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Congestion, packet loss and increased response-time due to network traffic are common problems in most networks. This results in lowered network efficiency and poor Quality of Service (QoS). A number of routing protocols have been developed to deal with network traffic. The goal of every network routing protocol is to direct the traffic from source to destination maximizing the network performance. The Ant Colony Optimization (ACO) based routing protocol is efficient when used to dynamically route network traffic. Currently, there are many variations of the ACO algorithm in the domain of network routing. Past work has been done by researchers to improve the performance of the algorithm. In this paper we first study and analyze the existing work in this field and weigh the pros and cons of the different modifications and variations of the algorithm. We then propose a modification to the ACO algorithm in order to improve the quality of service offered by a network by routing packets according to their priority. Packets that belong to time-sensitive services like VOIP will be given higher priority and routed differently from low priority packets like FTP. By doing so the proposed algorithm will improve the success rate of the high priority packets while still maintaining high overall throughput of the network by dropping low priority packets that form loops. We then implement this algorithm on NS2 network simulator. The algorithm is then tested to see how it dynamically adapts to network changes. We then conduct tests to calculate the success rate and throughput that is offered by the algorithm and compare the results to those of other ACO algorithms. Our results indicate that the proposed algorithm improves the overall performance of the network by striking a balance between throughput and success rate thanks to the priority mechanism.

Computer Networks; Routing; QoS; Ant Colony Optimization; Swarm Intelligence

[1] Debasmita Mukherjee and Sriyankar Acharyya, “Ant Colony Optimization Technique Applied in Network Routing Problem”, International Journal of Computer Applications, Volume-01, Issue-15, Page no (66-73), May 2012
[2] Chris Saliba and Reuben A. Farrugia, “Quality of Service Aware Ant Colony Optimization Routing Algorithm”, 15th IEEE Mediterranean Electrotechnical Conference, ISBN: 978-1-4244-5793-9, Page no (343-347) , April 26-28, 2010.
[3] Masaya Yoshikawa and Kazuo Otani, “Ant Colony Optimization Routing Algorithm with Tabu Search”, Proceedings of the International Mulitconference of Engineers and Computer Scientists 2010, Volume – III, ISBN: 978-988-18210-5-8, Page no (112-117) March 17-19, 2010.
[4] Vincent Verstraete, Matthias Strobbe, Erik Van Breusegem, Jan Coppens, Mario Pickavet and Piet Demeester, “AntNet: ACO routing algorithm in practice”, Proceedings of the 8e INFORMS Telecommunications Conference, 2006.
[5] Gianni Di Caro and Marco Dorigo, “Ant colonies for Adaptive Routing in Packet-Switched Communications Networks”, Lecture Notes in Computer Science, Volume 1498, Page no (673-682), June 2006.
[6] The Network Simulator – NS2, http://www.isi.edu/nsnam/ns/, August 2014.
[7] V. Laxmi, Lavina Jain and M.S. Gaur, "Ant Colony Optimization Based Routing on NS-2", International Conference on Wireless Communication and Sensor Networks (WCSN), India, December 2006.
[8] Gianni Di Caro and Marco Dorigo, “AntNet : Distributed Stigmergetic Control For Communications Network”, Journal of Artificial Intelligence Research, Volume-09, Issue-01, ISSN 1076–9757, Page no (317-365), August 1998.

Farhaan Jalia and Aruna Gawde, "Priority Mechanism for ant Colony Optimization in Network Routing," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.105-110, 2015.

Multi-document summarization refers to the process of automatic extraction of text from multiple sources which belong to same topic. With the increase in usage of internet large amount of data has been generated day by day. It is quite difficult for anyone to distinguish and summarize this vast information gathered from various sources. Multi document text summarization has solution for this problem. Multi document summarization assembles information from different sources and summarizes the information up to necessary length. In this paper preprocessing is applied to unprocessed documents and different features are extracted. And then CST relations are identified from these extracted features document. Finally summary is generated depending on identified CST relations.

Multi Document Summarization, CST Realtions, Feature Extraction, Extractive Summarization.

[1] V. Gupta and G. S. Lehal, "A survey of text summarization extractive techniques," Journal of Emerging Technologies in Web Intelligence, vol. 2, pp. 258-268, 2010.
[2] Yogan Jaya Kumar, Naomie Salim, Albaraa Abuobieda, Ameer Tawfik, “Multi Document summarization based on cross-document relation using voting technique”, International conference on computing, electrical and electronic engineering (ICCEEE), 2013.
[3] Y. J. Kumar and N. Salim, "Automatic multi document summarization approaches," Journal of Computer Science, vol. 8, pp. 133-140, 2011.
[4] Ultimate Research Assistant, http://en.wikipedia.org/wiki/Ultimate_Research_Assistant, 27 Jan,2015.
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[7] Md. Mohsin Ali , Monotosh Kumar Ghosh, and Abdullah-Al-Mamun, “Multi-document Text Summarization: SimWithFirst Based Features and Sentence Co-selection Based Evaluation”, International Conference on Future Computer and Communication, 2009
[8] Z. Zhang, S. Blair-Goldensohn, and D. R. Radev, "Towards CST-enhanced summarization," presented atthe Eighteenth national conference on Artificial intelligence, Edmonton, Alberta, Canada, 2002
[9] M. L. d. R. C. Jorge and T. A. S. Pardo, "Experiments with CST-based multidocument summarization," presented at the Proceedings of the 2010 Workshop on Graph-based Methods for Natural Language Processing, Uppsala, Sweden, 2010
[10] Z. Zhang, J. Otterbacher, and D. Radev, "Learning crossdocument structural relationships using boosting," presented at the Proceedings of the twelfth international conference on Information and knowledge management, New Orleans, LA, USA, 2003.
[11] Rajesh S.Prasad, Dr. U.V.Kulkarni, Jayashree R.Prasad, “A Novel Evolutionary Connectionist Text Summarizer (ECTS)”, published in proceedingASID’09 Proceedings of the 3rd international conference on Anti-Counterfeiting, security, and identification in communication, IEEE Press Piscataway, NJ, USA, 20 Aug 2009

Yogita Desai and P. P. Rokade, "Multi Document Summarization using Cross Document Relations," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.111-115, 2015.

Various graphical password schemes have been proposed as an alternative to text-based passwords. We propose and examine the usability and security of Persuasive Cued Click Points (PCCP) with a supportive sound signature, a cued-recall graphical password technique. Users click on one point per image for a sequence of images and they are asked to select a sound signature corresponding to each click-point. In PCCP, the next image will be based on the previous click-point. PCCP provides greater security than Pass Points because the number of images increases the workload for attackers. The cued-click point application in this project can be used to enter into a private SMS area, where the SMSs from specified numbers are kept hidden from the Inbox of the ANDROID mobile. As the solution to image gallery attacks digital watermarking is used in the images. In addition to these, we propose to implement a CAPTCHA which ensures the user to be a human and not any machine. Thus additional security to spambot can be ensured.

Graphical Password; Persuasive Cued Click Points; Cued Click Point; Sound Signature; Digital Signature; CAPTCHA; ANDROID; SMS; Spambot

[1] S. Chiasson, R. Biddle, and P. van Oorschot, “A Second Look at the Usability of Click-Based Graphical Passwords,” Proc. ACM Symp. Usable Privacy and Security (SOUPS), July 2007.
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[3] S. Chiasson, A. Forget, E. Stobert, P. van Oorschot, and R. Biddle, “Multiple Password Interference in Text and Click-Based Graphical Passwords,” Proc. ACM Conf. Computer and Comm. Security (CCS), Nov. 2009.
[4] E. Stobert, A. Forget, S. Chiasson, P. van Oorschot, and R. Biddle, “Exploring Usability Effects of Increasing Security in Click-Based Graphical Passwords,” Proc. Ann. Computer Security Applications Conf. (ACSAC), 2010.
[5] S. Chiasson, A. Forget, R. Biddle, and P.C. van Oorschot, “User Interface Design Affects Security: Patterns in Click-Based Graphical Passwords,” Int’l J. Information Security, vol. 8, no. 6, pp. 387-398, 2009.
[6] S. Chiasson, P. van Oorschot, and R. Biddle, “Graphical Password Authentication Using Cued Click Points,” Proc. European Symp. Research in Computer Security (ESORICS), pp. 359-374, Sept. 2007.
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Anjumol P S and Amina Beevi A, "Implementation of Persuasive Cued Click Points (PCCP) with the Integration of CAPTCHA and Sound Signature," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.116-119, 2015.

Mobile ad-hoc networks (MANETs) assume that mobile nodes volunteer collaborates in order to work appropriately. This Cooperation is a cost-intensive activity and some nodes can refuse to cooperate, leading to selfish node behaviour. Thus, the complete network performance could be seriously affected. The use of watchdogs is a well-known mechanism to detect selfish nodes. However, the detection process performed by watchdogs can fail, generating false positives and false negatives that can induce to wrong operations. Moreover, relying on local watchdogs alone can lead to poor performance when detecting selfish nodes, in term of precision and speed. This is especially important on networks with sporadic contacts, such as Delay Tolerant Networks (DTNs), where sometimes watchdog’s lack of enough time or information to detect the selfish nodes. Thus, Collaborative Contact-based Watchdog (CoCoWa) is proposed as a collaborative approach based on the diffusion of local selfish nodes awareness when a contact occurs, so that information about selfish nodes is quickly propagated. As shown in the paper, this collaborative approach will make the selfish node as trusted node by using AODV protocol and provide better security.

CoCoWa Architecture, Watchdog, Delay Tolerant Networks, Trust model, Security,Routing Protocol, AODV

[1] Morigere Subramanya Bhat, Shwetha .D, Manjunath .D and DevarajuJ.T.”Scenario Based Study of on denmand Reactive Routing Protocol for IEEE-802.11 and 802.15.4 Standards” ISSN: 2249-57 Vol 1(2), 128-135 published in October-November 2011.
[2] Ashish Bagwari,Raman Jee,Pankaj Joshi,Sourabh Bisht “Performance of AODV Routing Protocol with increasing the MANET Nodes and its effects on QoS of Mobile Ad hoc Networks” International Conference on Communication Systems and Network Technologies 2012.
[3] Xu Huang, Muhammad Ahmed and Dharmendra Sharma”Protecting from Inside Attacks in Wireless Sensor Networks” Ninth IEEE International Conference on Dependable, Autonomic and Secure Computing 2011.
[4]. Mishra and K. M. Nadkarni, “Security in wireless ad hoc networks – A Survey”, in TheHandbook of Ad Hoc Wireless Networks, M.Ilyas, Ed. Boca Raton: CRC Press, 2002, pp.30.1-30.51.
[5]. P. Papadimitratos and Z. Hass, “Securing Mobile Ad Hoc Networks”, in The Handbook of Ad Hoc Wireless Networks, M. Ilyas, Ed. Boca Raton: CRC Press, 2002, pp. 31.1-31.17
[6] S. Marti, T. Giuli, K. Lai, and M. Baker, ―Mitigating Routing Misbehavior in Mobile Ad Hoc Networks, Proc. MobiCom, Aug. 2000.
[7] J.-S. Lee, “A Petri net design of command filters for semiautonomous mobile sensor networks,” IEEE Trans. Ind. Electron., vol. 55, no. 4, pp.1835–1841, Apr. 2008.
[8] J. Parker, J. Undercoffer, J. Pinkston, and A. Joshi, “On intrusion detection and response for mobile ad hoc networks,” in Proc. IEEE Int. Conf.Perform., Computer, Commun, pp. 747–752, 2004.
[9] A. Patcha and A. Mishra, “Collaborative security architecture for black hole attack prevention in mobile ad hoc networks,” in Proc. Radio Wireless Conf, pp. 75–78, 2003.
[10] Sergio Marti, T. J. Giuli, Kevin Lai, and Mary Baker. Mitigating routing misbehavior in mobile ad hoc networks. In Mo-biCom '00: Proceedings of the 6th annual international conference on Mobile computing and networking, pages 255_265, New York, NY, USA, 2000. ACM.
[11] F. Kargl, A. Klenk, M. Weber, and S. Schlott, “Sensors for detection of misbehaving nodes in MANETs,” in Proc. Detection Intrusions Malware Vulnerability Assessment, pp. 83–97, 2004.

[12] Q. Li, S. Zhu, and G. Cao, “Routing in socially selfish delay tolerant networks,” in Proc. IEEE Conf. Comput. Commun, pp. 857–865, 2010.
[13] Y. Li, G. Su, D. Wu, D. Jin, L. Su, and L. Zeng, “The impact of node selfishness on multicasting in delay tolerant networks,” IEEE Trans. Veh. Technol., vol. 60, no. 5, pp. 2224–2238, Jun. 2011.
[14] M. Mahmoud and X. Shen, “ESIP: Secure incentive protocol with limited use of public-key cryptography for multihop wireless networks,” IEEE Trans. Mobile Comput., vol. 10, no. 7, pp. 997–1010, Jul. 2011.

G.Satyavathy and P. Anitha, "A Collaborative Contact-Based Watchdog CoCoWa for Detecting Selfish Nodes with Trust Model," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.120-123, 2015.

Network security is a complicated subject, historically only tackled by well-trained and experienced experts. However, as more and more people become “wired”, an increasing number of people need to understand the basics of security in a networked world. This document was written with the basic computer user and information systems manager in mind, explaining the concepts needed to read through the hype in the marketplace and understand risks and how to deal with them. Some history of networking is included, as well as an introduction to TCP/IP and internet working. We go on to consider risk management, network threats, firewalls, and more special-purpose secure networking devices. This is not intended to be a reference, nor is it a document describing how to accomplish specific functionality. It is hoped that the people will have a wider perspective on security in general, and better understand how to reduce and manage risk personally, at home, and in the workplace.

Network Security, TCP/IP, Cryptography, UUCP, Firewall, Denial of Service, UDP

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[6]. M. Curtin, “Snake Oil Warning Signs: Encryption Software to Avoid”, USENET
[7]. Abhinav Gupta, Prabhdeep Singh, “Improving the Performance of Mobile Wireless Sensor Networks Using Modified DBSCAN”, International Journal of Computer Sciences and Engineering (IJCSE), Volume 3, Issue 8, August 2015.

Abhinav V. Deshpande, "Introduction to Network Security," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.124-134, 2015.

Huge amounts of data are required to build internet search engines and therefore large number of machines to process this entire data. The Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of machines. The Hadoop having two modules 1. Hadoop distributed file system and 2. Map Reduce. The Hadoop distributed file system is different from the local normal file system. The HDFS can be implemented as single node cluster and multi node cluster. The large datasets are processed more efficiently by the multi node clusters. By using the hive query language on the Hadoop and increasing number of nodes the data will be processed fastest than with the fewer nodes.

Big Data, HDFS, Map Reduce, Hive,Join

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[3] Liu Liu, Jiangtao Yin, Lixin Gao, “Efficient Social Network Data Query Processing on MapReduce” ACM August 16, 2013.
[4] Stephen Kaisler, Frank Armour, J. Alberto Espinosa, William Money, “Big Data: Issues and Challenges Moving Forward” 1530-1605/12, Jan 2013.
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[8] http://hashprompt.blogspot.in/2014/06/multi-node-hadoop-cluster-on-ubuntu-1404.html, 7 April,2015

A.Vinay Kumar and A. Madhuri , "Query Optimization of Big Data Using Hive," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.135-139, 2015.

Data mining in the stream data handles quality and data analysis using extremely large and infinite amount of data and disk or memory with limited volume[2]. In such traditional transaction environment it is impossible to perform frequent items mining because it requires analyzing which item is a frequent one to continuously incoming stream data and which is probable to become a frequent item. This paper analyze a way to predict frequent items using linear regression model[5] to the continuously incoming one dimensional stream data like the time series data. By establishing the regression model from the stream data, it may be used as a prediction model to uncertain items. The proposing way will exhibit its effectiveness through experiment in stream data.

Data mining, Time Series Data, Regression Techniques, Stream Data

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[4]C. Giannella, J. Han, J. Pei, X. Yan, and P. S. Yu, :Mining Frequent Patterns in Data Streams at Multiple Time Granularities, In H. Kargupta, A. Joshi, K. Sivakumar, and Y.Yeshar(eds.), Next Generation Data Mining, AAAI/MIT, 2003.
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Pinki Sagar , "Regression Based Data Mining Techniques for Frequent Data Stream," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.140-143, 2015.

For mining association rule different algorithms are used such as Apriori, tree based algorithm which take too much computerized time to accomplish all the frequent items. These obstacles are eliminated by using GA and MLMS and also improving the performance. In this method used a multi level minimum support of data table as 0 and 1. Genetic algorithm is indiscriminate search algorithm model based on natural selection, works in an iteration manner and is very adequate in large amount of data. Genetic algorithm is implemented in Hadoop to reduce computation cost. Hadoop supports for manipulating large data and operate them in parallel manner for better performance. The optimal frequent items are access that satisfies fitness, support and confidence.

Association Rule, Apriori algorithm, Genetic algorithm, Hadoop ,MapReduce

[1] Mohammed Al-Maolegi, Bassam Arkok, “An Improved Apriori Algorithm for Association Rules”, Int. Jounal on Natural Language Computing, Volume-03, No.1, Page No (21-29), February 2014.
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[3] Nikky Suryawanshi Rai, Susheel Jain, Anurag Jain, “Mining Interseting Positive And Negative Association Rule Based On Improved Genetic Algorithm”, Int. Journal of Advanced Computer Science and Applications, Volume-05, No.1, Page No (160-165), 2014.
[4] D. Kerana Hanirex and K.P. Kaliyamurthie, “Mining Frequent Itemsets Using Genetic Algorithm”, Middle-East Journal of Scientific Research, 19 (6), Page No (807-810), 2014.
[5] Pratibha Bajpai, Dr. Manoj Kumar, “Genetic Algorithm- an Approach to Solve Global Optimization Problems”, Int. Jounal of Computer Science and Engineering, Volume-01, No-03, Page No (199-206).
[6] Apache Hadoop, http://hadoop.apache.org/ , Monday, April 6, 2015.
[7] Stephen Kaisler, Frank Armour, J. Alberto Espinosa, William Money, “Big Data: Issues and Challenges Moving Forward”, 46th Hawaii International Conference on System Sciences, Page No (995-1004), 2013.
[8] Srinath Parera, Thilina Gunarathane, “Hadoop MapReduce Cook Book”, [PACKT] publishing, ISBN: 9781849517287, Page No (5-115), Jan 2013.
[9] Apache HBase, http://hbase.apache.org/ , Friday, July 10, 2015.

Arsha Sultana and S. Madhavi , "Generating Optimized Association Rule for Big Data Using GA and MLMS," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.144-148, 2015.

A decade back television, radio, newspaper, magazines, and billboards were among the major channels that placed advertisements, but now the trend has changed. A completely new era of marketing has opened up. The advancement of the mobiles, smart phones, Internet and the World Wide Web (WWW) has incredibly changed the lives of people. Using the Internet and the WWW, users are able to express their information requests, navigate specific websites and perform e-commerce transactions. This paper explores innovative marketing strategies which fashion companies are employing to outshine in today’s cut-throat market competition.

Fashion Marketing, Smart Phones, Digital Marketing

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[3] Hanley, M. and Becker, M. (2008), “Cell phone usage and advertising acceptance among college students: a four-year analysis”, International Journal of Mobile Marketing, Vol. 3 No. 1, pp. 67-80.
[4] Jin, H.C. and Villegas, J. (2008), “Mobile phone users’ behaviors: the motivation factors of the mobile phone user”, International Journal of Mobile Marketing, Vol. 3 No. 2, pp. 4-14.
[5] Grant, I. and O’Donohoe, S. (2007), “Why young consumers are not open to mobile marketing communication”, International Journal of Advertising, Vol. 26 No. 2, pp. 223-46.
[6] Heinonen, K. and Strandvik, T. (2007), “Consumer responsiveness to mobile marketing”, International Journal of Mobile Communications, Vol. 5 No. 6, pp. 603-17.
[7] Roach, G. (2009), “Consumer perceptions of mobile phone marketing: a direct marketing innovation”, Direct Marketing: An International Journal, Vol. 3 No. 2, pp. 124-38.
[8] Barutc¸u, S. (2007), “Attitudes towards mobile marketing tools: a study of Turkish consumers”, Journal of Targeting, Measurement and Analysis for Marketing, Vol. 16 No. 1, pp. 26-38.
[9] Megdadi, Y.A.A. and Nusair, T.T. (2011), “Shopping consumer attitudes toward mobile marketing: a case study among Jordanian users”, International Journal of Marketing Studies, Vol. 3 No. 2, pp. 53-63
[10] Park, K. and Yang, S. (2006), “The moderating role of consumer trust and experiences: value driven usage of mobile technology”, International Journal of Mobile Marketing, Vol. 1 No. 2, pp. 24-32.
[11] Basheer, A. and Ibrahim, A.M.A. (2010), “Mobile marketing: examining the impact of trust, privacy concern and consumers’ attitudes on intention to purchase”, International Journal of Business and Management, Vol. 5 No. 3, pp. 28-41.
[12] Zhang, J. and Mao, E. (2008), “Understanding the acceptance of mobile SMS advertising among young Chinese consumers”, Psychology & Marketing, Vol. 25 No. 8, pp. 787-805.
[13] Hsu, C.L., Hsi-Peng, L. and Hsu, H. (2007), “Adoption of the mobile internet: an empirical study of multimedia message service (MMS)”, Omega, Vol. 35 No. 6, pp. 715-26.
[14] Mort, S. and Drennan, J. (2005), “Marketing m-services: establishing a usage benefit typology related to mobile user characteristics”, Journal of Database Marketing & Customer Strategy Management, Vol. 12 No. 4, pp. 327-41.
[15] Bauer, H.H., Reichardt, T., Barnes, S.J. and Neumann, M.M. (2005), “Driving consumer acceptance of mobile marketing: a theoretical framework and empirical study”, Journal of Electronic Commerce Research, Vol. 6 No. 3, pp. 181-91.
[16] Karjaluoto, H., Lehto, H., Leppa¨niemi, M. and Jayawardhena, C. (2008), “Exploring gender influence on customer’s intention to engage permission-based mobile marketing”, Electronic Markets, Vol. 18 No. 3, pp. 242-59.
[17] Barnes, S.J. and Scornavacca, E. (2004), “Mobile marketing: the role of permission and acceptance”, International Journal of Mobile Communications, Vol. 2 No. 2, pp. 128-39.
[18] Jayawardhena, C., Kuckertz, A., Karjaluoto, H. and Kautonen, T. (2009), “Antecedents to permission based mobile marketing: an initial examination”, European Journal of Marketing, Vol. 43 Nos 3/4, pp. 473-99.
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[21] Banerjee, S. and Dholakia, R.R. (2008), “Mobile advertising: does location-based advertising work?”, International Journal of Mobile Marketing, Vol. 3 No. 2, pp. 68-74.
[22] “Apparel Drives US Retail Ecommerce Sales Growth”; http://www.emarketer.com/newsroom/index.php/apparel-drives-retail-ecommerce-sales-growth.
[23] “The Top 7 Online Marketing Trends That Will Dominate 2014”; http://www.forbes.com/sites/jaysondemers/2013/09/17/the-top-7-online-marketing-trends-that-will-dominate-2014.
[24] “Digital Marketing Solutions for Fashion Labels”; http://www.slideshare.net/wilddreams/digital-marketing-solutions-for-faishion-labels.
[25] “Apparel Online Sales Statistics”; http://ecommerce-news.internetretailer.com/retailing/Apparel-Online-Sales-Statistics.
[26] “Smart fashion: the luxury brands embracing technology”; http://www.standard.co.uk/lifestyle/esmagazine/smart-fashion-the-luxury-brands-embracing-technology-9755935.html.
[27] “10 awesome innovations changing the future of fashion”; http://www.treehugger.com/sustainable-fashion/10-awesome-innovations-changing-future-fashion.html.
[28] “A Mini-Skirt Controlled By Your iPhone: Wearable Tech Lights Up NYFW”; http://www.ibtimes.com/mini-skirt-controlled-your-iphone-wearable-tech-lights-nyfw-video-1555426.
[29] “Apple, Burberry show off iPhone 5S photo, video technology”; http://www.cnet.com/news/apple-burberry-show-off-iphone-5s-photo-video-technology.
[30] “The digital hard-sell behind Burberry's traditional image”; http://www.theguardian.com/business/2013/jul/10/digital-burberry-image.
[31] “Burberry unveils monogramming visualizer for the holidays”; http://www.luxurydaily.com/burberry-unveils-monogramming-visualizer-for-the-holidays.
[32] “Neclumi: The Necklace That Responds to Movement and Sound”; http://www.fastcolabs.com/3037871/neclumi-the-necklace-that-responds-to-movement-and-soundOther references:-
[33] “Avatar-Based Marketing”; https://vhil.stanford.edu/news/2006/hbr-avatar-based-marketing.pdf
[34] Kaplan, Andreas M. and Michael Haenlein (2010), "Users of the world, unite! The challenges and opportunities of social media," Business Horizons, 53 (1), 59 - 68
[35] “The World's Top 10 Most Innovative Companies Of 2015 In Styl”; http://www.fastcompany.com/3041666/most-innovative-companies-2015/the-worlds-top-10-most-innovative-companies-of-2015-in-style.
[36] “The 13 Most Innovative Fashion Companies”; http://www.racked.com/2013/2/12/7687823/all-the-retail-companies-who-made-fast-companys-most-innovative-list.
[37] “NASTY GAL, MODCLOTH, STYLESEAT, SCIENCE, SNAPETTE, BIRCHBOX, AHALIFE”; http://www.fastcompany.com/most-innovative-companies/2013/nasty-gal-modcloth-styleseat-science-snapette-birchbox-ahalife.
[38] “SPEED THROUGH THE STORE”; http://www.fastcompany.com/most-innovative-companies/2013/target.
[39] “The World’s Top 10 Most Innovative Companies”; http://www.decodedfashion.com/fashion-tech-daily-the-worlds-top-10-most-innovative-companies-in-style-a-mini-skirt-controlled-by-your-iphone-ondon-fashion-week-arrives-us-tech-startups-an-endang/
[40] “Betty Boop Steps Into the 21st Century With Fashion App”; http://www.voice-online.co.uk/article/betty-boop-steps-21st-century-fashion-app.

Abhinav Garg and N.J.Rajaram, "Ingenious Fashion Marketing Comprehending Technology," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.149-156, 2015.

Recording the environmental conditions continuously is a mandatory process in industries. The tracing of the environment parameters leads to the production to continue in a hassle free manner. Using the analogue meters and recording it manually is always a tedious process and non-accurate. Replacing the whole process in a digital manner along with a sophisticated software reduce the time, effort and also produces more accurate results. Embedding sensors, transceivers with microcontroller with reduced power consumption and decreases the tendency of the damage occurrence in the production equipment. Precise monitoring with wireless sensors from a remote site is developed in this project.

Wireless communication, Transceivers, sensors, IoT, SPI, UART

[1] Sudhindra F, Annarao. S. J, Vani R. M, P.V. Hungund, “A Low Cost Short Range Wireless Embedded System for Multiple Parameter Control”, IJRET, Volume: 03 Issue: 02 | Feb-2014
[2] Lecture 6 – Introduction to the ATmega328 and Ardunio.
[3] Temperature and humidity monitoring systems for fixed storage areas Technical supplement to WHO Technical Report Series, No. 961, 2011/
[4] G. V. Satyanarayana, SD.Mazaruddin, Wireless Sensor Based Remote Monitoring System for Agriculture Using ZigBee and GPS.
[5] A. Goswami, T. Bezboruah and K.C. Sarma Design of An Embedded System For Monitoring and Controlling Temperature and Light.
[6] Circuits,www.engineersgarage.com,Mon,July 6,15
[7] Interfacing, www.circuitstoday.com,Tuesday,Aug11,15
[8] Wireless, www.hobbylist.com,Monday,August 17 2015

P. Usha Sri and B.Narasimha Swamy , "Wireless Atmospheric Data Logger for a Sensor Network," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.157-161, 2015.