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The enormous development in the computer and communication technology led to use various web based software applications using with the Internet. Cloud computing is an emerging technology where millions of clients and individuals use various cloud services like storage, software’s and infrastructure on rental basis. Tremendous increase in the number of users has led to some issues and problems. One of the main issues is balancing the work load and increasing the performance of the system. An efficient and dynamic algorithm called “Enhanced Round Robin scheduling algorithm” has been proposed in this paper for balancing the load. The work distribution to the various balancers by the main controller is done using two parameters: one is balancer status (idle, normal and overload) and the other is percentage of overall balancer status (idleness and normalness). Based upon these parameters, the balancers are sorted and stored in a list. The work is distributed to various balancers in a round robin fashion.

Main controller,Balancer, Nodes, Enhanced Round Robin Sceduling, Idle, Normal and Overload

[1] B. Adler, Load balancing in the cloud: Tools, tips and techniques, http://www.rightscale. com/info center/whitepapers/Load-Balancing-in-the-Cloud.pdf, 2012
[2] K. Nishant, P. Sharma, V. Krishna, C. Gupta, K. P. Singh, N. Nitin, and R. Rastogi, Load balancing of nodes in cloud using ant colony optimization, in Proc. 14th International Conference on Computer Modelling and Simulation (UKSim), Cambridgeshire, United Kingdom, Mar. 2012, pp. 28-30.
[3] Jasp reetKaur et al. “Comparison of load balancing algorithms in a Cloud”, International Journal of En gineerin g Research and Applications (IJERA), May-Jun 2012.
[4] K. Nuaimi, N. Mohamed, M. Nuaimi and J. Al-Jaroodi, “A Survey of Load Balancing in Cloud Computing: Challenes and Algorithmsg,” 2012 IEEE Second Symposium on Network Cloud Computing and Applications, 2012.
[5] T. Zuhori, T. Shamrin, R. Tanbin and F. Mahmud, “An Efficient Load Balancing Approach in Cloud Environment by using Round Robin Algorithm,” International Journal of Artificial Intelligence and Mechatronics, vol. 1, 2013, pp. 1-4.
[6] T. Kokilavani and G. Amalarethinam, “Load Balanced Min-Min Algorithm for Static Meta-Task Scheduling in Grid Computing” International Journal of Computer Applications, vol. 20, No. 2, April 2011, pp. 43-49.
[7] Rajesh George rajan,V. jeyakrishna “A survey on Load balancing in cloud computing environment”, International journal of advanced research in computer engineering, December 2013 .
[8] Ajaltouni, E. E., Boukerche, A., & Zhang, M. (2008). An Efficient Dynamic Load Balancing Scheme for Distributed Simulations on a Grid Infrastructure. 2008 12th IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications, 61-68. Ieee. doi:10.1109/DS-RT.2008.38
[9] Wang, S-C., K-Q. Yan, W-P. Liao and S-S. Wang, "Towards a load balancing in a three-level cloud computing network," in proc. 3rd International Conference on. Computer Science and Information Technology (ICCSIT), IEEE, Vol. 1, pp: 108-113, July 2010.
[10] Radojevic, B. and M. Zagar, "Analysis of issues with load balancing algorithms in hosted (cloud) environments." In proc. 34th International Convention on MIPRO, IEEE, 2011.
[11] K. S. Rashmi, V. Suma and M. Vaidehi, “Enhanced Load Balancing Approach to Avoid Deadlocks in Cloud,” Special Issue of International Journal of Computer Applications (0975 – 8887) on Advanced Computing and Communication Technologies for HPC Applications (ACCTHPCA), June 2012, pp. 31–35.
[12] Z. Chaczko, V. Mahadevan, Sh. Aslanzadeh and Ch. Mcdermid, “ Availability and Load Balancing in Cloud Computing, ” 2011 International Conference on Computer and Software Modeling, IACSIT Press, Singapore, vol.14, pp. 134-140.
[13] S.Penmatsa and A. T. Chronopoulos , Game- theoretic static load balancing for distributed systems, Journal of parallel and Distributing Computing, vol. 71, no.4, pp. 537-555, Apr.2011.
[14] Ruhi Guptha, Rekha Bhatia “An Enhanced and secure approach of load balancing in cloud computing” International Journal of computer Science and Engineering(IJCSE), vol.02, Issue.08, pp.112-116, Aug-2014.

G.Thejesvi and T.Anuradha, "Distribution of Work Load at Main Controller Level Using Enhanced Round Robin Scheduling Algorithm in A Public Cloud," International Journal of Computer Sciences and Engineering, Vol.3, Issue.12, pp.75-78, 2015.

Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model from the data mining perspective. The planning of several optimal tuning processes, the comparison of different designs (through graphics or the numeric results obtained), and the management of data files saved during the planned optimal tunings process. The developed tool was made available to students for them to solve a practical problem and, subsequently, the impact of its use was evaluated. There are techniques to learn the categories (clustering). Methods of pattern recognition are useful in many applications such as information retrieval, data mining.

MIMO,NLP,RGA,PIP

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Kalpana, "Graph Analysis with Big-data," International Journal of Computer Sciences and Engineering, Vol.3, Issue.12, pp.79-81, 2015.

Mobile ad hoc network (MANET) is a self-configuring network of mobile nodes formed anytime and anywhere without the help of a fixed infrastructure or centralized management. It has many potential applications in disaster relief operations, military network, and commercial environments. Due to dynamic, infrastructure-less nature, the ad hoc networks are vulnerable to various attacks. AODV is an important on-demand distance vector routing protocol for mobile ad hoc networks. It is more vulnerable to black & gray hole attack. In MANET, black hole is an attack in which a node shows malicious behavior by claiming false RREP (route reply) message to the source node and correspondingly malicious node drops the entire receiving packet. In fuzzy based IDS an intrusion detection system is presented for MANETs against black hole attack detection as well as prevention using fuzzy logic. But it has some issues such as the attack detection accuracy and speed are less, and also it emphasized on very limited features for data collection towards detection of very specific range of attacks. To overcome above issues, the Adaptive Neural Fuzzy Inference Systems (ANFIS) is proposed and detect black hole attack in MANETs. The proposed system will identify the attack over the node as well as provide the solute on to reduce the data loss over the network. Through simulations, the results prove the proficiency of proposed technique which detect the black hole and improves the network performance.

MANETs, ANFIS, Intrusion detection, Black hole Attack, AODV

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[8] MonitaWahengbam,” Intrusion Detection in MANET using Fuzzy Logic”, 978-1-4577-0748-3/12/$26.00 © 2012 IEEE.
[9] G.Kalpana, Dr..M.Punithavalli," fuzzy logic technique for gossip based reliable broadcasting in mobile ad hoc networks", Journal of Theoretical and Applied Information Technology 31st May 2013. Vol. 51 No.3.
[10] ElmarGerhards-Padilla,” Detecting Black Hole Attacks in Tactical MANETs using Topology Graphs”, 32nd IEEE Conference on Local Computer Networks 0742- 1303/07© 2007 IEEE.
[11] LathaTamilselvan “Prevention of Co-operative Black Hole Attack in MANET” JOURNAL OF NETWORKS, VOL. 3, NO. 5, MAY 2008
[12] K.Selvavinayaki, K.K.Shyam Shankar” “Security Enhanced DSR Protocol to Prevent Black Hole Attacks in MANET”International Journal of Computer Applications (0975-8887).volume7-volume11, October 2010.
[13] Jathe S.R, Dakhane D.M ,” A Review Paper on Black Hole Attack and Comparison of Different Black Hole Attack Techniques “International Journal of Cryptography and Security ISSN: 2249-7013 & EISSN: 2249-7021
[14] Rashid Sheikhl, Mahakal Singh Chande et.al “Security Issues in MANET: Review” 978-1- 4244-7202-4/10/$26.00 ©2010 IEEE
[15] Ochola EO,” A Review of Black Hole Attack on AODV Routing in MANET. Information Security South Africa Conference, Proceedings ISSA 2011.
[16] A. Mitra, R. Ghosh, A. Chakraborty, D. Srivastva, “An Alternative Approach to Detect Presence of Black HoleNodes in Mobile Ad-Hoc Network Using Artificial Neural Network” in IJARCSSE, 2013.
[17] G. Wahane, A. Kanthe, s”Techniques for detection of cooperative Black hole Attack in MANET” in IOSR-JCE, 2014.
[18] AvinashSavaliya, Hardik Patel, BhavikPandya,"Fuzzy Based IDS for Black Hole Detection and Prevention in MANET", www.academia.edu.

K.Santhi and V.Abinaya, "Intrusion Detection System for Black Hole Detection and Prevention in MANET Using Adaptive Neural Fuzzy Inference Systems," International Journal of Computer Sciences and Engineering, Vol.3, Issue.12, pp.82-88, 2015.

Visual information is the richest probably different existing information sources in our daily lives. The extraction of this information by processing systems and image analysis has attracted growing interest. The image processing is a process involving several stages, that it was born from the need to replace the human observer by the machine. He works in many fields such as medicine. A must in all image analysis process is the segmentation. By providing a compact description of the image, more exploitable than all the pixels, the image segmentation facilitates automatic interpretation of an image similar to human interpretation. Indeed, she was inspired by the human visual perception system that uses the concept of similarity and difference in order to locate and delineate the objects in an image. It can be defined as following: the image segmentation is a low-level process of creating a partition of the image into subsets called regions in a way that no region is empty; the intersection between the two regions is empty and covers all regions throughout the image. A region is a set of connected pixels having common properties that differentiate the pixels neighboring regions. This task although fluently although raised by the human visual system, is actually complex and remains a challenge for the image processing community despite several decades of research. Thus, several segmentation methods have been proposed in the literature, and can be classified into three major approaches: Approach area, Approach contour, cooperative approach. This article studies the problem of segmentation of MRI brain images. We worked precisely on cooperating more automatic classifiers to exploit complementarities between different methods or operators and increase the strength of the segmentation process. Our approach focuses on the FCM algorithm (Fuzzy c-means), the sum of degrees of membership of an individual given to all possible classes being 1. To make the algorithm robust to inaccuracies and ambiguous data that can considerably affect on the classes centers, we introduce the notion of ambiguity rejection.

Segmentation, C-means, MRI brain, thresholding, Otsu

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[16] Giovanna Menardi , ‘’a Nonparametric Clustering Method For Image Segmentation’’, http:// convegni.unica.it/cla dag2015 /files /2015 /10/Menardi.pdf.,Page No (1-4).
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[18] Ahmad Adel Abu Shareha, Mandava Rajeswari, Dhanesh Ramachandram,’’ Textured Renyi Entropy for Image Thresholding’’, Fifth International Conference on Computer Graphics, Imaging and Visualisation, DOl 10.1109/CGIV.2008.48 ,978-0-7695-3359_9/08 $25.00 © IEEE ,Page No (185-192),2008 .
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[22] Martin Lindquist,’’ Statistical Methods in functional MRI’’, Overview of fMRI Data Analysis, 04/02/13.Page No (1-8),2013.
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[33] Azzeddine Riahi ,’’MRI Image Segmentation by K-Means Clustering Method and Detection of Lesions’’, International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064, Volume 4 Issue 6, Page No(2484-2492),June 2015
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Azzeddine Riahi, "Image Segmentation Techniques Based on Fuzzy C-Means and Otsu, Applied to the Brain MRI in Tumor Detection," International Journal of Computer Sciences and Engineering, Vol.3, Issue.12, pp.89-101, 2015.

For several applications, segmented images gives better insight with increased accuracy and repeatability. Several segmentation algorithms were proposed for clinical purposes to diagnose, treatment and for tracking the progress of disease. Segmenting structures from medical images and reconstruction of specific anatomical shapes is difficult due to large size of datasets, complexity and variability of a given image. It is therefore, better to view the segmented images than the whole scan obtained from CT or MRI. Particularly, in surgical planning over diseased organ, segmented part is enough for visualization than the whole image. For example, if there is fracture in skull bone, it would be sufficient to view the fractured bone from a diagnostic image. Watershed segmentation is widely used in medical image processing applications because it is relatively fast in terms of computational time. An algorithm to segment the cranial vault bone based on Watershed method is presented. It is also implemented for few specific cranial vault abnormalities to demonstrate the results.

Watershed; segmentation; cranial vault; MRI; Lesions

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[21] P K Nayak M Ch, A K Mahapatra M Ch.” Primary reconstruction of depressed skull fracture - The changing scenario”. Indian Journal of Neurotrauma (IJNT) 2007, Vol. 5, No. 1, pp. 35-38.
[22] M. Seruya, Tan SY, Wray AC, Penington AJ, Greensmith AL, Holmes AD, Chong DK. “Total cranial vault remodeling for isolated sagittal synostosis: part I. Postoperative cranial suture patency”. Plast Reconstr Surg. 2013 Oct; 132(4): pp.602e-610e
[23] J. Rajiv. Iyengar, BS, Petra M. Klinge, Wendy Chen, Stephen R. Sullivan, Helena O. Taylor, “Management of craniosynostosis at an advanced age: Clinical findings and interdisciplinary treatment in a 17 year-old with pan-suture synostosis”. Interdisciplinary Neurosurgery. Vol. 2, Iss.1, 2015, pp. 61-64.

K. Prahlad Rao, "Image Segmentation of Cranial Vault for Clinical Analysis," International Journal of Computer Sciences and Engineering, Vol.3, Issue.12, pp.102-105, 2015.

Wireless communications has become a new field to growing rapidly in our recent life and creates a huge impact on almost all the features of our daily lives. An enormous technological transformation in the prior two decades has been provided a potential growth in the area of digital communication and lots of the most recent applications and technologies are to come each day as a result of these valid reasons. Digital modulation contributes to the growth of mobile communications by increasing the quality, speed and capacity of the wireless network. In the communication, the idea of modulation is a primary factor for the reason that without a scheme of appropriate modulation, it would be not possible to attain a planned flow. The offered bandwidth, allowable power and the level of inherent noise of the system are the constraints which must be taken into account in the development of communication systems. Because of the error free capacity in the digital modulation, it is chosen over the techniques of analogue modulation. The WiMax uses combinations of distinct modulation schemes such as BPSK, QPSK, 4-QAM and 16-QAM and it is a capable technology which provides video, data and high speed voice services. In this literature the review of documentation on the various digital modulation techniques that are typically used for wireless communication is presented.

Digital Modulation; Amplitude Shift Keying; Phase Shift Keying; Binary Phase Shift Keying (BPSK); Quadrature Phase Shift Keying (QPSK); QAM; Bit Error Rate

[1] J.T. Haitham, M.F.M Salleh, “Multi-carrier Transmission Techniques for Wireless Communication Systems: A Survey,” WSEAS Transactions on Communications, Vol. 8, Issue 5, pp. 215-225, 2009.
[2] B.W. Hiroshi, W.M. Ramjee, “Digital Communication,” Artech House, London, pp. 5-8, 2005.
[3] G. Tharakanatha, S.K. Mahaboob, V.B. Chanda, I. Hemalatha, “Implementation and Bit Error Rate analysis of BPSK Modulation and Demodulation Technique using MATLAB,” IJETT, vol. 4, Issue 9, 2013.
[4] V.Chauhan, M. Arora, R.S. Chauhan, “Comparative BER Performance of PSK based Modulation Techniques under Multipath Fading,” Advances in Applied Science Research, vol. 2, no. 4, pp. 521-524, 2011.
[5] J. Bagga, N. Tripathi, “Study and Comparison of Various Modulation Classification Techniques under Noisy Channel Conditions,” IJETAE, vol. 2, Issue 4, 2012.
[6] R. Pandey, K. Pandey, “An Introduction of Analog and Digital Modulation Techniques in Communication System,” Journal of Innovative Trends in Science Pharmacy & Technology, vol. 1, pp. 15-21, 2014.
[7] R. R.Bhambare, R.D. Raut., “A Survey on Digital Modulation Techniques for Software Defined Radio Applications,” IRACST, vol. 3, no. 3, 2013.
[8] R. Mongre, M. Kapoor, “Compative Analysis of Digital modulation Techniques on the Basis of their Bit Error Rate in VHDL,” IJETR, vol. 2, Issue 6, pp. 55-60, 2013.
[9] K. Hamid, I. Khider, A. Babiker, “Performance Evaluation of Digital Modulation Techniques on DS-WCDMA,” IJCA vol. 74, no. 8, 2013.
[10] J.D. Oetting, “A Comparison of Modulation Techniques for Digital Radio,” IEEE Transactions on Communications, vol. 27, no. 12, pp 1752-1762, 1979.
[11] B.Sklar, P.K.Ray, “Digital Communications, Fundamentals and Applications,” Second Edition, Pearson Education, Inc., 2001.
[12] T.Schilling, “Principles of Communications Systems,” Second Edition, Tata McGraw-Hill Publishing Company Limited, New Delhi.
[13] M.Tao, “Principles of Communications, Chapter-8: Digital Modulation Techniques,” Shanghai Jiao Tong University.
[14] O. A. Adeleke and R.O. Abolade, “Modulation Methods Employed in Digital Communication: An Analysis,” IJECS-IJENS, vol. 12 , no. 03, 2012.
[15] R. Singh, “MIMO System using Space-Time Block Code with Digital Modulation Techniques,” IJEER, vol. 1, Issue 1, pp. 26-31, 2013.
[16] A.Rehman, T.Khan, S.K.Chaudhry, “Study of Wi-Max Physical Layer under Adaptive Modulation Technique using Simulink,” IJSRET, vol. 1, Issue 5, pp 05-11, 2012.
[17] S. Gupta, H.Sharma, “Performance Investigation for Different Modulation Techniques in WCDMA with Multipath Fading Channels,” IJARCSSE, vol. 2, Issue 7, 2012.
[18] A. Islam, Z. Hasan, “Performance Evaluation of Wi-Max Physical Layer under Adaptive Modulation Techniques and Communication Channels,” IJCSIS, vol. 5, no. 1, 2009.
[19] S.A. Oyetunji, A.A. Akinninranye, “Performance Evaluation of Digital Modulation Techniques in AWGN Communication Channel,” IJERT, vol. 2, Issue 5, pp. 34-39, 2013.
[20] V.Tilwari, A.S. Kushwah, “Performance Analysis of Wi-Max 802.16e Physical Layer using Digital Modulation Techniques and Code Rates,” IJERA, vol. 3, Issue 4, pp. 1449-1454, 2013.
[21] P.Aulin,W.C.Sundberg, “Continuous Phase Modulation Part-1: Full Response Signaling,” IEEE Transactions on Communications, vol. 29, no. 3, pp. 196-209, 1981.
[22] S.Haykin, M. Moher, “Introduction to Analog and Digital Communications,” John Wiley & Sons, Inc., 2007.
[23] M.A.Masud, M.Samsuzzaman, M.A.Rahman, “Bit Error Rate Performance Analysis on Modulation Techniques of Wideband Code Division Multiple Access,” Journal of Telecommunications, vol. 1, Issue 2, pp. 8-14, 2010.

Shadbhawana Jain and Shailendra Yadav, "A Survey Paper on Digital Modulation Techniques," International Journal of Computer Sciences and Engineering, Vol.3, Issue.12, pp.107-111, 2015.

A mobile ad-hoc network (MANET) is a group of wireless mobile devices or nodes that communicate with each other without any help of a pre-installed infrastructure and centralized access points. Security is the most important concern for the functionality of network in MANET. MANET is unsecure from various attacks in the routing path and understanding the form of attacks is always the primary step towards the secured communication between mobile nodes. A number of attacks affect the safe exchange of information in MANET and among them the occurrence of black hole attack causes several limitations such as fault tolerance, packet loss, denial of service and jamming of network while transmitting data between nodes in the route. In order to preserve the security of MANET from attacks, routing protocols are important to ensure proper functioning of the path from source to destination nodes. In this research paper an improved dynamic source routing (IDSR) technique has been proposed to detect and remove the black hole attack nodes in the routing path and ensures reliable communication between nodes by constructing the black hole attack free route in MANET.

MANET, black hole attack, routing protocols, dynamic source routing technique

[1] Alex Hinds, Michael Ngulube, Shaoying Zhu, and Hussain Al-Aqrabi, “A Review of Routing Protocols for Mobile Ad-Hoc Networks (MANET)”, International Journal of Information and Education Technology, Vol. 3, No. 1, February 2013.
[2] S. Anusuya, Dr. S.Meenakshi, “A Review of Routing Protocols and Attacks in Mobile Ad-hoc Network”, International Journal of Advanced Research in Computer Engineering & Technology, Vol. 4, Issue 9, pp.3485-3493, September 2015.
[3] T.Ashish Bhole, Prachee ,N. Patil “Study Of Black hole Attack In MANET”, International Journal of Engineering and Innovative Technology, Vol.2, No. 4, October 2012.
[4] C-C Chiang, H-K Wu, W .Liu, M.Gerla “Routing in Clustered Multihop, Mobile Wireless Networks with Fading Channel”, In Proceedings of IEEE SICON, pp.197–211, 1997.
[5] H. Deng, W. Li, and Dharma P. Agrawal, “Routing Security in Ad Hoc Networks,” IEEE Communications Magazine, Special Topics on Security in Telecommunication Networks, Vol. 40, No. 10, pp. 70-75, October 2002.
[6] M. Girish Chandra ,S.G.HarishReddy,JaydipSen “A Mechanism for Detection of black hole attack in Manets”, In Proceeding of the 6th International Conference on Information, Communication and Signal Processing(ICICS07), Singapore, December 2010.
[7] HesiriWeerasinghe and Huirong Fu, Member of IEEE, “Preventing Cooperative Black Hole Attacks in Mobile Ad-hoc Networks”, Simulation implementation and evaluation, Vol. 2, No.3, July 2008.
[8] JaydipSen, M. Girish Chandra, Harihara S.G., Harish Reddy, P. Balamurlidhar (Embedded System Research Group, TCS), “Mechanism for Detection of Gray Hole Attack in Mobile Ad Hoc Networks”, IEEE 2007.
[9] D. B. Johnson, DA.Maltz and J.Broch “Dynamic Source Routing Protocol (DSR)”, ACM Digital Library, pp 210-215, October 1996.
[10] M.Mohanapriya, krishnamurthi “DSR protocol for detection and removal of black hole attack in MANET”, ELSEVIER computer and electrical engineering, pp.530-538, 2014.
[11] V. G. Muralishankar and Dr. E. George Dharma Prakash Raj, “Routing Protocols for MANET: A Literature Survey”, International Journal of Computer Science and Mobile Applications, Vol. 2, No.3, March 2014.
[12] D.B.Roy, R.Chaki and N.Chaki, “A New Cluster-Based black hole Intrusion Detection Algorithm for Mobile Ad Hoc Networks”, International Journal of Network Security and Its Application (IJNSA), Vol. 1, No.1, April, 2009.
[13] Shalini, Hidehisa Nakayama, Nei Kato, Abbas Jamalipour, and Yoshiaki Nemoto “Detecting Blackhole Attack on AODV-based Mobile Ad Hoc Networks by dynamic learning method”, International Journal of Network Security”, Vol.5, No.3, pp. 338-346, 2007.
[14] Tamilselvan, L.Sankaranarayanan, “Prevention of black hole Attack in MANET”, International Journal of networks Network Security, Vol.3, No.5, May 2008.

S.Anusuya and S.Meenakshi, "An Improved Dynamic Source Routing Protocol for Detection and Removal of Black Hole Attack in Mobile Ad-Hoc Network," International Journal of Computer Sciences and Engineering, Vol.3, Issue.12, pp.112-117, 2015.

In this paper, the proposed work states automatic control strategy for ventilation systems in energy-efficient buildings. To maintain indoor CO2 in the comfort zone to the accurate level and minimum ventilation rate is the main design goal of automatic ventilation controller. The system uses CO2 as the main indicator of human comfort. The intelligent system as compared to traditional ON/OFF or fixed ventilation system gives better results in energy saving and also provide high indoor air quality.

AVR controller, LCD, Sensor, CO2 Predictive model, KEIL µVision3

[1]. ANSI/ASHRAE Standard 62.1-2004: “Ventilation for Acceptable Indoor Air Quality”.American Society of Heating, Refrigerating and Air-Conditioning Engineers.
[2]. Tuan Anh Nguyen, Marco Aiello Distributed Systems Group, Johann Bernoulli Institute for Mathematics and Computer Science,“Energy intelligent buildings based on user activity: A survey”, University of Groningen, Nijenborgh 9, 9747 AG Groningen, The Netherlands
[3]. M. Wigginton, J. Harris,” Intelligent Skins”, Architectural Press, Oxford, 2002.
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[6]. A. Kamthe, L. Jiang, M. Dudys, A. Cerpa, SCOPES: “Smart cameras object position estimation system”, in: Proceedings of the 6th European Conference on Wireless Sensor Networks, EWSN ’09, Springer-Verlag, Berlin, Heidelberg, 2009, pp.279–295.
[7]. V.L. Erickson, Y. Lin, A. Kamthe, B. Rohini, A. Surana, A.E. Cerpa, M.D. Sohn, S.Narayanan, “Energy Efficient building environment control strategies using realtime occupancy measurements”, in: Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy Efficiency in Buildings, BuildSys ’09, ACM, New York, NY, USA, 2009, pp. 19–24.
[8]. Y. Agarwal, B. Balaji, R. Gupta, J. Lyles, M. Wei, T. Weng, “Occupancy-driven energy management for smart building automation”, in: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, BuildSys ’10, ACM, New York, NY, USA, 2010, pp. 1–6.
[9]. W. J. Fisk and A. T. de Almeida, “Sensor-based demand-controlled ventilation: a review,” Energy Building, vol. 29, no. 1, pp. 35–44, 1998.

Rupam S. Rote and Rupali R. Jagtap, "Automatic Ventilation Control System for Energy Efficient buildings using CO2 Sensors," International Journal of Computer Sciences and Engineering, Vol.3, Issue.12, pp.118-125, 2015.

This research paper presents a review of data mining algorithms which is useful to predict the accuracy in the educational domain. It discusses and compares various data mining algorithms in the educational domain. Nowadays it is becoming increasingly important to develop powerful tools for analysis of the enormous data that is stored in databases and data warehouses, and mining such data and arriving at values focused on interesting knowledge from it. Modern organizations focus and put on much thrust in developing data mining procedures and derive benefit from it. Data mining is a process of inferring knowledge from such huge data. There are three major components in Data Mining Viz Clustering, Classification and Association Rules. By discussing the various algorithms this research paper has the scope to extend it further, in order to develop it further in the direction of new and innovative algorithms. The main aim of the paper is to study the effectiveness of various algorithms in the educational environment.

Clustering, Classification, Association rule, Data mining, innovative algorithms

[1]. Hemlata Sahu et al. A Brief Overview on Data Mining Survey, International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume 1, Issue 3, pp. 114-121. ISSN 2249-6343.
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[3]. Shuai,Qin,zhou Ming-quan. Cloud computing promotes the progress of m-learning, Uncertainty Reasoning and knowledge Engineering. International conference on IEEE, vol.2, no.,pp. 162-164,4-7 Aug.2011.
[4]. Hirsch, Benjamin, Jason WP Ng., “Education beyond the Cloud: Anytime-anywhere learning in a smart campus environment”, Internet Technology and Secured Transactions (ICITST), International conference on IEEE, vol., no., pp. 718-723, 11-14 Dec.2011.
[5]. Carsten Ullrich, Ruimin Shen, Ren Tong and Xiaohong Tan. A mobile live video learning system for large scale learning system design and Evaluation. IEEE Transactions on learning Technologies. Vol .3, No.1, January –March 2010.
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Uma, L. Jayasimman, and V. Upendran, "A Survey on Data Mining Algorithms," International Journal of Computer Sciences and Engineering, Vol.3, Issue.12, pp.126-129, 2015.

A mobile ad hoc network (MANET) is auto-configuring network without any infrastructure. It is temporary network created by, mobile nodes which are capable to communicating with each other without the use of network infrastructure. Ad hoc networks are vulnerable to many type of security attacks due to their open medium, dynamic topology, distributed co-operation. For successful data transfer nodes are depend on each other. To believe on the other node for wireless data transmission, consider as trust problem. Our aim in this review is to present schemes which are mainly focused on security based on trust value of node in MANET. This work proposes a approach for trust calculation based on Trust proxy node (TPN). The route discovery is achieved through a routing decision based on trust sequence certificate exchange by Trust proxy node (TPN). Trust proxy node (TPN) is an additional node having extra responsibility for trust calculation of Black hole node. Trust proxy node (TPN) will act as monitoring node for routing decision. After the trust value index is calculated the TPN node issues a certificate to every node in its network. To participate in routing the nodes must have two trust index (TI) certificates & can be consider as a reliable node by TPN. The directory of this certificate is maintained in a Trust Index (TI) Table. This TI table is shared with the server by this centralized TPN. This TPN will also monitors the behavior of nodes in a specific range continuously to avoid unwanted action.

MANET, AODV, TPN(Trust proxy node), Milisious Node, Black Hole

[1] D He, C Chen, S Chen, J Bu & A B. Vasilakos, “ReTrust:Attack Resistant & Lightweight Trust for Sensor Network” in IEEE Transaction on IT in volume- 16/ No 4 Page No (623-632), July 2012.
[2] Z Min & Z Jiliu, “Cooperative Black Hole Attack Prevention for Mobile Ad Hoc Networks” in IEEE Transaction ISBN 978-0-7695- 3686 Page No(26-30),16-17 May 2009.
[3] S Ramaswamy, H Fu, M Sreekantaradhya, J Dixon & K Nygard, “Prevention of Cooperative Black Hole Attack in Wireless Ad Hoc Networks” in Department of Computer Science, IACC 258, Page No(1-7) March, 2008.
[4] K E Defrawy& G Tsudik, “Privacy-Preserving Location-Based OnDemand Routing in MANETs” in IEEE Transaction of selected Journal in communication, Volume 29 Issue 10, Page No(1926-1934), Dec. 2011.
[5] Xu Li, R Lu, X Liang, & X Shen, “Side Channel Monitoring: Packet Drop Attack Detection in Wireless Ad Hoc Networks” in IEEE ICC, Nov.2011.
[6] E. A .Mary Anita & V. Vasudevan, “Black Hole Attack Prevention in Multicast Routing Protocols for Mobile Ad hoc networks using Certificate Chaining” in IJCA (0975 – 8887) Volume 1 – No. 12, Page No (1-8), Jan 2010.
[7] P H. Yu and U W. Pooch, “Chapter on Security and Dynamic Encryption System in Mobile Ad-Hoc Network” in A&M University, Dept of CSE Texas, USA, ISBN 978-953-307-402-3 Page No(469-490), Jan 2011
[8] G. S. Mamatha& Dr. S. C. Sharma, “A Robust Approach to Detect and Prevent Network Layer Attacks in MANETS” in IJCSS Volume 4, Issue 3, page No(275-284), July 2010.
[9] J Cal, P Yi, J Chen, Z Wang & N Liu, “An Adaptive Approach to Detecting Black and Gray Hole Attacks in Ad Hoc Network” in IEEE Proceedings, 1550-445X/10, 2010.
[10] R Karandikar, R K Khanuja, S Shukla, “Proposed solution to prevent Black Hole Attack in MANET” in IJRIM ,Vol 2, Issue 2, ISSN 2231- 4334, Page No(1-5), Feb 2012.
[11] H Weerasinghe and H Fu “Preventing Cooperative Black Hole Attacks in Mobile Ad Hoc Networks: Simulation Implementation and Evaluation” in IJCA, Volume 2 ,No 3 July, 2008.
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Amit Saraf and Megha Singh, "A Trust Proxy Node (TPN) Based Black hole Attack Detection Mechanism in MANET Using AODV," International Journal of Computer Sciences and Engineering, Vol.3, Issue.12, pp.130-135, 2015.