help of the resource broker. This paper provides a brief review on cloud computing, its types, service delivery models, cloud architecture, state-of-the-art and the research issues in cloud computing.

Cloud Service Provider (CSP), Virtual Machines (VM), Cloud Architecture, Resource Broker

[1] Rabi Prasad Padhy, Manas Ranjan Patra and Suresh Chandra Satapathy, “ Cloud Computing: Security Issues and Research Challenges”, IRACST - International Journal of Comput.er Science and Information Technology & Security (IJCSITS) Vol. 1, No. 2, December 2011
[2] Felix Meixner and Ricardo Buettner,” Trust as an Integral Part for Success of Cloud Computing”, The Seventh International Conference on Internet and Web Applications and Services(ICIW 2012).
[3] Sakshi kathuria, "A Survey on Security Provided by Multi-Clouds in Cloud Computing", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.1, pp.23-27, 2018.
[4] Anitya Kumar Gupta, Srishti Gupta, "Security Issues in Big Data with Cloud Computing", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.27-32, 2017
[5] V.K. Saxena, S. Pushkar, "Privacy Preserving using Encryption Proxy in Data Security", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.36-41, 2017
[6] XiaoJun Chen, Jing Zhang, Junhuai Li and Xiang Li, “Resource Virtualization Methodology for on-demand allocation in cloud computing systems, Springer-Service oriented computing and application, Vol.7, Issue 2, June 2013, pp. 77-100.
[7] R. Jamina Priyadarsini and L. Arockiam, “Failure Management in Cloud: An Overview”, international Journal of Advanced Research in Computer and Communication Engineering, vol.2, Issue 10, October 2013.
[8] Foram F Kherani and Prof.Jignesh Vania, “Load Balancing in cloud computing”, 2014 IJEDR Volume 2, Issue 1, ISSN: 2321-9939.
[9] Dr. Amit Agarwal and Saloni Jain, “Efficient Optimal Algorithm of Task Scheduling in Cloud Computing Environment”, International Journal of Computer Trends and Technology (IJCTT) – volume 9 number 7– Mar 2014.
[10] Rajarshi Roy Chowdhury, “Security in Cloud Computing”, International Journal of Computer Applications (0975 – 8887) Volume 96– No.15, June 2014.
[11] Maram Mohammed Falatah and Omar Abdullah Batarfi, “Cloud Scalability Considerations”, International Journal of Computer Science & Engineering Survey (IJCSES) Vol.5, No.4, August 2014.
[12] Jasbir Kaur and Supriya Kinger, “Efficient Algorithm for Fault Tolerance in Cloud Computing”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (5), 2014, 6278-6281.
[13] Eli WEINTRAUB and Yuval COHEN, “Cost Optimization of Cloud Computing Services in a Networked Environment”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 6, No. 4, 2015.
[14] Priyanka Sankhla and Sohit Agarwal, “Efficient resource Utilization in Cloud Computing Using Revised ROSP Algorithm (ERROSP)”, International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169, Volume: 4 Issue: 1 159 – 162.
[15] Cloud Computing by George Reese.
[16] http://thecloudtutorial.com-15.10.2013

R. Jemina Priyadarsini, "State-Of-The-Art and Research Issues in Cloud Computing", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.29-33, 2018.

YouTube, with large number of content creators, has turn into the ideal destination for watching videos online. Through the associate program, YouTube allows pleased creators to monetize their popular videos. Of significant consequence for content creators is which meta-level features (e.g. title, tag, thumbnail) are most receptive for promoting video status. The attractiveness of videos also depends on the social dynamics, i.e. the interface of the content creators (or channels) with YouTube users. The peer to peer (P2P) file distribution applications have owed a considerable amount of today’s Internet traffic. Along with various P2P file sharing protocols, BitTorrent is the mainly widespread and trendy one that attracts monthly a quarter of a billion users from all over the world. Comparable to other P2P file sharing protocols, BitTorrent is frequently used for unlawful sharing of copyright protected files such as movies, music and TV series. To obstruct this enormous amount of illegal file distributions, anti-P2P companies have arisen to place against these applications (specially the BitTorrent). And our proposed approach Diffie Hellman algorithm ensures the secure transmission of data over a secure channel and enhances the performance of this proposed approach.

[1] (2014, March 16) An estimate of infringing use of the internet. [Online]. Available: http://documents.envisional.com/docs/ Envisional-Internet Usage-Jan2011.pdf
[2] (2014, March 16) Google trends. [Online].Available:http://www.google.com/trends/explore#q=BitTorrent,kazaa,gnutella,edonkey,opennap&date=today%2012-m&cmpt=q
[3] (2014, Jan 5) BitTorrent. [Online]. Available: http://en. wikipedia.org/wiki/ Bit Torrent
[4] (2014, March 16) The eclipse foundation open source community website. [Online]. Available: http://eclipse.org
[5] (2014, March 16) Linux.org website. [Online]. Available: http://linux.org
[6] (2014, March 16) Comparison of BitTorrent clients. [Online]. Available: http://en.wikipedia.org/wiki/Comparison of BitTorrent clients
[7] P. Dhungel, D. Wu, and K. W. Ross, “Measurement and mitigation of Bit Torrent leecher attacks,” Computer Communications, vol. 32, no. 17, pp. 1852–1861, 2009.
[8] G. Siganos, J. M. Pujol, and P. Rodriguez, “Monitoring the BitTorrent monitors: A bird’s eye view,” in Passive and Active Network Measurement. Springer, 2009, pp. 175–184.
[9] (2014, March 16) Media defenders official website.[Online].Available: http://www.mediadefender.com
[10] (2014, March 16) Copyright at mit. [Online].Available:http://web.mit.edu/copyright/dmca-notices.html

P. Jayalakshmi , "A Confidential and Efficient Query in the Large Scale Attack", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.34-37, 2018.

A collection of mobile devices forms a network called Mobile Adhoc Network (MANET) which establishes communications with the help of intermediate nodes without a fixed infrastructure. The quality of the MANET depends on many parameters like delay, jitter, bandwidth and throughput. Though there are many techniques available for improving the performance through guaranteeing higher throughput, MANET is in need of new techniques for improving throughput which could lead towards higher goodput. There are various interferences exist which affects the throughput. In this research work, the throughput of the networks is increased by the proposed mechanism named as LIRANT which considers the interference of the nodes during the communication. This technique is implemented by having the Ant Colony Optimization along with the least interference routing technique. The simulation result shows that after incorporating the improved least interference routing with ACO, MANET performs well with increased throughput.

MANET, ACO, interference, throughput

[1] Mohammad Ilyas, Imad Mahgoub, “Mobile computing handbook”, Auerbach Publications, 2005
[2] Amol B.Suryawanshi, Baljit Kaur Saini, "Survey on Various Routing Protocols in Ad-hoc Networks", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.174-178, 2017.
[3] Gianni A. Di Caro, Frederick Ducatelle, and Luca M. Gambardella, “Ant Colony Optimization for Routing in Mobile Ad Hoc Networks in Urban Environments”, IDSIA / USI-SUPSI Dalle Molle Institute for Artificial Intelligence Galleria 2, 6928 Manno, Switzerland, Technical Report No. IDSIA-05-08 May 2008.
[4] R. Schoonderwoerd, O. Holland, J. Bruten, and L. Rothkrantz. Antbased load balancing in telecommunications networks. Adaptive Behavior, 5(2):169–207, 1996.
[5] G. Di Caro and M. Dorigo. "AntNet: Distributed stigmergetic control for communications networks", Journal of Artificial Intelligence Research (JAIR), 9:317–365, 1998
[6] Rajesh Dhakad, Abhinesh Kumar, "Drop of RERR Leads Routing Loop in AODV", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.5, pp.6-9, 2017.
[7] Bibhash Roy, Suman Banik, Parthi Dey, Sugata Sanyal, Nabendu Chaki, "Ant Colony based Routing for Mobile Ad-Hoc Networks towards Improved Quality of Services", Journal of Emerging Trends in Computing and Information Sciences, VOL. 3, NO. 1, January 2012
[8] Umesh Kumar Singh, Jalaj Patidar and Kailash Chandra Phuleriya, "On Mechanism to Prevent Cooperative Black Hole Attack in Mobile Ad Hoc Networks", International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.1, pp.11-15, 2015
[9] Leena Pal, Pradeep Sharma, Netram Kaurav and Shivlal Mewada, "Performance Analysis of Reactive and Proactive Routing Protocols for Mobile Ad-hoc –Networks", International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.5, pp.1-4, 2013.
[10] M. Aissani, M. Fenouche, H. Sadour, and A. Mellouk, “Ant-DSR: cache maintenance based routing protocol for mobile ad-hoc networks,” AICT 2007. The Third Advanced International Conference in Telecommunications, pp. 35–35. 2007
[11] J. Alex Pontes Martins, S. Luis O. B. Correia, J. Celestino J´unior, “Ant-DYMO: A Bio-Inspired Algorithm for MANETS”, 17th International Conference on Telecommunications, 2010
[12] Jianping Wang, E. Osagie, P. Thulasiraman, R. Thulasiram, "Hopnet: a hybrid ant colony optimization routing algorithm for mobile ad hoc network", Elsevier - Ad Hoc Networks, Vol 7, No. 4, 2009
[13] A. Massayuki Okazaki and A. Augusto Frohlich, “AD-ZRP: Antbased Routing Algorithm for Dynamic Wireless Sensor Networks”, 18th International Conference on Telecommunications, 2011
[14] Gustavo B. Figueiredo, Nelson L. S. da Fonseca, Jos´e A. Suruagy Monteiro, "A Minimum Interference Routing Algorithm", Proceedings of IEEE International Conference on Communications, France, June 2004
[15] Geert Heijenk, Fei Liu, "Interference-based routing in multi-hop wireless infrastructures", Computer Communications, Vol. 29, No. 13, 2006
[16] Jochen H. Schiller, "Mobile Communications", Addison-Wesley, 2003, pp.339-340

P. Calduwel Newton, M. Syed Khaja Mohideen , "LIRANT: An Improved Ant Colony Optimization mechanism with Least Interference Routing for Enhancing Throughput of MANET", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.38-42, 2018.