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nThis research paper represents an overview regarding the Wireless Broadband network technology. This is focus on the history, tools, standards and implementation of Wi-Fi network. The wireless term refers to the transmission of the information over a medium without requiring wire. In present days, wireless technology has become an essential part of various type of wireless device. This research paper is used to understand the problem associated with the implementation of theses WLAN’s and purpose recommendation and measures to solve these problem and risk factor.n

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1. Gast, Matthew, “802.11 Wireless Networks: The Definitive Guide”, 2nd Edition, O’Reilly Media, Inc.,2005.
2. Ni, Qiang, Romdhani, Lamia and Turletti, Thierry, “A Survey of QoS Enhancements for IEEE 802.11 Wireless LAN”, Journal of Wireless Communication and Mobile computing,Vol.4, No.5, 2004, pp547-566.
3. Mani Subramanium, “Network Management-Principles and Practices”, 2nd Edition, Pearson, 2013.
4. P. E. Rybski, S. E. Stoeter, M. Gini, D. F. Hougen, and N. P. Papanikolopoulos,””Performance of a distributed robotic system using shared communications channels,”” IEEE Trans. Robot. Autom., vol.18, no. 5, pp. 713-727, Oct. 2004.
5. V. K. Kongezos and C. R. Allen, “”Wireless communication between A.G.V.’s (autonomous guided vehicle) and the industrial network C.A.N. (controller area network),”” in Proc. IEEE Int. Conf. Robotics and Automation, 2002, pp. 434-437.
6. J.-D. Decotignie and P. Pleineveaux, “”Asurvey on industrial communication networks,”” Ann. Telecomm., vol. 48, no. 9, p. 435ff, 1993.
7. F. Hernandez-Campos, M. Karaliopoulos, M. Papadopouli, and H. Shen, “”Spatio-temporal modeling of traffic workload in a campus WLAN,”” In Second Annual International Wireless Internet Conference, Boston, USA, 2006, pp. 265-272.
8. Andrew Miceli, “Wireless Technician’s Handbook, Second Edition”, Artech House, 2003.
9. Dr.M.Sengaliappan , Dr.K.Kumaravel, “Analysis Study of Wireless Technology and its Communication Standards Using IEEE 802.11”, IJARSET Vol. 4, Issue 6 , June 2017.

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nWireless sensing network is con- sisting of sensors and sink that is self- organized and infrastructure less network. En- ergy plays a very important role for the life- time, coverage and connectivity of network. In this paper, proposed protocol has two level heterogeneous wireless sensor network in which Residual Energy and distance of node from base station in which every sensing ele- ment node is taking into account to maximize the life time of network. Simulation results show that the proposed protocol improves the performance of network.n

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1. Ravendra Singh, Itika Gupta and A. K. Daniel , “Position based energy-efficient clustering protocol under noisy environment for sensor networks using fuzzy logic technique,” (27-29 Aug. 2014); London, UK,IEEE. 09 October 2014.
2. Loscri V, Morabito G, Marano S. A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH). In IEEE vehicular technology conference 2005 Sep 25 (Vol. 62, No. 3, p. 1809). IEEE; 1999.
3. Lindsey S, Raghavendra CS. PEGASIS: Power-efficient gathering in sensor information systems. InProceedings, IEEE aerospace conference 2002 Mar 9 (Vol. 3, pp. 3-3). IEEE.
4. Vipul Narayan and A.K Daniel, ”A Noval Protocol for Detection and Optimization of Overlapping Coverage in Wireless Sensor Network ” International Journal of Engineering and Advanced Technology,2019;8(6S):1-6.
5. Ashok Kumar Rai and A.K. Daniel , ” An Energy Efficient Routing Protocol for Wireless Sensor Network” Test Engineering and Management,ISSN:0913- 4120,pp.12556-12563, May-June 2020.
6. Vipul Narayan,A.K Daniel and Ashok Kumar Rai , ”Energy Efficient Two Tier CH Based Protocol for Wireless Sensor Network ” International Conference electrical and Electronics Engineering, Feb 2020.
7. Pooja Chaturvedi, AK Daniel “An energy efficient node scheduling protocol for target coverage in wireless sensor net works” Fifth International Conference on Communication Systems and Network Technologies pp.138-142,april 2015.
8. Pooja Chaturvedi, AK Daniel “Trust Based Target Coverage Protocol for Wireless Sensor Networks Using Fuzzy Logic” International Conference on Distributed Computing and Internet Technology pp.188-192, 2016.
9. Pooja Chaturvedi, AK Daniel “Recovery of holes problem in wireless sensor networks” International Conference on Information Communication and Embedded Systems (ICICES2014),pp.1-6,Feb 2014.
10. Pooja Chaturvedi, AK Daniel “A novel sleep/wake protocol for target coverage based on trust evaluation for a clustered wireless sensor network” International Journal of Mobile Network Design and Innovation, Inderscience Publishers (IEL),2017.
11. Vipul Narayan and A.K.Daniel. “Wireless Sensor Network Design Consideration” NationalConfrence on Emerging Dimension in Artificial Intelligence and Soft Computing (TECHCON- 2019) held on October 19- 20,(2019).
12. Vipul Narayan, and A.K.Daniel. “Multi-Tier Cluster Based Smart Farming Using Wireless Sensor Network” 3rd International Conference on Big Data & Computational Intelligence (ICBDCI-2020).
13. G. Smaragdakis, I. Matta, A. Bestavros,“SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks.” in: Second International Workshop on Sensor and Actor Network Protocols and Applications (SANPA 2004), 2004.
14. A. Kashaf, N. Javaid, Z. A. Khan and I. A.Khan, ”TSEP: Threshold-sensitive stable election protocol for WSNs” International conference on Fronteirs of Information Technology, Feb 2013.
15. Parul Saini, Ajay. K. Sharma, “E-DEEC- Enhanced Distributed Energy Efficient Clustering scheme for heterogeneous WSN”, Parallel Distributed and Grid Computing (PDGC), IEEE, 2010 1st International Conference on, 6 January 2011.
16. T. N. Qureshi, N. Javaid, A.H. Khan, A. Iqbal, E. Akhtar, M. Ishfaq, “BEENISH: Balanced Energy Efficient Network Integrated Super Heterogeneous Protocol for Wireless Sensor Networks”, The 4th International Conference on Ambient Systems, Networks and Technologies (ANT 2013), Volume 19, 24 June 2013, Pages 920-925.
17. A. Preethi, E. Pravin, D. Sangeetha, “Modified Balanced Energy Efficient Network Integrated Super Heterogeneous Protocol”, 2016 Fifth International Conference On Recent Trends In Information Technology, © 2016 IEEE.

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nIn order to do an automated evaluation of various retinal illnesses such as Diabetic retinopathy, Glaucoma, and Macular Edema, fundus images must be pre-processed first. For many reasons, it’s difficult to accurately detect the optic disc. Many blood vessels cross the optic disc, making it difficult to discern the disc’s boundaries in fundus images. Lesion regions in diabetic retinopathy look very much like an optic disc’s colour and texture, so an automated retinal image analysis system must identify and remove these areas. DR is diagnosed early in this study using machine learning (ML) approaches. For example: Bayesian Classification; K-Means Clustering; PNN; SVM; and Bayesian Classification In order to determine the most effective strategy, these options will be weighed against one another and evaluated. For training and testing, a total of 300 fundus images are processed. Using image processing techniques, these raw photos are processed to extract the features. The results of an experiment show that PNN, Bayes Classifications, SVM, and K-Means Clustering are all more accurate than 94% of the time. It appears that SVM is the best method for detecting early signs of degenerative disease.n

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1. Parashuram Bannigidad and Asmita Deshpande, “A Hybrid Approach for Digital Fundus Images using Image Enhancement Techniques”, International Journal of Computer Engineering and Applications, Vol. XII, Issue I, 2017, pp.122-131.
2. Parashuram Bannigidad and Asmita Deshpande, “A Multistage Approach for exudates detection in fundus images using texture features with k-NN classifier”, International Journal of Advanced Research in Computer Science, Vol. 9, No. 1, 2018, pp.1-5.
3. Parashuram Bannigidad and Asmita Deshpande, “Exudates Detection in Digital Fundus Images using GLCM features with SVM classifier”, International Journal of Modern Electronic and Communication Engineering, Vol. 6, Issue. 6, 2018, pp.184-189.
4. B. Dorizzi, G. Tozatto, R. Varej, E. Ottoni, and T. Salles, ‘‘Diabetic retinopathy detection using red lesion localization and convolutional neural networks ~ o Andre a,” vol. 116, no. November 2019, 2020, 10.1016/ j. compbiomed.2019.103537.
5. Sakshi Gunde, A.A., Gupta, S.D., 2020. Diabetic retinopathy detection using nonmydriatic fundus images. Our Herit 1, 141–145.
6. Leeza, M., Farooq, H., 2019. Detection of severity level of diabetic retinopathy using Bag of features model. IET Comput. Vis. 13 (5), 523–530. https://doi.org/ 10.1049/cvi2.v13.510.1049/iet-cvi.2018.5263.
7. S. S. Rahim, V. Palade, C. Jayne, A. Holzinger, and J. Shuttleworth, ‘‘Detection of Diabetic Retinopathy and Maculopathy in EYe Fundus images using Fundus image processing,” vol. 9250, pp. 275–284, 2015, 10.1007/978-3-319-23344-4.
8. Soomro, T.A., Gao, J., Khan, T., Hani, A.F.M., Khan, M.A.U., Paul, M., 2017. Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: A survey. Pattern Anal. Appl. 20 (4), 927–961. https:// doi.org/10.1007/s10044-017-0630-y.
9. J. Hemanth and J. Anitha, “”Hybrid clustering method for optic disc segmentation and feature extraction in retinal images””, World Congress on Information and Communication Technologies, Trivandrum, 2012, pp. 320-325.
10. Juan Xua, Opas Chutatapeb, Eric Sungc, Ce Zhengd, Paul ChewTec Kuand, “Optic disk feature extraction via modified deformable model technique for glaucoma analysis”, Pattern Recognition, Vol. 40, Issue 7, 2007, pp. 2063-2076.
11. Arturo Aquino, Manuel Emilio Gegúndez-Arias, and Diego Marí, “Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques”, IEEE transactions on medical imaging, Volume. 29, no. 11, 2010, pp. 1860-1869.
12. Muhammad Abdullah, Muhammad Moazam Fraz, and Sarah A. Barman, “Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm”, PeerJ4: e2003; DOI10.7717/peerj.2003.
13. J. Sivaswamy, S. R. Krishnadas, G. Datt Joshi, M. Jain and A. U. Syed Tabish, “”Drishti-GS: Retinal image dataset for optic nerve head (ONH) segmentation””, 2014, IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing, 2014, pp. 53-56.
14. Image Database. (2007). DIARETDB1-Standard Diabetic Retinopathy Database Calibration level 1 [online]. Available from http://www.it.lut.fi/project/imageret/diaretdb1/
15. Wikipedia. (2022). Sensitivity and specificity. [online]. Available from https://en.wikipedia.org/wiki/Sensitivity_and_specificity.

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