Volume-7 , Issue-5 , May - 2019 Go Back

This survey focus the comparison between Cloud Computing and Fog computing, So that we can analyzes which is more required with their techniques and why for security purpose, The Internet of Thing is required further secured platform other than the cloud computing to find the best and secure way as possible. Cloud computing is reliable for using by the companies or the organizations, but it is not more trustable to prevent the critical tasks. The Internet of Things are facing several problems with the use of cloud computing also and prevention is more required for the advanced Infrastructure to promote the critical tasks of the cloud computing users, which can handle and prevent all of the transactions of Internet of Things without taken any type of risks. As, we observed numbers of devices are increasing, which connects to IoT. Hence, the fundamental issues of insecurity are increasing. Prevention or security is more required as a first priority for the users, those have been connected to the Internet of Things by the service of cloud computing. Fog computing has made of the appearance as a solution to this problem and it can tackle insecurity issues during process the data of IoT.

Fog Computing, Cloud Computing, Internet of Things, Security Issues, Fogging

[1] Z. Huaqing, Y. Zhang, Y. Gu, D. Niyato, and Z. Han, "A Hierarchical Game Framework for Resource Management in Fog Computing", IEEE Communications Magazine 55, No. 8, pp.52-57, 2017.
[2] Stojmenovic, Ivan, "Fog computing: A cloud to the ground support for smart things and machine-to-machine networks", Telecommunication Networks and Applications Conference (ATNAC), Australasian, IEEE, pp.117-122, 2014.
[3] Rahmani, M. Amir, T. N. Gia, B. Negash, A. Anzanpour, I. Azimi, M. Jiang, and P. Liljeberg, "Exploiting smart e-health gateways at the edge of healthcare internet-of-things: a fog computing approach" Future Generation Computer Systems 78, pp.641-658, 2018.
[4] Mukherjee, Mithun, L. Shu, D. Wang, "Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges", IEEE Communications Surveys & Tutorials, 2018.
[5] Skourletopoulos, Georgios, C.X. Mavromoustakis, G. Mastorakis, J. M. Batalla, C. Dobre, J. N. Sahalos, R. I. Goleva, N. M. Garcia, "Game Theoretic Approaches in Mobile Cloud Computing Systems for Big Data Applications: A Systematic Literature Review", Mobile Big Data, Springer, Cham, pp.41-62. 2018.
[6] L. Francesc, D. Lezzi, J. Ejarque, R. M. Badia, "An Architecture for Programming Distributed Applications on Fog to Cloud Systems", European Conference on Parallel Processing, Springer, Cham, pp. 325-337, 2017.
[7] V. Kumar, A. A. Laghari, S. Karim, M.Shakir, A. A. Brohi, “Comparison of Fog Computing & Cloud Computing”, I.J. Mathematical Sciences and Computing, Vol. 1, pp.31-41, 2019.
[8] S. Aguru, B. M. Rao, “Data Security In Cloud Computing Using RC6 Encryption and Steganography Algorithms” International Journal of Scientific Research in Computer Sciences and Engineering, Vol.7, Issue 1, pp.6-9, 2019.
[9] P. Devi, “Attacks on Cloud Data: A Big Security Issue”, IJSRNSC, Vol. 6, Issue 2, pp.15-18, 2018.

Jaishree Jain, Ajit Singh, "Survey on Fog Computing and Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.752-756, 2019.

Critical incidents targeting National Critical Infrastructures are happening more and more often. Attacks, that happens to be both more sophisticated and persistent, can even replicate life. As per CERT-In’s data, the number of cyber security incidents reported in the years: 2014-16 are more than 45000 and in 2017 (till June) are approx 27,482. Wannacry, Erebus & Petya are some big cyber-attacks, which crippled more than 10,000 organizations and 200,000 individuals in over 100 countries. From the above data, it’s notable that the number of cyber security incidents has been growing steadily in India. The goal of this examination is to survey the relative performance of some notable hybrid classification techniques. We used KDD CUP 99 data to play out a controlled experiment in which the data characteristics are efficiently changed to present defects, for example, nonlinearity, multi-co-linearity, unequal covariance, and so forth. Our analyses recommend that datasets attributes significantly impact the classification execution of the strategies. Here we created and analyzed the diverse hybrid strategies in soft computing such as GWO-EBG, GWO-KNN, GWO-SVM and GWO-GRNN. The results of the diverse hybrid strategies can help in the structure of classification frameworks in which several classification techniques can be utilized to expand the reliability and consistency of the classification.

Intrusion detection systems (IDS), SVM, Gray wolf optimizer (GWO), Entropy Based Graph, KNN etc

[1] D.K. Srivastava, K. S. Patnaik and L Bhambhu, “Data Classification: A Rough - SVM Approach”, in Contemporary Engineering Sciences, Vol. 3 no. 2, 2010, pp 77 – 86.
[2] S. Mirjalili, S. M. Mirjalili, A. Lewis, “Grey wolf optimizer”, Advances in Engineering Software, vol. 69, 2014, pp. 46-61.
[3] KDD cup 1999 data, http:// kdd.ics.uci.edu/ databases/kddcup99/kddcup99.html .
[4] Durgesh Srivastava, Nachiket Sainis and Dr. Rajeshwar Singh, “Classification of various Dataset for Intrusion Detection System”, in International Journal of Emerging Technology and Advanced Engineering, Volume 8, Issue 1, January 2018.
[5] H. Günes, Kayacık, A, NurZincir-Heywood, Malcolm I. Heywood, “Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets”, in Third Annual Conference on Privacy, Security and Trust, October 12-14, 2005
[6] Chet Langin, Shahram Rahimi, “Soft computing in intrusion detection: the state of the art”, J ambient Intel Human Compute (2010), 1:133–145, Springer.
[7] Lin, Wei-Chao, Shih-Wen Ke, and Chih-Fong Tsai. "CANN: An intrusion detection system based on combining cluster centers and nearest neighbors", in Knowledge-based systems, 2015.
[8] Donald F. Specht, “A General Regression Neural Network”, in IEEE transactions on neural networks, Vol. 2, No. 6. November 1991.
[9] Benmessahel, Ilyas, Kun Xie, and Mouna Chellal. "A new evolutionary neural networks based on intrusion detection systems using multiverse optimization", Applied Intelligence 2017.
[10] Durgesh Srivastava, L Bhambhu, “Data classification using support vector machine” Journal of Theoretical and Applied Information Technology, 12(1), 2010.
[11] Alaa Tharwat, “Classification assessment methods”, in Applied Computing and informatics, 2018.
[12] Okeh UM and Okoro CN, “Evaluating Measures of Indicators of Diagnostic Test Performance: Fundamental Meanings and Formulars”, in Journal of Biometrics & Biostatistics, Vol.3, Issue 1, 2012
[13] Hossam Faris, Ibrahim Aljarah, “Grey wolf optimizer: a review of recent variants and applications”, in Neural Computing and Applications, 2018.
[14] Jitendra Kumar, Satish Chandra, “Intrusion detection based on key feature selection using Binary GWO”, in International conference on computing for sustainable global development, 2016.
[15] Qiang Li, Huiling Chen, Hui Huang, “An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis” , in Computational and Mathematical Methods in Medicine, Article ID 9512741, 15 pages, 2017.
[16] Durgesh Srivastava, Rajeshwar Singh and Vikram Singh, “Performance Evaluation of Entropy Based Graph Network Intrusion Detection System (E-Ids)”, in Jour of Adv Research in Dynamical & Control Systems, Vol.- 11, 02-Special Issue, 2019
[17] Durgesh Srivastava, Rajeshwar Singh, Vikram Singh, “An Intelligent Gray Wolf Optimizer: A Nature Inspired Technique in Intrusion Detection System (IDS)”, in Journal of Advancements in Robotics. 2019; 6(1): 18–24p
[18] Durgesh Srivastava, L Bhambhu, “Data classification using support vector machine” Journal of Theoretical and Applied Information Technology, 12(1), 2010.
[19] Ebrahim Bagheri, Wei Lu, Mahbod Tavallaee and Ali A. Ghorbani , “A Detailed Analysis of the KDD CUP 99 Data Set”, in IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009.

Durgesh Srivastava, Rajeshwar Singh, Vikram Singh, "Analysis of different Hybrid methods for Intrusion Detection System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.757-764, 2019.

Every vehicle owner wants maximum protection of his vehicle; otherwise thief can easily steal the vehicle. Modern vehicles are becoming smarter by the incorporation of higher computing power, connectivity solutions and advances in communication. This paper introduces design of theft prevention system. Three stages are proposed to protect the vehicle from theft. 1. when the user accesses the vehicle by the vehicle key and entered the wrong password, the power remain disable. 2. If engine is cracked by any other means like bypassing the key switch, the second level comes by sending “ALERT” message to the owner of the vehicle. 3. In case, owner identifies that vehicle is stolen by other way, he has to send “STOP” message to predefined number, so that ignition of vehicles engine will be turned off and brakes will be applied to the vehicle automatically, which is third security. Once that vehicle engine is turned OFF, after every five minutes microcontroller reads geographical location from GPS and sends one URL to owner through GSM module. Owner can open this URL and identify exact location of vehicle on Google Map.

GPS, GSM, matrix Keypad, microcontroller, SMS

[1] Montaser, N.R.,& Mohammad, A.A. “Senior Member”, IACSIT, A.A Sharaf, “Intelligent Anti- Theft and Tracking System for Automobiles” International Journal of Machine Learning and Computing, Vol. 2, No. 1, February 2012.
[2] Sot, S. “MMS Based Vehicle Security System” International Journal of Electronics and Computer Science Engineering. ISSN-2277-1956.
[3] Kiruthiga, N., &Latha, L. “A Study of Biometric Approach for Vehicle Security System Using Fingerprint Recognition”, International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 1, Issue 2, October 2014.

[4] Iman M. Almomani ;Nour Y. Alkhalil ; Enas M. Ahmad ; Rania M. Jodeh “Ubiquitous GPS vehicle tracking and management system” Applied Electrical Engineering and Computing Technologies (AEECT), 2011 IEEE Jordan Conference on 6-8 Dec. 2011

[5] Mohammed Abuzalata,MuntaserMomani, Sayel Fayyad and Suleiman Abu-Ein “A Practical Design of Anti-Theft Vehicle Protection System Based on Microcontroller”American Journal of Applied Sciences 9 (5): 709-716, 2012 ISSN 1546-9239 pg. 709-716
[6] K. Shruthi, P. Ramaprasad, R. Ray, M. A. Naik, S. Pansari, "Design of An Anti-theft vehicle Tracking System with a Smartphone Application", 2015 International Conference on Information Processing (ICIP) Vishwakarma Institute of Technology, Dec 16–19, 2015.
[7] Muhammad Ali Mazidi, Janice GillispieMazidi, Rolin D. Mckinlay “The 8051 Microcontroller and Embedded Systems” Second edition, Pearson publication, India
[8] V. Pankaj, B. J.S., "Design And Development Of GPS-GSM Based Tracking System With Google map Based Monitoring", International Journal of Computer Science Engineering and Applications (IJCSEA), vol. 3, no. 3, June 2013.

Mahesh Pawaskar, Manisha Samant, Adesh Hardas, "Microcontroller Based Anti-theft Vehicle system," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.765-768, 2019.

Graph theoretical concepts are hugely used by the applications of computer science. Especially in research areas of computer science such as networking, image capturing, data mining, image segmentation etc. Fuzzy labeling graphs produce more precision, flexibility, and compatibility to the system compared to the classical and fuzzy graphs. They have large number of applications in Physics, Chemistry, Computer Science, and other branches of mathematics. In this paper a new concept of fuzzy distance two labeling is introduced. Some properties related to product fuzzy graph and fuzzy distance two labeling graph have been discussed. This paper also considers the properties of fuzzy distance two labeling circular graph with appropriate explanation.

Fuzzy distance two labeling graph, product fuzzy graph, fuzzy bridge and fuzzy cut node, fuzzy circular graph

[1] A. Rosenfeld, “Fuzzy Graph”, In: L. A. Zadeh, K. S. Fu, and M. Shimura, Editors, Fuzzy sets and their Applications to Cognitive and Decision Process Academic press, New York, pp.77-95, 1975.
[2] A. Nagoorgani, and V. T. Chandrasekaran, “A first look at fuzzy graph theory”, Allied Publishers Pvt. Ltd., 2010.
[3] A. Nagoorgani, “Fuzzy bipartite graphs”, Journal of Quantitative methods, Vol.2 , pp.54-60, 2006.
[4] A. Nagoorgani, and D. Rajalaxmi (a) subahashini, “Properties of fuzzy labeling graph”, Applied Mathematical Sciences, Vol.6, Issue. 70 , pp.3461-3466, 2012.
[5] J. N. Mordeson and P. S. Nair, “Fuzzy Graphs and Fuzzy hyper- graphs”, Physica-Verlag, Heidelberg, 2000.
[6] J. N. Mordeson and Y. Y. Yao, “Fuzzy cycles and fuzzy trees”, The Journal of Fuzzy Mathematics, Vol.10, Issue.1, pp.189-202, 2002.
[7] J. N. Mordeson and Chang-Shyh Peng, “Operation on fuzzy graphs”, Information Sciences, Vol.79 , pp.169-170, 1994.
[8] K. R. Bhutani and A. Rosenfeld, “Strong arcs in fuzzy graph”, Information sciences, Vol.152 , pp.319-322, 2003.
[9] L.A. Zadeh, “Fuzzy sets” Information and control, Vol.8, pp.338-353, 1965.
[10] M. Delagado, J. L. Verdegay and M. A. Villa, “On fuzzy tree definition”, European Journal of Operation Research, Vol.22 pp.243-249, 1985.
[11] M. S. Sunitha and A. Vijayakumar, “A characterization of fuzzy trees”, In- formation Science, Vol.113 , pp.293-300, 1999.
[12] P. Bhattacharya, “Some remarks on fuzzy graphs”, Pattern Recognition Letters, Vol.6, pp.297-302, 1987.
[13] R. T. Yeah and S. Y. Bang, “Fuzzy relations, fuzzy graphs and their applications to clustering analysis”, In: L. A. Zadeh, K. S. Fu, M. Shimura, Eds, Fuzzy Sets and their Applications, Academic Press, pp.125-149, 1975.
[14] S. Mathew and M. S. Sunitha, “Types of arcs in a fuzzy graph”, Information Science, Vol.179 , pp.1760-1768, 2009.
[15] V. Ramaswamy and B. Poornima, “Product Fuzzy Graphs”, International Journal of Computer Science and Network Security, Vol.9, Issue.1, pp.114-119, 2009.

Anuj Kumar, P. Pradhan, "Some Properties of Fuzzy Distance two Labeling Graph," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.769-775, 2019.

People who travel very often have a fear of losing their baggage during connecting flights, trains, Bus or any transport vehicle. Although the occurrence of such a case is found to be about 5%, it is important to note that the regular air traffic is in millions. This IoT device can be installed in every baggage through which the users can always keep track of their baggage whenever it has misplaced or lost etc., In this paper the lost bags are easily tracked with low cost and efficiently we can search our bags through android application and IoT device this helps them locate the lost baggage easily. The IoT device communicates with the mobile device to give real-time location.

Node MCU, Neo 6m, Thingspeak, Dyno Tag, Trackdot, Konas Bags,Android and IoT

[1]. Madakam, Somayya, R. Ramaswamy, and SiddharthTripathi. “Internet of Things (IoT): A Literature Review”, Journal of Computer and Communications, 2015, pp.164-168.
[2]. Catarinucci, Luca, et al. “An IoT-Aware Architecture for Smart Healthcare Systems” Internet of Things Journal, IEEE, 2015, pp.515-526.
[3]. Redondi, Alessandro, et al. "An integrated system based on wireless sensor networks for patient monitoring, localization and tracking." Ad Hoc Networks11.1 (2013): pg no. 39-53.
[4]. Castillejo, Pedro, et al. "Integration of wearable devices in a wireless sensor network for an E-health application." Wireless Communications, IEEE 20.4 (2013): pg no. 38-49.
[5]. Occhiuzzi, Cecilia, et al. "NIGHT-Care: a passive RFID system for remote monitoring and control of overnight living environment." Procedia Computer Science 32 (2014): pg no. 190-197.
[6]. Catarinucci, Luca, “Switched-beam antenna for wireless sensor network nodes”, Progress in Electromagnetics Research, 2013, pp.193-207.
[7]. Mainetti, Luca, Luigi Patrono, and Antonio Vilei., “Evolution of wireless sensor networks towards the internet of things: A survey.”, Software, Telecommunications and Computer Networks (SoftCOM), 2011 19th International Conference on. IEEE.
[8]. De Donno, Danilo, Luca Catarinucci, and Luciano Tarricone, “A battery-assisted sensorenhanced RFID tag enabling heterogeneous wireless sensor networks”, Sensors Journal, IEEE ,2014, pp.1048-1055.
[9]. Colella, Riccardo,. “Advances in the design of smart, multi-function, RFID-enabled devices”, Antennas and Propagation Society International Symposium (APSURSI), IEEE, 2014.
[10]. Anuradha T., Chandrakala G, GeetaKalshetty, Suma Paddki, “Android Based Student Information System.” International Journal of Advanced Research in Computer and Communication Enginnering, 2017, Volume 6, Issue 8, pp.397-382.
[11]. Anuradha T.,“The monitoring of Water Quality in Iot Environment.” IJRST, March 2018, Volume 4, Issue 5, pp.903-907.
[12]. Anuradha T., “Smart Door Locking System using RFID Reader in IoT Environment”, IJCSE, Volume 7, Issue 5, 2019, Accepted, To be Printed.
[13]. Anuradha T., Shweta Jadhav, Sridevi Mahamani, “Smart Water Despenser and Monitoring Water Level in IoT and Android Environment”, IJCSE, Volume 7, Issue 5, 2019, Accepted, To be Printed.

Anuradha T, Mohammed Abdur Rahman, Juned Khan, "Smart Lost Baggage Tracking Using Android and IoT Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.776-780, 2019.

The process of building the machines intelligent is called Artificial intelligence. Doing the work with foresight with the given environment is called as intelligence. To understand the people feelings and choices we use computers. These computer systems are trained with intelligent computer programs. So artificial intelligence has become a vital topic in human life and varying this life enormously. This artificial intelligence has occupied its importance in many domains like education, health and safety also and changed the lifestyle also. For generating the character animation speech animation is a main and time taking feature. In the existing system for the given input speech to produce a natural-looking animation, we used a simple and effective deep learning approach. It uses the sliding window predictor by using the phoneme label input series and it learns the arbitrary nonlinear mapping to mouth activities. One of the important parameters in the human communication is nonverbal gestures and also, these ought to be considered by speech-driven face animation system. In this paper, we utilize the neural systems to recognize the real-time speech-driven face animation with appearance. By utilizing the MU-based facial movement following algorithm we can gather an audio-visual training database. The visual portrayal of facial distortions is called as movement units (MUS). By preparing the arrangement of neural systems with the assistance of the gathered audio training database we can develop a real-time audio-to-MUP mapping.

Artificial intelligence, neural networks, machine learning algorithms, speech animation, phoneme label,MUS

[1] Aryan Singh DPS “Faridabad Artificial Intelligence in Various Domains of Life”, International Journal of Computer Science and Information Technologies, Vol. 7 (5) 2016, 2353-2355.
[2] Ratnesh Kumar Shukla, Ajay Agarwal, Anil Kumar Malviya “An Introduction of Face Recognition and Face Detection for Blurred and Noisy Images” , International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.39-43 , June (2018).
[3] K. Nagao and A. Takeuchi, “Speech dialogue with facial displays,” in Proc. 32nd Ann. Meet. Assoc. Comput. Linguistics (ACL-94), 1994, pp. 102–109.
[4] K. Waters and J. M. Rehg et al., “Visual Sensing of Humans for Active Public Interfaces,” Cambridge Res. Lab., CRL 96-5, 1996.
[5] Unnati Chawda, Shanu K Rakesh, “Implementation and Analysis of Depression Detection Model using Emotion Artificial Intelligence” Vol.-7, Issue-4, April 2019.
[6] K. Mase, “Recognition of facial expression from optical flow,” ICICE Trans., vol. E74, pp. 3474–3483, Oct. 1991.
[7] A. Lanitis, C. J. Taylor, and T. F. Cootes, “A unified approach to coding and interpreting face images,” in Proc. International Conference of Computer Vision, 1995, pp. 368–373.
[8] M. J. Black and Y. Yacoob, “Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion,” in Proc. International Conference of Computer Vision, 1995, pp. 371–384.
[9] Y. Yacoob and L. Davis, “Recognizing human facial expressions from long image sequences using optical flow,” IEEE Transactions of Pattern Analysis Machine Intel., vol. 18, pp. 636–642, Jan. 1996.
[10] Chaitanya Gupte, Shruti Gadewar “Diagnosis of Parkinson’s Disease using Acoustic Analysis of Voice” Int. J. Sc. Res. In Network Security and Communication, Volume-5, Issue-3, June 2017.
[11] I. A. Essa and A. Pentland, “Coding, analysis, interpretation, and recognition of facial expressions,” IEEE Trans. Pattern Anal. Machine Intel., vol. 10, pp. 757–763, July 1997.
[12] M. Nahas, H. Huitric, and M. Saintourens, “Animation of a b-spline figure,” Visual Comput., vol. 3, pp. 272–276, 1988.
[13 H. Li, P. Roivainen, and R. Forchheimer, “3-D motion estimation in model-based facial image coding,” IEEE Transactions of Pattern Analaysis Machine Intel., vol. 15, pp. 545–555, June 1993.
[14] T. F. Cootes and C. J. Taylor et al., “Active shape models—Their training and application,” Computer Vision Image Understanding, vol. 61, no. 1, pp. 38–59, Jan. 1995.
[15] D. W. Massaro, J. Beskow, and M. M. Cohen et al., “Picture my voice: Audio to visual speech synthesis using artificial neural networks,” in Proc. AVSP’99, 1999.
[16] D. DeCarlo and D. Mataxas, “Optical flow constraints on deformable models with applications to face tracking,” International Journal of Computer Vision, vol. 38, no. 2, pp. 99–127, 2000.

K. Rajasekhar, C. Usharani, A. Mrinalini, "Real Time Face Driven Speech Animation Using Neural Networks in with Expressions," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.781-786, 2019.

With the advent of technology, the computational demands of users are increasing day by day. Cloud Computing is among the most trending technologies satisfying the computationally intensive demands of users. Cloud computing has exploited virtualization technology to provide on demand provisioning of resources, results in increased complexity of cloud infrastructure, thus faults are inevitable. These faults may result in failure causing serious loss to the organizations. Techniques used for fault management usually require additional resources increasing the consumption of energy. Moreover, cloud infrastructure also consumes a lot of energy and is the major contributor to carbon content. Growing demands and limited renewable resources had led to serious energy crises. Thus energy efficient fault tolerant solutions are needed to tolerate faults and provide reliable, scalable and flexible availability of cloud services, preventing system failure and minimizing energy consumption at the same time. Fault tolerance and energy efficiency are the crucial issues which must be simultaneously considered in order to ensure availability, performance, and reliability of the cloud computing services. This paper describes the basic concepts of faults, errors, and failures. It also discusses different fault tolerance strategies and the trade-off between energy efficiency and fault tolerance.

Checkpointing, Energy efficiency, Fault Tolerance, Migration, Replication

[1] F. Salfner, M. Lenk, M. Malek, “A survey of online failure prediction methods”, ACM Computing Surveys (CSUR),vol no 42(3), pp.10,2010
[2] R. Jhawar,V. Piuri, “Fault tolerance management in IaaS clouds” In Satellite Telecommunications (ESTEL), 2012 IEEE First AESS European Conference, pp. 1-6,2012.
[3] A. Avižienis, J.C. Laprie, B. Randell, and C. Landwehr. “Basic concepts and taxonomy of dependable and secure computing,” IEEE Transactions on Dependable and Secure Computing, vol. 1, no. 1, pp. 11–33, 2004.
[4] M. Grottke, R. Matias, and K. Trivedi, “The fundamentals of software aging,” in Proc. IEEE International Conference on Software Reliability Engineering Workshops, Nov. 2008.
[5] S. Marc, Robert W. Wisniewski, J.A. Abraham, S.V.Adve, S. Bagchi, P. Balaji, J. Belak et al. "Addressing failures in exascale computing." International Journal of High Performance Computing Applications, vol. 28, no. 2, pp. 129-173, 2014.
[6] F.Gartner, “Fundamentals of fault-tolerant distributed computing in asynchronous environments,” ACM Computing Surveys, vol. 31, no. 1, pp. 1–26, Mar. 1999.
[7] B. Javadi, J. Abawajy, and R. Buyya, “Failure-aware resource provisioning for hybrid cloud infrastructure,” Journal of Parallel and Distributed Computing, vol. 72, no. 10, pp. 1318 – 1331, 2012.
[8] M.Lackovic, D.Talia, R.T. Calasanz, J.Banares, and O.Rana, “A taxonomy for the analysis of scientific workflow faults,” Proceedings of the 13th IEEE International Conference on Computational Scienceand Engineering,pp.398–403, 2010
[9] J. Wei, L. Rashid, K. Pattabiraman and S. Gopalakrishnan, "Comparing the effects of intermittent and transient hardware faults on programs", Dependable Systems and Networks Workshops, IEEE, pp. 53-58, 2011.
[10] M. Castro , B. Liskov, “Practical byzantine fault tolerance and proactive recovery”, ACM Transactions on Computer Systems (TOCS), v.20 n.4, p.398-461, 2002.
[11] R. Jhawar, V. Piuri, "Fault Tolerance and Resilience in Cloud Computing Environments" in Computer and Information Security Handbook, Morgan Kaufmann, 2013.
[12] Q. Zhang, L. Cheng, R. Boutaba, “Cloud computing: state-of-the-art and research challenges”, J Internet Serv Appl , springer, 2010, pp. 7–18, 2010.
[13] B.P. Rimal , E. ChoI “A taxonomy and survey of Cloud Computing Systems”, IEEE Fifth International Joint Conference on INC, IMS and IDC, pp. 44-51, 2009
[14] C. N. Höfer, G. Karagiannis, “Cloud computing services: taxonomy and comparison”, Journal of Internet Services and Applications, Springer, pp.81-94, 2011.
[15] R. Buyya and Chee Shin Yeo, "Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility", Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2009
[16] C.C. Meixner, C.Develder, M.Tornatore, B. Mukherjee, “A Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications”, IEEE Communiation Sureys, pp. 2244-2281 2016
[17] P.D. Kaur, Kanupriya, “Fault Tolerance Techniques and Architectures in Cloud Computing-A Comparative Analysis”, International Conference on Green Computing and Internet of Things, IEEE, pp. 1090-1095, 2015
[18] T. Mastelic , A. Oleksiak , H. Claussen , I. Brandic , J.M. Pierson , A. V. Vasilakos, “Cloud Computing: Survey on Energy Efficiency”, ACM Computing Surveys (CSUR), v.47 n.2, p.1-36, 2015
[19] E. Feller, L. Rilling, C. Morin, R. Lottiaux, and D. Leprince "Snooze: A Scalable, Fault-Tolerant and Distributed Consolidation Manager for Large-Scale Clusters," Proc. 2010 IEEE/ACM Int`l Conference on Green Computing and Communications & Int`l Conference on Cyber, Physical and Social Computing, pp. 125-132, 2010
[20] A. Beloglazov, R. Buyya, “Energy Efficient Allocation of Virtual Machines in Cloud Data Centers”, 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, 2010
[21] D. Sun,Guiran, Chang, C. Miao, X. Wang, “Analyzing, modeling and evaluating dynamic adaptive fault tolerance strategies in cloud computing environments”, The Journal of Supercomputing 66, Vol no. 1, pp. 193-228, 2013
[22] C. Engelmann, G. R. Vallee, T. Naughton, and S. L. Scott “Proactive Fault Tolerance Using Preemptive Migration”, 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing, IEEE, pp 252-257, 2009
[23] Liu, Jialei, S. Wang, Ao Zhou, S.A P Kumar, and R. Buyya “Using Proactive Fault - Tolerance Approach to Enhance Cloud Service Reliability”, IEEE Transactions on Cloud Computing, 2017
[24] Goiri, F. Julia, J. Guitart, and J. Torres, "Checkpoint-based fault tolerant infrastructure for virtualized service providers," Proc. IEEE/IFIP Network Operations and Management Symposium (NOMS`10), pp. 455-462, 2010
[25] P. Das, P. M. Khilar, “VFT: A Virtualization and Fault Tolerance Approach for Cloud Computing”, Proceedings of 2013 IEEE Conference on Information and Communication Technologies(ICT 2013) , (pp. 473-478) , 2013
[26] A. Zhou, S. Wang, Z. Zheng, C. Hsu, M. Lyu, and F. Yang, "On cloud service reliability enhancement with optimal resource usage," IEEE Transactions on Cloud Computing, pp. 452-466, 2016
[27] H. Jin, L. Deng, S. Wu, X. Shi, and X. Pan. “Live virtual machine migration with adaptive, memory compression”, In Cluster Computing and Workshops, 2009. CLUSTER ’09. IEEE Inter-national Conference on, pages 1 –10, 2009
[28] F. Ma, F. Liu, and Z. Liu, "Virtual machine migration based on improved pre-copy approach," In Proc. IEEE Int`l Conf. Software Engineering and Service Sciences, pp.230-233, 2010.
[29] X. Zhang, Z. Huo, Jie Ma, Dan Meng, “Exploiting Data Deduplication to Accelerate Live Virtual Machine Migration”, 2010 IEEE International Conference on Cluster Computing, pp 88-97, 2010
[30] B. Hu, Z. Lei, Y. Lei, D. Xu, and J. Li, “A time-series based precopy approach for live migration of virtual machines”, IEEE 17th International Conference on Parallel and Distributed Systems, pp. 947-952, 2011
[31] J. Arputharaj Johnson “Optimization of migration downtime of virtual machine in Cloud”IEEE, pp. 1-5, 2013
[32] Y. Ma, H. Wang, J. Dong, Y. Li,and S. Cheng, “ME2: efficient live migration of virtual machine with memory exploration and encoding”, IEEE International Conference on Cluster Computing, pp 610-613, 2012.
[33] D. Jung, S. Chin, K. Chung, H. Yu, "VM migration for fault tolerance in spot instance based cloud computing," in Grid and Pervasive Computing, Springer, vol. 7861, pp. 142-151, 2013.
[34] B. Jiang, J. Wu, X. Zhu, D. Hu, “Priority-Based Live Migration of Virtual Machine”, Springer, pp. 376–385, 2013.
[35] C. Kim, C. Jeon, W. Lee, S. Yang, “A Parallel Migration Scheme for Fast Virtual Machine Relocation on a Cloud Cluster.” The Journal of Supercomputing, Springer, pp: 4623–4645, 2015.
[36] Y. Zhang, Z. Zheng, and M. R. Lyu. "BFTCloud: A byzantine fault tolerance framework for voluntary-resource cloud computing." In Cloud Computing (CLOUD), 2011 IEEE International Conference, pp. 444-451, 2011.
[37] B. Egger, Y. Cho, C. Joe, E.Park, J. Lee “Effcient Checkpointing of Live Virtual Machine Migration”, IEEE Transactions on Computers, pp. 3041 – 3054, 2016.
[38] M. Zhao, F. DUgard, K.A. Kwait, C.A. Kamhoua “Multi-level VM replication based survivability for mission-critical cloud computing”, IEEE International Symposium on Inegrated Network Management, 2015.
[39] D. Bruneo, S. Distefano, F. Longo, A. Puliafito, M. Scarpa. "Workload-based software rejuvenation in cloud systems."IEEE Transactions on Computers, vol. 62, no. 6, pp.1072-1085, 2013.
[40] L. Silva, J. Alonso, and J. Torres, “Using Virtualization to Improve Software Rejuvenation,” IEEE Trans. Computers, vol. 58, no. 11, pp. 1525-1538, 2009.
[41] K. Kourai, S.Chiba “A Fast Rejuvenation Technique for Server Consolidation with Virtual Machines”, IEEE International Conference on Dependable Systems and Networks, pp. 245-255, 2007.
[42] Y. Abe, R. Geambasu, K. Joshi, M. Satyanarayanan “Urgent Virtual Machine Eviction with Enlightened Post-Copy”, 12th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, pp. 51-64, 2016.
[43] M.R. Hines, K. Goplanan “Post-Copy Based Live Virtual Machine Migration Using Adaptive Pre-Paging and Dynamic Self-Ballooning”, Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments, pp.51-60, 2009.
[44] K. Su, W. Chen, G. Li, Z. Wang “RPFF: A Remote Page-fault Filter for Post-copy Live Migration”, IEEE International Conference on Smart City/SocialCom/SustainCom together with DataCom 2015 and SC2 2015, pp.936-943, 2015.
[45] I. Egwutuoha, S. Chen, D. Levy, B. Selic, R. Calvo, “Energy efficient fault tolerance for high performance computing (hpc) in the cloud”, Sixth International Conference on Cloud Computing (CLOUD), IEEE, , pp. 762–769, 2013.
[46] H. Asai “P2V Migration with Post-copy Hot Cloning for Service Downtime Reduction”, IEEE Third International Conference on Cloud and Green Computing, pp. 1-8, 2013.
[47] J. Ansel, K. Arya, and G. Cooperman, “DMTCP: Transparent checkpointing for cluster computations and the desktop,” in Proc. of the Int’l Parallel and Distributed Processing Symp. (IPDPS). Rome, Italy: IEEE, 2009, pp. 1–12.
[48] S. Agarwal, R. Garg, M. S. Gupta, and J. E. Moreira. Adaptive incremental checkpointing for massively parallel systems. ICS Proceedings of the 18th Annual International Conference on Supercomputing, pp. 277–286, 2004.
[49] R. Rajachandrasekar, A. Venkatesh, K. Hamidouche, D. K.Panda “Power-Check: An Energy-Efficient Checkpointing Framework for HPC Clusters”, 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 261-271, 2015
[50] P. Chi, Cong Xu, T. Zhang, X. Dong, Y. Xie, “Using Multi-Level Cell STT-RAM for Fast and Energy-Efficient Local Checkpointing”, IEEE/ACM International Conference, pp. 301-308, 2014
[51] R. Melhem, D. Mosse, E. Elnozahy, “The Interplay of Power Management and Fault Recovery in Real-Time Systems” IEEE Transactions on Computers,, VOL. 53, NO. 2, pp.217-231, 2004
[52] M. Salehi, M. K. Tavana, S. Rehman, M. Shafique, A. Ejlali, and J. Henkel, “Two-State Checkpointing for Energy-Efficient Fault Tolerance in Hard Real-Time Systems”, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol 24, No 7, pp.2426-243, 2016
[53] S. Di, Y. Robert, F. Vivien, D. Kondo, C. Wang, F. Cappello1, “Optimization of Cloud Task Processing with Checkpoint-Restart Mechanism”, High Performance Computing, Networking, Storage and Analysis (SC), 2013 International Conference, pp. 1-12, 2013
[54] B. Nicolae, F. Cappello “BlobCR: Efficient Checkpoint-Restart for HPC Applications on IaaS Cloudsusing Virtual Disk Image Snapshots”, Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, ACM, pp. 1-36,2011
[55] D. Boru, D. Kliazovich, F. Granelli, P. Bouvry, A. Zomaya, "Energy-Efficient Data Replication in Cloud Computing Datacenters", CCSNA Cluster Computing, Vol no. 18(1), pp.385-402, 2015
[56] M. A. Haque, H. Aydin, D. Zhu, "Energy-Aware Standby-Sparing "Technique for Periodic Real-Time Applications", Sparing "Technique for Periodic Real-Time Applications", Computer Design (ICCD), 2011 IEEE 29th International Conference, pp. 190-197, 2011.
[57] B. Mills, T. Znati, R. Melhem, K. B. Ferreira, R. E. Grant, “Energy Consumption of Resilience Mechanisms in Large Scale Systems” 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, IEEE, pp. 528-535, 2014
[58] W. Lang, J.M. Patel, J.F.Naughton “On Energy Management, Load Balancing and Replication”, ACM SIGMOD Record, pp. 35-42, 2009
[59] H. Goudarzi, M. Pedram “Energy-Efficient Virtual Machine Replication and Placement in a Cloud Computing System” 2012 IEEE Fifth International Conference on Cloud Computing, pp. 750-757, 2012
[60] X. Cui, T. Znati, R. Melhem, ”Adaptive and Power-Aware Resilience for Extreme-Scale Computing” 2016 Intl IEEE Conferences, pp 1-9, 2016
[61] Y. Lin, H. Shen, “EAFR: An Energy-Efficient Adaptive File Replication System in Data-Intensive Clusters”, IEEE Transactions on Parallel and Distributed Systems, pp. 1017-1030, 2017
[62] X. You, L. Zhou, J. Huang, J. Zhang, C. Jiang and J. Wan, “E2ARS : An Energy-Effective Adaptive Replication Strategy in Cloud Storage System, Applied Mathematics & Information Sciences An International Journal, pp. 2409-24019, 2013
[63] D. Ibtesham, D. DeBonis, D. Arnold, K.B. Ferreira. "Coarse-Grained Energy Modeling of Rollback/Recovery Mechanisms." InDependable Systems and Networks (DSN), 2014 44th Annual IEEE/IFIP International Conference on, pp. 708-713. IEEE, 2014.
[64] N. El-Sayed, B. Schroeder. "To checkpoint or not to checkpoint: Understanding energy-performance-I/O tradeoffs in HPC checkpointing." Cluster Computing (CLUSTER), 2014 IEEE International Conference, pp. 93-102, 2014.
[65] E.A.S Sarma, J.N. Maggo, A.S. Sachdeva, “India’s energy scenario in 2020” 17th World Energy Congress, Houston, TX, pp. 13-18, 1998
[66] F. Ahmad, T. N. Vijaykumar “Joint optimization of idle and cooling power in data centers while maintaining response time”, ACM SIGPLAN, pp. 243–256, 2010
[67] Indian Energy Outlook 2015 , http://www.worldenergyoutlook.org/india
[68] W. Deng, F.Liu, H. Jin, B. Li, D. Li, “Harnessing renewable energy in cloud datacenters: opportunities and challenges” IEEE Network, pp. 48-55, 2014.
[69] M. Ghamkhari, H. Mohsenian-Rad, “Energy and Performance Management of Green Data Centers: A Profit Maximization Approach”, IEEE Transactions on Smart Grid, pp. 1017-1025, 2013
[70] E.K. Lee, H. Viswanathan, D. Pompili, “Proactive Thermal-aware Resource Management in Virtualized HPC Cloud Datacenters”, IEEE Transactions on Cloud Computing, pp. 1-14, 2015
[71] A. Beloglazov, J. Abawajy, R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for Cloud Computing”, Future generation Computer System, pp.755-768, 2012
[72] A. Dalvandi, M. Gurusamy, K.C. Chua “Time-aware VMFlow Placement, Routing and Migration for Power Efficiency in Data Centers” IEEE Transactions on Network and Service Management, pp. 349-362, 2015
[73] A. Beloglazov, R. Buyya, “Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers”, Concurrency and Computation: Practice and Experience (CCPE), Wiley Press, pp. 755-768, 2012.
[74] F. Farahnakian, A. Ashraf, T. Pahikkala, P. Liljeberg, J. Plosila, I. Porres, H. Tenhunen, “Using Ant Colony System to Consolidate VMs for Green Cloud Computing”, IEEE Transactions on Services Computing, pp. 184-198, 2015
[75] K.K. Nguyen, M. Cheriet “Environment-aware Virtual Slice Provisioning in Green Cloud Environment”, ” IEEE Transactions on Service Computing, pp. 507-519, 2015
[76] L. Wang, F. Zhang, J. A. Aroca, A.V. Vasilakos, K. Zheng, C. Hou, D. Li, and Z. Liu, “GreenDCN: A General Framework for Achieving Energy Efficiency in Data Center Networks”, IEEE Journal on Selected Areas in Communications, pp. 4-15, 2014
[77] S. Wang, A. Zhou, C.H Hsu, X. Xiao, F. Yang, “Provision of Data-intensive Services through Energy- and QoS-aware Virtual Machine Placement in National Cloud Data Centers”, IEEE Transactions on Emerging Topics in Computing, pp. 290-300, 2016
[78] J. Moore , J.S. Chase, P. Ranganathan, “Weatherman: Automated, Online, and Predictive Thermal Mapping and Management for Data Centers”, IEEE International Conference on Autonomic Computing, pp. 155-164, 2006
[79] K. Singh, S. Kaushal, “Energy Efficient Resource Provisioning Through Power Stability Algorithm in Cloud Computing”, Proceedings of the International Congress on Information and Communication Technology, Springer, pp 255-263, 2016
[80] E. Sheme , N Frashëri , S. Holmbacka, S. Lafond, D. Lucanin , “Datacenters powered by renewable energy: A case study for 60 degrees latitude north”, Software, Telecommunications and Computer Networks (SoftCOM), 2016.
[81] D.Zhu, R. Melhem, D. Mossé. "The effects of energy management on reliability in real-time embedded systems." IEEE/ACM International Conference on Computer Aided Design, ICCAD, pp. 35-40, 2004.
[82] V. Sundriyal, M. Sosonkina, “Initial Investigation of a Scheme to Use Instantaneous CPU Power Consumption for Energy Savings Format”, E2SC `13 Proceedings of the 1st International Workshop on Energy Efficient Supercomputing, 2013
[83] Y. Ren, J. Suzuki, C. Lee, A.V. Vasilakos, S. Omura, “Balancing Performance, Resource Efficiency and Energy Efficiency for Virtual Machine Deployment in DVFS-enabled Clouds: An Evolutionary Game Theoretic Approach”, GECCO Comp `14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation,pp. 1205-1212, 2014
[84] M. Basoglu, M. Orshansky, M. Erez, “NBTI-Aware DVFS: A New Approach to Saving Energy and Increasing Processor Lifetime”, IEEE International Symposium on Low-Power Electronics and Design, pp. 253-258, 2010
[85] B. Meroufeland, G. Belalem “Adaptive time-based coordinated checkpointing for cloud computing workflows”, Scalable Computing: Practice and Experience, pp. 153–168, 2014
[86] D. Jung, S.H. Chin, K.S. Chung, H. Yu, J. Gil “An Efficient Checkpointing Scheme Using Price History of Spot Instances in Cloud Computing Environment”, IFIP International Federation for Information Processing, pp. 185-200, 2011
[87] A. Kumar, A. Bashir, "Improved EDF Algorithm for Fault Tolerance with Energy Minimization." In Computational Intelligence & Communication Technology (CICT), 2015 IEEE International Conference on, pp. 370-374, 2015.
[88] T.Hea, A.Toosib, R. Buyyaa, “Performance Evaluation of Live Virtual Machine Migration in SDN-enabled Cloud Data Centers”, Journal of Parallel and Distributed Computing, Elsevier, pp. 1-40, 2019.

Kamaljit Kaur, Kuljit Kaur, "A Survey on Energy-Aware Fault Tolerant Strategies in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.787-800, 2019.

RFID tagging is an ID system that uses small radio frequency identification devices for identification and tracking purpose. An RFID tagging system includes the tag itself, a read/write device, and a host application for data collection, processing, and transmission .In simple words an RFID uses electromagnetic fields to transfer data over short distances. RFID is useful to identify people, to make transactions.

Arduino Uno, RFID Reader, two RFID tags, Arduino Uno cable, Jumper wires, CD driver,Battery,IoT(Internet of things technology)

[1]. D. L. Wu, Wing W. Y. NG, D. S. Yeung, and H. L. Ding, “A brief survey on current RFID applications,” in Proc. International Conference on Machine Learning and Cybernatics, Baoding, July 12-15, 2009, pp. 2330-2334.
[2]. B. Yan and D. Y. Lee, “Design of spot ticket management system based on RFID,” in Proc. International Conference on Networks Security, Wireless Communications and Trusted Computing, 2009, pp. 496-499.
[3]. G. Ostojic, S. Stankovski, and M. Lazarevic, “Implementation of RFID technology in parking lot access control system,” in Proc. Annual RFID Eurasia Conference, 2007, pp. 1-5.
[4]. N. Ahmad, S. Butler, and U. Ramachandran, “GuardianAngel: An RFID based indoor guidance and monitoring system,” 2010, pp. 546-551.
[5]. K. S. Huang and S. M. Tang, “RFID applications strategy and deployment in bike renting system,” in Proc. ICACT 2008, pp. 660-663.
[6]. S. Lahiri, RFID sourcebook, IBM Press, Westford, Massachusetts, 2006
[7]. F. Lourenco and C. Almeida, “RFID based monitoring and access control system,” in Proc. INFORUM, 2009.
[8]. R. Weinstein, “RFID: A technical overview and its application to the enterprise,” IT Professional, vol. 7, no. 3, May-June 2005, pp. 27-33.
[9]. X. L. Meng, Z. W. Song, and X. Y. Li, “RFID-Based security authentication system based on a novel face-recognition structure,” in Proc. WASE International Conference on Information Engineering, 2010, pp. 97-100.
[10]. D. L. Wu, Wing W. Y. NG, Patrick P. K. Chan, H. L. Ding, B. Z. Jing, and D. S. Yeung, “Access control by RFID and face recognition based on neural network,” in Proc. International Conference on Machine Learning and Cybernatics, July 11-14, 2010, pp. 675-680.
[11]. M. A. Mazidi, J. C. Mazidi, and R. D. Mckinaly, The 8051 Microcontroller and Embedded Systems, Pearson Education, 2006.

Anuradha T, "Smart Door Locking System using RFID Reader in IoT Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.801-805, 2019.

Inability to speak make difficult to convey Message. People with disability use sign language for communication. Mute deaf uses hand gestures to convey there say. This hand glove prototype will help them to communicate with others. The model is developed with the help of an electronic device which translates the sign language into speech so that they can communicate. On hand glove, shirt buttons are stitched which work as a point of contact for the different gesture. The generated gestures are being captured by the Arduino Uno which transmits the corresponding instruction to the mobile application with the help of Bluetooth sensor, the mobile application responds with voice. Not only it will help mute deaf but it will also help half paralysis patient to covey the daily routine or basic say in an easy way. The goal of this paper is to provide a simple solution for fewer instructions with maximum accuracy including innovative and cost-cutting hardware solution.

Bluetooth sensor, hand glove, shirt buttons [tap buttons], Arduino Uno

[1] “Deaf-Mute Communication Interpreter” by Anbarasi Rajamohan, Hemavathy R., Dhanalakshmi M. International Journal of Scientific Engineering and Technology (ISSN : 2277-1581) Volume 2 Issue 5, pp : 336-341, 1 May 2013
[2] Satjakarn Vutinuntakasame, “An Assistive Body Sensor Network Glove for Speech- and Hearing- Impaired Disabilities”, Proceedings of the 2011 IEEE Computer Society International Conference on Body Sensor Networks
[3] Kunal Kadam, Rucha Ganu, Ankita Bhosekar, Prof. S. D. Joshi, “American Sign Language Interpreter”, Proceedings of the 2012 IEEE Fourth International Conference on Technology for Education
[4] “A new instrumented approach for translating American Sign Language into sound and text” by Hernandez-Rebollar, J. L. ; Kyriakopoulos, N. ; Lindeman, R.W. Automatic Face and Gesture Recognition, 2004.
[5] “Sign language recognition using sensor gloves” by Mehdi, S.A.; Khan, Y. N. Neural Information Processing, 2002.ICONIP `02. Proceedings of the 9th International Conference,Volume: 5 Publication Year: 2002 , IEEE Conference Publications.

Palash.Y.Ingle, Raj kumar, "Let’s talk model for converting Gesture to voice using hand glove and IOT," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.806-809, 2019.

About 71% of earth is covered with water but sadly only 2.5% of it is used for drinking purpose, the reason for this is with rise in population, pollution and climate change, humans waste a lot of water due to our negligence. In this paper the automatic water dispenser and water level monitoring is been proposed using sensors in IoT environment. For a automatic water dispenser they used node MCU and ultrasonic sensor in IoT environment. Here the manual taps are replaced with a smart taps that opens and closes on its own automatically due to this saving of water is achieved which is a biggest challenge nowadays. This technique changes the lifestyle of the public since they don’t need to operate the tap manually through their hands. In this paper not only saves the water but the smart water dispenser sends a notification when the level of water becomes low in the dispenser through an app based on android to the authorized person. Once the authorized person receives notification for low water level, the android application will provide to order water to water cans or water tanks.

Arduino, Servo motor, Ultrasonic sensor, IR sensor, Jumper wires, Power Bank, memory card, IoT

[1]. Poonam J. Chavan, Manoj Mechkul “IoT Based Water quality Monitoring”, IJMTER Journal, Vol 3, 2016, pp.746-750.
[2]. Aaina Venkateshwaran, Harsha Menda P. prof. Priti Bodar “An IoT based system for water quality monitoring” ,IJRCCE, 2017, pp. 2510-2515.
[3]. Mithali Borbade, Shruthi Danve “Real Time Water Quality monitoring system” IJIRCE journal, Vol 3, 2015, water pp.5046-5068.
[4]. Anuradha T, Bhakti, Chitra R, pooja D. “IOT based low cost system for monitoring of water quality in real time”, International Research Journal of Engineering and Technology, Vol 05, Issue 05, pp. 1658-1663.
[5]. Anuradha T. “The monitoring of water quality in IoT Environment”, IJSRT, March 2018, Volume 4, Issue 5, pp.
[6]. Jyoti Bhatt, Jignesh Pataliya “IoT based water quality monitoring system”, IJIEE journal, Vol 4, 2016, pp. 44-48.
[7]. S.Geeta, S.Goutham “Internet of Things enabled real time water quality monitoring system” springer open journal Vol 5, pp. 1-19, 2017.
[8]. Pradeep Kumar M. Manisha J. Praveen Sha R. Proiserin V. Suganya Devi, “The real time monitoring of water quality in IoT Environment”, Vol 5, 2016, pp.4419-4427.
[9]. A.D Ausilio, “Arduino: At low cost multipurpose lab equipment Behaviour research methods”, 2012, pp.305-313
[10]. Yogita patil, Ramandeep singh, “smart tank management system for residential colonies using Atmega 128A microcontroller”, international journal nof scientific & engineering research, volume 5, issue 6, june-2014.
[11]. Supriya R. Khaire, Revati M. Wahul, “water quality data transfer and monitoring system in IOT environment. Volume 2, Issue 6, November-December 2017.
[12]. Anuradha T, “ Smart Door Locking System using RFID Reader in IoT Environment”, IJCSE, Volume 7, Issue 5,may 2019, To be Printed.
[13]. Anuradha T, “Smart Lost Baggage Tracking Using Android and IoT”, IJCSE, Volume 7, Issue 5, May 2019, To be Printed.

Anuradha T, Shweta Jadhav, Sridevi Mahamani, "Smart Water Dispenser and Monitoring Water Level in IoT and Android Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.810-814, 2019.