Volume-7 , Issue-3 , Mar - 2019 Go Back

Now a day’s internet is very popular and widely used for information generation and broadcasting. If current trend is observed, then most of the organization/labs/institute uses “PDF” (Portable Document Format) document to release their official/research report. PDF document has many benefits, hence popularly used for publishing information on the web. if this widely published information extracted and re-processed then this information can be useful inputs for many research and development projects. In this research paper we introduced information extraction algorithm, which extracts information from the pdf document using free libraries. To be specific, we have targeted PDF documents comprising Kanhan River water quality data, which is freely published over the internet. To present this information beautifully, extracted information is geo-mapped and re-published in the public domain which helps in observing and validating Kanhan River water quality data at different geographical locations.

PDF Extraction, data generation, Extraction, Kanhan River, information system

[1] Dr. G. K. Khadse, P. M. Patni, P.S. Kelkar, S. Devotta, "Qualitative evaluation of Kanhan River and its tributaries flowing over central Indian plateau", Environ Monit Assess. 2008 Dec; 147 (1-3):83-92. Epub 2007 Dec 22.
[2] Margaret H. Dunham, “Data Mining Introductory & Advanced Topics”, Pearson Education
[3] Dinesh A. Lingote1*, Girish S. Katkar2, Ritesh Vijay 3, R. B. Biniwale4, "Responsive Information generation system for Kanhan River, an effective information system for river modeling", International Journal of Computer Science and Engineering (IJCSE, E-ISSN: 2347-2693), Vol.-6, Issue-12, Dec 2018
[4] Library org.apache.pdfbox.* is attributed as it is used for reading PDF document.
[5] Mehrdad Jalali, Norwati Mustapha et al,” A Recommender System Approach for Classifying User Navigation Patterns Using Longest Common Subsequence Algorithm”, American Journal of Scientific Research ISSN 1450-223X Issue 4 (2009), pp 17-27
[6] K. A. Smith and A. Ng, Web page clustering using a self-organizing map of user navigation patterns, Decision Support Syst. 35(2) (2003) 245–256
[7] Nacim Fateh Chikhi, Bernard Rothenburger, Nathalie Aussenac-Gilles “A Comparison of Dimensionality Reduction Techniques for Web Structure Mining”, Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, P.116-119 ,2007
[8] Poonam Devi, "Attacks on Cloud Data: A Big Security Issue", International Journal of Scientific Research in Network Security and Communication, Volume-6, Issue-2, April 2018
[9] P.V. Nikam, D.S. Deshpande, "Different Approaches for Frequent Itemset Mining", International Journal of Scientific Research in computer science and Engineering, Vol.6, Issue.2, pp. 10-14, April (2018)

D. A. Lingote, Girish S. Katkar, "Interactive Data Extraction Algorithm to Extract Data from The Pdf Document, Helpful in Generating Water Quality Data of The Kanhan River," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.550-556, 2019.

A robust and secure technique is required to protect multimedia data as it can be easily produced as illegal copies. Digital watermarking is used for Intellectual Property Rights protection and authentication. This paper present the comparative performance analysis of second order IWT-SVD based image watermarking with others technique at different scaling factor to satisfies both imperceptibility and robustness.The experimental results show the effectiveness of proposed image watermarking scheme. Performance of methodology is evaluated using different fidelity parameters like as peak signal noise ratio (PSNR) and normalized correlation coefficient (NCC).

Image Watermarking, Integer Wavelet Transforms, Singular Value Decomposition, Discrete Wavelet Transform, PSNR, NCC

[1] W. Bender, D. Gruhl, N. Morimoto, and A. Lu, “Techniques for data hiding”, IBM System Journal, Vol. 35, NOS 3&4, pp. 313-336, 1996.
[2] E. T. Lin, and E. J. Delp, “A review of data hiding in digital images”, Proc. of the Image Processing, Image Quality, Image Capture Systems Conference, Vol. 299, pp. 274-278, April, 1999.
[3] R. Liu, and T. Tan, “An SVD-based watermarking scheme for protecting rightful ownership”, IEEE Transactions on Multimedia, Vol. 4, No. 1, pp. 121-128, March, 2002.
[4] Kundur, Deepa, and D. Hatzinakos, “A robust digital image watermarking method using wavelet-based fusion”, Proc. IEEE International Conference on Image Processing, Vol. 1, pp. 544-547, October, 1997.
[5] W. Sweldens, “The lifting scheme: A construction of second generation wavelets”, SIAM J. Math. Anal., Vol. 29, No. 2, pp. 511–546, 1998.
[6] Z. Zhou, B Tang, and X. Liu, “A Block-SVD based image watermarking method”, IEEE The Sixth World Congress on Intelligent Control and Automation, Vol. 2, pp. 10347-10351, June, 2006.
[7] A. Miyazaki, and F. Uchiyama, “An image watermarking method using the lifting wavelet transform”, IEEE International Symposium on Intelligent Signal Processing and Communications, pp. 155-158, December, 2006.
[8] Loukhaoukha, and J. Y. Chouinard, “Hybrid watermarking algorithm based on SVD and lifting wavelet transform for ownership verification”, IEEE, 11th Canadian Workshop on Information Theory, CWIT 2009, pp. 177-182, May, 2009.
[9] L. Hu, and F. Wan, “Analysis on wavelet coefficient for image watermarking”, IEEE International Conference on Multimedia Information Networking and Security (MINES), pp. 630-634, 2010.
[10] M. Thapa, Dr S. K. Sood, and A. P. M. Sharma, “Digital image watermarking technique based on different attacks”, International Journal of Advanced Computer Science and Applications, Vol. 2, 2011.
[11] Kashyap, Nikita, and G. R. Sinha, “Image watermarking using 2-level DWT”, Advances in Computational Research 4.1, pp. 42-45, 2012.
[12] Navnidhi, “Various digital image watermarking techniques and wavelet transforms”, International Journal of Emerging Technology and Advanced Engineering 2.5, pp. 363-366, 2012.
[13] M. Ibrahim, M. M. Rahman, and M. Iqbal, “Digital watermarking for image authentication based on combined DCT, DWT and SVD transformation”, arXiv preprint arXiv:1307.6328, 2013.
[14] A. Kala, and K. Thaiyalnayaki, “Robust lossless image watermarking in integer wavelet domain using SVD”, International Journal of Computer Science Engineering, pp. 30-35, 2013.
[15] Makbol, Nasrin, and B. E. Khoo, “A new robust and secure digital image watermarking scheme based on the integer wavelet transform and singular value decomposition”, Digital Signal Processing 33, pp. 134-147, October, 2014.
[16] P. Gupta and G. Parmar, “Image watermarking using IWT-SVD and its comparative analysis with DWT-SVD”, Proc. IEEE International Conference on Computer, Communications and Electronics (COMPTELIX-2017), pp. 527-531, July, 2017.
[17] USC-SIPI image database http://sipi.usc.edu/database/database.php

Manoj Kumar, Mahendra Kumar Pandey, Sanjay Patsariya, "Comparative Performance Analysis of Second Level IWT-SVD Based Robust Image Watermarking," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.557-562, 2019.

A new method for dorsal hand vein pattern recognition is presented in the paper. To improve the recognition ratio, the vein skeleton extracting with little distortion is very important. Firstly, our method acquires a clean, skeleton with little distortion after a series of processes: size and gray normalizing, Gaussian low pass and wiener filtering, adaptive thresholding segmenting, area thresholding, morphological opening and closing, conditional thinning, spurs pruning. Then, the seven corrected moment invariants of the vein skeleton are extracted as the feature vector. At last, the feature vector is input into KNN for training and recognition. Experiment shows the algorithm achieves a higher recognition ratio of 97.75%.

Vein pattern recognition; Preprocessing; Segmenting; Feature extraction; Nearest Neighbor Classifier (KNN)

[1] Zahra Honarpisheh, Karim Faez, "An Efficient Dorsal Hand Vein Recognition Based on Firefly Algorithm", International Journal of Electrical and Computer Engineering (IJECE), Vol. 3, pp. 30-34, 2013.
[2] P. Ramsoful and M. Heenaye Mamode Khan, "Feature extraction Techniques for dorsal hand vein pattern," Third International Conference on Innovative Computing Technology (INTECH 2013), IEEE Transaction, London, pp. 49 -53, 2013.
[3] Shangqing Wei, ``International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE)” Changchun, China, pp.1696-1693,2011.
[4] Yiding Wang , Yun Fan , Hand vein recognition based on multiple keypoints sets, 5th IAPR International Conference on Biometrics (ICB), IEEE Transaction, pp.367-371, 2012.
[5] Ajay Kumar, Personal Authentication using Hand Vein Triangulation and Knuckle Shape, IEEE Transactions on Image Processing, vol. 38, pp. 2127-2136, 2009.
[6] Amioy Kumar, M. Hanmandlu, “Biometric Authentication Based on Infrared Thermal Hand Vein Patterns", Digital Image Computing: Techniques and Applications, IEEE, pp.331-338, 2009.
[7] Zhang Y., Han X., Ma S., "Feature Extraction of Hand-Vein Patterns Based on Ridgelet Transform and Local Interconnection structure Neural Network", Intelligent Computing in Signal Processing and Pattern Recognition,.Springer, Berlin, Heidelberg, vol 345, pp.870-875, 2006.
[8] Tim, Jerman, Beyond Frangi, "an improved multiscale vesselness _lter", Medical Imaging 2015: Image Processing, edited by Sebastien Ourselin, Martin A. Styner, Proc. of SPIE Vol. 9413, pp.94132A-l -94132A-11,2015.
[9] J. Yang, L. Zhang, J. Yang, D. Zhang, "From classifiers to discriminators: A nearest neighbor rule induced discriminant analysis", Pattern Recognit., vol. 44, no. 7, pp. 1387-1402, 2011.

N. S. Zulpe, B. M. Sontakke, "A Dorsal Hand Vein Pattern Recognition using Invariant Moment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.563-566, 2019.

Face recognition system is used to identify a person by comparing a face image in a database record. Face recognition is comparing and matching human beings with their faces. Face occlusion detection is also part of face recognition. Face occlusion is one of the major problems in face recognition. Facial occlusion is different from another kind of challenge in the field of artificial intelligence (AI). Occlusion means some area of the face is hidden behind an object like sunglasses, hand, and mask, etc. This paper gives brief information about face detection and recognition from occluded face images. This paper includes face occlusion detection methods like SVM, LGBPHS, S – LNME, and LBP, etc. that are used to recognize an occluded human face from a database record. This paper contains some publicly available datasets: Occluded LFW dataset, FERFT datasets, WebV-Cele dataset, Bosphorus dataset, UMB (University of Milano Bicocca) datasets and so on.

Face Recognition, Face Detection, Face Occlusion Detection, Convolution Neural Networks (CNN), Datasets

[1]. Ganguly, Suranjan, Debotosh Bhattacharjee, and Mita Nasipuri. "Depth based Occlusion Detection and Localization from 3D Face Image." International Journal of Image, Graphics & Signal Processing 7.5 (2015).
[2]. Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
[3]. G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” Technical Report 07-49, University of Massachusetts, Amherst, Tech. Rep., 2007
[4]. G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” Technical Report 07-49, University of Massachusetts, Amherst, Tech. Rep., 2007.
[5]. Z.-N. Chen, C.-W. Ngo, W. Zhang, J. Cao, and Y.-G. Jiang, “Nameface association in web videos: a large-scale dataset, baselines, and open issues,” Journal of Computer Science and Technology, vol. 29, no. 5, pp. 785–798, 2014.
[6]. Danisman, Taner, et al. "Automatic facial feature detection for facial expression recognition." Fifth International Conference on Computer Vision Theory and Applications (VISAPP) 2010. Vol. 2. 2010.
[7]. Colombo, Alessandro, Claudio Cusano, and Raimondo Schettini. "UMB-DB: A database of partially occluded 3D faces." 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE, 2011.
[8]. Y. Su, Y. Yang, Z. Guo and W. Yang, "Face recognition with occlusion," 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, 2015, pp. 670-674.
[9]. Min, Rui, Abdenour Hadid, and Jean-Luc Dugelay. "Efficient detection of occlusion prior to robust face recognition." The Scientific World Journal 2014 (2014).
[10]. Min, Rui, Abdenour Hadid, and Jean-Luc Dugelay. "Improving the recognition of faces occluded by facial accessories." Face and Gesture 2011. IEEE, 2011.
[11]. Zhang, Wenchao, et al. "Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition." Tenth IEEE International Conference on Computer Vision (ICCV`05) Volume 1. Vol. 1. IEEE, 2005.
[12]. Oh, Hyun Jun, et al. "Occlusion invariant face recognition using selective LNMF basis images." Asian Conference on Computer Vision. Springer, Berlin, Heidelberg, 2006.
[13]. Dagnes, Nicole & Vezzetti, Enrico & Marcolin, Federica & Tornincasa, Stefano. (2018). Occlusion detection and restoration techniques for 3D face recognition: a literature review. Machine Vision and Applications. 1-25. 10.1007/s00138-018-0933-z.

Kashyap Patel, Hemant Yadav, "A Survey: Face Detection and Recognition from Occluded images," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.567-570, 2019.

The Internet of Things (IoT) is the system of physical gadgets mainly used in vehicles, home apparatuses and different things inserted with hardware, programming, sensors, actuators and availability that empowers these articles to interface and trade information. Agribusiness is the essential occupation in numerous nations for ages. Especially farming assumes imperative job in the advancement of agrarian nation like India, and Internet of Things (IoT) is one of the quickest creating innovations all through the India. However at this point because of relocation of individuals from rustic to urban there is block in agribusiness. So to beat this issue we have a few applications incorporated with IoT which help the general population with less human work in farming. Also, in this paper we are going to see a review give an account of a portion of the gadgets which are utilized in farming alongside IoT like sensors and small scale controllers.

IoT, Agriculture, Sensor, Micro-controller, Survey

[1]“A Novel Technology for Smart Agriculture Based on
IoT with Cloud Computing”, Mahammad Shareef Mekala Research scholar, Dr P.Viswanathan Associate Professor.
[2]“IoT Based Control and Automation of Smart
Irrigation SystemAn Automated Irrigation System Using Sensors, GSM, Bluetooth and Cloud Technology”, Monica M1, B.Yeshika2, Abhishek G.S2, SanjayH.A3,SankarDasiga4 UG Student, Department of Information Science andEngineering, UG Students, Department of Electronics and Communication Engineering, Professor & Head of Department, Information Science and Engineering, Senior Professor, Electronics and Communication Engineering.
[3]“Cloud Based Data Analysis and Monitoring of Smart Multi-level Irrigation System Using IoT”,
Sanket Salvi1, Pramod Jain S.A2, SanjayH.A3,Harshita T.K4, M. Farhana4, Naveen Jain4, Suhas M V4.
[4]“Wireless Sensor and Actuator System for Smart Irrigation on the Cloud”, Nelson Sales, Orlando Remédios SenseFinity, Artur Arsenio, Universidade da Beira Interior.
[5] “IoT Based Smart Irrigation Monitoring And Controlling System”, Shweta B. Saraf, Dhanashri H. Gawali
[6]“SMART AGRICULTURE MANAGEMENT SYSTEM”, Pushpalatha S1, Shreyas B2, Syed Nadeem Hussain3, Sadhan Kumar4, Pramoda CS5
[7]“International Conference onMicro Electronic and Mechanical Systems” MEMS,Kyoto, Japan, 2012, pp. 65–68.
[8]Goli, K. M., Maddipatla, K., & Sravani, T. (2011). “Integration of wireless technologies for sustainable agriculture”. International Journal of Computer Science & Technology, 2(4), 83-85.
[9]Zhenyu Liao; Sheng Dai; Chong Shen, "Precision agriculture monitoring system based on wireless sensor networks," Wireless Communications and Applications (ICWCA 2012), IET International Conference on ,vol., no., pp.1,5, 8-10 Oct. 2012.
[10] Moummadi, K., Abidar, R., Medromi, H., "Generic model based on constraint programming and multi-agent system for M2M services and agricultural decision support," Multimedia Computing and Systems (ICMCS), 2011 International Conference on, vol., no., pp.1,6, 7-9 April 2011

M. Kirubakaran, P.Lavanya, George Gabriel Richard Roy, "Survey on Application of IoT in Agriculture," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.571-573, 2019.

Digital Image Security is still recent topic of research in computer science engineering. Images are being shared from one device to another device. Security of digital data becomes important for many reasons such as confidentiality, authentication and integrity. Digital watermarking has emerged as an advanced technology to enhance the security of digital images. The insertion of watermark in images can authenticate it and guarantee its integrity. The watermark must be generally hidden does not affect the quality of the original image. In this paper, we discuss on different spatial and frequency domain watermarking methods and Various attacks like Translation, Rotation and Scaling will be addresses to make the approach RST invariant are also discuss.

Discrete Wavelet Transform, Discrete Cosine Transform, Singular Value Decomposition, RST Attack

[1] P. Singh and R. S. Chadha, “A survey of digital wa- termarking techniques, applications and attacks,” International Journal of Engineering and Innova- tive Technology, vol. 2, no. 9, pp. 165–175, 2013.
[2] B. L. Gunjal and R. Manthalkar, “An overview of transform domain robust digital image water- marking algorithm,” Journal of Emerging Trends in Computing and Information Sciences, vol. 2, no. 1, pp. 37–42, 2010-11.
[3] M. Saqib and S. Naaz, “Spatial and frequency do- main digital image watermarking techniques for copyright protection,” International Journal of En- gineering Science and Technology, pp. 691–698, 2017.
[4] H. Tao, L. Chongmin, J. M. Zain, and A. N. Ab- dalla, “Robust image watermarking theories and techniques: A review,” Journal of Research and Technology, vol. 12, pp. 122–135, 2014.
[5] L. K. Saini and V. Shrivastava, “A survey of digi- tal watermarking techniques and its applications,” International Journal of Computer Science Trends and Technology, vol. 2, pp. 70–73, 2014.
[6] N. Rathi and G. Holi, “Securing medical im- ages by watermarking using dwt-dct-svd,” Inter- national Journal of Computer Trends and Technol- ogy, vol. X, pp. 1–8, 2014.
[7] B. Ram, “Digital image watermarking technique using discrete wavelet transform and discrete co- sine transform,” International Journal of Advance- ments Research and Technology, vol. 2, no. 4, pp. 19–27, 2013.
[8] E. Sonia, E. N. K. Garg, and E. G. Singh, “A sur- vey on digital image watermarking,” International Journal of Advanced Research in Computer Engi- neering and Technology, vol. 3, pp. 2054–2057, 2014.
[9] S. M. Mousavi, A. Naghsh, and S. A. R. Abu- Bakar, “Watermarking techniques used in medi- cal images: a survey,” Journal of Digital Imaging, vol. 27, no. 6, pp. 714–729, 2014.
[10] R. v. Totla and K.S.Bapat, “Comparison analysis of watermarking in digital image using dct and dwt,” International Journal of Scientific and Research Publications, vol. 3, pp. 1–4, 2013.
[11] M. S. Islam and U. P. Chong, “A digital image wa- termarking algorithm based on dwt dct and svd,” International Journal of Computer and Commu- nication Engineering, vol. 3, no. 5, pp. 356–360, 2014.
[12] T. Bhuyan, V. kumar Srivastva, and F. Thakkar, “Shuffled svd based robust and secure digital image watermarking,” International Conference on Electrical, Electronics, and Optimization Tech- niques, pp. 1229–1233, 2016.
[13] J. sahu and D. shukla, “Digital image watermark- ing method 4 level dwt-dct on the basis of psnr,” International Journal of Engineering Development and Research, vol. 3, pp. 1008–1012, 2015.
[14] A. Kaur and J. Singh, “Digital image watermark- ing techniques: A review,” International Journal of Advanced Research in Computer Science, vol. 8, no. 8, pp. 714–718, 2017.
[15] N.Senthikumaran and S.Abinaya, “Comparison analysis of digital image watermarking using dwt and lsb technique,” International Conference on Communication and Signal Processing, pp. 448– 451, 2016.
[16] Anushka and A. Saxena, “Digital image water- marking using least significant bit and discrete co- sine transformation,” International Conferenceon Intelligent Computing ,Instrumentation and Control Technologies, pp. 1582–1585, 2017.
[17] O. Hosam and N. B. Halima, “A hybrid roi- embedding based watermarking technique using dwt and dct transform,” Journal of Theoretical and Applied Information Technology, vol. 81, no. 3, pp. 514–528, 2015.
[18] M.Jamali, S.Samavi, N.Karimi, S. Soroushmehr, K. Ward, and K. Najarian, “Robust watermarking in non-roi of medical images based on dct- dwt,” IEEE Transactions on Image Processing, pp. 1200–1203, 2017.
[19] R. Keshavarzian, “A new roi and block based watermarking scheme using dwt,” Iranian Confer- ance on Electrical Engineering, pp. 1323–1328, 2012.
[20] P. Singh, B. Raman, and M. Misra, “Region of interest based robust watermarking scheme exploit- ing the homogeneity analysis,” International Con- ference on Intelligent Computing and Control Systems, pp. 797–803, 2017.
[21] M. Hamidi1, M. E. Haziti, and H. C. and Mo- hammed El Hassouni, “Hybrid blind robust im- age watermarking technique based on dft-dct and arnold transform,” Springer, vol. 77, no. 20, pp. 27181–27214, 2018.
[22] D. Sudiana and D. Apriyadi, “Dft-svd and dct-svd domain digital image watermarking: Implementa- tion and performance analysis,” Optics and Remote Sensing Research Group, 2015.
[23] H. Cai, H. Liu, M. Steinebach, and X. Wang, “A roi-based self-embedding method with high recov- ery capability,” IEEE Transactions on Image Pro- cessing, pp. 1722–1726, 2015.
[24] H. L. Khor, S.-C. Liew, and J. M. Zain, “Region of interest-based tamper detection and lossless re- covery watermarking scheme (roi-dr) on ultra- sound medical images,” Journal of Digital Imag- ing, vol. 30, no. 3, pp. 328–349, 2017.
[25] R. Choudhary and G. Parmar, “A robust im- age watermarking technique using 2-level discrete wavelet transform (dwt),” IEEE International Con- ference on Communication, Control and Intelligent Systems, pp. 120–124, 2016.
[26] A. Chen and X. Wang, “An image watermarking scheme based on dwt and dft,” International Journal of Computer Technology and Applications, 2017.
[27] S. D. Degadwala and D. S. Gaur, “4-share vcs based image watermarking for dual rst attacks,” International Journal of Computer Technology and Applications, pp. 902–912, 2018.
[28] E. Najafi, “A robust embedding and blind ex- traction of image watermarking based on discrete wavelet transform,” Mathematical Sciences, vol. 11, no. 4, pp. 307–318, 2017.
[29] Y. Wang1, J. Liu1, Y. Yang1, D. Ma1, and R. Liu1, “3d model watermarking algorithm robust to geometric attacks,” The Institution of Engineering and Technology, vol. 11, no. 10, pp. 822–832, 2017.
[30] D. V. Singh, “Digital watermarking: A tutorial,” Multidisciplinary Journals in science and technol ogy, pp. 10 – 19, 2011.
[31] A. D’Silva and N. Shenvi, “Data security using svd based digital watermarking techniques,” Interna tional Conference on Trends in Electronics and In- formatics, pp. 382–386, 2017.
[32] N. Sahu and A. Chugh, “A survey on digital im- age watermarking techniques based on frequency domain,” International Journal of Current Trends in Engineering and Technology, vol. 3, pp. 56–59, 2017.
[33] A. Sheshasaayee and S. D, “Analysis of techniques involving data hiding and watermarking,” International Conference on Innovative Mechanisms for Industry Applications, pp. 593–596, 2017.
[34] H. Li, S. Wang, W. Song, and Q. Wen, “A novel watermarking algorithm based on svd and zernike moments,” Springer Berlin Heidelberg, pp. 448– 453, 2005.
[35] P. Parashar and R. K. Singh, “A survey: Digital im- age watermarking techniques,” International Jour- nal of Signal Processing, Image Processing and Pattern Recognition, vol. 7, no. 6, pp. 111–124, 2014.
[36] M. S. Islam and U. P. Chong, “A digital image wa- termarking algorithm based on dwt dct and svd,” International Journal of Computer and Commu- nication Engineering, vol. 3, no. 5, pp. 356–360, 2014.
[37] L. K. Saini and V. Shrivastava, “A new hybrid dwt- dct algorithm for digital image watermarking,” International Journal of Advance Engineer ing and Research Development, vol. 1, pp. 1–8, 2014.
[38] M. Singh, A. Singhal, and A. Chaudhary, “Digital image watermarking techniques: A survey,” International Journal of Computer Science and Telecommunications, vol. 4, pp. 51–55, 2013.
[39] R. Kaur and H. Singh, “Image watermarking in dct, dwt and their hybridization using svd: A survey,” International Journal of Innovations in Engineer- ing and Technology, vol. 4, pp. 376–379, 2014.
[40] N. Mittal and A. S. Bisen, “Digital watermaking using svd and dwt, fft, dct based method : A sur- vey,” International Journal of Innovations in Engi- neering and Technology, vol. 3, pp. 40–45, 2017.
[41] A. Singh1 and D. A. Sharma, “Digital image water- marking techniques: A survey,” International con- ference on science, technologies and management, pp. 620–625, 2017.
[42] R. Patel and Prof.A.B.Nandurbarkar, “Implemen- tation of dct dwt svd based watermarking algo- rithms for copyright protection,” International Re- search Journal of Engineering and Technology, vol. 2, pp. 340–344, 2015.
[43] S. Kaur, “A digital image watermarking technique based on dwt,” International Journal of Computer and IT, pp. 1–6, 2015.
[44] M. H. Arya, S. S. Bundela, V. Kushwah, and P. Jain, “A survey on digital watermarking tech- niques,” International Journal of Engineering Technology Science and Research, vol. 3, pp. 1–6, 2016.
[45] H. Lala, “Digital image watermarking using discrete wavelet transform,” International Research Journal of Engineering and Technology, vol. 4, pp. 1682–1685, 2017.

Rashmika N Baria, Mahesh M Goyani, "A Suvery on DWT-DCT Based RST Attacks Invariant Watermarking Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.574-583, 2019.

The nodes in Mobile Ad Hoc Network are mobile continuously moves in limited area and having fixed range of communication but it is possible to vary range according to requirement of functioning. The mobile nodes are energy dependent and consume energy in every commotion like sending packets, sensing neighbour and receiving. The attacker in network is consumes the valuable energy resource by flooding large amount of data packets to all mobiles unnecessary. In this research we proposed the security scheme for three attacks like Blackhole attack, Jamming attack and Vampire attack. The malicious functioning of attacker are different one is packet dropping and rest of two is flooding unwanted packets.. In this research work the proposed security scheme is identified the flooding status of jamming as well as Vampire attacker. The proposed scheme is collect the information of attacker on the basis of heavy flooding unnecessary energy consumption. The attacker information is also broadcast to other nodes to not communicate with attacker after blocking communication capability of nodes The Jamming attacker aim is to consume the resources at most of the time because of that the routing performance is affected. The proposed work is provides the reliable routing scheme to improve network performance in minimum energy cost. The minimum energy consumption enhances the possibility of communication, which enhance network lifetime. The performance of attack, existing scheme and proposed security scheme is evaluated through performance metrics like percentage of receiving, Overhead, data drop and Average energy consumption.

Attack, Energy, flooding, Routing, Security, DTN

[1] Lalit Kulkarni, Debajyoti Mukhopadhyay and Jagdish Bakal “Analyzing Security Schemes in Delay Tolerant Networks” Proceedings of the International Conference on Data Engineering and Communication Technology, Springer Science Business Media Singapore 2017.
[2] T. Sasilatha, S. Vidhya and P. Suresh Mohan Kumar “Detection and Elimination of Black Hole and Grey Hole Attack on MANET” International Journal of Pure and Applied Mathematics Volume 116 No. 24 2017, 235-242.
[3] Sofia Anna Menesidou , Vasilios Katos , and Georgios Kambourakis “Cryptographic Key Management in Delay Tolerant Networks: A Survey” Future Internet 2017, 3, 26; doi:10.3390/fi9030026 Published: 27 June 2017.
[4] Signe Rüsch, Dominik Schürmann, Rüdiger Kapitza, Lars Wolf,“Forward Secure Delay-Tolerant Networking” CHANTS’17, October 20, 2017, Snowbird, UT, USA © 2017 Association for Computing Machinery.
[5] Feng Li,Yali Si,Ning Lu, Zhen Chen, and Limin Shen, “A Security and Efficient Routing Scheme with Misbehavior Detection in Delay-Tolerant Networks” Hindawi Security and Communication Networks Volume 2017, Article ID 2761486.
[6] Veerpal Kaur, Simpel Rani, “Prevention/Detection Methods of Black Hole Attack: A Review” Advances in Wireless and Mobile Communications. ISSN 0973-6972 Volume 10, Number 4 (2017), pp. 747-756.
[7] Sanjay Ramaswamy, Huirong Fu, Manohar Sreekantaradhya, John Dixon and Kendall Nygard “Prevention of Cooperative Black Hole Attack in Wireless Ad Hoc Networks”IEEE 2016.
[8] Vaishali Mittal, “Prevention and Elimination of Gray Hole Attack in Mobile Ad-Hoc Networks by Enhanced Multipath Approach” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 4 Issue 5, May 2015.
[9] D. Boneh, X. Boyen, and E.-J. Goh. 2005. Hierarchical Identity Based Encryption with Constant Size Ciphertext. Springer, Berlin, Heidelberg, 440–456.
[10] R. Canetti, S. Halevi, and J. Katz. 2003. A Forward-Secure Public-Key Encryption Scheme. Springer, Berlin, Heidelberg, 255–271.
[11] M. D. Green and I. Miers. 2015. Forward Secure Asynchronous Messaging from Puncturable Encryption. In 2015 IEEE Symposium on Security and Privacy. 305–320.
[12] R. Ostrovsky, A. Sahai, and B. Waters. 2007. Attribute-Based Encryption with Non-Monotonic Access Structures. In ACM CCS ’07. 195–203.

Prachi Tiwari, S. Veenadhari, "Data link and Network Layer Attack Prevention in DTN Mobile Ad-hoc Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.584-590, 2019.

Big data will be transformative in every sphere of life. But Just to Process and analyze those data is not enough, human brain tends to find pattern more efficiently when data is represented visually. Data Visualization and analytics plays important role in decision making in various sector. Many Visual analytics methods have been proposed across disciplines to understand large-scale structured and unstructured social media data. Current Big data Visualization approaches often reduce high dimension data to low dimension, and omit some data trends or relationships. In exploratory analysis of multivariate datasets, performing an analytical task is often necessary. Such tasks may include extracting characteristics subsets and comparing them.In social network, thousands of people produce data at the same time, and huge amount of data will be produce in seconds. In this paper, survey on a Real time Information Visualization and Analysis framework– RIVA[2]. RIVA to collect data from the social networks, such as Twitter, by using Spark Cloud computing platform to discover popular topics around the world.

visual analytic method, unstructured data, structured data, social media data, big data visualization, Apache spark, BladeGraph

[1] Hiroaki Kobayashi, Hiroko Suzuki, Kazuo Misue, “A Visualization Technique t Support Searching and Comparing features of Multivariate Datasets”. 201519th International Conference on Information Visualization.
[2] Yong-Ting Wu, He-YenHsieh, Xanno K. Sigalingging, Kaun-Wu Su, Jenq-Shiou Leu”, RIVA: A Real-time Information Visualization and Analysis Platform for Social Media Sentiment Trend.” 2017 9th international Congress on Ultra Modem Telecommunication and Control System and Workshop.
[3] A .Inselberg “ ThePlane With parallel coordinates”, The Visual Computer,Vol. 1, No. 4, pp. 69-91, 1985.
[4] D.B.Carr, R.J.Littlefield. W.L. Nicholson, and J.S. Littlefield, “Scatterplot Matrix Technique for Large N”, In Journal of the American statistical Association, Vol. 82, No.398,pp. 424-436, 1987.
[5] A. Lex, M. Streit, C. Partl, K. Kashofer, and D. Schmalstieg,“Comparative analysis of multidimensional, quantitativedata”, IEEE Transactions on Visualization and Computer
Graphics, Vol. 16, No. 6, pp. 1027–1035, 2010.
[6] J.-F . Im, M. J.McGuffin, and R.Leung, “GPLOM:The Generalized Plot Matrix for Visualizing Multidimensional Multivariate Data”, IEEE Transactions on Visualization and Computer Graphics, Vol. 20, No. 12 , pp. 2023-2032,2014.
[7] S. Gratzl, N. Gehlenborg, A. Lex, H. Pfister, and M. Streit,“Domino: Extracting, Comparing, and Manipulating Subsets across Multiple Tabular Datasets”, IEEE Transactionson Visualization and Computer Graphics, Vol. 20, No. 12,pp. 2023–2032, 2014.
[8] J. Kehrer, H. Piringer, W. Berger, and E. M. Gr¨oller, “AModel for Structure-Based Comparison of Many Categoriesin Small-Multiple Displays”, IEEE Transactions on Visualizationand Computer Graphics, Vol. 19, No. 12, pp. 2287–2296,2013.
[9] T. Pham, R. Hess, C. Ju, E. Zhang, and R. Metoyer, “Visualizationof Diversity in Large Multivariate Data Sets”,IEEE Transactions on Visualization and Computer Graphics,Vol. 16, No. 6, pp. 1053–1062, 2010.Letter Symbols for Quantities, ANSI Standard Y10.5-1968.
[10] W. .Javed, B. MDonnel, and N. Elmqvist,”Graphical perception of multiple time series”, IEEE Transaction on Visualization and Computer Graphics, Vol. 16, No. 6, pp. 927-934, 2010.
[11] T. Pham, R. Hess, C. Ju, E. Zhang, and R. Metoyer, “Visualizationof Diversity in Large Multivariate Data Sets”,IEEE Transactions on Visualization and Computer Graphics,Vol. 16, No. 6, pp. 1053–1062, 2010.Letter Symbols for Quantities, ANSI Standard Y10.5-1968.
[12] Inc. Cisco System, “Cisco Visual Networking Index: Global MobileData Traffic Forecast Update,2015-2020,” February 3, 2016.

Komal Javalkoti, Vipul Joshi, Pooja Shah, "Big Data Visualization Techniques of Social Media: A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.591-594, 2019.

The road traffic causes worst condition and severe side effects. These affects can be reduced in case density of traffic can be predicted in advance. The number of vehicles is growing as the population growth so the traffic management systems are required that handles traffic. Today traffic becomes very big issues in the world that leads to increased accidents and pollution. Towards this aspect intelligent transportation system is worked upon by many researchers. This work analysed a previous work that has been done towards the intelligent transportation system. The merits and demerits of various techniques also highlighted through this approach. Literature survey is presented interactively in the comparative form for best possible approach selection for future enhancement. Parametric comparison includes metrics classification accuracy, error rate , true positive rate , false positive rate and sensitivity.

Intelligent transportation system, traffic, metric

[1] K. Zheng, H. Meng, P. Chatzimisios, L. Lei, and X. Shen, “An SMDP-Based Resource Allocation in Vehicular Cloud Computing Systems,” IEEE Trans. Ind. Electron., vol. 62, no. 12, pp. 7920–7928, 2015.
[2] W. Min and L. Wynter, “Real-time road traffic prediction with spatio-temporal correlations,” Transp. Res. Part C, vol. 19, no. 4, pp. 606–616, 2011.
[3] A. V. Chernov, M. A. Butakova, V. D. Vereskun, and O. O. Kartashov, “Situation awareness service based on mobile platforms for multilevel intelligent control system in railway transport,” 24th Telecommun. Forum, TELFOR 2016, pp. 1–4, 2017.
[4] W. Guo, D. Brennan, K. Pavkova, and P. T. Blythe, “Department of Industrial and Reformatory Schools : report of the Secretary for the year 1883,” 1885.
[5] I. Kabashkin, “Reliable v2x communications for safety-critical intelligent transport systems,” Proc. - 2017 Adv. Wirel. Opt. Commun. RTUWO 2017, vol. 2017-January, pp. 251–255, 2017.
[6] Z. Kljaiü, E. Briški, H. Vojvodiü, and N. Amin, “Benefits of Utilisation of GPS Error Mitigation Models for Intelligent Transport Systems,” pp. 1121–1125, 2018.
[7] I. Malygin, V. Komashinsky, and V. V. Tsyganov, “International experience and multimodal intelligent transportation system of Russia,” Proc. 2017 10th Int. Conf. Manag. Large-Scale Syst. Dev. MLSD 2017, 2017.
[8] A. Paier, “The end-to-end intelligent transport system (ITS) concept in the context of the european cooperative ITS corridor,” 2015 IEEE MTT-S Int. Conf. Microwaves Intell. Mobility, ICMIM 2015, 2015.
[9] A. Papola, F. Tinessa, V. Marzano, and A. Mautone, “Quantitative overview of efficiency and effectiveness of public transport in Italy: The importance of using ITS,” 5th IEEE Int. Conf. Model. Technol. Intell. Transp. Syst. MT-ITS 2017 - Proc., pp. 895–900, 2017.
[10] Y. A. Seliverstov, I. G. Malygin, V. I. Komashinskiy, A. A. Tarantsev, N. V. Shatalova, and V. A. Petrova, “The St. Petersburg transport system simulation before opening new subway stations,” Proc. 2017 20th IEEE Int. Conf. Soft Comput. Meas. SCM 2017, pp. 284–287, 2017.
[11] K. Siddiqi, A. D. Raza, and S. S. Muhammad, “Visible light communication for V2V intelligent transport system,” Proc. 2016 Int. Conf. Broadband Commun. Next Gener. Networks Multimed. Appl. CoBCom 2016, pp. 2–5, 2016.
[12] X. Wang and S. Yan, “Design and implementation of intelligent public transport system based on GIS,” 2011 Int. Conf. Electr. Inf. Control Eng. ICEICE 2011 - Proc., pp. 4868–4871, 2011.
[13] S. V. Kumar and L. Vanajakshi, “Short-term traffic flow prediction using seasonal ARIMA model with limited input data,” Eur. Transp. Res. Rev., vol. 7, no. 3, pp. 1–9, 2015.
[14] A. D. REZWAN-AL-ISLAM KHAN, BJORN LANDFELDT, “Predicting Travel Times in Dense and Highly Varying Road Traffic Networks using STARIMA Models .,” IEEE, no. February, 2012.
[15] F. Chen, P. Deng, J. Wan, D. Zhang, A. V Vasilakos, and X. Rong, “Data Mining for the Internet of Things : Literature Review and Challenges,” IEEE, vol. 2015, no. i, 2015.
[16] T. Jena and J. R. Mohanty, “GA-Based Customer-Conscious Resource Allocation and Task sScheduling in Multi-cloud Computing,” Arab. J. Sci. Eng., 2017.
[17] F. Guo, R. Krishnan, and J. W. Polak, “Short-term traffic prediction under normal and incident conditions using singular spectrum analysis and the k-nearest neighbour method,” IEEE ACCESS, pp. 1–6, 2015.
[18] S. D. Thepade, S. Vasaikar, N. Bhavsar, R. More, and A. Bhatkhande, “Vehicle traffic density estimation using bayes, rule, tree family data mining classifiers applied on background subtracted traffic images.,” IEEE ACCESS, pp. 87–92, 2016.
[19] G. Zhu, K. Song, and P. Zhang, “A Travel Time Prediction Method for Urban Road Traffic Sensors Data,” 2015 Int. Conf. Identification, Information, Knowl. Internet Things, pp. 29–32, 2015.
[20] J. He, W. Shen, P. Divakaruni, L. Wynter, and R. Lawrence, “Improving Traffic Prediction with Tweet Semantics,” IEEE ACCESSIEEE ACCESS, pp. 1387–1393, 2016.

Rohit Jangral, Sandeep Sharma, "Intelligent Transportation Mechanisms Used for Predicting on Road Traffic," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.595-598, 2019.

Wireless sensor network (WSN) is a group of spatially dispersed and dedicated sensors for monitoring the physical or environmental parameters and communicating the collected data to a central base station through wireless links. Each sensor node consists of a sensor, microcomputer, transceiver and power source. The gathered data is forwarded through multiple nodes to the central base station. This requirement demands deploying number of nodes in the hostile environment, which might lead to the malfunction or failure of nodes due to power depletion, environmental impacts, radio intrusion, asymmetric communication links, and interruption of sensor nodes. Hence, fault tolerance is one of the critical issues in WSN’s. The recent developments in WSN have led to considerable improvements in protocols and fault tolerant mechanisms that are proposed to achieve higher data reliability, accuracy, energy saving, enhance network lifetime and minimize failure of components. This paper discusses and analyses the various fault tolerance mechanisms to identify the strength and weakness of these methods with prime focus on centralized and distributed network environments.

Wireless sensor network, fault tolerance, energy efficiency, centralized network, distributed network

[1] Walaa M. Elsayed, Sahar F. Sabbeh and Alaa M. Riad, A Distributed Fault Tolerance Mechanism for Self-Maintenance of Clusters in Wireless Sensor Networks, Arab J SciEng (Springer), 2017.
[2] Indrajit Banerjee, Prasenjit Chanak, Hafizur Rahaman and Tuhina Samanta, Effective fault detection and routing scheme for wireless sensor networks, Computers and Electrical Engineering (Elsevier), 2013.
[3] B. R. Tapas Bapu, K. Thanigaivelu and A. Rajkumar, Fault Tolerance in Wireless Sensor Networks – A Survey, World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering Vol: 9, No:2, 2015.
[4] Hossein Karimia, Omid Medhatib, Hossein Zabolz adeha, Ali Eftekharic, Fateme Rezaeib, Shahin BehrozianDehnob, and Amir Abasjama lpoorb, Implementing a reliable, fault tolerance and secure framework in the wireless sensor-actuator networks for events reporting, The International Conference on Advanced Wireless, Information, and Communication Technologies (AWICT 2015), pp. 384 – 394.
[5] Samira Chouikhia, In`es El Korbia, Yacine Ghamri-Doudanec, and Leila AzouzSaidanea, A Survey on Fault Tolerance in Small and Large Scale Wireless Sensor Networks, Computer Communications, 2015.
[6] Markus Lanthaler, Self-Healing Wireless Sensor Networks, pp . 1-10.
[7] Gaurav Gupta and Mohamed Younis Dept, Fault-Tolerant Clustering of Wireless Sensor Networks, IEEE, pp. 1579 - 1584, 2003.
[8] Xin-Ming Huang, Jing Deng, Jing Ma and Zeyu Wu, Fault Tolerant Routing For Wireless Sensor Grid Networks, IEEE Sensors Applications Symposium, pp. 66-70, February 2006.
[9] Iman Saleh, Hesham El-Sayed, and Mohamed Eltoweissy, A Fault Tolerance Management Framework for Wireless Sensor Networks, IEEE, 2006.
[10] Gayathri Venkataraman, Sabu Emmanuel and Srikanthan hambipillai, A Cluster-Based Approach to Fault Detection and Recovery in Wireless Sensor Networks, IEEE ISWCS, pp. 35- 39, 2007.
[11] Myeong-Hyeon Lee and Yoon-Hwa Choi, Fault detection of wireless sensor networks, Computer Communications (Elsevier), pp. 3469–3475, 2008.
[12] Themistoklis Bourdenas and Morris Sloman, Towards Self-healing in Wireless Sensor Networks, Body Sensor Networks, pp.15-20, 2009.
[13] Peng Jiang, A New Method for Node Fault Detection in Wireless Sensor Networks, Sensors, pp. 1282- 1294, 2009.
[14] Muhammad Zahid Khan, Madjid Merabti, Bob Askwith and FaycalBouhafs, A Fault-Tolerant Network Management Architecture for Wireless Sensor Networks, 2010.
[15] Mojoodi . A, M. Mehrani , F. Forootan and R.Farshidi, Redundancy Effect on Fault Tolerance in Wireless Sensor Networks, Global Journal of Computer Science & Technology, Volume 11 Issue,pp. 35-39, 2011.
[16] Abhishek Kashyap, Samir Khuller and Mark Shayman, Relay placement for fault tolerance in wireless networks in higher dimensions, Computational Geometry: Theory and Applications (Elsevier) pp. 206–215, 2010.
[17] Ataul Bari, Arunita Jaekel, Jin Jiang and Yufei Xu, Design of fault tolerant wireless sensor networks satisfying survivability and lifetime requirements, Computer Communications (Elsevier), pp. 320–333, 2011.
[18] Arunanshu Mahapatroaa and Pabitra Mohan Khilarb, Transient Fault Tolerant Wireless Sensor Networks, Procedia Technology C3IT-2012, pp. 97 – 101, 2012.
[19] Sushruta Mishra, Lambodar and Aarti Pradhan, Fault Tolerance in Wireless Sensor Networks, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 10, pp.146- 153, 2012.
[20] Prasenjit Chanak, Tuhina Samanta, and Indrajit Banerjee, Fault-Tolerant multipath routing Scheme For energy efficient wireless Sensor networks, International Journal of Wireless & Mobile Networks (IJWMN) Vol. 5, No. 2, pp.33-45, 2013.
[21] Ayasha Siddiqua, Shikha Swaroop, Prashant Krishan and Sandip Mandal, Distance Based Fault detection in wireless sensor network, International Journal on Computer Science and Engineering (IJCSE), Vol. 5 No. 05, pp. 368-375, 2013.
[22] Yunxia Feng, Shaojie Tang and Guojun Dai, Fault Tolerant Data Aggregation Scheduling with Local Information in Wireless Sensor Networks, TSINGHUA SCIENCE AND TECHNOLOGY, Volume 16, Number 5,pp451-463, 2011.
[23] Anshika Bhalla, Soumya Tiwari, and Nikita Kaushik, Tolerating Node Failures in WSN, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 6, Issue 4, pp. 165-172, 2016.
[24] Amir Zare and Samad Nejatian, A New Method to Increase Fault Tolerance in Wireless Sensor Networks (WSNFT), IJCSNS International Journal of Computer Science and Network Security, VOL.17 No.5,pp. 309-311, 2017.
[25] Walaa Elsayed, Mohamed, Elhoseny, Sahar Sabbeh and AlaaRiad, Self –maintenance model for Wireless Sensor Networks, Computers and Electrical Engineering (Elsevier), pp. 1-14, 2017.
[26] Gholamreza Kakamanshadi, Savita Gupta and Sukhwinder Singh,A Survey on Fault Tolerance Techniques in Wireless Sensor Networks , IEEE International Conference on Green Computing and Internet of Things (ICGCIoT), pp.168- 173, 2015.
[27] Zhe Wang and Naftaly H. Minsky, Fault Tolerance in Heterogeneous Distributed Systems, 2014.
[28] HosseinKarimia, OmidMedhatib, HosseinZabolzadeha, Ali Eftekharic, FatemeRezaeib, ShahinBehrozianDehnob, and Amir Abasjamalpoorb, Implementing a reliable, fault tolerance and secure framework in the wireless sensor-actuator networks for events reporting, The International Conference on Advanced Wireless, Information, and Communication Technologies (AWICT 2015), pp. 384 – 394, 2015.
[29] Samira Chouikhia, In`es El Korbia, YacineGhamri-Doudanec, and Leila AzouzSaidanea, A Survey on Fault Tolerance in Small and Large Scale Wireless Sensor Networks,Computer Communications, 2015.
[30] Nancy Alrajei and Huirong Fu, A Survey on Fault Tolerance in Wireless Sensor Networks, Proceedings of the 2014 ASEE North Central Section Conference , pp.1-18. 2014.
[31] AnshikaBhalla, Soumya Tiwari and Nikita Kaushik, Tolerating Node Failures in WSN, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 6, Issue 4, pp. 165-172, 2016.
[32] C.AhilaJerlin and N.Rajkamal, Fault Tolerance in Wireless Sensor Networks, International Journal of Innovative Research in Advanced Engineering (IJIRAE), Issue 2, Volume 2, pp. 142- 146, 2015.
[33] Neethu Ann Jaison and Manoj.R, Hole Detection and Energy Efficient Hole Healing for Wireless Sensor Networks, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 5, pp. 1027-1031, 2015.
[34] Muhammad Tahir, Fazlullah Khan, Syed Roohullah Jan, NaziaAzim, Izaz Ahmad Khan and Farman Ullah, EEC: Evaluation of Energy Consumption in Wireless Sensor Networks, International Journal of Engineering Trends and Applications (IJETA) Volume 3 Issue 2, pp. 57-65, 2016.
[35] ArunanshuMahapatro andPabitra Mohan Khilar, Online Distributed Fault Diagnosis in Wireless Sensor Networks, Wireless PersCommun (2013), pp.1931–1960, 2013.
[36] MdAzharuddin, PratyayKuila and Prasanta K. Jana, A Distributed Fault-tolerant Clustering Algorithm for Wireless Sensor Networks, IEEE InternationalConference on Advances in Computing, Communications and Informatics (ICACCI), pp. 997 – 1002, 2013.
[37] Tapas Bapu B R and L.C. SiddannaGowd, A Novel Fpga Implementation for a Self Healing Reconfigurable System in Wireless Sensor Network, African Journal of Basic & Applied Sciences, pp. 152-160, 2017.
[38] SibaMitra and Ajanta Das, Distributed Fault Tolerant Architecture for Wireless Sensor Network, Informatica 41 (2017), pp. 47–58 47, 2017.
[39] Krishna P. Sharma and T. P. Sharma, rDFD: reactive distributed fault detection in wireless sensor networks, Wireless Netw (Springer), 2016.
[40] Sukhwinder Sharma, Rakesh Kumar Bansal and Savina Bansal, Issues and Challenges in Wireless Sensor Networks, IEEE International Conference on Machine Intelligence Research and Advancement, pp. 58- 62, 2013.
[41] Yun Wang, Yanping Zhang, Jiangbo Liu and Rahul Bhandari, Coverage, Connectivity, and Deployment in Wireless Sensor Networks, Recent Development in Wireless Sensor and Ad-hoc Networks, Signals and Communication Technology (Springer), pp.25- 44, 2015.
[42] Yi Zou and Krishnendu Chakrabarty, Sensor Deployment and Target Localization in Distributed Sensor Networks, ACM Transactions on Embedded Computing Systems, Vol. 3, No. 1,pp. 61–91, 2004.
[43] PearlAntiland AmitaMalik, Hole Detection for Quantifying Connectivity in Wireless Sensor Networks: A Survey, Journal of Computer Networks and Communications, pp. 1 -11, 2014.
[44] C.AhilaJerlin and N.Rajkamal, Fault Tolerance In Wireless Sensor Networks, International Journal of Innovative Research in Advanced Engineering (IJIRAE), Issue 2, Volume 2, pp. 142- 146, 2015
[45] Md Azharuddin, PratyayKuila and Prasanta K. Jana, Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks, Computers and Electrical Engineering(Elsevier), 2014.
[46] Jianping Song, Song Han, Aloysius K. Mok, Deji Chen and Mark Nixon, Centralized Control of Wireless Sensor Networks for Real-Time Applications.
[47] Poonam M. Mahajan, WSN Infrastructure and Application, International journal of scientific research in network security and communication.(IJSRNSC), Issue-1,Volume-6,2018
[48] L. B. Bhajantri, Fuzzy Logic Based Fault Detection in Distributed Sensor Networks, International Journal of Scientific Research and Engineering,(IJSRCSE), Issue 2,Volume-6,pp.27-32,2018.

N. Priya, P. B. Pankajavalli, "Review and future directions of Fault Tolerance schemes and applied techniques in Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.599-606, 2019.