Volume-5 , Issue-6 , Jun - 2017 Go Back

In recent years, the quantity of data between organizations, companies and governments has been produced and transmitted with extremely increased in number. Privateness preserving is without doubt one of the primary challenges in a computer world, when you consider that of the large amount of sensitive information on the internet. Additionally, with the quick increase of data mining technologies hidden relationships between items in databases can now be exposed with ease, for the reason of decision making or to determine user’s preferences. In the existing work, k-anonymity method used for the safety of sensitive data from the leakage or distribution to unauthorized users. However it is not sufficient for the protection of attribute disclosure. This method is also difficult to reverse the data to get the content. To overcome this problem, we performed the Animal Migration Optimization on the basis of age and then encryption is performed using RSA algorithm for achieving the security of the data and preserve from heavy data loss.

Privacy Preservation, Data Modification, Privacy preserving techniques , Animal Migration Optimization and RSA Algorithm.

[1] M. Patel, A. Hasan , S.Kumar, "A Survey: Preventing Discovering Association Rules for Large Data Base", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.2, pp.30-32, 2013.
[2] Bullarao Domathoti, Rajia Begum and Nageswara Rao.P, "Privacy Preserving Collaborative Auditing Data Storage Scheme in Cloud Computing", International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.212-218, 2015.
[3] Md. Riyazuddin, Dr.V.V.S.S.S.Balaram, Md.Afroze, Md.JaffarSadiq, M.D.Zuber, “An Empirical Study on Privacy Preserving Data Mining”, ISSN: 2231-5381, Page 687, Volume3Issue6- 2012.
[4] Y. Lindell and B. Pinkas, “Privacy preserving data mining”, J. Cryptology, 15(3):177–206, 2002.
[5] R. Rajeswari and Mrs R. Kavitha ,”Privacy Preserving Mechanism for anonymizing data streams in data mining”, International conference on current research in Engineering Science and Technology(ICCREST-2016).
[6] Tong Li, Zheli Liu, Zin Li, Chunfu Jia and Kuan-Ching Li, “A Cryptographic Data Publishing System”, J. Comput. Syst. Sci. (2016), http://dx.doi.org/10.1016/j.jcss.2016.12.004.
[7] J. Jesu Vedha Nayahi and V. Kavitha,” Privacy and utility preserving data clustering for data anonymization and distribution on Hadoop”, Future Generation Computer Systems, 0167-739X/© 2016 Elsevier.
[8] Surbhi Sharma and Deepak Shukla, “Efficient multi-party privacy preserving data mining for vertically partitioned data”, Inventive Computation Technologies (ICICT), 10.1109/INVENTIVE.2016.7824852, © 2017 IEEE.
[9] Prakash, M., and G. Singaravel. "An approach for prevention of privacy breach and information leakage in sensitive data mining." Computers & Electrical Engineering 45 (2015): 134-140.
[10] V.K. Saxena, S. Pushkar, "Privacy Preserving using Encryption Proxy in Data Security", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.36-41, 2017.
[11] L. Sweeney,“An Achieving k-Anonymity Privacy Protection using Generalization and Suppression”, International Journal of Uncertainty, Fuzziness and Knowledge-Based System, 2002, pp571-588.
[12] Xiangtao Li, Jie Zhang and Minghao Yin, “Animal migration optimization: an optimization algorithm inspired by animal migration behavior,” Neural Comput & Applic (2014) 24:1867–1877.
[13] https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data

Nivedita Bairagi, Punit K. Johari, "A novel approach for privacy preserving using Animal Migration Optimization and RSA algorithm," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.193-197, 2017.

Most of the security solutions use routers, firewalls, and intrusion detection systems implemented to tightly control, access to networks from outside authors. Cloud computing breaks these organizational bounds. When the data is present in the cloud, it resides outside the organizational bounds. Hence, a user loses control over their data. Another problem is, most of the time users are anxious about uploading private and confidential files for online backup due to concern that the service provider might use it inappropriately. So, providing security at the required level is a major concern. The existing solution is data-centric access control solution with an enriched role-based approach in which security is focused on protecting user data regardless the cloud service provider that holds it. In this, Novel rule-based and proxy re-encryption technique are used to protect the authorization model and increase the performance. The authorization model is rule-based file access control, i.e. permissions granted based on authority rules provided by the data owner. In this existing solution, the authorization model contains limited privileges which restrict modification and deletion. The proposed systems consist of two types of data viz. Normal data and Sensitive data. The user can upload the file and select the type of data. Protecting normal data by using proxy re-encryption technique and sensitive data by using rummage technique. The rummage technique which encrypts the original data into meaningful (readable) format, hence attacker gets confused to identify encrypted data. The authorization model is rule-based file access control that contains privileges like access, modify, delete, etc.

Encryption technique, Data-centric security, Cloud computing, Role-based access control, Authorization

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[15] Jean-Maries Borello and Ludovic Me, Code Obfuscation Techniques for Metamorphic Viruses, Springer, 2008.
[16] Wroblewski., General method of program code obfuscation. In Proc. International Conference on Software Engineering Research and Practice (SERP 02), pages 153–159, 2002.
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P.B. Gajeli, P.S. Yalagi, "Data Security in Public Cloud for Authorization," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.198-202, 2017.

One of the main vision of Internet of Things (IoT) is to equip real-life physical objects with computing and communication power so that they can interact with each other for the social good. As one key members of IoT, Internet of Vehicles (IoV) has seen steep advancement in communication technologies. Here, a vehicle can easily exchange safe, efficient, infotainment, and comfort-related information with other vehicles and infrastructures using vehicular ad hoc networks (VANETs). We introduce a cloud-based VANETs theme to propose cyber-physical architecture for the Social IoV (SIoV). SIoV is a vehicular example of the Social IoT (SIoT), where vehicles are the key social members in the machine-to-machine vehicular social networks. We have identified the social structures of SIoV components, their relationships, and the interaction types that manage the overall system . Also we have defined the tNote message, that describes how vehicles can share transport related safe, efficient and comfort notes to the cloud and other infrastructure following the Dedicated Short Range Communication (DSRC) standard.

Social Network Of Vehicles, Internet Of Things, Internet of Vehicles, Vehicular Ad-hoc Network

[1]. Kazi Mazudul Alam, Mukesh Saini, and Abdulmotaleb el Saddik, “Toward Social Internet of Vehicles: Concept, Architecture, and Applications,” IEEE Access (volume 3), Apr 27. 2015, pp. 343-357
[2]. K.M. Alam, M. Saini, and A. EI Saddik, tNote: A Social Network of Vehicles under Internet of Things,” in Internet of Vehicles – Technologies and Services, Springer-Verlag, 2014,pp. 738-745.
[3]. M. Al-Hader, N.Ahmad and A.R. Sharif, “ Smart City Components Architecture,” in Proc. Int. Conf. Comput. Intell.,Modelling simulation, sep. 2009, pp. 93-97.
[4]. K.M. Alam, M. Saini, D.T. Ahmed, and A.EI Saddik, “ VeDi: A Vehicular Crowd-sourced Video Social Network for VANETs,” in Proc. IEEE 39th conf. Local comput.Netw(LCN), SEP. 2014, pp. 738-745.
[5]. S. Haller, “The things in the Internet of Things”, in Proc. Internet Things conf. 2010, pp. 1-3.

Jamsheera M., Riyadh M., "A Study on Social Internet of Vehicles," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.203-205, 2017.

The whole world is filled with a huge collection of digital data, digital images, and videos or can be anything that can be stored in a digitized manner. This data doesn`t mean essentially anything. It is stored in an unorganized manner without any interpretation. Image Mining is an energetic concept for researchers. When there is a need to extract necessary information from the massive collection of image database through image mining techniques then this concept came into the picture. In this research paper, the proposed work is done through two steps. One is feature extraction, extract the features of images by RGBHist as a color feature and Edge Histogram Descriptor as a shape feature has taken to create feature dataset. While in second step K-Medoid clustering algorithm is applied to make good clusters and retrieval process is done from the clusters to increase the accuracy of the system. Manhattan similarity method is used a matching purpose from the query image. Three Database is used in this paper for testing the proposed image mining system.

Image Mining, RGB histogram descriptor, Edge Histogram Descriptor (EHD), Content Based Image Retrieval (CBIR), Clustering, K-Medoid Clustering Algorithm, Data Mining, Manhattan Similarity Measure,

[1] Maria Fayez, Soha Safwat and Ehab Hassanein,"Comparative Study of Clustering Medical Images”, 978-1-4673-8460-5/16 IEEE, SAI Computing Conference, London, UK, pp. 312–318
[2] Snehal Mahajan, Dharamaraj Patil, ”Image Retrieval Using Contribution-based Clustering”, Algorithm with Different Feature Extraction Techniques”978-1-4799-3064-7/14 IEEE
[3] Ruziana Mohamad Rasli, T Zalizam et al,"Comparative Analysis of Content Based Image Retrieval Technique using Color Histogram. A Case Study of GLCM and K-Means Clustering”, 978-0-7695-4668-1/12 IEEE DOI 10.1109/ISMS.2012.111
[4] Y. Poornima, P.S. Hiremath,”Efficient Modelling of Visual Art Color Image Clustering”, International Journal of Computer Applications (0975 – 8887) Volume 91 – No.11, April 2014
[5] Geethu Varghese, Dr. Arun Kumar M N "Content Based Image Retrieval using Feature Extraction and K-Means Clustering" International Journal for Innovative Research in Science and Technology Vol.3, ISSN:04 September(2016)-4238, pp.291-302
[6] Rupinder Kaur, Navleen Kaur,”Content Based Image Mining Technique for Image Retrieval Using Optimized Hybrid Clustering”, International Journal of Computer Trends and Technology (IJCTT) – Volume 11 number 3 – May 2014
[7] Ankita Tripathi, Shivam Pandey,”An Improved and Efficient Image Mining Technique for Classification of Textual Images Using Low-Level Image Features”, 10.1109@inventive.2016.7823220
[8] Amit Khatami, and Saeed Mirghasemi,"A New Color Space Based on K-medoids Clusterings for Fire Detection", 978-1--4799-8697-2/15, IEEE, DOI 10.1109/SMC. 2015.481,pp 2755-2760
[9] Katta Sugamya, Suresh Pabboju, Dr. A.VinayaBabu.” A CBIR Classification using support vector machines”, International Conference on Advances in Human Machine Interaction (HMI - 2016), 978-1-4673-8810-8/16 IEEE.
[10] Monika Sahu, Madhup Shrivastava “Image Mining: A New Approach for Data Mining Based on Texture”, 978-0-7695-4872-2/12 IEEE DOI 10.1109/ICCCT.2012.11
[11] Dong Kwon Park, Yoon Seok Jeon, and Chee Sun Won, ” Efficient Use of Local Edge Histogram Descriptor," ACM workshop on Multimedia, November 2000, pp. 51-55
[12] Sawet Somnupong,and Kanokwan Khiewwan,”Content –Based Image Retrieval using a Combination of Color Correlograms and Edge Direction Histogram,",JESSE,978-1-5090-2033-1/16 IEEE.
[13] Aruna Bhat, “K-Medoids Clustering Using Partitioning Around Medoids For Performing Face Recognition," IJSCMC, Vol 3, No 3, August 2014, DOI: 10.14810/IJSCMC.(2014).3301,pp-1-12
[14] Swarndeep Saket J. and. Sharnil Pandya,"Implementation of Extended K-Medoids Algorithms to Increase Efficiency and Scalability using Large Dataset", International Journal of Computer Applications(0975-8887) Volume 146- No. 5, July (2016),pp-19-23
[15] Prabhjeet Kaur and Kamaljit kaur, "Review of Different Existing Image Mining Techniques," International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), Volume 4, Issue 6 June (2014), pp 518-524
[16] Swati Agarwal, A.K. Verma, Preetvanti Singh,”Content Based Image Retrieval using Discrete Wavelet Transform and Edge Histogram Descriptor”, 978-1-4673-5986-3, (2013), IEEE
[17] Priti Maheshwary, and Namita Srivastav “Retrieving Similar Image Using Color Moment Feature Detector and K-means Clustering of Remote Sensing Image,” 978-0-7695-3504-3/08, IEEE
[18] Neethu Joseph.c, Aswathy Wilson," Retrieval of Images using Data Mining Techniques”,978-1-4799-6629-5/14 IEEE

[19] Yogita Mistry, D. T. Ingole, and M. D. Ingole,” Efficient Content Based Image Retrieval Using Transform and Spatial Feature Level Fusion”, The 2nd International Conference on Control, Automation and Robotics. 978-1-4673-9859-61/16 IEEE.
[20] Naveena A K and N K Narayanan,"Image Retrieval using Combination of Color, Texture and Shape Descriptor,"©(2016) IEEE, pp-958-962
[21] Ming Zhang, Ke Zhang,and Qinghe Feng, Jianzhong Wang,Jun Kong and Yinghua Lu," A novel image retrieval based on hybrid information descriptors,”1047-3203/©(2014)Elsevier.
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[24] Shubhangi P. Meshram, Dr. Anuradha D. Thakre,and Prof. Santwana Gudadhe “Hybrid Swarm Intelligence Method for Post Clustering Content Based Image Retrieval,” 7th International Conference on Communication Computing and Virtualization (2016), Elsevier B.V., pp. 509–515.
[25] Rajkumar Jain, and Punit Johari, ”An Improved Approach of CBIR using Color Based HSV Quantization and Shape Based Edge Detection Algorithm,” 978-1-5090-0774-5/16 ©2016 IEEE
[26] Jaimala Jha Dr. Sarita Sign Bhaduaria “Review of Various Shape Measures for Image Content Based Retrieval” International Journal of Computer & Communication Engineering Research Nov. (2015).
[27] Ravi Devesh, Jaimala Jha,”An Efficient Approach for Monuments Image Retrieval using Multi-Visual Descriptors.” In MCCS (2017),unpublished
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[29] Ruchi Jayaswal, Jaimala Jha,"A Hybrid Approach For Image Retrieval using Visual Descriptors”, ISBN: 978-1-5090-6471-7/17/$31.00 ©(2017) IEEE, unpublished
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[31] Ansari Mohd Aquib, Dixit Manish,”An Image Retrieval Framework: A Review", International Journal of Advanced Research in Computer Science,[S.l.], v. 8, n. 5, p. 692-699, June 2017. ISSN 0976-5697.

Ruchi Jayaswal, Jaimala Jha , Ravi Devesh , "An Effective Method of Image Mining using K-Medoid Clustering Technique," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.206-214, 2017.

Energy aware routing protocols can be ordered into energy saver and energy administrator. Energy saver protocols diminish energy utilization completely. The greater part of them attempt to locate the most limited way amongst source and goal to diminish energy utilization. Yet, energy administrator protocols adjust energy utilization in network to maintain a strategic distance from network dividing. Discovering best route just in light of energy adjusting thought may prompt long way with high postponement and declines network lifetime. Then again, discovering best route just with the briefest separation thought may prompt network apportioning. This paper enhances SEER routing protocol. Conventional SEER is just energy saver and has poor thought regarding energy adjusting. Our proposed protocol, named PEAR, considers energy adjusting and ideal separation both. It finds a reasonable tradeoff between energy adjusting and ideal separation by learning automata idea. We reproduce and assess routing protocols by NS2 simulator.

Sensor Network, Energy Aware, Routing Protocol, SEER Protocol

[1] Backhyun Kim, and Iksoo Kim, “Energy Aware Routing Protocol in Wireless Sensor Networks”, in IJCSNSVOL.6 No.1, January 2006.
[2] Peter K.K. Loh and Yi Pan, “An Energy-Aware Clustering Approach for Wireless Sensor Networks”, I.J. Communications, Network and System Sciences, 2009, 2, 91-168.
[3] V. Prasad, VS. Sunsan, "Multi path dynamic routing for data integrity and delay Minimization differentiated services in wireless sensor network", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.4, pp.20-23, 2016.
[4] Adel Gaafar A.Elrahim, Hussein A.Elsayed, Salwa El Ramly, Magdy M. Ibrahim, “An Energy Aware WSN Geographic Routing Protocol”, Universal Journal of Computer Science and Engineering Technology, 1 (2), 105-111, Nov. 2010.
[5] U. Korupolu, S. Kartik, GK. Chakravarthi, "An Efficient Approach for Secure Data Aggregation Method in Wireless Sensor Networks with the impact of Collusion Attacks", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.26-29, 2016.
[6] KazemSohraby, Danielminoli and TaiebZnati, “Wireless Sensor Networks: Technology, Protocols, and Applications”, published by John Wiley & Sons, Inc., Hoboken ew Jersey, 2007.
[7] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless Sensor Networks: A Survey” Computer Networks, Vol. 38, No.4, pp.393-422, 2002.
[8] Wu, Zhengyu; Song, Hantao; Jiang, Shaofeng; Xu, Xiaomei, “Energy-Aware Grid Multipath Routing Protocol in Mobile Ad Hoc network (MANET)”, IEEE Asia International Conference on modeling & simulation (AMS), pp. 36-41, 2007.
[9] Shanti.C and Sahoo.A. “DGRAM: A Delay Guaranteed Routing and MAC Protocol for Wireless Sensor Networks”, IEEE Transactions on Mobile Computing, Vol.9, No.10, pp.1407-1423, 2010.
[10] Zhengyu Wu, Xiangjun Dong and Lin Cui, “A Grid-based Energy Aware Node-Disjoint Multipath Routing Algorithm for Mobile Ad Hoc networks (MANETs)”, IEEE International Conference on Computing, Networking and Communications (ICNC), pp. 244-248, 2007.
[11] R. Nathiya, S.G. Santhi, "Energy Efficient Routing with Mobile Collector in Wireless Sensor Networks (WSNs)", International Journal of Computer Sciences and Engineering, Vol.2, Issue.2, pp.36-43, 2014.
[12] R.V. Pawar, S.S. Mahajan, "Performance Analysis of Wireless Sensor Network Using Cognitive Radio Concept", International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.136-137, 2014.
[13] Gurbinder Singh Brar, Shalli Rani, Vinay Chopra, Rahul Malhotra, Houbing Song and Syed Hassan Ahmed, “Energy Efficient Direction-Based PDORP Routing Protocol for WSN”, Published in: IEEE Access, Volume: 4, Page(s): 3182 – 3194.
[14] Sathish Kumar S and Dr. A. Grace Selvarani, "Improving Energy Efficiency by Using Tree-Based Routing Protocol for Wireless Sensor Network", International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.201-206, 2015.
[15] Shusen Yang, Xinyu Yang, Julie A. McCann, Tong Zhang, Guozheng Liu and Zheng Liu, “Distributed Networking in Autonomic Solar Powered Wireless Sensor Networks”, Published in: IEEE Journal on Selected Areas in Communications, Volume: 31, Issue: 12, December 2013.
[16] Degan Zhang, Guang Li, Ke Zheng, Xuechao Ming and Zhao-Hua Pan, “An Energy-Balanced Routing Method Based on Forward-Aware Factor for Wireless Sensor Networks”, Published in: IEEE Transactions on Industrial Informatics, Volume: 10, Issue: 1, Feb. 2014.
[17] Subramanian Ganesh and Ramachandran Amutha, “Efficient and secure routing protocol for wireless sensor networks through SNR based dynamic clustering mechanisms”, Published in: Journal of Communications and Networks, Volume: 15, Issue: 4, Aug. 2013.
[18] Aditya Singh Mandloi and Vinita Choudhary, "An Efficient Clustering Technique for Deterministically Deployed Wireless Sensor Networks", International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.1, pp.6-10, 2013.
[19] Lili Zhang and Yan Zhang, “Energy-Efficient Cross-Layer Protocol of Channel-Aware Geographic-Informed Forwarding in Wireless Sensor Networks”, Published in: IEEE Transactions on Vehicular Technology, Volume: 58, Issue: 6, July 2009.

A.Santha Devi, V. Vinoba, "PEAR: Protected Based Energy- Aware Routing For WSNs," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.215-222, 2017.

Now everything has evolved under Internet of Things. This domain embodied in a wide spectrum of networked products, systems, and sensors, which take advantage of advancements in computing power, electronics miniaturization, and network interconnections offering new ventures undiscovered previously. The IoT is the inter connection of physical devices (aka smart devices) equipped with electronics, software, sensor, actuators, and network connectivity enabling different components for data collection and exchange. The IoT allows objects to be sensed or controlled remotely across existing network infrastructure enabling direct embodiment of the physical world into digital world, leading to improved efficiency, accuracy and economic benefit apart from reduction in human intervention. The main objective of proposed system is to provide automation system with low cost & effective solution to electronic appliances.

IOT, Webserver, Beagle Bone Black, Android Apps, Smartphone

[1] BS. Dhande, US. Pacharaney, "Railway Management System using IR sensors and Internet of Things Technology", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.1, pp.12-15, 2017.
[2] Aditya Singh Mandloi and Vinita Choudhary, "An Efficient Clustering Technique for Deterministically Deployed Wireless Sensor Networks", International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.1, pp.6-10, 2013.
[3] R. Nathiya, S.G. Santhi, "Energy Efficient Routing with Mobile Collector in Wireless Sensor Networks (WSNs)", International Journal of Computer Sciences and Engineering, Vol.2, Issue.2, pp.36-43, 2014.
[4] D. Maheshwari, “Raspberry Pi Technology”, Splint International Journal of Professionals, Vol.3, Issue.1, pp.1-4, 2016.
[5] R. Gorli , "Interlinking OF IoT, Big data, Smart Mobile app with Smart Garbage Monitoring", International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.70-74, 2017.
[6] Hasan Omar Al-Sakran, “Intelligent Traffic Information System Based on Integration of Internet of Things and Agent Technology”, International Journal of Advanced Computer Science and Applications, Vol.6, Issue2, pp.37-43, 2015.
[7] Sadiya Shakil and Vineet Singh, "Security of Personal Data on Internet of Things Using AES", International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.35-39, 2016.
[8] Nannan He, Han-Way Huang, Brian David Woltman,” The Use of Beagle Bone Black Board in Engineering Design and Development”, ASEE, 2014.
[9] P K Mishra, M. R Pradhan and M.Panda, "Internet of Things for Remote Healthcare", International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.106-111, 2016.
[10] Pranay Kujur and Kiran Gautam, "Smart Interaction of Object on Internet of Things", International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.15-19, 2015.
[11] Swati H Chungde,” A Smart City Approach for Child Tracking with Video Streaming”, International Journal of Innovative Research in Science, Engineering and Technology, 2016, vol.5.
[12] A.C. Buchade, "Sophisticated Parking Availability Prediction System in IoT Network", International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.132-136, 2017.
[13] Atzori L, Iera A, Morabito G., “The internet of things: A survey”, Computer networks, Vol.54, Issue.15, pp.2787-2805, 2010.
[14] Pooja Kanase, Sneha Gaikwad, “Smart Hospitals Using Internet of Things (IoT)”, IRJET, 2016, vol.03.
[15] Nannan He, Han-Way Huang, Brian David Woltman,” The Use of Beagle Bone Black Board in Engineering Design and Development”, ASEE, 2014.
[16] L. Atzori, A. Iera and G. Morabito, “The Internet of Things: A survey”, Comput. Netw, 2010, vol. 54, pp 2787-2805.
[17] L. Da Xu, W. He and S. Li, “Internet of Things in industries: A survey”, IEEE, 2014, vol. 10, pp 2233-2243.
[18] C. Zhu, V. Leung, L. Shu and E. C. H Ngai, “Green Internet of Things for smart world”, IEEE, 2015, vol. 3, pp 2151-2162.
[19] Shrungashri Chaudhary and Mudit Kapoor , "Design and Implementation of Reservation Of Parking Spaces Using RFID and GSM Technology", International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.188-191, 2015.
[20] https://beagleboard.org/black.

N.B. Gawade, S. B. Shinde, "Remote Access to Home Appliances: A Survey," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.223-226, 2017.

Cloud computing is defined as that type of computing which shows the development of potential, grid and parallel computing. It is the fastest new paradigm for delivery of services via internet. In this, the client can access software resources and valuable information over a network. It is the internet based computing in which resources are accessed via internet. In practice, the cloud computing faces the number of challenges like reliability, portability and shared access etc. Moreover, cloud computing faces the large quantity of cloud users, their tasks and data. Hence, to schedule the tasks efficiently, scheduling is done. In this paper, a Deadline Aware Particle Swarm Optimization (DAPSO) Algorithm is used which provides efficient and better results. Due to its fast convergence property, it is much better than Particle Swarm Optimization (PSO) algorithm. It is used to optimize the task scheduling algorithm which results in better performance and profit.

Cloud computing, scheduling, Task Scheduling Algorithms, Particle Swarm Optimization (PSO), Task scheduling, Scheduling Types, Deadline Aware Particle Swarm Optimization (DAPSO).

[1] Dr. M. Sridhar and Dr. G. Rama Mohan Babu, R.V.R & J.C College of Engineering, Guntur, INDIA, 2015 IEEE International Advance Computing Conference (IACC).
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[3] Xingquan Zuo, Member, IEEE, Guoxiang Zhang, and Wei Tan, Member, IEEE Transactions on Automation Science & Engineering, Vol. 11, No.2, 2014
[4] Nuttapong Netjinda, Booncharoen
Sirinaovakul, Tiranee Achalakul Department of Computer Engineering King Mongkut’s University of Technology Thonburi
Bangkok,
[5] BU Yanping1, 2 ZHOU Wei3 YU Jinshou1 1.Research Institute of Automation, East China University of Science and Technology, Shanghai 200237 China; 2.
[6] A. Salman, “Particle swarm optimization for task assignment Problem”, Microprocessors and Microsystems, Vol. 26, No.8, pp.363–371, 2009.
[7] Azadi Khalili and S eyed Morteza, School of Electrical and Computer
Engineering Kashan University, Babamir, 2015 23rd Iranian Conference on Electrical Engineering (ICEE).
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[9] Lizheng Guo, Shuguang Zhao, Shigen Shen, and Changyuan Jiang, “Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm”, Journal of Networks, Vol.7, No.3, 2012.
[10] ChienHung Chen, JennWei Lin, and SyYen Kuo, Fellow, IEEE.
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Algorithm for Task Scheduling on The Cloud Computing Environment”, International Journal of Computers and Technology, Vol. 13, No. 9, 2014.
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Shruti, Meenakshi Sharma, "Task Scheduling and Resource Optimization in Cloud Computing Using Deadline-Aware Particle Swarm Technique," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.227-231, 2017.

In this digital world, authentication of legitimate user is highly important for secure banking transactions and activities, One Time Password (OTP) security is one of them. Online banking requires some kind of authentication to verify whether it is performed by legitimate user or not. By the help of One Time Password it can be performed securely. When someone perform any online transaction, he/she would be asked to input One Time Password which has been sent to his/her registered mobile number, if it is legitimate user then transaction would be successfully performed otherwise no one can do any fraudulent activity without having One Time Password. But what happens when someone has stolen your mobile phone and by having your username and password along with your mobile phone, he/she would be able to perform successful transactions. For this case we require much more tenable process to secure our banking account from any kind of fraudulent activities. This paper has been proposed for review of existing systems which provide security in the field of authentication.

Component OTP, Authentication, Legitimate, MATLAB, Mobile

[1] Ramesh K and Ramesh S, “Implementing One Time Password Based Security Mechanism for Securing Personal Health Records in Cloud”, IEEE Transaction, 2014.
[2] John Jacob, Kavya Jha, Paarth Kotak and Shubha Puthran, “Mobile Attendance using Near Field Communication and One-Time Password”, IEEE Transaction, 2015.
[3] Swapnoneel Roy,Matt Rutherford and Charlene H. Crawshaw, “Towards Designing and Implementing a Secure One Time Password (OTP) Authentication System”, IEEE Transaction, 2016.
[4] Shubham Srivastava and Sivasankar M, “On The Generation of Alphanumeric One Time Passwords”, IEEE Transaction, 2016.
[5] Eddy Prasetyo Nugroho, Rizky Rachman Judhie Putra, Iman Muhamad Ramadhan, “SMS Authentication Code Generated by Advance Encryption Standard (AES) 256 bits Modification Algorithm and One Time Password (OTP) to Activate New Applicant Account”, IEEE Transaction, 2016.
[6] TE-YUAN LIN and CHIOU-SHANN FUH, “Considerations of Emerging Cloud Computing in Financial Industry and One-Time Password with Valet Key Solution”, IEEE Transaction, 2016.
[7] Joyce Soares and A.N.Gaikwad, “Fingerprint and Iris Biometric Controlled Smart Banking Machine Embedded with GSM Technology for OTP”, IEEE Transaction, 2016.
[8] Fuqiang Zhang and Lin Chen, “OTP_SAM: DHCP security authentication model based on OTP”, IEEE Transaction, 2016.

Rachita Dubey, Jijo S.Nair, "A Review on Secured One Time Password (OTP) Based Authentication & Validation System," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.232-236, 2017.

There are many data mining algorithms which exist today for searching patterns from transactional databases. Most of them work only on precise data. But there are also situations in which these conventional algorithms fail, situations in which Data is uncertain in nature. Uncertain data can be explained as the one where items have probabilistic values associated with them. These probabilities express the likelihood of these items to be present in the transactions. In mining, the search tree produced is also one of the major factor of concern. The search space produced when dealing with uncertain data is much larger due to the presence of existential probabilities. This problem worsens when dealing with Big data. Considering all the above factors and concerns, an algorithm is specified and explained ahead. It allows users to express the interest in terms of constraints and uses the Map Reduce programming model to mine uncertain Big data for frequent patterns that satisfy the user-specified constraints. By using these user-specified constraints as inputs, the algorithm greatly reduces the search space for Big data mining of uncertain data, and returns only those patterns the users are interested in.

Big data models and algorithms, Big data analytics, Uncertain data mining, Frequent pattern mining

[1] C.K.-S. Leung , R. K. MacKinnon, F. Jiang, “Reducing the Search Space for Big Data Mining for Interesting Pattern from Uncertain Data” , 2014 IEEE International Congress on Big Data, pp.315-322, 2014.
[2] J.V. Patel, K. J. Panchal, “A Modified Approach to Mine Frequent Patterns from Uncertain Data”, 2015 1st International Conference (NGCT), pp.612-615, 2015.
[3] C.K.-S. Leung, “Mining uncertain data”, WIREs Data Mining and Knowledge Discovery, Vol.1 ,Issue.4, pp.316–329, July-Aug. 2011.
[4] S. Madden, “From databases to big data,” IEEE Internet Computing, Vol.16, Issue.3, pp. 4–6, May–June 2012.
[5] Azzini, P. Ceravolo, “Consistent process mining over Big data triple stores”, IEEE Big Data Congress 2013, pp. 54–61, 2013.
[6] Ӧlmezoğullari, I. Ari, “Online association rule mining over fast data”, IEEE International Congress on Big Data 2013, pp.110–117, 2013.
[7] P. Agarwal, G. Shroff, P. Malhotra, “Approximate incremental big-data harmonization”, IEEE Big Data Congress 2013, pp.118–125, 2013.
[8] Yang , S. Fong, “Countering the concept-drift problem in big data using iOVFDT”, IEEE Big Data Congress 2013, pp.126–132, 2013.
[9] C.K.Chui, B.Kao, E.Hung, “Mining Frequent Itemsets from Uncertain Data”, LNCS 2007, pp.47-58, 2007.
[10] C.K.-S. Leung , F. Jiang, “Frequent itemset mining of uncertain data streams using the damped window model”, ACM SAC 2011, pp.950–955, 2011.
[11] C.K.-S. Leung , F. Jiang, “Frequent pattern mining from time-fading streams of uncertain data”, DaWaK 2011 (LNCS 6862), pp. 252-264, 2011.
[12] Y. Tong, L. Chen, Y. Cheng, P.S. Yu, “Mining frequent itemsets over uncertain databases”, PVLDB, Vol.5, Issue.11, pp.1650–1661, July 2012.
[13] C.K.-S. Leung, M.A.F. Mateo, D.A. Brajczuk, “A tree-based approach for frequent pattern mining from uncertain data”, PAKDD 2008 (LNAI 5012), pp. 653–661, 2008.
[14] C.K.-S. Leung , S.K. Tanbeer, “Fast tree-based mining of frequent itemsets from uncertain data”, DASFAA 2012 (LNCS 7238), pp. 272–287, 2012.
[15] C.K.-S. Leung, S.K. Tanbeer, “PUF-tree: A compact tree structure for frequent pattern mining of uncertain data”, PAKDD 2013 (LNCS 7818), pp.13–25, 2013.
[16] D.N. Goswami, Anshu Chaturvedi., C.S. Raghuvanshi,”An Algorithm for Frequent Pattern Mining Based On Apriori”, International Journal on Computer Science and Engineering(IJCSE), Vol.2, Issue.4, pp.942-947, 2010.
[17] J.Dean, S.Ghemawat, “MapReduce: simplified data processing on large clusters”, CACM, Vol.51, Issue.1, pp.107-113, Jan. 2008.
[18] M.Y.Lin, P.Y.Lee, S. C. Hsueh, “Apriori based frequent itemset mining algorithms on MapReduce”, ICUIMC 2012, art.76, 2012.

H.V. Sapte, S.S. Pallati, P.P. Pandit, A.S. Joshi, V. Jumb, "Uncertain Big Data Strategical Miner," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.237-243, 2017.

In this paper, a new display technology which is more power efficient and provides less interference along with interaction between users is designed. It displays the data entered in touch screen to the connected nodes and vice versa. The input data is entered in touch screen by touching the screen or using a stylus. The touch screen system has a built in data buffer that enables the user to enter data without waiting for the touch screen to be refreshed. When touch screen is refreshed, data from data buffer is sent to a computer and processed. The computer stores the data which is received from the data buffer and transmits the data to connected nodes through ZigBee transceiver. A maximum of 65,536 nodes can receive the data that is transmitted from the ZigBee transceiver within a range of 100 meters. A node can be added or removed easily from the computer by using their IP address which also decreases the interference between nodes of adjacent networks. ZigBee networks are more power efficient and can determine the pending data available in it. Data is received in the connected nodes via ZigBee transceiver attached in them. The nodes also have the capability to transmit messages by their ZigBee transceiver which is displayed in a segment of the screen. Hence an interaction between the users is enabled. If data in a node gets lost, it can be retrieved from the personal computer (PC) itself. This technology can augment the active interaction between the users in classroom and video conferencing. Embedded C and JAVA programming languages are used for the implementation.

Touch screen, ZigBee, SC-FDMA, MATLAB, IFDMA, and PAPR

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[6] H. Qin, W. Zhang, “ZigBee-Assisted Power Saving Management for Mobile Devices,” IEEE Transactions on Mobile Computing, Vol.13, Issue.12, pp.2933–2947, 2014.
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[10] S. Shekhar, A. K. Jain, and P. Nukala, “Data Interface Buffer Compensation Scheme for Fast Calibration,” in Proceedings of the IEEE 18th International Symposium on Quality Electronic Design (ISQED), 2017.
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D.Allin Joe, Karthikumar R, P.Pavithra, "A low Power Wireless Interaction System using IFDMA in ZigBee Communication," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.244-248, 2017.