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Alzheimer’s disease was the leading cause of death. There is no cure and no effective treatment for Alzheimer’s disease. The challenges will increase in intensity as demographic changes, particularly the aging of baby boomers, take hold. High prediction of Alzheimer’s, developed in therapy, and appropriate care modalities that likely observe significant investment. The Alzheimer’s disease Neuro Imaging Initiative (ADNI) is an ongoing, longitudinal study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer’s disease (AD). The required accomplishments of ADNI have been as follows: the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF).In the recent system, a large number of people are suffering from brain related diseases. Studying and finding the solutions for those diseases is the requirement of our need. Dementia is one such disease of the brain. This is most reason for the loss of cognitive functions such as reasoning, memory and other mental abilities which may be due to trauma or normal ageing. Alzheimer’s disease is one of the most dangerous mental disorders which accounts to 60-80% of mental disorders. Diagnosis of this disease at an early stage will help the patients to lead a quality life for the remaining tenure of their life. The focus of the work is to have a review on different neuro psychological tests, the various algorithms used for the purpose of diagnosis, and the tool that may be used for the analysis.

Alzheimer’s disease, Diagnosis, MCI

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[28] Klunk WE, Mathis CA. “The future of amyloid-beta imaging: a tale ofradionuclides and tracer proliferation”. Curr Opin Neurol;21:683–7, 2008.
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A. Nancy, S. Vijaykumar, "Analysis Of Diagnostic System For Alzheimer’s Disease", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.55-58, 2018.

Mobile-Ad hoc networks are qualify by a lack of infrastructure, and by a random and quickly varying network topology; thus the need for a rich dynamic routing protocol that can adapt such an environment. In general, with the high mobility environment and high load network traffic, network performance perhaps took down causing packet loss or increase overhead. TCP optimization in mobile ad hoc networks MANETs is a challenging issue because of some unequaled characteristics of MANETs. Packet losses in MANETs are primarily due to congestion and frequent link losers but in case of wireless networks packet losses are accruing mainly due to congestion. Aims of this article is to Comparative analysis of transport layer perspective, it is very important to regard Transmission Control Protocol (TCP) as well for MANETs because of its broad application, and also demonstrates the several parameters comparison of Transmission Control Protocols solutions for Mobile ad-hoc wireless network

MANET, TCP-F, TCP-ELFN, TCP-BuS, ATCP and Split-TCP

[1]. Masoud Alfragani Ali and Raghav yadav,”MATCP Modified-ATCP for Mobile Ad hoc Networks”,International Journal of Computer Applications, Vol(147), Iss(3), pp.10-14, 2016.
[2]. Molia,Hardik k and Rashmi Agrawal,”A comprehensive study of cross layer approaches for improving TCP performance in wireless networks”,2015 international conference on computing and communication technologies,2015.
[3]. Sakshi Bhatia, SanjeevRana, Rajneesh kumarGujral,”Security aware congestion control mechanism on SPLIT-TCP overmanet”, Inter. Journal of Sci and Research publications, Vol(4), Iss(2), pp.1-8, 2014.
[4]. Ranjeet V. Bidwe,”Different Transmission Control Protocol variants in wireless environment”, Inter Journal on Recent and Innovation trends computing and communications, Vol(3), Iss(8), pp.5409-5416, 2015.

A.Thilaka, V. Uma Devi, "A Comparative Analysis and Classification of TCP Solutions for MANET", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.59-62, 2018.

The attention is routinely mentioned to furnish a window into the health of a person for it`s only in the e Diabetic retinopathy (DR) is a significant eye disease originating from diabetes mellitus ye that one can surely see the exposed flesh of the subject without utilizing invasive tactics. There are quantities of diseases, primarily vascular disorder that depart telltale markers within the retina. Micro aneurysms (MAs) are early signs of DR, so the detection of these lesions is predominant in an efficient screening application to satisfy medical protocols. Retinal photos provide enormous knowledge on pathological alterations brought on via regional ocular disorder which exhibits diabetes, hypertension, arteriosclerosis, cardiovascular disease and stroke. Computer-aided evaluation of retinal picture performs a significant position in diagnostic procedures. Nonetheless, computerized retinal segmentation is problematic by means of the fact that retinal photographs are by and large noisy, poorly contrasted, and the vessel widths can fluctuate from very giant to very small. This paper grants photo processing systems similar to darkish object detection to analyze the situation or increase the enter photograph so as to make it suitable for further processing and beef up the visibility of vessels in color fungus portraits. Then we are able to put in force okay-way clustering algorithm to segment the vessels and automate classification procedure headquartered on support vector computing device to provide regional know-how about arteries and veins. And finally predict cardio vascular diseases and other ailments utilizing CRAE and CRVE measurements.

Image processing, Eye components, Disease diagnosis, Cardio vascular diseases, Classification, Support Vector machine

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[2] Kaur, Manvir, and Rajneesh Talwar. "Automatic Extraction of Blood Vessel and Eye Retinopathy Detection." European Journal of Advances in Engineering and Technology 2.4 (2015): 57-61.
[3] Wang, Shuangling, et al. "Hierarchical retinal blood vessel segmentation based on feature and ensemble learning." Neurocomputing 149 (2015): 708-717.
[4]Vidyashree, M. R., M. V. Usha, and vtuewit."Locating the optic nerve and blood vessel in a retinal images using graph partition method." (2015).
[5] Annunziata, Roberto, et al. "Leveraging multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation." IEEE journal of biomedical and health informatics 20.4 (2016): 1129-1138.
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M. Arulkothaipriya, "An Image Mining Technique Using Support Vector Machine Based Retinal Image Classification", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.63-69, 2018.

Cloud computing contextual is a computing model for dealing and accessing services over the internet. It delivers variety of services to the recipient for on-demand. The very important service of the cloud environment is data storage. The data storage in the internet is varying day by day and the data size also varying based on the users need. The end users required the best security mechanism’s. The motivation of this research is to encrypt and decrypt data efficiently and effectively protect the stored data. All over the world the data center is placed in many different locations to maintain and monitor the user data. It is more reliable storage but it has many securities related problems and different kinds issues. The problem is how to secure the data in cloud storage, to protect the data from unauthorized user’s access, data is supposed to either encrypted format or unreadable form. This paper analysis the various cloud data storage security algorithms.

Component, Formatting, Style, Styling, Insert (key words)

[1] Shakeeba S. Khan, Prof.R.R. Tuteja,” Security in Cloud Computing using Cryptographic Algorithms”, International Journal of Innovative Research in Computer and Communication Engineering , Vol. 3, Issue 1, January 2015.
[2] Sheren A. El-Booz, Gamal Attiya and Nawal El-Fishawy, “A secure cloud storage system combining time-based one-time password and automatic blocker protocol”, El-Booz et al. EURASIP Journal on Information Security ,2016.
[3] Fortine Mata, Michael Kimwele, George Okeyo, “Enhanced Secure Data Storage in Cloud Computing Using Hybrid Cryptographic Techniques (AES and Blowfish)” , International Journal of Science and Research, Volume 6 Issue 3, March 2017.
[4] S. Balamurugan, Dr. S. Sathyanarayana, Enhanced Security as a Service to Protect Data in Public Cloud Storage”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, Issue 4, April 2016.
[5] Adamu Ismail Abdulkarim, Boukari Souley, “An Enhanced Cloud Based Security System Using RSA as Digital Signature and Image Steganography” International Journal of Scientific & Engineering Research Volume 8, Issue 7, July-2017.
[6] A.M. Vengadapurvaja, G. Nisha, R. Aarthy, N. Sasikaladevi, “An Efficient Homomorphic Medical Image Encryption Algorithm For Cloud Storage Security”, Science direct- 7th International Conference on Advances in Computing & Communications, Cochin, India , August 2017.
[7] Dr. L. Arockiam, S. Monikandan, “Data Security and Privacy in Cloud Storage using Hybrid Symmetric Encryption Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 8, August 2013
[8] Sawase Akanksha and B.M.Patil, “A Secure Multiowner Dynamic Groups Data Sharing In Cloud”, International Journal of Advances in Engineering & Technology, Feb., 2016.
[9] Anshu Chaturvedi, D.N.Goswami, Rakesh Prasad Sarang, “Privacy Algorithms to Improve the Secure Framework for Cloud Computing Environment” International Journal of Advanced Research in Computer and Communication Engineering, Vol. 6, Issue 4, April 2017.
[10] Dr. R. Sugumar, K. Arul Marie Joycee,”DSCESEA: Data Security in Cloud using Enhanced Symmetric Encryption Algorithm” International Journal of Engineering Research & Technology, Vol. 6 Issue 10, October – 2017.
[11] Dr. R. Sugumar, K. Arul Marie Joycee ,“Ensure and Secure Data Confidentiality in Cloud Computing Environment using Data Obfuscation Technique”, International Journal Of Advanced Studies In Computer Science And Engineering, Volume 6, Issue 12, 2017.

Ramalingam Sugumar, K. Arul Marie Joycee, "ADSSCCE: Analysis of Data Storage Security in Cloud Computing Environment", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.70-75, 2018.

As there is an exponential growth of data in every field of life, the assessment and extraction of data from the massive data sets has derived as a dreadful challenge in this golden era of big data. Conventional security methods cannot be adapted to big data due to its massive volume, and range. Undoubtedly, mining fruitful information from this massive data has been a universal interest for the organizations having large dataset. Big data life cycle includes three phases such as data generation, data storage, and data processing. In big data process, distributed systems are adapted since it needs large storage and high computational power. As many parties are engaged in these systems, the possibility of the violation in security concerns increases. In addition, since big data contains individual’s personal information, privacy is the foremost security concern. The main objective of this paper is to present an exhaustive overview of the privacy preservation mechanisms in big data life cycle. The modern privacy-preserving methods such as the generalization are capable of effectively managing the privacy assaults on a sole data set, whereas the protection of privacy for multiple data sets continues to be hard. Therefore, with intention of conserving the secrecy of multiple data sets, it is desirable to initially anonymize whole data sets and thereafter encrypt them before amassing or exchanging them in cloud. The challenges in existing mechanisms and eventual research discussions relevant to privacy preservation in big data are mentioned. The security techniques to protect the data set from being accessed by illegal users are also discussed.

Big Data, Conventional, Data Privacy, Mining, Security

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Mining,” SIGKDD ’03, August 24-27, 2003, Washington, DC, USA.
[8] ArisGkoulalas-Divanis, &GrigoriosLoukides, “Revisiting Sequential Pattern Hiding to Enhance Utility”,
ACM, August 2011.
[9] Yehuda Lindell, Benny Pinkas, ―Secure Multiparty Computation for Privacy-Preserving Data Mining, The Journal of Privacy and Confiden-tiality, Vol. 1, no. 1,, pp. 1-39, 2009.
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Manjula GS, T. Meyyappan, "An Exhaustive Review of the Privacy Preservation and Security Mechanisms in Big Data Life Cycle", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.76-83, 2018.

In Medical industry there are many diseases that makes a patient critical among them diabetes is one of the major disease that affect most of the people in early stage. Diabetes (or Diabetes Mellitus) is a group of metabolic diseases, chronic, in which there are high blood sugar levels and affects the body’s ability to use the energy found in food over a prolonged period. Researchers are finding effective methods for the prediction of diabetes. The main goal is to analysis the performance of various data mining techniques in the diabetes dataset for efficient extraction of valuable patterns. For doing so WEKA software was used as a mining tool for diagnosing the useful pattern. The Pima Indian diabetes dataset are used for the analysis. The dataset was applied in various classification algorithms to analysis the performance to identify an effective model that predict diabetes disease. In this, the analysis is done by applying attribute evaluator to enhance the accuracy then applying Naive Bayes, Bayes Net, J48 and Random Forest and the performance are compared. Through this study, Naive Bayes Algorithm provides better classification accuracy, when compared with classification algorithms like Bayes Net, J48 and Random Forest.

Diabetes, Health care, Naive Bayes, Bayes Net, J48 and Random Forest, WEKA

[1] Asma A Aljarullah. Decision tree discovery for the diagnosis type-2 diabetes. International conference on innovation in information technology. 2011; p. 303-7.
[2] Aiswarya Iyer, Jeyalatha S and Sumbaly Ronak. Diagnosis of diabetes using classification mining techniques. International Journal of Data Mining & Knowledge Management Process. 2015; 5:1-14. 2.
[3] “Bayes Net”, Wikipedia, Aug 2018.
[4] ChaitraliDangare, S. and SulabaApte,S.Improved study of disease prediction using data mining classification techmiques. Int.J.Comp.Appl., 2012,47(10):75-88.
[5] Global Diabetes Community, http://www.diabetes.co.uk/diabetes_care/blood-sugar-level-ranges.html
[6] Ianchao Han J, Juan C Rodriguze, Beheshti Mohsen. Diabetes Data Analysis and Prediction model discovery. Second International conference on future generation com- munication and networking. 2011; p. 96-9. 13.
[7] “J48”, Wikipedia, March 2018.
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[10] “Naïve Bayes”, Wikipedia, March 2018.
[11] P.Yasodha and M. Kannan, "Analysis of a Population of Diabetic Patients Databases in WekaTool", International Journal of Scientific & Engineering Research, vol. 2, no. 5, 2011.
[12] “Random Forest”, Wikipedia, March 2018.
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[15] Stutz J., P. Cheeseman. (1996) Bayesian classification (autoclass): Theory and results. In Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press
[16] Uswa Ali Zia, Dr. Naeem Khan “Predicting Diabetes in Medical Datasets Using Machine Learning Techniques” International Journal of Scientific & Engineering Research Volume 8, Issue 5, May-2017, ISSN 2229-5518
[17] Velide Phani Kumar and Velide Lakshmi. A Data Mining Approach for Prediction and Treatment of diabetes Disease. International Journal of Science Inventions Today. 2014; 3:73-9.

G. Paul Davidson, D. Ravindran, "Performance Analysis of Diabetes Disease using Classification Algorithms by WEKA", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.84-88, 2018.

Now days it is difficult to predict the changes in season especially during rainy season. In rainy season it is very difficult to prevent the wet clothes from the rain. So there is need for human intervention to continuously monitor this. Keeping a person continuously watching for rain is too much stupidity as a waste of time. As the advancement in science technology is developing, the human comfort & needs are also increasing proportionally. Thus, it is important to take a small step towards the comfort ability and save our time. Thus, in this paper we have proposed a system which includes combination of sensor technology and Internet of Things (IoT). This proposed electro mechanical system(Arduino Uno, Sensor, Transistor and Diode) which continuously monitors the rain in rainy season and automatically takes back the clothes from rain to protect from wet and send fast information to user using GSM (Global System for Mobile) mobile device using SMS (Short Messaging System).

Internet of Things (IoT), Arduino Uno, Rain Drop Sensor, BC547 Transistor, 1N4007 Diode, DC Motor, GSM

[1] Lu Tan, Neng Wang, “Future Internet: The Internet of Things”, 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), 2010.
[2] https://en.wikipedia.org/wiki/Internet_ of_Things, Jun 25 (2016).
[3] S. Dharmadhikari, N. Tamboli, N. Gawali and N.N. Lokhande “Automatic Wiper System” in International Journal of Computer Technology and Electronics Engineering Vol. 4, No. 2, April 2014 pp.15-18.[4] Pearson, D.,: ‘Rain detector’, US Patent App. 12/963,728, Dec. 9 2010.

M. Hemalatha, "Clothes Protection from Rain Based on Internet of Things (IOT)", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.89-91, 2018.

Ultrasound Placenta image are usually low in resolution which may lead to loss of characteristics features of the image. Discarded at birth, the placenta is a highly complex and fascinating organ. During the course of a pregnancy, it acts as the lungs, gut, kidneys, and liver of the fetus. Ultrasound is a diagnostic technique which has many purposes though it is typically used by a Gynecologist to check the fetus in the mother`s womb during pregnancy. Image Segmentation is very important in many medical reputation applications. This survey aims at providing an insight about different 2-Dimensional and 3-Dimensional Placenta image segmentation techniques and to help better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.

Placenta Imaging; Image Segmentation; Image Processing; 2-Dimensional image segmentation; 3-Dimensional image segmentation

[1] Pinaki Pratim Acharjya and Dibyendu Ghoshal ” Segmentation of Medical Images Using Morphological Approach” International Journal of Scientific & Engineering Research, Volume 4, Issue 7, July-2013, ISSN 2229-5518.
[2] Ali Abdo Mohammed Al-Kubati, Jamil A. M. Saif, Murad A. A.Taher” Evaluation of Canny and Otsu Image Segmentation” International Conference on Emerging Trends in Computer and Electronics Engineering (ICETCEE`2012) March 24-25, 2012 Dubai.
[3] Poonam Dhankhar1, Neha Sahu2” A Review and Research of Edge Detection Techniques for Image Segmentation” IJCSMC, Vol. 2, Issue. 7, July 2013, pg.86 – 92.
[4] G. Malathi and Dr. V. Shanthi, “Statistical Measurement of Ultrasound Placenta Images Complicated by Gestational Diabetes Mellitus Using Segmentation Approach”, Journal of Information Hiding and Multimedia Signal Processing, Volume 2, Number 4, October 2011.
[5] T. Hata, H. Tanaka, ”Three-dimensional ultrasound evaluation of the placenta”, Elesevier journal, November 2010. [5]Image Segmentation Techniques Rajeshwar Dass, priyanka, Swapna Devi IJECT Vol. 3, Issue 1, Jan. - March 2012.
[6] G. Malathi and Dr. V. Shanthi” Thickness based Characterization of Ultrasound Placenta Images using Regression Analysis”, International Journal of Computer Applications (0975 – 8887) Volume 3 – No.7, June 2010.
[7] “Histogram Based Classification of Ultrasound Images of Placenta”, G.Malathi, Dr.V.Shanthi, International Journal of Computer Applications, Feb 2010, Volume 1 – No. 16.
[8] “Wavelet Based Features For Ultrasound Placenta Images Classification”, G. Malathi, Dr.V.Shanthi, 2nd International Conference on Emerging Trends in Engineering and Technology, Nagpur, December 2009.
[9] Roy-Lacroix ME, Moretti F, Ferraro ZM, Brosseau L, Clancy J, Fung-Kee-Fung K.” A comparison of standard two-dimensional ultrasound to three-dimensional volume sonography for routine second-trimester fetal imaging.” J Perinatol. 2017 Apr;37(4):380-386. doi: 10.1038/jp.2016.212. Epub 2017 Jan 26

J. James Manoharan, "Comparison on Two-Dimensional Ultrasound to Three-Dimensional Placenta Image using Segmentation Techniques", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.92-95, 2018.

The technology is the knowledge of science put into practical use to solve some problems. The impact of technology in modern life is immeasurable. The most indispensable technology which is helpful in all aspects of life is the Machine Learning (ML). It is the most recent approach to digital transformation which has benefits as well as risks to mankind. It helps us to process with large data in minimum time. This paper includes the difference of Machine learning and Artificial Intelligence. It also provides the description about the machine learning, methods, process types, algorithms, various applications and challenges towards healthcare, business, fraud detection, social media and other internet activities.

Machine Learning, Artificial Intelligence, algorithm, process

[1] Machine Learning and Deep Learning Methods for Cybersecurity, Yang Xin ; Lingshuang Kong ; Zhi Liu ; Yuling Chen ; Yanmiao Li ; Hongliang Zhu ; Mingcheng Gao, ISSN: 2169-3536, IEEE Access, 15 May 2018.
[2] A survey of machine learning algorithms for big data analytics, S. Athmaja, M. Hanumanthappa, Vasantha Kavitha, 2017 International Conference on Innovations in Information, Embedded and Communication Systems, IEEE.
[3] A Survey on Machine Learning: Concept, Algorithms and Applications Kajaree Das1 , Rabi Narayan Behera, ISSN : 2320-9798 on International Journal of Innovative Research in Computer and Communication Engineering.
[4] A Survey on Application of Machine Learning Techniques in Optical Networks, Francesco Musumeci, Member, IEEE, Cristina Rottondi, Member, IEEE, Avishek Nag, Member, IEEE, Irene Macaluso, Darko Zibar, Member, IEEE, Marco Ruffini, Senior Member, IEEE, and Massimo Tornatore, Senior Member, IEEE.
[5] Caligtan, P. C. Dykes, "Electronic health records and personal health records", Semin Oncol Nurs, vol. 27, pp. 218-228, 2011.
[6] A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View, Qiang Liu ; Pan Li ; Wentao Zhao ; Wei Cai ; Shui Yu ; Victor C. M. Leung, IEEE, 13 February 2018.
[7] L. Zhou, S. Pan, J. Wang, A. V. Vasilakos, "Machine learning on big data: Opportunities and challenges", Neurocomputing, vol. 237, pp. 350-361, May 2017.
[8] Cour, T. and Sapp, B. and Taskar, B. Learning from partial labels, Journal of Machine Learning Research, Volume 12, 1501-1536 2012
[9] Alex Smola and S.V.N. Vishwanathan, Introduction To Machine Learning, Cambridge University Press 2008.
[10] Mohssen Mohammed Muhammad Badruddin Khan Eihab Bashier Mohammed Bashier, Machine Learning Algorithms and Applications, 2017 by Taylor & Francis Group, LLC.

S.Jenila, D. Ananthi, "A Brief Study on Machine Learning", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.96-98, 2018.

The wireless network places very keen role on today’s network. This network has tremendous potentiality to work towards the users present day aspiration in communication. The efficiency of this network has been improved further with the help clustering the network. These clusters need to communicate through gateways. In this paper a novel gateway identification procedure based on Fuzzy logic has been proposed. In order to substantiate this study the work also covered the experimental results with the help of C++ as a programming language.

Fuzzy logic,AODV,gateway

[1] S.Thirumurugan, “Direct sequenced C-IAODV Routing Protocol”, International Journal of computer science and technology,vol.1,issue.2, Dec 2010,pp.108-113.
[2] S.Thirumurugan,“C-AODV: Routing Protocol for Tunnel’s Network”, International Journal of computer science and technology,vol.2,issue.1, Mar2011,pp.113-116.
[3] S.Thirumurugan, “ PAC - A Novel approach For Clustering Mechanism in Adhoc Network” , ICSCCN’11,pp 593-598.
[4] S.Thirumurugan, “Analysis of Clustering Techniques in ad hoc network”,
[5] Jieming Wu, WenhuYu.“Optimization and improvement based on K-Means Cluster algorithm”, Second International Symposium on Knowledge Acquisition and Modeling,2009,pp.335-339.
[6] G.S.Malkin and M.E. Steenstrup, “Distance-Vector Routing,” In Routing in Communications Networks, edited by M.E. Steenstrup, Prentice Hall, 1995, pp. 83-98
[7] C.Perkin and P.Bhagwat. Routing over Multihop Wireless Network of Mobile computers.SIGCOMM’94 : Computer communications Review, 24(4):234-244,Oct 1994.
[8] David B. Johnson and David A. Maltz, “Dynamic Source Routingin Ad Hoc Wireless Networks”, In Mobile Computing, dited by Tomasz Imielinski and Hank Korth, Kluwer Academic Publishers, 1996.
[9] D. Johnson, Y. Hu, D. Maltz, “The Dynamic Source Routing Protocol (DSR) for Mobile Ad- hoc Networks for IPv4”. RFC 4728. February, 2007.
[10] Charles E_ Perkins, Sun Microsystems Laboratories, Elizabeth M_ Royer Dept of Electrical and Computer Engineering. “Ad hoc On Demand Distance Vector Routing”,Proceedings of the 2nd IEEE Workshop on Mobile Computing .Systems and Applications, New Orleans, LA, February 1999, pp. 90-100.

M. Ramesh Kannan, S. Thirumurugan, "Fuzzy Based Cluster Gateway Election Protocol for Ad hoc Networks", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.99-102, 2018.