Volume-7 , Issue-11 , Nov - 2019 Go Back

Cloud computing is an internet based computing technology that provide on demand computing for end users. Normally, data centers allocation for application on statically based. But today so many data centers have a problem how to reduce energy consumption? Due to increase use of cloud services and infrastructure by various cloud providers, uses of energy day by day increase that’s why energy consumption increase lots. Large numbers of data centers that consume lots of energy which increase the level of co2. For decrease energy consumption of data centers need to develop efficient VM migration algorithm which will provide utilization of the VM. Energy conservation then becomes essential, in order to decrease operation costs and increase the system reliability. Using VM consolidation and VM migration data centers per- form efficient energy saving. In this paper discuss Comparative Architecture and Algorithm Study for Energy-aware Social Networking Virtualized Data Centers.

Cloud Computing, Virtualization, Allocation of virtual machines, Quality Of Service (QoS), Energy Aware VM Allocation, Social Networking

[1] Eugen Feller, Louis Rilling, Christine Morin, Energy-Aware Ant Colony Based Workload Placement in Clouds, INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE, ISSN 0249-6399, inria-00594992, version 1
- 23 May 2011.
[2] Si-Yuan Jing, Shahzad Ali, Kun She,Yi Zhong, State-of-the- art research study for green cloud computing, The journal of super computing, Springer, July 2013.
[3] P.Mell, and T. Grance, The NIST defination of cloud computing, Special publications 800-145, 2013.
[4] Rajkumar Buyya, Anton Beloglazov and Jemal Abawajy, Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements and Open Challenges, In proceedings of the international conference on parallel and distributed processiong techniques and applica- tions (PDPTA), 2010.
[5] L. Minas and B. Ellison, Energy Efficiency for Information Technology: How to Reduce Power Consumption in Servers and Data Centers. Intel Press, Aug. 2009.
[6] Anton Beloglazov, Rajkumar Buyya, Young Choon Lee, and Albert Zomaya, A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems,2010 IEEE
[7] G. Wei, J. Liu, J. Xu, G. Lu, K. Yu, and K. Tian, The on-going evolutions of power management in Xen,. Intel Corporation, Tech. Rep., 2009, Tech. Rep., 2009.
[8] VMware Inc., vSphere resource management guide,. 2009.
[9] Qumranet Inc, KVM: kernel-based virtualization driver,. White Paper, 2006.
[10] Chia-Ming Wu, Ruay-Shiung Chang, Hsin-Yu Chan, A green energy-efficient scheduling algorithm using the DVFS tech- nique for cloud datacenters, Future Generation Computer Sys- tems, 2013 Elsevier
[11] Chaima Ghribi, Makhlouf Hadji and Djamal Zeghlache , Energy Efficient VM Scheduling for Cloud Data Centers: Exact allocation and migration algorithms, ExtremeGreen workshop, During CCGrid 2013 May 13-16, 2013, Delft, The Nether- lands.
[12] C. S. Yeo and R. Buyya. A Taxonomy of Market-based Resource Management Systems for Utility-driven Cluster Computing. Software: Practice and Experience, 36(13):1381- 1419, 10 Nov. 2006.

Prashantkumar Oza, Dipesh Kamdar, Nikhil Patel, "Comparative Architecture and Algorithm Study for Energy-aware Social Networking Virtualized Data Centers," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.141-144, 2019.

In this work, I have a tendency to gift information science system to model and visualize student flow patterns supported electronic student data of a university. The datasets utilized by eCamp were antecedently disconnected and solely maintained and accessed in a much siloed manner by freelance field offices. At a campus-level, our models and image show however students create selections among many potential majors, as students step by step progress towards their sophomore, junior, and senior year. At a department-level, the scholar flow patterns unconcealed by eCamp show however every course plays a special role inside a syllabus. ECamp more dives all the way down to the roughness of the precise categories offered in every semester. At that level, eCamp shows however students navigate from one set of categories in one semester to a different set in a very enchant semester. I’d wish to build a deeper set of analytics mistreatment a lot of discourse info with further information sources like pedagogue info of every category, student help info, and student admission info. Previously, comprehensive info regarding student progression patterns in the slightest degree of those levels was merely unavailable. to it finish, we have a tendency to additionally demonstrate however insights into such student flow patterns will support analytical tasks involving student outcomes, student retention, and syllabus style.

Big Data Applications, Data Analysis, Data Visualization

[1] G. Gee et al., “An open letter to college and university leaders: College completion must be our priority.” A report by National Commission on Higher Education Attainment. American Council on Education, 2011.
[2] L. Soares, “Post-traditional learners and the transformation of postsecondary education: A manifesto for college leaders,” American Council on Education, vol. 118, 2013.
[3] W. S. Swail, “A different viewpoint on student retention,” Higher Learning Research Communications, vol. 4, no. 2, 2014.
[4] V. Tinto, “Promoting student completion one class at a time,” in Symp. at the Retention 360 Conference, 2010.
[5] M. Raji, J. Duggan, B. DeCotes, J. Huang, and B. V. Zanden, “Visual progression analysis of student records data,” in Proc. of Workshop on Visualization in Data Science (VDS), Oct 2017.
[6] P. Attewell, S. Heil, and L. Reisel, “Competing explanations of undergraduate noncompletion,” American Educational Research Journal, vol. 48, no. 3, pp. 536–559, 2011.
[7] M. R. Clark, “Negotiating the freshman year: Challenges and strategies among first-year college students,” Journal of College Student Development, vol. 46, no. 3, pp. 296–316, 2005.
[8] J. Grann and D. Bushway, “Competency map: visualizing student learning to promote student success,” in Proc. of the 4th Intl Conference on Learning Analytics and Knowledge. ACM, 2014, pp. 168–172.
[9] G. D. Kuh, J. Kinzie, J. H. Schuh, and E. J. Whitt, Student success in college: Creating conditions that matter. John Wiley & Sons, 2011.
[10] M. J. Lutz, J. R. Vallino, K. Martinez, and D. E. Krutz, “Instilling a software engineering mindset through freshman seminar,” in Frontiers in Education Conf. (FIE), 2012, 2012.
[11] R. Mazza and V. Dimitrova, “Generation of graphical representations of student tracking data in course management systems,” in Proc. of the 9th Information Visualisation Conf., 2005.
[12] “Visualising student tracking data to support instructors in web-based distance education,” in Proc. of the 13th Intl World Wide Web Conf. on Alternate Track Papers & Posters, 2004.
[13] U. Rueda, M. Larranaga, M. Kerejeta, J. A. Elorriaga, and A. Arru- ˜ arte, “Visualizing student data in a real teaching context by means of concept maps,” in International Conf. on Knowledge Management I-Know, vol. 5, 2005.
[14] H. Siirtola, K.-J. Raiha, and V. Surakka, “Interactive curriculum visualization,” in 17th International Conference Information Visualisation (IV). IEEE, 2013, pp. 108–117.
[15] S. Gama and D. Goncalves, “Visualizing large quantities of educational datamining information,” in 18th International Conference on Information Visualisation (IV). IEEE, 2014, pp. 102–107.
[16] D. Wortman and P. Rheingans, “Visualizing trends in student performance across computer science courses,” ACM SIGCSE Bulletin, vol. 39, no. 1, pp. 430–434, 2007.
[17] B. Johnson and B. Shneiderman, “Tree-maps: a space-filling approach to the visualization of hierarchical information structures,” in Proc. of IEEE Visualization, Oct 1991, pp. 284–291.
[18] D. H. Huson, D. C. Richter, C. Rausch, T. Dezulian, M. Franz, and R. Rupp, “Dendroscope: An interactive viewer for large phylogenetic trees,” BMC bioinformatics, vol. 8, no. 1, p. 460, 2007.
[19] H. Zhang, S. Gao, M. J. Lercher, S. Hu, and W.-H. Chen, “Evolview, an online tool for visualizing, annotating and managing phylogenetic trees,” Nucleic acids research, vol. 40, no. W1, pp. W569–W572, 2012.
[20] Y. Zhu, L. Sun, A. Garbarino, C. Schmidt, J. Fang, and J. Chen, “Pathrings: a web-based tool for exploration of ortholog and expression data in biological pathways,” BMC bioinformatics, vol. 16, no. 1, p. 165, 2015.

N.S. Hima Bindu, R. Swathi, K. Sreedivya, "Analyzing and Predicting Students Flow Visualization," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.145-147, 2019.

Today pollution has been one amongst the foremost issues to cope with for any country. In South Asia it`s hierarchical because the sixth most dangerous killer. One doesn`t very understand the harmful impact of a pollution if he/she has not full- fledged within the initial place in 2016, a World Health Organisation (WHO) study found that fourteen of the twenty world’s most contaminated cities belonged to Asian nation. Kanpur, in province, emerged because the town with the best PM2.5 level, standing at 173 (17 times more than the limit set for safety). Air pollution doesn`t acknowledge geographical boundaries. even as contaminated air from rural areas travels into cities, cities too contribute towards rural pollution. Thus, it`s crucial f or anti- pollution efforts to be coordinated across completely different levels. Urban-rural and inter-state responses square measure integral to crafting undefeated solutions. luckily, the govt of Asian nation (GoI) has well-versed the pollution epidemic with a nation-wide programme. this is often possible to own terribly positive impact on the health of all voters, particularly town dwellers. The Air Quality Life Index indicates that if national standards with relation to air quality square measure met, expectancy would go up by 2 years.

Dangerous killer, harmful, World health organisation(WHO), Government of India, pollution epidemic

[1]. Tiwari, R. Sam and S. Shaikh, "Analysis and prediction of churn customers for telecommunication industry," 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, 2017,pp.218222.doi:10.1109/ISMAC.2017.8058343
[2]. S. Navadia, P. Yadav, J. Thomas and S. Shaikh, "Weather prediction: A novel approach for measuring and analyzing weather data," 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, 2017, pp. 414-417.
doi: 10.1109/I-SMAC.2017.8058382
[3]. S. Shaikh, S. Rathi and P. Janrao, "IRuSL: Image Recommendation Using Semantic Link," 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN), Tehri, 2016, pp.305-308.doi: 10.1109/CICN.2016.66
[4]. S. Shaikh, S. Rathi and P. Janrao, "Recommendation System in E-Commerce Websites: A Graph Based Approached," 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, 2017, pp. 931-934.doi: 10.1109/IACC.2017.0189
[5]. Aarnio Pet al. (2005). The concentrations and composition of and exposure to fine particles (PM2.5) in the Helsinki subway system. Atmospheric Environment, 39(28):5059–5066.
[6]. Abbey DEet al. (1999). Long-term inhalable particles and other air pollutants related to mortality in nonsmokers. American Journal of Respiratory and Critical Care Medicine, 159(2):373–382.
[7]. Almeida-Silva M, Almeida SM, Freitas MC, Pio CA, Nunes T, Cardoso J. Impact of Sahara dust transport on Cape Verde atmospheric element particles. J Toxicol Environ Health A. 2013;76(4-5):240–51.
[8] Alnawaiseh NA, Hashim JH, Md Isa Z. Relationship between vehicle count and particulate air pollution in Amman, Jordan. Asia Pac J Public Health. 2012;27(2):NP1742–51.
[9]..Alolayan MA, Brown KW, Evans JS, Bouhamra WS, Koutrakis P. Source apportionment of fine particles in Kuwait City. Sci Total Environ. 2013;448:14–25.
[10]. Amador-Muñoz O, Bazan-Torija S, Villa-Ferreira SA, Villalobos-Pietrini R, Bravo-Cabrera JL, Munive-Colín Z, et al. Opposing seasonal trends for polycyclic aromatic hydrocarbons and PM10: health risk and sources in southwest Mexico City. Atmos Res. 2013;122:199–212.

Khan Salman Tasawar, Khan Moin Mohd Umar, Piyush Kishore Vaidya, Shakila Shaikh, "Analyses the Pollution Data and Suggest Measure to Reduce Pollution," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.148-153, 2019.

Collisions which occur in a channel during data transmissions result in re-transmissions and this causes energy dissipation. In order to reduce this effect, a new Medium Access Control (MAC) Protocol is designed. In this paper, the existing and current mechanisms of WSNs have been studied and their shortcomings identified. Accordingly the design parameters of IEEE 802.15.4 CSMA/CA based WSNS hse been extended and a new design parameter has been evolved which reduces the collisions and prevents simultaneous data transmission by nodes. This paper suggests minor modifications to the current mechanism of IEEE 802.15.4 CSMA/CA by incorporating re-transmission limits of the nodes with packet collision probability.

WSN, IEEE 802.15, Medium access control (MAC), Received signal strength indicator (RSSI), Clear channel assessment (CCA), Number of backoffs (NB), Backoff exponent (BE), Contention Window (CW), Guaranteed time slots (GTS), Contention access period (CAP), Deterministic synchronous multi-channel extension (DSME), Personal area Network (PAN), Carrier

[1] IEEE 802.15.4, Wireless Medium Access control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless personal Area Networks (LR-WPANs)”, 2006
[2] IEEE Standard for Local and Metropolitan Area networks – Part 15.4: Low rate Personal Area Networks (LR-WPANs), 2011
[3] IEEE Standard for Local and Metropolitan Area networks – Part 15.4: Low rate Personal Area Networks (LR-WPANs), Amendment 1: MAC Sublayer, 2012
[4] Misic, J., Shafi, S., Misic, V.B., 2006. Performance of a beacon enabled IEEE 802.15.4 cluster with downlink and uplink traffic. IEEE Trans. Parallel Distrib. Syst. 17 (April (4)), 361–376.
[5] W.Ye, J. Heidemann, D. Estrin, Medium Access Control with Coordinated Adaptive Sleeping for Windows Sensor Networks, IEEE/ACM Transactions on Networking, Volume:12, Issue: 3, Pages 493 – 506, June 2004
[6] Rajendran, V., Obraczka, K., Garcia-Luna-Aceves, J.J., 2003. Energy- efficient, collision free medium access control for wireless sensor networks. Proceedings of the ACM SenSys 03, Los Angeles, California, 5–7 November, pp. 181–192
[7] Tay, Y.C., Jamieson, K., Balakrishnan, H., 2004. Collision-minimizing CSMA and its applications to wireless sensor networks. IEEE J. Sel. Areas Commun. 22 (6), (Pages: 1048 V 1057).
[8] Rasheed, M.B., Javaid1, N., Haider, A., Qasim, U., Khan, Z.A., Alghamdi, T.A., 2014. an energy consumption analysis of beacon enabled slotted CSMA/CA IEEE 802.15.4. WAINA.
[9] Mehta, A., Bhatti, G., Sahinoglu, Z., Viswanathan, R., Zhang, J., 2009. Performance analysis of beacon-enabled IEEE 802.15.4 MAC for emergency response applications. ANTS
[10] Pollin, S., Ergen, M., Ergen, S.C., Bougard, B., Van der Perre, L., Moermann, I., Bahai, A., Varaiya, P., Catthoor, F., 2008. Performance analysis of slotted carrier sense IEEE 802.15.4 medium access layer. IEEE Trans. Wirel. Commun. 7 (9), (September)
[11] Zhao, H., Wei, J., Sarkar, N.I., 2016. E-MAC: an evolutionary solution for collision avoidance in wireless ad hoc networks. J. Netw. Comput. Appl. 65 (April), 1–11, (Pages).[where is 12]
[12] Chen, C.-P., Jiang, J.-A., Mukhopadhyay, S.C., Suryadevara, N.K., 2014. Performance measurement in wireless sensor networks using time-frequency analysis and neural networks. Proceedings of IEEE International Instrumentation and Measurement Technology Conference, May, pp. 1197–1201.
[13] Faridi, A., Palattella, M.R., Lozano, A., Dohler, M., Boggia, G., Grieco, L.A., Camarda, P., 2010. Comprehensive evaluation of the IEEE 802.15.4 MAC layer performance with Retransmissions. IEEE Trans. Veh. Technol. 59 (8), 3917–3932, October
[14] Sahoo, P.K., Sheu, J.-P., 2008. Modeling IEEE 802.15.4 based Wireless Sensor Network with packet retry limits. In: Proceedings of the 5th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks, Oct.
[15] Doudou, M., Djenouri, D., Badache, N., Bouabdallah, A., 2014. Synchronous contentionbased MAC protocols for delay-sensitive wireless sensor networks: a review and taxonomy. J. Netw. Comput. Appl. 38, 172–184, (Pages)
[16] Lee, H., Lee, H., 2016. Modeling and analysis of an energy-efficient MAC protocol for wirelesssensor networks. In: Proceedings of the International Conference on Information Networking (ICOIN), 402–405.
[17] Alvi, A.N., Naqvi, S.S., Bouk, S.H., Javaid, N., Qasim, U., Khan, Z.A., 2012. Evaluation of Slotted CSMA/CA of IEEE 802.15.4. In: Proceedings of the International Conference on Broadband, Wireless Computing, Communication and Applications, pp. 391– 396.
[18] Park, P., Marco, D., Fischione, P., Johansson, C., K.H., 2013. Modeling and optimization of the IEEE 802.15.4 protocol for reliable and timely communications. IEEE Transactions on Parallel and Distributed Systems, vol. 24, no.3, March, pp. 550–564.
[19] Ning Weng, I.-Hung Li, Lucas Vespa, 2011. Information quality model and optimization for 802.15.4-based wireless sensor networks. J. Network and Computer Applications, Volume 34 Issue (6), 1773–1783, (Pages)

Sumit Saha, Ankur Dumka, Priti Dimri, "Design of Medium Access Control Protocol for IEEE 802.15.4 Based WSNS to Reduce Collisions and Prevent Simultaneous Data Transmission by Nodes," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.154-172, 2019.

OCR based Aadhar & driving license info Extraction System may be a time period embedded system that mechanically acknowledges the kind of document whether or not it`s a Aadhar or driving license and extract the out there info from it. There square measure several applications starting from advanced security systems to common official work. OCR primarily based Aadhar & driving license info Extraction System has advanced characteristics because of various effects like totally different pattern in numerous Aadhar Card, totally different Spacing in text etc. Most of the OCR primarily based info Extraction System square measure designed mistreatment proprietary tools like MATLAB that takes a protracted method and time and conjointly will have many limitations and conjointly they`re unable to sight pattern and can`t extract the desired info severally. this concept presents an efficient technique of implementing OCR primarily based Aadhar & driving license info Extraction System mistreatment Free software package together with Python and therefore the Open pc Vision Library.

Software Requirement, Python, OpenCV, Tesseract

[1] Rafi, Ali, Faraz, Athaul, “OCR Engine to extract Food-items and Prices from Receipts Images via Pattern matching and heuristics approach”, SMIU, 1st International Conference on computing and related technologies, 2017 [Souvik Das “The Development of a Microcontroller Based Low Cost Heart Rate Counter for Health Care Systems” International Journal of Engineering Trends and Technology- Volume4Issue2- 2013.
[2] Chaki, Nabendu, Soharab Hossain Shaikh, and Khalid Saeed. "A comprehensive survey on image binarization techniques." In Exploring Image Binarization Techniques, pp. 5-15. Springer India, 2014.
[3] Zhang, Mi, Anand Joshi, Ritesh Kadmawala, Karthik Dantu, Sameera Poduri, and Gaurav S. Sukhatme. "OCRdroid: A Framework to Digitize Text Using Mobile Phones." In MobiCASE, pp. 273-292. 2009.
[4] Brisinello, Matteo, Ratko Grbić, Matija Pul, and Tihomir Anđelić. "Improving Optical Character Recognition Performance for Low Quality Images." In 59th International Symposium ELMAR-2017. 2017.
[5] ZHAO, Yan, Yue CHEN, and Shi-gang WANG. "Corrected fast SIFT image stitching method by combining projection error." Optics and Precision Engineering 6 (2017): 029.

Kothagattu Surya Teja, Kunam Siri Chandana, R. Srinivas, B. Prasanthi, "Aadhar & Driving License Information Extraction System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.173-176, 2019.

The power consumption of wireless access networks has become a major economic and environmental issue. Providing dedicated low cost power supply to cell sites located in the rural and sub-urban areas of developing countries is most challenging, as most of the rural areas are not connected to the electricity grid and, even though they are connected, the availability of the supply is very limited to provide uninterrupted power supply. This paper developed a Solar Powered Micro-Inverter Grid connected System as an alternative solution to the problems encountered with power supply in cell sites. The configuration of the Solar Powered Micro-Inverter Grid connected System examined in this paper include a Solar Power System, Diesel generator, battery bank and Grid. Analysis of results shows that, after fifteen (15) years of operation, the reliability figures of solar power system is much higher than 80%. But that of the generator is approaching 15%. Comparing these figures with the general bath tub, it can be seen that the reliability of generator after five years of operation degrades by 60%. It was also found that, the developed Solar Powered Micro-Inverter Grid connected System has very high reliability figures with the Mean Time Before Failure (MBTF) of about twenty three (25) years before complete failure as compared to eight (8) years for generator system.

Base Transceivers Station (BTS), energy conservation, power consumption, Solar, Renewable energy, Micro-Inverter, Switch Mode Power Supply (SMPS).

[1] Bright, (2011). “Green Energy”. Retrieved from http://www.wirefreedirect.com on 16th March 2016.
[2] Climate, (2011), “Nigeria Climate”. Retrieved from http://www.climatetemp.info/nigeria on 6th March 2016.
[3] ITU, (2011). Retrieved from http://www.itu.int/newsroom/press_releases/2011/12.html on 20th March 2016.
[4] Margot. D., Emmeric, T., Wout, J., Luc, M.,(2011). “Modeling and optimization of Power Consumption in wireless access network”. Published by Computer Communications, Elsevier.
[5] Mishra. A. J, (2007), “Advanced Cellular Networks Planning and Optimization 2/2.5/3G Evolution to 4G”, John Wiley and Sons Ltd ISBN 13 978-0-470-01471-4, pp 1-12
[6] Nasir F., Mujahid Y. M., Olayiwola W. B., Abubakar A., Agbakoba J., and Mohammed I. G., (2011). “Energy Conservation through Site Optimization for Mobile Cellular Systems”. (BTS Optimization), Epistemic in Science Engineering & Technology, Vol.2, No. 1, p 26-33.
[7] Solar, (2011). Retrieved from http://www.solar-energy-advantages-blog.com/ on 18th March 2016.
[8] Vanguard, (2011). “Airtel Goes Green” Retrieved from http://www.vanguardngr.com/2011/12/airtel-goes-green-with-e-site/ on 12th April 2016.
[9] Vincenzo Mancuso and Sara Alouf, (2011). INRIA Sophia Antipolis Mediterranee “Reducing Costs and Pollution in Cellular Networks”. Published in IEEE Communication Magazine.
[10] Wissam Balshe, (2011), “Power system considerations for cell tower applications”. Published by Cummins Power Generation Inc., USA. Retrieved from www.cumminspower.com on 12 April 2016.
[11] World Weather (2009), Temperature, Average monthly min-max Temperature, Maiduguri, Nigeria.htm
[12] N. Faruk,. A.A Ayeni,. M. Y. Muhammad, L.A.Olawoyin,. A. Abubakar,. J. Agbakoba, O. Moses., (2012). “Powering Cell Sites for Mobile Cellular Systems using Solar
[13] Mr. Gunter Schmitt, “The Green Base Station”. Published by El Tek V Alere Deutschland GmbH, Frankfurt, Germany.
[14] Newmar (2017). “Negative 48 Volts Power; What, Why and How”. Published by Newmar Powering Tech Services. Retrieved from http://www.newmarpower.com on 15th November 2017.
[15] Ghaddor (2010). “The Power Supply for the BTS site”. Published by Ghaddor Machinery Co. saida Lebanon. Retrieved from http://www.ghaddor.com on 20th April 2016.
[16] Francine (2011). “The Micro-Inverter”. Published by Enphase Energy. Retrieved from http://www.enphaseenergy.com on 20th April 2016.

Alumona T.L., Oranugo C.O., Eze C.E., Onyeyili T.I., "Analysis of Solar Powered Micro-Inverter Grid Connected System for a Cellular Communication Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.177-192, 2019.

There are large numbers of sensor nodes deployed within wireless sensor networks. These nodes contain batteries of extremely small size. Moreover, these nodes are generally deployed at distant locations. Therefore, replacing these batteries is not a good option. Hence, in WSN, the efficient use of battery is a main challenge. The base paper proposes a novel routing algorithm called opportunistic routing. This algorithm is used to route data packets in wireless sensor networks. The source node stores the data on the intermediate node in this sort of routing. The source node will move close to the sink node or base station. This node delivers data to the sink node. The priority is assigned to the data stored on the intermediate node. The data with higher priority is transmitted first to the sink node. A simulation tool named NS2 is used for the simulation of proposed algorithm. It has been analyzed that proposed algorithm gives better performance in terms of different performance metrics.

WSN, Opportunistic, priority queue, gateway

[1] Samaneh Rashidibajgan, Robin Doss, “Privacy-preserving history-based routing in Opportunistic Networks”, Elsevier, volume 3, pp. 244 – 255, 2019.
[2] Chuan Zhu, Shuai Wu, Guangjie Han, Lei Shu and Hongyi WU, “A Tree-Cluster-Based Data-Gathering Algorithm for Industrial WSNs with a Mobile Sink”, IEEE Access, volume 3, pp 381-396, 2015.
[3] Chu Du, ZhangBing Zhou, Lei Shu, “An Efficient Technique of Scheduling Mobile Sinks in Hybrid WSN”, IEEE Annual conference of the industrial electronics society, pp 3885-3891, 2014.
[4] Cong Wang, Ji Li, Fan Ye, and Yuanyuan Yang, “NETWRAP: An NDN Based Real-Time Wireless Recharging Framework for Wireless Sensor Networks”, IEEE International conference on mobile Ad-Hoc and sensor system, pp 173-181, 2013.
[5] D Anand, Dr. H.G. Chandrakanth and Dr. M. N. Giriprasad, “An Energy Efficient Distributed Protocol for Ensuring Coverage and Connectivity (E3C2) of Wireless Sensor Networks”, International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.3, No.1, 2012.
[6] Dahnil, D. P., Singh, Y. P., & Ho, C. K. “Energy-efficient cluster formation in heterogeneous Wireless Sensor Networks: A comparative study”, Advanced Communication Technology (ICACT)”, 13th International Conference, pp. 746-751, 2011.
[7] Euisin Lee, Soochang Park, Fucai Yu, and Sang-Ha Kim, “Communication Model and Protocol based on Multiple Static Sinks for Supporting Mobile Users in Wireless Sensor Networks”, IEEE Transactions on Consumer Electronics, Vol. 56, No. 3, pp 1652-1660, 2010.
[8] Fangxin Chen, Lejiang Guo, Chang Chen, “A survey on Energy Management in Wireless Sensor Networks,” Elsevier B.V., 2012.
[9] Geetha. V, Pranesh.V. Kallapur, Sushma Tellajeera, “Clustering in Wireless Sensor Networks: Performance Comparison of LEACH & LEACH-C Protocols Using NS2”, Elsevier International conference on Computer, Communication, Control and Information Technology, Volume 4, pp 163-170, 2012.
[10] Ammar Hawbani, Xingfu Wang, Yaser Sharabi, Aiman Ghannami, Hassan Kuhlani, Saleem Karmoshi, “LORA: Load-Balanced Opportunistic Routing for Asynchronous Duty-Cycled WSN”, IEEE Transactions on Mobile Computing, Volume: 18, Issue: 7, 2019.
[11] Hajer Ben Fradj, Rajoua Anane, Ridha Bouallegue, “Energy Consumption for Opportunistic Routing Algorithms in WSN”, IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), 2018.
[12] Hajer Ben Fradj, Rajoua Anane, Mehdi Bouallegue, Ridha Bouallegue, “A range-based opportunistic routing protocol for Wireless Sensor networks”, 13th International Wireless Communications and Mobile Computing Conference (IWCMC), 2017.
[13] Xinguo Wang, Xi Wu, Xinming Zhang, “Optimizing Opportunistic Routing in Asynchronous Wireless Sensor Networks”, IEEE Communications Letters, Volume: 21, Issue: 10, 2017.
[14] Nagesh Kumar, Yashwant Singh, “An energy efficient and trust management based opportunistic routing metric for wireless sensor networks”, Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), 2016.
[15] Mayank Sharma, Yashwant Singh, “Middle Position Dynamic Energy Opportunistic Routing for Wireless Sensor Networks”, International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015.

Diksha Rani, Yogesh Kumar, "Privacy Preserving in Opportunistic Routing for Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.193-197, 2019.

Machine Learning (ML) is the subfield in Artificial Intelligence (AI) that works dynamically to solve several issues. ML mainly focused on understanding the structure of the data and selecting the specific model based on the given dataset. Nowadays plant diseases are becoming very dangerous to farmers. Various plant diseases are identified by many researchers based on the pathogen. Several visible and invisible features are present to identify plant diseases. Visible features such as shape, size, silting are most widely used to analyze the condition of the plant. In this paper, the adaptive clustering algorithm (ACA) is introduced to detect diseases in plants. To show the disease-affected region the fuzzy c-means (FCM) clustering approach is adopted to highlight the disease-affected region with red patches which are called clusters. To improve the performance of the proposed approach the feature subset selection is used to increase the effectiveness and scalability. The output results show the performance of the ACA.

Machine learning (ML), ACA, AI and K-Means

[1] F. Fina, P. Birch, R. Young, J. Obu, B. Faithpraise, and C. Chatwin, "Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters," Int. J. Adv. Biotechnol. Res., vol. 4,no. 2, pp. 189–199, Jul. 2013.
[2] M. A. Ebrahimi, M. H. Khoshtaghaza, S. Minaei, and B. Jamshidi, "Vision-based pest detection based on SVM classification method," Comput. Electron. Agricult., vol. 137, pp. 52–58, May 2017.
[3] S. R. Dubey and A. S. Jalal, "Adapted approach for fruit disease identification using images," Int. J. Comput. Vis. Image Process., vol. 2, no. 3, pp. 44–58, Jul. 2012.
[4] L. Li, S. Zhang and B. Wang, "Plant Disease Detection and Classification by Deep Learning—A Review," in IEEE Access, vol. 9, pp. 56683-56698, 2021, doi: 10.1109/ACCESS.2021.3069646.
[5] X. Liu, W. Min, S. Mei, L. Wang and S. Jiang, "Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach," in IEEE Transactions on Image Processing, vol. 30, pp. 2003-2015, 2021, doi: 10.1109/TIP.2021.3049334.
[6] Jayme G.A. Barbedo, Factors influencing the use of deep learning for plant disease recognition, Biosystems Engineering, Volume 172, 2018, https://doi.org/10.1016/j.biosystemseng.2018.05.013.
[7] P. Shah, H. B. Prajapati and V. K. Dabhi, "A survey on detection and classification of rice plant diseases," 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), pp. 1-8, 2016. doi: 10.1109/ICCTAC.2016.7567333.
[8] L. Shanmugam, A. L. A. Adline, N. Aishwarya and G. Krithika, "Disease detection in crops using remote sensing images," 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), pp. 112-115, 2017. doi: 10.1109/TIAR.2017.8273696.
[9] Q. Wu, Y. Chen, and J. Meng, "DCGAN-based data augmentation for tomato leaf disease identification," IEEE Access, vol. 8, pp. 98716–98728, 2020.
[10] M. A. H. Abas, N. Ismail, A. I. M. Yassin and M. N. Taib, "VGG16 for plant image classification with transfer learning and data augmentation", Int. J. Eng. Technol., vol. 7, pp. 90-94, Oct. 2018.
[11] P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, "Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks," IEEE Access, vol. 7, pp. 59069–59080, 2019.
[12] B. Wang and D. Wang, "Plant leaves classification: A few-shot learning method based on Siamese network," IEEE Access, vol. 7, pp. 151754–151763, 2019.
[13] W. Yang, C. Yang, Z. Hao, C. Xie, and M. Li, "Diagnosis of plant cold damage based on hyperspectral imaging and convolutional neural network," IEEE Access, vol. 7, pp. 118239–118248, 2019.
[14] tansal, P., Yadav, H. and Sunkaria, R.K., “Impulse noise removal usinm MDBUTMF gith histomram estimation”, In Advance Computing Conference (IACC), International on, pp. 468-471, IEEE, 2015.
[15] Ramakrishnan M., Sahaya Anselin Nisha, “Groundnut leaf disease detection and classification by usinm back probamation almorithm”, In Communications and Signal Processing (ICCSP), International Conference on, pp. 0964-0968, IEEE, 2015.

Anuradha Anumolu, Shaheda Akthar, "Adoptive Clustering Algorithm with Feature Subset Selection Method to find the Plant Diseases," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.198-202, 2019.