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The Internet of Things (IoT) visualize seamlessly connecting the physical objects with the Internet. This in turn combines the physical world into the digital world. There are several mechanisms available that reduces the need for external human intervention for maintaining and configuring deployed. They must be scalable and efficient, since the number of deployed objects is expected to grow exponentially in the next few years. Resource discovery is a problem of finding useful devices and their services in order to accomplish a task, and it constitutes a major challenge for IoT. In this paper a survey on various service discovery mechanisms is performed and an MQTT protocol based IoT service search has been proposed. The proposed work uses a clustering and optimal distribution mechanism on the IoT services to find the optimal distribution value and return the services. Experimental results have shown significant improvement in service discovery time.

Internet of Things, MQTT protocol, Resource discovery, Services

[1] Charith Perera, Prem Prakash Jayaraman, Arkady Zaslavsky, Dimitrios Georgakopoulos, Peter Christen, “Sensor Discovery and Configuration Framework for the Internet of Things Paradigm”, 2014 IEEE World Forum on Internet of Things (WF-IoT).
[2] Simone Cirani, Luca Davoli, Gianluigi Ferrari, Rémy Léone, Paolo Medagliani, Marco Picone, and Luca Veltri, “A Scalable and Self-Configuring Architecture for Service Discovery in the Internet of Things”, IEEE INTERNET OF THINGS JOURNAL, VOL. 1, NO. 5, OCTOBER 2014.
[3] Soumya Kanti Datta, Rui Pedro Ferreira Da Costa, Christian Bonnet,” Resource Discovery in Internet of Things: Current Trends and Future Standardization Aspects”, IEEE 2015.
[4] Sabriansyah Rizqika Akbar, Wijaya Kurniawan, Mochammad Hannats Hanafi Ichsan, Issa Arwani, Maystya Tri Handono ,”Pervasive Device and Service Discovery Protocol In XBeeSensor Network”,IEEE 2016.
[5] Pablo Calcina Ccori. Laisa Caroline Costa De Biase Marcelo Knorich Zuffo Fl´avio Soares Corrˆea da Silva,”Device discovery strategies for the IoT”, 2016 IEEE International Symposium on Consumer Electronics.
[6] Stefana Chirila, Camelia Lemnaru and Mihaela Dinsoreanu,”Semantic-based IoT device discovery and recommendation mechanism”, 2016 IEEE.
[7] Juan Li,Nazia Zaman, Honghui Li,”A decentralized Locality-preserving Context-aware Service Discovery Framework for the Internet of Things”,2015 IEEE International Conference on Services Computing.
[8] Elli Rapti, Catherine Houstis, Elias Houstis, Antony Kargeorgos.,”A Bio-inspired Service Discovery and Selection Approach for IoT applications, 2016 IEEE International Conference on Services Computing.
[9] Anne H.Ngu, Marion Gutierrez, Vangelis Metsis, Surya Nepal and Quan Z.Sheng,”IoT Middleware: A survey on Issues and Enabling Technologies”, 2017 IEE Internet of Things Journal, Vol 4, NO.1.

[10] Takashi Ikebe, Hirofumi Noguchi, Naoto Hoshikawa,”Distributed Live Data Search Architecture for Resource Discovery on Internet of Things”, 2016 IEEE.
[11] Pedro R. J. Pêgo, Luís Nunes,”Automatic Discovery and Classifications of IoT Devices”.
[12] Antonio J. Jara, Pablo Lopez, David Fernandez, Jose F. Castillo, Miguel A. Zamora and Antonio F. Skarmeta,”Mobile Digcovery: A Global Service Discovery for the Internet of Things”, 2013 27th International Conference on Advanced Information Networking and Applications Workshops.
[13] Zhiyuan Li, Rulong Chen, Lu Liu, Geyong Min,” Dynamic Resource Discovery Based on Preference and Movement Pattern Similarity for Large-Scale Social Internet of Things”, IEEE Internet of Things Journal, VOL. 3, NO. 4, AUGUST 2016.
[14] Farzad Khodadadi, Amir Vahid Dastjerdi, Rajkumar Buyya,” Simurgh: A Framework for Effective Discovery, Programming, and Integration of Services Exposed in loT”, 2015 International Conference on Recent Advances in Internet of Things (RioT) Singapore, 7-9 April 2015.

Nihaala Najeeb, Shelbi Joseph, "IoT Service Search using MQTT Protocol," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.292-297, 2019.

Contradictory nature of read and write stability and stability with speed necessitates the use of FinFET device which has less statistical variability, sensitivity and more on current. In this work, we explored three different FinFET device structures. These structures are used to form three different 6T SRAM cells. All the simulations are done with the help of Sentaurus TCAD. Three SRAM cells are compared to reduce access time and enhance data stability. We found that ADSE FinFET SRAM achieve significant improvement in access time as compared to Underlap FinFET SRAM cell without degradation of cell stability. On the other hand High-k spacer SRAM cell shows noteworthy increase in stability over other two cells with somewhat slower response.

ADSE, FinFET, High-k spacer SRAM, Underlap

[1]. Jawar Singh, Saraju P.Mohanty, Dhiraj K. Pradhan, “Robust SRAM Designs and Analysis,” Available at www.springer.com, 2013
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[3]. V. A. A. Goud et al, “Atomistic Tight-Binding Based Evaluation of Impact of Gate Underlap on Source to Drain Tunneling in 5nm Gate Length Si FinFETs,” IEEE DRC, p. 51,52, June 2013.
[4]. V. Trivedi, J. G. Fossum, and M. M. Chowdhury, “Nanoscale FinFETs with gate–source/drain underlaps,” IEEE Trans. Electron Devices, vol. 52, no. 1, pp. 56–62, Jan. 2005.
[5]. FinFET from Device to Architecture 2014 (online) Available: http://dx.doi.org/10.1155/2014/365689
[6]. Pankaj Kumar Pal, Shivam verma, B.K.kaushik, “Statistical variability and sensitivity Analysis of dual-k spacer FinFET,” IEEE Trans. On electronic devices and solid state circuits, 2015.
[7]. D. Pham, L. Larson, and J.-W. Yang, “FinFET device junction formation Challenges,” in Proc. Int. Workshop Junction Technol., May 2006, pp. 73–77.
[8]. C. R. Manoj and V. R. Rao, “Impact of high-κ gate dielectrics on the device and circuit performance of nanoscale FinFETs,” IEEE Electron Device Lett., vol. 28, no. 4, pp. 295–297, Apr. 2007.
[9]. P.K.Pal, B.K.Kaushik, and S.Dasgupta “Investigation of symmetric dual k spacer trigate FinFET from delay perspective,” IEEE Trans. on Electron Devices,” vol.61, no.11, pp-3579-3585, Nov.2014.
[10]. J.P.colinge, FinFET and Other Multi Gate Transistor, a series on Integrated Circuits and Systems,” Springer.com” August 2007
[11]. Sherif A. Tawfik, Zhiyu Liu, and Volkan Kursun, “ Independent gate and Tied – Gate FinFET SRAM circuits,” IEEE ICM - December 2007.

Ashish Raturi, Kamlesh Bhatt, "Improved stability and access time Using Different 6T SRAM Cells at Low Voltage," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.298-301, 2019.

Biometric identification makes utilization of physical and behavioral traits to recognize an individual. It really is currently a measurable physical feature which is believed far very much reliable and safer than passwords. It authenticates secure access and helps in gaining access to data through fingerprints or DNA which are the biological information of human beings. Many biometric systems have recently been developed and so are being utilized to authenticate the individual identity. Iris recognition systems are being used broadly and have became efficient at individual recognition with high precision and practically perfect coordination. The features extracted from iris of both eye of the same person varies, this helps it to be more secured method of authentication in comparison to other biometric systems. This paper offers a review of different methods and algorithms utilized by different experts and their undertake performance of iris recognition system along with identification of gap for potential work.

Biometric Authentication; Iris recognition system; Iris database; Iris recognition review; segmentation; feature extraction; normalization; localization; matching

[1] M. Singh and S. K. Sharma, “Review Paper A Review on Implementation of Biometric Iris Recognition,” IJCSE, vol. 6, no. 5, pp. 630–635, 2018.

[2] N. Taneja, M. Shabaz, and V. Khajuria, “Iris Detection Using Segmentation Techniques,” Int. J. Comput. Sci. Eng., vol. 6, no. 9, pp. 442–444, 2018.
[3] J. Daugman, “How Iris Recognition Works,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 21–30, Jan. 2004.
[4] A. J. Dixit and K. S. Kazi, “IRIS Recognition by Daugman ‟ s Method,” vol. IV, no. Vii, pp. 90–93, 2015.
[5] K. Hajari, U. Gawande, and Y. Golhar, “Neural Network Approach to Iris Recognition in Noisy Environment,” Phys. Procedia, vol. 78, no. December 2015, pp. 675–682, 2016.
[6] H. Rai and A. Yadav, “Iris recognition using combined support vector machine and Hamming distance approach,” Expert Syst. Appl., vol. 41, no. 2, pp. 588–593, 2014.
[7] A. Nithya and R. Krithiga, “Iris Recognition Based on GLCM and FFT Feature Set Fusion,” Int. J. Pure Appl. Math., vol. 113, no. January, pp. 57–66, 2017.
[8] M. A. Afreen and I. D. Judith, “IRIS Recognition using hybrid Technique , Methods of Moment and K Means Algorithm,” no. February, pp. 2–5, 2018.
[9] O. Kaudki and K. Bhurchandi, “Edge Processing Technique,” 2018 9th Int. Conf. Comput. Commun. Netw. Technol., pp. 1–6, 2018.
[10] R. S. Shubhika Ranjan, Prabu Sevugan, P.Swarnalatha, Magesh Gopu, “Iris Recognition System,” vol. 4, no. 12, pp. 354–356, 2017.

Prabhat Kumar, Manish Ahirwar, Anjna Deen, "A Survey on Iris Recognition System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.302-307, 2019.

The plant disease detection is the approach which is applied to predict disease type from the input image. The plant disease detection has the two phases which are feature extraction and classification. In the previous years, various techniques has been designed for the plant disease detection. The various classifications methods has been designed for the plant disease detection like SVM, decision etc. In this paper, various plant disease detection techniques are reviewed and analyzed in terms of certain parameters

Plant Disease detection, SVM, Classification, Feature extraction

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[7] Bashir Sabah, Sharma Navdeep, “Remote area plant disease detection using image processing”, IOSR J Electron Commun Eng 2012;2(6):31–4. ISSN: 2278-2834.
[8] Naikwadi Smita, Amoda Niket, “Advances in image processing for detection of plant diseases”, Int J Appl Innov Eng Manage 2013;2(11).
[9] Patil Sanjay B et al., “Leaf disease severity measurement using image processing”, Int J Eng Technol 2011;3(5):297–301.
[10] Chaudhary Piyush et al., “Color transform based approach for disease spot detection on plant leaf”, Int Comput Sci Telecommun 2012;3(6).
[11] Vijai Singh, A.K. Misra, “Detection of plant leaf diseases using image segmentation and soft computing techniques”, Information Processing in Agriculture 4 (2017) 41 – 4 9
[12] Shunping Ji, Chi Zhang, Anjian Xu, Yun Shi and Yulin Duan, “3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images”, Remote Sens. 2018, 10, 75
[13] Tom Brosch, Lisa Y.W. Tang, Youngjin Yoo, David K.B. Li, Anthony Traboulsee, and Roger Tam, “Deep 3D Convolutional Encoder Networks with Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation”, 2016, IEEE
[14] Guillermo L. Grinblat, Lucas C. Uzal, Mónica G. Larese, Pablo M. Granitto, “Deep learning for plant identification using vein morphological patterns”, Computers and Electronics in Agriculture 127 (2016) 418–424
[15] Jayme Garcia Arnal Barbedo, Luciano Vieira Koenigkan, Thiago Teixeira Santos, “Identifying multiple plant diseases using digital image processing”, Biosystem Engineering 147, 2016, 104-116
[16] Mads Dyrmann, Henrik Karstoft, Henrik Skov Midtiby, “Plant species classification using deep convolutional neural network”, Biosystems Engineering 151, 2016, 72-80
[17] Jiang Lu, Jie Hu, Guannan Zhao, Fenghua Mei, Changshui Zhang, “An in-field automatic wheat disease diagnosis system”, Computers and Electronics in Agriculture 142 (2017) 369–379
[18] Halimatu Sadiyah Abdullahi, Ray E. Sheriff, Fatima Mahieddine, “Convolution Neural Network in Precision Agriculture for Plant Image Recognition and Classification”, 2017, IEEE
[19] Tisen Huang, Rui Yang, Wenshan Huang, Yiqi Huanga, Xi Qiao, “Detecting Sugarcane Borer Diseases Using Support Vector Machine”, 2017, Information Processing in Agriculture
[20] Sue Han Lee, Chee Seng Chan, Simon Joseph Mayo, Paolo Remagnino, “How deep learning extracts and learns leaf features for plant classification”, Pattern Recognition 71 (2017) 1–13

Gazzal Thukral, Lal Chand, "Analysis of various Plant Disease detection Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.308-311, 2019.

Mobile Ad hoc Network (MANET) is an infrastructure free network that is self-configurable and is dynamic in nature. In MANET nodes communicate with each other independently since there is no centralised controller. An extensive variety of routing protocols for MANETs have been suggested by researchers to excel the restrictions of wired routing protocols. Congestion in MANET is a restriction in the implementation of MANET as it can affect the efficiency of the network and may lead to packets loss, increased average delay during transmission or reception of data. This paper discusses various routing protocols to control congestion problem in MANET. Also various performance parameters have been evaluated here like packets transmitted, packets collided, packets that got into error, throughput and average delay by using AODV (Ad- Hoc On Demand Distance Vector), DSR (Dynamic Source Routing) and ZRP (Zone Routing Protocol) protocols by taking 5,10 and 20 nodes using the software NETSIM Simulator.

MANET, ZRP, AODV, DSR

[1] H. Gupta and P. Pandey (2013).Survey of routing base congestion control techniques under MANET.IEEE International Conference On Emerging Trends in Computing, Communication and Nanotechnology (ICECCN), 241-244.
[2] Sandeep Dalal, Renu Ruhil (2014).Routing based Congestion Control Metric:RFR. International Journal of Computer Applications, 99(11), 20-24.
[3] Akhtar N., Khan Khattak M.A., Ullah A., Javed M.Y. (2019). Congestion Aware and Adaptive Routing Protocols for MANETs: A Survey. Recent Trends and Advances in Wireless and IoT-enabled Networks.159-169.
[4] V.K Sharma, M. Kumar (2017). Adaptive Congestion Control Scheme in Mobile ad- hoc networks. Peer-to-Peer Netw. Appl. 10(3), 633-657.
[5] S Hemlatha, P.C. senthil Mahesh (2018). Energy optimization in Directional Advanced Intruder Handling AODV Protocol in MANET. Swansea Printing Technology Ltd.14, 935- 960,
[6] D. Bhattacharyya, A. Chatterjee, B. Chatterjee, A. K. Saha and A. Santra (2018). A novel approach to energy efficient low cost routing in MANET by reduction in packet size. IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC).679-684.
[7] Geetika Maheshwari, Mahesh Gour, Umesh Kumar Chourasia (2014). A Survey on Congestion Control in MANET. International Journal of Computer Science and Information Technologies. 5(2), 998-1001.
[8] Bhupinder Kaur, Vaibhav Pandey (2015). A Survey on Congestion Control Techniques in MANETs. International Journal of Advanced Research in Computer Science and Software Engineering. 5(5), 1441-1446.
[9] N. Sharma, A. Gupta, S. S. Rajput and V. K. Yadav (2017). Congestion Control Techniques in MANET: A Survey. Second International Conference on Computational Intelligence & Communication Technology (CICT). 280-282.
[10] Sapna Khurana, Suresh Kumar (2017). A Study of Congestion Control in MANET: Review. International Journal of Engineering Technology Science and Research IJETSR. 4(5), 94-98.
[11] Bhawna Ahlawat ,Vikas Nandal (2018). A Review Paper On - Analysis And Performance Evaluation For Congestion Control Routing Protocols In Manet. Intermational Journal of Computer Science and Engineering. 6 (12), 466-470.
[12] Ashish K. Maurya, Dinesh Singh, and Ajeet Kumar (2013). Performance Comparison of DSR, OLSR and FSR Routing Protocols in MANET Using Random Waypoint Mobility Model. International Journal of Information and Electronics Engineering. 3(5).
[13] Y. Mai, F. M. Rodriguez and N. Wang (2108). CC-ADOV: An effective multiple paths congestion control AODV. IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). 1000-1004.
[14] Prakhar Gupta, Pratyaksh Goel, Pranjali Varshney and Nitin Tyagi (2018). Reliability Factor Based AODV Protocol: Prevention of Black Hole Attack in MANET. Advances in Intelligent Systems and Computing. 271-279.
[15] Payal, Deepak Sharma, Suresh Kumar (2018). Performance Evaluation of Reactive Routing Protocols Using IEEE 802.15.4 Application in Designed Wireless Sensor Network. International Journal of Computer Sciences and Engineering. 6(4), 90-96.
[16] Shaily Mittal, Prabhjot Kaur (2009). Performance Comparision of Aodv, Dsr And Zrp Routing Protocols in Manet’s. International Conference on Advances in Computing, Control, and Telecommunication Technologies. 165-168.
[17] M.W Lee, G. Hwang (2018). Adaptive Contention Window Control Scheme in Wireless Ad Hoc Networks. IEEE Communication Letters. 22(5), 1062-1065.
[18] Varun Kumar Sharma, Mahesh kumar (2019). Adaptive Load Distribution approach based on Congestion control schemes in ad- hoc networks. International Journal of Electronics. 106(1), 48-68.
[19] D. A. Tran and H. Raghavendra (2006). Congestion Adaptive Routing in Mobile Ad Hoc Networks. IEEE Transactions on Parallel and Distributed Systems. 17(11), 1294-1305.
[20] Anju, Sugandha Singh (2015). Modified AODV for Congestion Control in MANET. International Journal of Computer Science and Mobile Computing. 4(6), 984-1001.
[21] Sukhwinder Singh, Vijay Laxmi (2017). A Review on Techniques for Controlling the Congestion in MANET. IJCSN - International Journal of Computer Science and Network. 6(3).
[22] Gupta, S. Sinha (2018). Effect of Varying Pause Time on Performance of QoS Parameters in MANET. Proceedings of International Conference on Recent Advancement on Computer and Communication. 34,105-113.
[23] H. Sai Srujan, A. Sudhakar (2018). Improving QoS Parameters Based on Traffic Type for AODV Routing Protocol in MANET. International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI). 26, 31-41.
[24] Sandeep Kumar , Suresh Kumar (2015). Study of MANET: Characteristics, Challenges, Applications, Routing Protocol and Security Attacks. International Journal of R & D in Engineering Science and Management. 2(5), 266-274.

Bhawna Ahlawat, Vikas Nandal, "Performance Analysis of Congestion Control In MANET Using Different Routing Protocols," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.312-319, 2019.

Bayesian Network (BN), a probabilistic reasoning approach have been widely used in ecological decision-making to deal with uncertain information nevertheless, very few instances of its usage in crop pest management. This paper focuses on how to deal with uncertain agro-ecological information for decision–making in pest management. In the study, a Bayesian network was developed for selecting appropriate management option of fruit borer (Helicoverpa armigera) and leaf minor (Liriomyza trifolii), key insect-pest of tomato based on the tentative agro-ecological information besides crop condition that farmers provided. Validation of the method resulted in 76% accuracy for fruit borer and 82% for leaf minor. Application of the method thus developed in Decision Support Systems (DSSs) of agriculture with applies Information and Communication Technology (ICT) would automate and speed up the process of providing insect-pest management decision support to the farmers. Thus, it will not only save the crop worth crores of rupees but also help in reduction of excessive and irrational usage of pesticides thus saving the environment and human health.

Fruit borer, Leaf minor, Tomato, Decision-making, ICT, DSS, Pest Management

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Niranjan Singh, Neha Gupta, "Development and Validation of Bayesian Network Method for Decision-Support System of Insect-Pest Management in Tomato," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.320-325, 2019.

In today`s era the massive online social networking places like Facebook, WeChat and WhatsApp, etc where are different-different working background peoples including students linked/connected to each other through a social networking media, and sharing of our thought, picture, videos with our family and friends. The main motive of this study is to highlight the default privacy setting and security of Facebook is breach due to the fake account, fake news and third parties on these sites. Facebook is a virtual online meeting space for the world where a huge volume of information is shared. Every second user of Facebook doesn`t have any knowledge about privacy setting. Privacy matter is strongly related to third parties to access Facebook user information. The risk with this platform is that it may fall within the wrong hands and people become a victim of hacking, identify theft, cyberstalking, child soliciting, and Abuse, etc.

Social media, Hacking, Facebook, Fake Account, cyberstalking

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Akhil Kumar, Sapna Jain, Rajkumar Yadav, "Flaws in Privacy and Security of Facebook," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.326-331, 2019.

The paradigm of Internet of Things (IoT) is paving the way for a world, where many of our daily objects will be interconnected and will interact with their environment in order to collect information and automate certain tasks. Such a vision requires, among other things, seamless authentication, data privacy, security, robustness against attacks, easy deployment, and self-maintenance. Such features can be brought by blockchain, a technology born with a cryptocurrency called Bitcoin. In this paper, a thorough review on how to adapt blockchain to the specic needs of IoT in order to develop Blockchain-based IoT (BIoT) applications is presented. After describing the basics of blockchain, the most relevant BIoT applications are described with the objective of emphasizing how blockchain can impact traditional cloud-centered IoT applications. Then, the current challenges and possible optimizations are detailed regarding many aspects that affect the design, development, and deployment of a BIoT application. Finally, some recommendations are enumerated with the aim of guiding future BIoT researchers and developers on some of the issues that will have to be tackled before deploying the next generation of BIoT applications.

Cryptocurrency, Bitcoin, Encrypted, Currency, Bitpay, Exchange Rate

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Granthi Sukhvinder Singh, Surendar Singh, "A Review on the Use of Block chain for the Internet of Things," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.332-358, 2019.

RPL is the IPv6 routing protocol for low-power and lossy networks, standardized by IETF in 2012 as RFC6550. Specifically, RPL is designed to be a simple and inter-operable networking protocol for resource-constrained devices in indus-trial, home, and urban environments, intended to support the vision of the Internet of Things with thousands of devices interconnected through multihop mesh networks. More than four-years have passed since the standardization of RPL, and we believe that it is time to examine and understand its current state. In this paper, we review the history of research efforts in RPL; what aspects have been (and have not been) inves-tigated and evaluated, how they have been studied, what was (and was not) implemented, and what remains for future inves-tigation. We reviewed over 97 [41] RPL-related academic research papers published by major academic publishers and present a topic-oriented survey for these research efforts. Our survey shows that only 40.2% of the papers evaluate RPL through experiments using implementations on real embedded devices, ContikiOS and TinyOS are the two most popular implementations (92.3%), and TelosB was the most frequently used hardware platform (69%) on testbeds that have average and median size of 49.4 and 30.5 nodes, respectively. Furthermore, unfortunately, despite it being approximately four years since its initial standardization, we are yet to see wide adoption of RPL as part of real-world systems and applications. We present our observations on the reasons behind this and suggest directions on which RPL should evolve.

RPL, IPv6, routing protocol, Internet of Things (IoT), low-power and lossy networks (LLN), Cooja

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Manish Mishra, Piyush Shukla, Rajeev Pandey, "A Survey on Different Tools Used for Simulation of Routing Protocol for low-Power and Lossy Networks (RPL)," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.359-365, 2019.

Cloud computing is an emerging computing technology and trend in research field today. It is an internet based computing model that shares pool of computing resources such as servers, storage, computer networks, applications and services to other devices on demand over a network on a leased basis. Many industries such as banking, healthcare and education are moving towards cloud due to efficiency of services provided by the pay per use pattern based on resources such as processing power used, transactions carried out, bandwidth consumed, data transferred or storage space occupied. Cloud computing is aimed at providing IT as a service to the cloud users on demand basis with greater flexibility, availability, reliability and scalability. There are various issues such as load balancing, management of energy, privacy and security which hinders their growth. Serverless architecture, Security and load balancing are critical issues in cloud computing in present era. This paper presents review on use of cloud computing, its various models, cloud computing architecture and various research challenges in cloud computing.

Cloud computing, Software as a service (SaaS), Platform as a service (PaaS), Infrastructure as a service ( Iaas), NIST(National Institute of standards and technology)

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Dayanand, Ragini Kumari, Anubhav Patrick, Disha Mohini Pathak, "A Review on Cloud Computing, It’s Overview and Current Research Challenges," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.366-372, 2019.