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Contactless payment systems are one which uses credit and debit cards, key fob, smartcards and other mobile devices that use radio frequency identification or other types to make the payments a secured one. It is not the one which involves close physical proximity. It involves wifi networks or broad area cellular network to perform the transaction. In this paper we present a exploration on the selected terminology of contactless payments, its genealogy, the securities involved on contactless payments. The present study relates generally to transaction processing techniques and associates systems. The paper contributes to study of the various digital payment mechanisms and their transparency in doing the financial transactions. The study relates to methods and systems for monitoring immediacy contactless payment transactions and assisting consumers with use of their payment gateways in carrying out the proximity contactless payment transactions.

Contactless Payments, Smartcards, Mobile Devices, Near-Field Communication (NFC), Point-of-Scale (POS), Radio Frequency Identification (RFID)

[1]. Shirsha Ghosh , Alak Majumder, Joyeeta Goswami, Abhishek Kumar, “Swing-Pay: One Card Meets All User Payment and Identity Needs: A Digital Card Module using NFC and Biometric Authentication for Peer-to-Peer Payment”, IEEE Consumer Electronics Magazine, Volume: 6, Issue: 1, Jan. 2017
[2]. Thomas S. Poole, Paul Young Moreton, “System and method for providing contactless payment with a near field communications attachment”, U.S. Patent No. 9,183,490. 10 Nov. 2015
[3]. Jason Lee James, “Middle device used to receive temporary onetime payment information from a NFC enabled smart device or digital wallet to process payment on a non NFC enabled point of sale terminal”, US Patent App. 15/241,027, 2018
[4]. I. A. Brusakova, A. G. Budrin, S. A. Borodulina, A. S. Lebedeva, “Efficiency assessment of contactless fare payment technology implementation on public transport”, IEEE International Conference on Soft Computing and Measurements (SCM), 2017
[5]. Tri B. Joewono, Bekti A. Effendi, Hansen S. A. Gultom, Ranto P. Rajagukguk, “Influence of Personal Banking Behaviour on the Usage of the Electronic Card for Toll Road Payment”, Transportation Research Procedia,Volume 25, pp 4454-4471, 2017
[6]. Nicholas Akinyokun, Vanessa Teague, “Security and Privacy Implications of NFC-enabled Contactless Payment Systems”, ARES `17 12th International Conference Article No. 47, 2017
[7]. Deepali Kayande, Elsa Rebello, Shweta Sharma, Monica Tandel, “Overview of a payment solution for NFC-Enabled Mobile phones”, International Conference on ICT in Business Industry & Government (ICTBIG), 2016
[8]. Constantinos Vasilios Priporas, Nikolaos Stylos, Anestis K. Fotiadis, “Generation Z consumers` expectations of interactions in smart retailing: A future agenda”, Computers in Human Behavior, Volume 77, December 2017
[9]. Annie Singla, Kamal Jain, Ajay Gairola, “Delving into Security of Networks – Time’s Need”, ISROSET-Int.J.Sci. Res. in Network Security & Communication Vol-2, Issue-3, PP(1-8) Oct 2014,E-ISSN: 2321-3256

P. Edith Linda, S. Vijay Anand, R. Srividhya, "An Efficient E-Wallet Mechanism Protecting Privacy, Security and Unobservability," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.608-611, 2019.

Any kind of attitude, through or judgment that occurs due to any feeling is known as a sentiment which is also known as opinion mining. The sentiments of individuals towards particular elements are analyzed in this approach. To gather sentiment information, web or internet is the best known source. A platform that is accessed socially by various users to post their views is known as Twitter. The messages that are posted by these users are known as tweets. The properties of Tweets are highly unique due to which new challenges have raised. In comparison to several other domains, the sentiment analysis requires higher analysis studies. This research work is based on the sentiment analysis of product reviews of Amazon data. To apply sentiment analysis the technique of feature extraction and classification is applied. For the sentiment analysis in the previous work, the SVM technique is applied and which is replaced with the KNN technique.

SA (Sentiment Analysis), SVM (Support Vector Machine), KNN (K-Nearest Neighbor).

[1] A.Pak and P. Paroubek. “Twitter as a Corpus for Sentiment Analysis and Opinion Mining”, In Proceedings of the Seventh Conference on International Language Resources and Evaluation, pp.1320-1326, 2010.
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[3] Go, R. Bhayani, L.Huang, “Twitter Sentiment Classification Using Distant Supervision”, Stanford University, Technical Paper, 2009.
[4] L. Barbosa, J. Feng, “Robust Sentiment Detection on Twitter from Biased and Noisy Data”, COLING 2010: Poster Volume, pp. 36-44.
[5] Bifet and E. Frank, “Sentiment Knowledge Discovery in Twitter Streaming Data”, In Proceedings of the 13th International Conference on Discovery Science, Berlin, Germany: Springer, pp. 1-15,2010.
[6] Agarwal, B. Xie, I. Vovsha, O. Rambow, R. Passonneau, “Sentiment Analysis of Twitter Data", In Proceedings of the ACL Workshop on Languages in Social Media, pp. 30-38,2011 .
[7] Dmitry Davidov, Ari Rappoport, “Enhanced Sentiment Learning Using Twitter Hashtags and Smileys”, Coling 2010: Poster Volume pages 241-249, Beijing, August 2010.
[8] Ketan Sarvakar, Urvashi K Kuchara, “Sentiment Analysis of movie reviews: A new feature-based sentiment classification”, Isroset-Journal (IJSRCSE) Vol.6, Issue.3, pp.8-12, 2018.
[9] M. Vidhyalakshmi, P. Radha, “Social Hash Tag Techniques Using Data Mining- A Survey”, Isroset-Journal (IJSRCSE) Vol.6, Issue.3, pp.86-92, 2018.
[10] A. Jenita Jebamalar, “Open Access Article Efficiency of Data Mining Algorithms Used In Agnostic Data Analytics Insight Tools”, Journal (IJSRNSC) Vol.6, Issue.6, pp.14-18, 2018.
[11] Rashmi H Patil , Siddu P Algur,” Sentiment Analysis by Identifying the Speaker’s Polarity in Twitter Data”, International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), 2017.
[12] Metin Bilgin,Izzet Fatih Senturk,”Sentiment analysis on twitter data with semi supervised DOC2 Vec”, Akgül, E.S., Ertano,C. ve Diri, B., "Twitter verileri ileduygu analizi.", Pamukkale University Journal of Engineering Sciences, 22(2), (2016): 106-110.
[13] Chintan Dedhia, Mrs Jyoti Ramteke, “Ensemble model for Twitter Sentiment Analysis”, International Conference on Inventive Systems and Control (ICISC-2017).
[14] Adyan Marendra Ramadhani, Hong Soon Goo, “Twitter Sentiment Analysis using Deep Learning Methods”,7th International Annual Engineering Seminar (InAES), Yogyakarta, Indonesia,2017.
[15] Paramita Ray and Amlan Chakrabarti,” Twitter Sentiment Analysis for Product Review Using Lexicon Method”, International Conference on Data Management, Analytics and Innovation (ICDMAI) Zeal Education Society, Pune, India, Feb 24-26, 2017
[16] Zahra Rezaei, Mehrdad Jalali, “Sentiment Analysis on Twitter using McDiarmid Tree Algorithm”, 7th International Conference on Computer and Knowledge Engineering (ICCKE 2017), Ferdowsi University of Mashhad, October 26-27 ,2017.
[17] M.Trupthi, Suresh Pabboju, G.Narasimha, “Sentiment Analysis on Twitter using Streaming API”, IEEE 7th International Advance Computing Conference, 2017.
[18] Rasika Wagh, Payal Punde, “Survey on Sentiment Analysis using Twitter Dataset”, Proceedings of the 2nd International conference on Electronics, Communication and Aerospace Technology (ICECA 2018).

Kajal, Prince Verma, "The Hybrid Approach for Sentimental Analysis of Twitter Data," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.612-617, 2019.

Energy-efficient cluster head selection algorithms are significant in Wireless Sensor Networks (WSN) to improve the lifetime of the networks. Various cluster head selection algorithms were designed in WSN to enhance the network lifetime. The tiny sensor nodes are grouped to form a cluster and clustering is an important technique in WSN. The selection of cluster heads greatly affects the throughput of the network. Still, it is a challenging task. Different approaches and algorithms have been proposed for the efficient CH selection in WSNs. In this paper, a brief survey is made about the various CH selection algorithms in the recent scenario. The advantages and disadvantages of the most important algorithms are highlighted.

Wireless Sensor Network (WSN), Cluster Head (CH), Energy Efficient, Network Lifetime, Throughput

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[3] D. Shinghal and N. Srivastava, "Wireless sensor networks in agriculture: for potato farming," Neelam, Wireless Sensor Networks in Agriculture: For Potato Farming (September 22, 2017), 2017.
[4] M. M. Afsar and M.-H. Tayarani-N, "Clustering in sensor networks: A literature survey," Journal of Network and Computer Applications, vol. 46, pp. 198-226, 2014.
[5] A. A. Abbasi and M. Younis, "A survey on clustering algorithms for wireless sensor networks," Computer communications, vol. 30, pp. 2826-2841, 2007.
[6] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks," IEEE Transactions on wireless communications, vol. 1, pp. 660-670, 2002.
[7] M. Elshrkawey, S. M. Elsherif, and M. E. Wahed, "An enhancement approach for reducing the energy consumption in wireless sensor networks," Journal of King Saud University-Computer and Information Sciences, vol. 30, pp. 259-267, 2018.
[8] C. Li, J. Bai, J. Gu, X. Yan, and Y. Luo, "Clustering routing based on mixed integer programming for heterogeneous wireless sensor networks," Ad Hoc Networks, vol. 72, pp. 81-90, 2018.
[9] D. Jia, H. Zhu, S. Zou, and P. Hu, "Dynamic cluster head selection method for wireless sensor network," IEEE Sensors Journal, vol. 16, pp. 2746-2754, 2016.
[10] A. Zahedi, M. Arghavani, F. Parandin, and A. Arghavani, "Energy Efficient Reservation-Based Cluster Head Selection in WSNs," Wireless Personal Communications, vol. 100, pp. 667-679, 2018.
[11] M. Alagirisamy and C.-O. Chow, "An energy based cluster head selection unequal clustering algorithm with dual sink (ECH-DUAL) for continuous monitoring applications in wireless sensor networks," Cluster Computing, vol. 21, pp. 91-103, 2018.
[12] T. M. Behera, S. K. Mohapatra, U. C. Samal, M. S. Khan, M. Daneshmand, and A. H. Gandomi, "Residual Energy Based Cluster-head Selection in WSNs for IoT Application," IEEE Internet of Things Journal, 2019.
[13] K. Wei-xin, R. A. Wagan, and A. A. Wagan, "Energy and Delay Efficient Routing Protocol (EDERP) for Threshold based Cluster Head Selection in Heterogeneous WSN," in Proceedings of the 2018 10th International Conference on Machine Learning and Computing, 2018, pp. 288-294.
[14] J.-S. Leu, T.-H. Chiang, M.-C. Yu, and K.-W. Su, "Energy efficient clustering scheme for prolonging the lifetime of wireless sensor network with isolated nodes," IEEE communications letters, vol. 19, pp. 259-262, 2015.
[15] S. Mahajan, J. Malhotra, and S. Sharma, "An energy balanced QoS based cluster head selection strategy for WSN," Egyptian Informatics Journal, vol. 15, pp. 189-199, 2014.
[16] F. Khan, T. Gul, S. Ali, A. Rashid, D. Shah, and S. Khan, "Energy Aware Cluster-Head Selection for Improving Network Life Time in Wireless Sensor Network," in Science and Information Conference, 2018, pp. 581-593.
[17] A. Sarkar and T. S. Murugan, "Cluster head selection for energy efficient and delay-less routing in wireless sensor network," Wireless Networks, vol. 25, pp. 303-320, 2019.
[18] D. R. Prasad, P. Naganjaneyulu, and K. S. Prasad, "Bio-Inspired Approach for Energy Aware Cluster Head Selection in Wireless Sensor Networks," in Computer Communication, Networking and Internet Security, ed: Springer, 2017, pp. 541-550.
[19] B. Singh and D. K. Lobiyal, "A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks," Human-Centric Computing and Information Sciences, vol. 2, p. 13, 2012.
[20] P. S. Rao, P. K. Jana, and H. Banka, "A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks," Wireless networks, vol. 23, pp. 2005-2020, 2017.
[21] S. Poolsanguan, C. So-In, K. Rujirakul, and K. Udompongsuk, "An enhanced cluster head selection criterion of LEACH in wireless sensor networks," in 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2016, pp. 1-7.
[22] T. Ahmad, M. Haque, and A. M. Khan, "An Energy-Efficient Cluster Head Selection Using Artificial Bees Colony Optimization for Wireless Sensor Networks," in Advances in Nature-Inspired Computing and Applications, ed: Springer, 2019, pp. 189-203.
[23] S. R. Samal, S. Bandopadhaya, A. Pathy, V. Poulkov, and A. Mihovska, "An Energy Efficient Head Node Selection For Load Balancing In A Heterogeneous Wireless Sensor Network," in 2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2018, pp. 1428-1433.
[24] M. Bhardwaj, "Faulty Link Detection in Cluster based Energy Efficient Wireless Sensor Networks," International Journal of Scientific Research in Network Security and Communication, vol. 5, pp. 1-8, 2017.

S. Tamilselvi, S. Rizwana, "A Review on Cluster Head Selection Algorithms for Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.618-622, 2019.

Software cost estimation SCE is directly related to quality of software. The paper presents a hybrid approach that is an amalgamation of algorithmic (parametric models) and non-algorithmic (expert estimation) models. Algorithmic model uses COCOMO II while non algorithmic utilizes Neuro-Fuzzy technique that can be further used to estimate accuracy in irregular functions. For generalization of the model, Neuro-fuzzy membership functions have been used and simulated using mathematical tool MATLAB. The main objective of this research is to investigate the role of fuzzy logic technique in improving the effort estimation accuracy using COCOMO II by characterizing inputs parameters using Gaussian, trapezoidal and triangular membership functions and comparing their results. NASA (93) dataset is used in the evaluation of the proposed Fuzzy Logic COCOMO II. After analyzing the results it had been found that effort estimation using Gaussian member function yields better results for maximum criterions when compared with the other methods

COCOMO II, Estimation, Neuro-Fuzzy, Reliability, Membership function, Soft Computing, Software Effort Estimation, Gaussian Membership Function

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[5] Randy K. Smith, Joanne E. Hale and Allen S. Parrish, “An Empirical Study Using Task Assignment Patterns to Improve the Accuracy of Software Effort Estimation”, IEEE Transactions on Software Engineering, vol. 27, issue-3, pp. 264-267, 2011.
[6] Shubhangi Mahesh Potdar, Manimala Puri and Mahesh P. Potdar, “Literature Survey on Algorithmic Methods for Software Development Cost Estimation”, International Journal of Computer Technology & Applications, vol. 5, issue-1, ISSN: 2229-6093, pp. 183-188, 2014.
[7] Chemuturi K.M, “Software Estimation Best Practices, Tools and Techniques: A Complete Guide for Software Project Estimators”, J. Ross Publishing Inc, pp. 49-65, 2009.
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[9] Vahid Khatibi, Dayang N. A. Jawawi “Software Cost Estimation Methods: Review”, Journal of Emerging Trends in Computing and Information Sciences, vol. 2, issue- 1, 2011.
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[15] Chetan Nagar, “Software efforts estimation using Use Case Point approach by increasing technical complexity and experience factors”, International Journal of Computer Sciences and Engineering, ISSN:0975-3397, vol.3, issue-10, pp. 3337-3345, 2011.
[16] N. Karunanitthi, D. Whitley and Y.K Malaiya, “Using Neural Network in Reliability Prediction”, IEEE Transaction on software engineering, vol. 9, issue-4, pp. 53-59, 1992.
[17] T. J. Mc Cabe, “A complexity measure”, IEEE Transaction on software engineering vol. 2, issue-4, pp. 308-320, 1976.
[18] A.J. Albrecht and J. E. Gaffney, “Software function, source lines of code and development effort prediction: A software science validation”, IEEE Transaction on Software Engineering, vol. 9, issue-6, pp. 639-647, 1983.

Ritu, Kamna Solanki, Amita Dhankhar, Sandeep Dalal, "An Analysis of Software Reliability Estimation Using Fuzzy Logic Function With Cocomo Ii Model," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.623-626, 2019.

The data mining is the approach which extracts useful information from the rough information. The clustering is the approach of data mining which cluster the similar and dissimilar type of information. The clustering techniques is of various type which hierarchal clustering, density based clustering and so on. The IDBSCAN algorithm is the density based clustering algorithm. The density based clustering has the various algorithms. In this research work, the I-DBSCAN algorithm is improved using the PSO algorithm to increase accuracy of clustering. The proposed methodology is implemented in MATAB and results are analyzed in terms of accuracy.

Clustering, Hierarchal, I-DBSCAN, PSO (Particle Swarm Optimization)

[1] Anand M. Baswade, Kalpana D. Joshi and Prakash S. Nalwade, “A Comparative Study Of K-Means and Weighted K-Means for Clustering,” International Journal of Engineering Research & Technology, Volume 1, Issue 10, December-2012
[2] Neha Aggarwal, Kirti Aggarwal and Kanika Gupta, “Comparative Analysis of k-means and Enhanced K-means clustering algorithm for data mining,” International Journal of Scientific & Engineering Research, Volume 3, Issue 3, August-2012
[3] Ahamed Shafeeq B M and Hareesha K S, “Dynamic Clustering of Data with Modified Means Algorithm,” International Conference on Information and Computer Networks, Volume 27, 2012
[4] Manpreet Kaur and Usvir Kaur, “Comparison Between K-Mean and Hierarchical Algorithm Using Query Redirection”, International Journal of Advanced Research in Computer Science and Social , Volume 3, Issue 7, July 2013 ISSN: 2277 128X
[5] Tapas Kanungo , David M. Mount , Nathan S. Netanyahu Christine, D. Piatko , Ruth Silverman and Angela Y. Wu, “An Efficient K-Means Clustering Algorithm: Analysis and Implementation ,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 24, July 2002
[6] Amar Singh and Navot Kaur, “To Improve the Convergence Rate of K-Means Clustering Over K-Means with Weighted Page Rank Algorithm,” International journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 8, August 2012
[7] Amar Singh and Navot Kaur, “To Improve the Convergence Rate of K-Means Clustering Over K-Means with Weighted Page Rank Algorithm,” International journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 8, August 2012.
[8] Harpreet Kaur and Jaspreet Kaur Sahiwal, “Image Compression with Improved K-Means Algorithm for Performance Enhancement,” International Journal of Computer Science and Management Research, Volume 2, Issue 6, June 2013
[9] Osamor VC, Adebiyi EF, Oyelade JO and Doumbia S “Reducing the Time Requirement of K-Means Algorithm” PLoS ONE, Volume 7, Issue 12, 2012
[10] Azhar Rauf, Sheeba, Saeed Mahfooz, Shah Khusro and Huma Javed, “Enhanced K-Mean Clustering Algorithm to Reduce Number of Iterations and Time Complexity,” Middle-East Journal of Scientific Research, pages 959-963, 2012
[11] Kajal C. Agrawal and Meghana Nagori, “Clusters of Ayurvedic Medicines Using Improved K-means Algorithm,” International Conf. on Advances in Computer Science and Electronics Engineering, 2013.
[12] M. N. Vrahatis, B. Boutsinas, P. Alevizos and G. Pavlides, “The New k-Windows Algorithm for Improving the k-Means Clustering Algorithm,” Journal of Complexity 18, pages 375-391, 2002.
[13] Chieh-Yuan Tsai and Chuang-Cheng Chiu, “Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm,” Computational Statistics and Data Analysis, pages 4658-4672, Volume 52, 2008
[14] Guangchun Luo, Xiaoyu Luo, Thomas Fairley Gooch, Ling Tian, Ke Qin,” A Parallel DBSCAN Algorithm Based On Spark”, 2016, IEEE, 978-1-5090-3936-4
[15] Dianwei Han, Ankit Agrawal, Wei−keng Liao, Alok Choudhary,” A novel scalable DBSCAN algorithm with Spark”, 2016, IEEE, 97879-897-99-4
[16] Nagaraju S,Manish Kashyap, Mahua Bhattacharya,” A Variant of DBSCAN Algorithm to Find Embedded and Nested Adjacent Clusters”, 2016, IEEE, 978-1-4673-9197-9
[17] Jianbing Shen, Xiaopeng Hao, Zhiyuan Liang, Yu Liu, Wenguan Wang, and Ling Shao,” Real-time Superpixel Segmentation by DBSCAN Clustering Algorithm”, 2016, IEEE, 1057-7149
[18] Ilias K. Savvas, and Dimitrios Tselios,” Parallelizing DBSCAN Algorithm Using MPI”, 2016, IEEE, 978-1-5090-1663-1
[19] Ahmad M. Bakr , Nagia M. Ghanem, Mohamed A. Ismail,” Efficient incremental density-based algorithm for clustering large datasets”, 2014, Elsevier Pvt. Ltd.
[20] Saefia Beri, Kamaljit Kaur,” Hybrid Framework for DBSCAN Algorithm Using Fuzzy Logic”, 2015, IEEE, 978-1-4799-8433-6
[21] Karlina Khiyarin Nisa, Hari Agung Andrianto, Rahmah Mardhiyyah,” Hotspot Clustering Using DBSCAN Algorithm and Shiny Web Framework”, 2014, IEEE, 978-1-4799-8075-8

Neha, Prince Verma, "I-DBSCAN Algorithm with PSO for Density Based Clustering," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.627-632, 2019.

Passwords have always been one of the simplest security methods, weak passwords, default passwords can easily be cracked using brute force attack and dictionary attack which is very dangerous for security of systems across the globe. Security and privacy issues are the challenges faced in many systems. Blacklisted Password Authentication System is an attempt to decrease the efficiency of Dictionary, Brute force Attack which can be implemented in any authentication system without any significant changes.

Brute force Attack, Dictionary Attack, Authentication, Blacklisted Password

[1] Yan Zhao, Shiming Li, et al. “Secure and Efficient User Authentication Scheme Based on Password and Smart Card for Multiserver Environment.” Hindawi Security and Communication Networks , 2018.
[2] Kolias, Constantinos, et al. "DDoS in the IoT: Mirai and other botnets." Computer Society IEEE. pp. 80-84, 2017.
[3] Antonakakis, Manos, et al. "Understanding the mirai botnet." 26th USENIX Security Symposium. pp. 1093-1110, 2017.
[4] Ari Juels, et al. “Honeywords: Making Password-Cracking Detectable”, ACM, pp. 145-156, 2013.
[5] Vasundhara R.Pagar, Rohini G.Pise, “Strengthening Password Security through Honeyword and HoneyEncryption Technique”, IEEE, pp. 827-831, 2017.
[6] Sohaib Khan, Fawad Khan, “Attempt based Password”, in Proceedings of 13th International Bhurban Conference on Applied Science and Technology. IEEE, pp. 300-304, 2016.
[7] C.E. Shannon, "A mathematical theory of communication," Bell System Technical Journal, pp. 379-423, 1948.
[8] Taha, Mariam M., et al. "On password strength measurements: Password entropy and password quality." In ICCEEE, IEEE, 2013.
[9] Wantong Zheng, et al. “CombinedPWD: A New Password Authentication Mechanism using Separators between Keystrokes”, 13th ICCIS Conference IEEE, pp. 557-560, 2017.
[10] Aakansha Gokhale,et al., “A Study of Various Passwords Authentication Techniques” International Journal of Computer Applications(0975–8887) International Conference on Advances in Science and Technology (ICAST), 2014
[11] Pa, Yin Minn Pa, et al. "Iotpot: A novel honeypot for revealing current iot threats." Journal of Information Processing 24.3, pp. 522-533,2016.
[12] John the Ripper password cracker, http://www.openwall.com/john/

Payal, Suman Sangwan, Arun Malik, "Blacklisted Password Authentication System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.633-635, 2019.

Recent years have witnessed the extension of cloud computing paradigm to the IoT devices. In cloud computing, traffic rate and data transmission time are very high which causes delay and reduces Quality of Service. In order to counter these limitations, many researchers introduced a concept of fog layer. The concept of fog layer has many limitations such as the availability of limited number of servers for serving the incoming requests. To counter the resource limitation problem of fog layer, this paper proposes a mechanism which not only utilizes the cloud resources optimally but also helps to maintain the Quality of Service (QoS). The main purpose of this paper is to present Quality of Service aware model for fog computing and decreases SLA violation in fog computing environment. In this paper, we models and evaluate our proposed mechanism in CloudSim- a framework for modeling and simulation.

Fog Computing, Quality of service (QoS), Internet of things (IoT), Virtual Machines (VMs)

[1] C. C. Byers, “Architectural imperatives for fog computing: Use cases, requirements, and architectural techniques for fog-enabled IoT networks,” IEEE Communications Magazine, vol. 55, no. 8, pp. 14–20, 2017.
[2] R. S. Montero et al., “Extending the cloud to the network edge,” IEEE Computer, vol. 50, no. 4, pp. 91–95, 2017.
[3] A. Al-Fuqaha et al., “Internet of things: A survey on enabling technologies, protocols and applications,” IEEE Communication Surveys Tutorials, vol. 17, no. 4, pp. 2347-2376, 2015.
[4] L. M. Vaquero and L. Rodero-Merino, “Finding your way in the fog: Towards a comprehensive definition of fog computing,” ACM SIGCOMM Computer Communications Review, vol. 44, no. 5, pp. 27–32, 2014.
[5] F. Bonomi et.al. , “Fog computing and its role in the internet of things,” in Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16, 2012.
[6] Chiang et.al. , “Fog computing and Networking: Part 1 [Guest editorial]” IEEE Communications Magazine, 55(4), pp.16-17, 2017.
[7] Yousefpour et.al. , “Fog Computing: Towards Minimizing Delay in the internet of things” at IEEE 1st international conference on edge computing, PP. 17-24, 2017.
[8] Yousefpour et.al. , “QoS-aware dynamic fog service provisioning” arXiv: 1802.00800v1 [cs.NI], pp. 1-10, 2018.
[9] Maiti et.al., “Mathematical modeling of QoS-Aware fog computing Architecture for IoT services” at emerging technologies in data mining and information security, Advances in Intelligent Systems and Computing 814, pp. 13-19, 2019.
[10] Hong et al., “Dynamic module deployment in a fog computing platform” in Network Operations and management symposium (APNOMS), 2016 18th Asia-Pacific, pp. 1-6, 2016.
[11] Kapsalis et al., “A Cooperative Fog Approach for Effective Workload Balancing” IEEE Cloud Computing, vol. 4, no. 2, pp. 36-45, 2017.
[12] Souza et.al., “Handling service allocation in combined fog-cloud scenarios”, in Communications (ICC), 2016 IEEE International Conference on, pp. 1–5, 2016.
[13] Skarlat et.al. , “Resource provisioning for IoT services in the fog,” in Service-Oriented Computing and Applications (SOCA), 2016 IEEE 9th International Conference on, pp. 32–39, 2016.
[14] Skarlat et.al. , “Towards QoS-aware fog service placement,” in Fog and Edge Computing (ICFEC), IEEE 1st International Conference, pp. 89–96, 2017.
[15] Sarkar et.al. , “Assessment of the suitability of fog computing in the context of internet of things,” IEEE Transactions on Cloud Computing, no. 99, pp. 1–1, 2015.
[16] Buyya et.al. , “Latency-Aware Application Module Management for Fog Computing Environments”, ACM Trans. Internet Technol. 19, 1, Article 9 (November 2018), 21 pages, 2018.
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Shikha Kamboj, Jitender Kumar, "A Quality of Service Aware Model for Fog Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.636-640, 2019.

Load balancing is the research dimension in the field of mobile ad hoc networks. From the several previously conducted research works it is inferred that clustering based load balancing approach offers better solution. Many protocols are proposed formerly, and multipath routing seems to be better one. This research work aims to make use of grey wolf optimization technique for clustering the nodes. Conventional multipath routing strategy is employed along with adaptive load balancing approach. Simulation settings are made and the performance metrics namely packet delivery ratio, throughput, packets drop, overhead and delay are taken into account for evaluating the efficiency of the approach.

MANET, load balancing, clustering, grey wolf optimization, packet delivery ratio, throughput, delay

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P. Aruna Devi, K. Karthikeyan, "Grey Wolf Optimization based Clustered On – Demand Load Balancing Scheme (GWO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.641-649, 2019.

Software requirements are adapting by the customer to adjust in new environment because business environment is very dynamic in current era. Struggling for appropriate agile processes for development environments of Software developers and project managers is going on till the appropriate process is not matched. Need to adapt in a complex business environment is being faced by organization for helping them in continuous change and transformation. Organization agility is being gaining strategic advantages and market success in these conditions, for maintaining and achieving requirement of agility are agile techniques, architectures, tools, methods and able to react to change requirements in real time. In this research paper various agile family methodologies like AM, XP, Scrum Development, Feature FDD, DSDM, ASD, Kanban, LSD, Scrumban, RAD, Crystal, AUP, DAD has been studied and compared on the basis of various parameters along with their relationship. The research will help future developers to get new ideas about the methods for development along with selection of the right methodology for the product development.

Agile, XP, FDD, DSDM, Scrumban, Crystal, AUP and DAD

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Rakesh Kumar, Priti Maheshwary, Timothy Malche, "Inside Agile Family: Software Development Methodologies," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.650-660, 2019.

In Vehicular Adhoc Networks the position information of vehicles has to be exchanged between the communicating vehicles as it plays a huge responsibility in avoiding fatal accidents. Many applications rely upon the safety message which also requires location information. The vehicles within the communication range exchanges these messages. In real time scenario the direct communication between the vehicles can be blocked by the presence of road side or dynamic obstacles creating a Non Line Of Sight between the vehicles. A HYBRID MULTI-PERSPECTIVE NLOS LOCALIZATION FRAMEWORK (HM-NLOS-LF) is proposed which uses the merits of Range based, Range Free and Range optimized to identify the NLOS based on the NLOS Factor. If the NLOS Factor between 0.3 and 0.5 Range based optimization is initiated. In contrary, the NLOS Node Localization Module enforces the range-free and range-optimizations-based NLOS node detection when the NLOS determination Factor between 0.5 and 0.8 respectively.

Non Line Of Sight; Range Based; Range Optimization; Range Free

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A. Amuthan, R. Kaviarasan, "Hybrid Multi-perspective NLOS Localization Framework (HM-NLOS-LF) for effective node localization in VANETs," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.661-670, 2019.