TOP CITED UBICOMPUTING ARTICLES IN 2013 - International Journal of Ubiquitous Computing (IJU)

TOP CITED UBICOMPUTING ARTICLES IN 2013

INTERNATIONAL JOURNAL OF UBICOMP (IJU) ISSN: 0975 - 8992 (ONLINE) ISSN 0976 - 2213(PRINT)

http://www.airccse.org/journal/iju/index.html

Citation Count – 26 HMR LOG ANALYZER: ANALYZE WEB APPLICATION LOGS OVER HADOOP MAPREDUCE Sayalee Narkhede and Tripti Baraskar, University of Pune, Pune

ABSTRACT In today’s Internet world, log file analysis is becoming a necessary task for analyzing the customer’s behavior in order to improve advertising and sales as well as for datasets like environment, medical, banking system it is important to analyze the log data to get required knowledge from it. Web mining is the process of discovering the knowledge from the web data. Log files are getting generated very fast at the rate of 1-10 Mb/s per machine, a single data center can generate tens of terabytes of log data in a day. These datasets are huge. In order to analyze such large datasets we need parallel processing system and reliable data storage mechanism. Virtual database system is an effective solution for integrating the data but it becomes inefficient for large datasets. The Hadoop framework provides reliable data storage by Hadoop Distributed File System and MapReduce programming model which is a parallel processing system for large datasets. Hadoop distributed file system breaks up input data and sends fractions of the original data to several machines in hadoop cluster to hold blocks of data. This mechanism helps to process log data in parallel using all the machines in the hadoop cluster and computes result efficiently. The dominant approach provided by hadoop to “Store first query later”, loads the data to the Hadoop Distributed File System and then executes queries written in Pig Latin. This approach reduces the response time as well as the load on to the end system. This paper proposes a log analysis system using Hadoop MapReduce which will provide accurate results in minimum response time.

KEYWORDS Hadoop, MapReduce, Log Files, Parallel Processing, Hadoop Distributed File System.

FOR MORE DETAILS:

http://airccse.org/journal/iju/papers/4313iju04.pdf

VOLUME LINK: http://www.airccse.org/journal/iju/vol4.html

REFERENCES [1] S.Sathya Prof. M.Victor Jose, (2011) “Application of Hadoop MapReduce Technique to Virtual Database System Design”, International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), pp. 892-896.

[2] Yulai Yuan, Yongwei Wu_, Xiao Feng, Jing Li, Guangwen Yang, Weimin Zheng, (2010) “VDBMR: MapReduce- based distributed data integration using virtual database”, Future Generation Computer Systems, vol. 26, pp. 1418-1425.

[3] Jeffrey Dean and Sanjay Ghemawat., (2004) “MapReduce: Simplified Data Processing on Large Clusters”, Google Research Publication.

[4] Konstantin Shvachko, Hairong Kuang, Sanjay Radia, Robert Chansler, (2010) “The Hadoop Distributed File System”, Mass Storage Systems and Technologies(MSST), Sunnyvale, California USA, vol. 10, pp. 1-10.

[5] C.Olston, B.Reed, U.Srivastava, R.Kumar, and A.Tomkins, (2008) “Pig latin: a not-so-foreign language for data processing”, ACM SIGMOD International conference on Management of data, pp. 1099– 1110.

[6] Tom White, (2009) “Hadoop: The Definitive Guide. O’Reilly”, Scbastopol, California.

[7] M.Zaharia, A.Konwinski, A.Joseph, Y.zatz, and I.Stoica, (2008) “Improving mapreduce performance in heterogeneous environments” OSDI’08: 8th USENIX Symposium on Operating Systems Design and Implementation.

[8] Mr. Yogesh Pingle, Vaibhav Kohli, Shruti Kamat, Nimesh Poladia, (2012)“Big Data Processing using Apache Hadoop in Cloud System”, National Conference on Emerging Trends in Engineering & Technology.

[9] Cooley R., Srivastava J., Mobasher B., (1997) “Web mining: informationa and pattern discovery on world wide web”, IEEE International conference on tools with artificial intelligence, pp. 558- 567.

[10] Liu Zhijing, Wang Bin, (2003) “Web mining research”, International conference on computational intelligence and multimedia applications, pp. 84-89.

[11] Yang, Q. and Zhang, H., (2003) “Web-Log Mining for predictive Caching”, IEEE Trans. Knowledge and Data Eng., 15( 4), pp. 1050-1053.

[12] P. Nithya, Dr. P. Sumathi, (2012) “A Survey on Web Usage Mining: Theory and Applications”, International Journal Computer Technology and Applications, Vol. 3, pp. 1625-1629.

[13] Andrew Pavlo, Erik Paulson, Alexander Rasin, Daniel J. Abadi, David J. DeWitt, Samuel Madden, Michael Stonebraker, (2009) ”A Comparison of Approaches to Large-Scale Data Analysis”, ACM SIGMOD’09.

[14] Gates et al., (2009) “Building a High-Level Dataflow System on top of Map-Reduce: The Pig Experience”, VLDB 2009, Section 4.

[15] LI Jing-min, HE Guo-hui, (2010) “Research of Distributed Database System Based on Hadoop”, IEEE International conference on Information Science and Engineering (ICISE), pp. 1417-1420.

[16] T. Hoff, (2008) “How Rackspace Now Uses MapReduce and Hadoop To Query Terabytes of Data”.

[17] Apache-Hadoop,http://Hadoop.apache.org

AUTHORS Sayalee Narkhede is a ME student of IT department in MIT-Pune under University of Pune. She has Received BE degree in Computer Engineering from AISSMS IOIT under University of Pune. Her research interest includes Distributed Systems, Cloud Computing.

Tripti Baraskar is an assistant professor in IT department of MIT- Pune. She has received her ME degree in Communication Controls and Networking from MIT Gwalior and BE degree from Oriental Institute of Science and Technology under RGTV. Her research interest includes Image Processing

Citation Count – 16

UBIQUITOUS HEALTHCARE MONITORING SYSTEM USING INTEGRATED TRIAXIAL ACCELEROMETER, SPO2 AND LOCATION SENSORS Ogunduyile O.O1, Zuva K2, Randle O.A1 and Zuva T1, 1

Tshwane University of Technology, South Africa and 2University of Botswana, Botswana

ABSTRACT Ubiquitous healthcare has become one of the prominent areas of research inorder to address the challenges encountered in healthcare environment. In contribution to this area, this study developed a system prototype that recommends diagonostic services based on physiological data collected in real time from a distant patient. The prototype uses WBAN body sensors to be worn by the individual and an android smart phone as a personal server. Physiological data is collected and uploaded to a Medical Health Server (MHS) via GPRS/internet to be analysed. Our implemented prototype monitors the activity, location and physiological data such as SpO2 and Heart Rate (HR) of the elderly and patients in rehabilitation. The uploaded information can be accessed in real time by medical practitioners through a web application.

KEYWORDS Android smart phone, Ubiquitous Healthcare, Web application server, Wireless Body Area Networks

FOR MORE DETAILS: http://airccse.org/journal/iju/papers/4213iju01.pdf VOLUME LINK:

http://www.airccse.org/journal/iju/vol4.html

REFERENCES [1]. Olugbara, O.O., Ojo, S.O. and Adigun, M.O. (2011). A Grid enabled framework for ubiquitous healthcare service provisioning, Advances in Grid Computing. Zoran Constantinescu (Ed.), InTech, Croatia, ISBN: 978953-307-301-9, 229-252.

[2]. Shaikh, A., Memon, M., Memon, M. and Misbahuddin, M. (2009). The Role of Service Oriented Architecture in Telemedicine Healthcare System. International Conference on Complex, Intelligent and Software Intensive Systems CISIS ‘09. IEEE, Fukuoka, Japan, 208 – 214.

[3]. Purwar, A., Jeong, D. and Chung W. (2007). Activity Monitoring from Real-Time Accelerometer data using sensor Network. International Conference on Control, Automation and Systems, COEX, Seoul Korea., 2402 – 2406.

[4]. Asad M.K., Pervez Z., Lee S and Lee Y.K (2011). Intelligent Healthcare Service Provisioning Using Ontology with Low – Level Sensory Data. KSII Transactions on Internet and Information Technology Systems 5(11). 2016 – 2033.

[5]. Sneha, S., Virginia, L. and Bick, M. (2010). Ubiquitous and Pervasive Computing in Healthcare. 15th Americas Conference on Information Systems. San Francisco, California (2010) [6]. Sneha, S and Varshney, U. (2006). Ubiquitous healthcare: a new frontier in e-health. Proceedings of Americas Conference on Information Systems (2006), 319.

[7]. Wang, H. Choi,H., Agoulmine, N., Deen, M.J. and Hong, J.W. (2010). Information-based sensor tasking wireless body area networks in U-health systems. CNSM 2010. 517 – 522.

[8]. Rotariu C and Manta V (2012). Wireless System for Remote Monitoring of Oxygen Saturation and Heart Rate. Proceedings of the Federated Conference on Computer Science and Information Systems pp. 193–196 [9]. Patil D.D., Mantri S.T., Himangi Milind Pande H.M., Wadhai V.M and Kharat M.U (2012). Feature Extraction Techniques for Mining ECG Signals in WBAN for Healthcare Applications. International Journal of Advances in Computing and Information Research (IJACIR). 1(1).

[10]. Latre, B., Braem, B., Moerman, I., Blondia, C. and Demeester, P. (2011). A survey on wireless body area networks. Journal of Wireless Networks, 17(1), 1 – 18.

[11]. Kim, S. (2002). Ubiquitous healthcare: the OnkoNet mobile agents architecture. Proceeding Workshop on Mobile Computing in Medicine, 105 – 118.

[12]. Jovanov, E., Milenkovic, A., Otto, C. and De Groen, P.C. (2005). A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. Journal of Neuro Engineering and Rehabilitation, 2005, 2(1), 6 [13]. Jung, J., Ha, K., Lee, J., Kim, Y. and Kim, D. (2008). Wireless body area network in a ubiquitous healthcare system for physiological signal monitoring and health consulting. International Journal of Signal Processing, Image Processing and Pattern Recognition, 1(1), 47 – 54.

[14]. Yick, J. Mukherjee, B. and Ghosal, D. (2008). Wireless sensor networks survey. Computer Networks, 52.

2292 2330.

[15]. Tapia, I.D., Rodriguez, S., Bajo, J., Corchado, J.M. and Garcia, O. (2010). Wireless sensor networks for data acquisition and information fusion: a case study. 13th IEEE International Conference on Information Fusion. Salamanca, Spain, 1 – 8.

[16]. Nikolidakis, S.A., Georgakakis, E., Giotsas, V., Vergados, D.D and Douligeris, C. (2010). Secure ubiquitous healthcare system based on IMS and the HL7 standards. Proceedings of the 3rd International Conference on Pervasive Technologies Related to Assistive Environments,(PETRA’10), Samos Greece, 42, 1 – 7.

[17]. Olugbara, O.O., Adigun, M.O., Ojo, S.O and Mudali, P. (2007). Utility grid computing and body area network as enabler for ubiquitous rural e-healthcare service provisioning. Proceedings of IEEE 9th International Conference on e-Health Networking, Application and Services, 19-22 June, 2007, Taipei, Taiwan. 202 – 207 [18]. Hao, Y. and Foster, R. (2008). Wireless body sensor networks for health-monitoring. Physiological Measurement, 29(11), 27 – 56.

[19]. Bouten, C., Koekkoek, K., Verduin, M., Kodde, R. and Janssen, J. (1997). A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Transactions on Biomedical Engineering, 44(3), 136 – 147.

[20]. Mendelson, M., Duckworth, R.J. and Comtois, G. (2006). A Wearable Reflectance Pulse Oximeter for Remote Physiological Monitoring. 2006 International Conference of the IEEE Engineering in Medicine and Biology Society (2006), 912 – 915.

[21]. Watthanawisuth, N., Lomas,L., Wisitsoraat, A. and Tuantranont, A. (2010). Wireless Wearable Pulse Oximeter for Health Monitoring using ZigBee Wireless Sensor Network. International conference on Electrical Engineering/Electronics and Information Technology (ECTI-CON), Chiang Mai, 575 – 579.

[22]. Morón, M.J., Luque J.R., Gómez-Jaime,A., Casilari, E. and Díaz-Estrella,A (2009). Prototyping of a remote monitoring system for a medical Personal Area Network using Python. 3rd International Conference on Pervasive Computing Technologies for Healthcare, 2009. PervasiveHealth 1 – 5.

Citation Count – 6

SIX DEGREES OF SEPARATION TO IMPROVE ROUTING IN OPPORTUNISTIC NETWORKS

Carlos O. Rolim1, Valderi R. Q. Leithardt1, Anubis G. Rossetto1, Tatiana F. M. dos Santos2, Adriano M. Souza2, Claudio F. R. Geyer 1, 1

Federal University of Rio Grande do Sul (UFRGS), Brazil and 2Federal University of Santa Maria (UFSM), Brazil

ABSTRACT Opportunistic Networks are able to exploit social behavior to create connectivity opportunities. This paradigm uses pair-wise contacts for routing messages between nodes. In this context we investigated if the “six degrees of separation” conjecture of small-world networks can be used as a basis to route messages in Opportunistic Networks. We propose a simple approach for routing that outperforms some popular protocols in simulations that are carried out with real world traces using ONE simulator. We conclude that static graph models are not suitable for underlay routing approaches in highly dynamic networks like Opportunistic Networks without taking account of temporal factors such as time, duration and frequency of previous encounters.

KEYWORDS Opportunistic Networks, Routing Algorithm, Data Dissemination, Complex Networks, Small-World Networks, Graphs

FOR MORE DETAILS: http://airccse.org/journal/iju/papers/4313iju02.pdf VOLUME LINK: http://www.airccse.org/journal/iju/vol4.html

REFERENCES [1] S. K. Das, “Pervasive computing vs. cyber-physical systems: A perspective from smart environments,” in 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, 2012, pp. 105– 105.

[2] M. Conti, S. Giordano, M. May, and A. Passarella, “From opportunistic networks to opportunistic computing,” IEEE Communications Magazine, vol. 48, no. 9, pp. 126–139, Sep. 2010.

[3] C. Boldrini, M. Conti, and A. Passarella, “Design and performance evaluation of ContentPlace, a socialaware data dissemination system for opportunistic networks,” Computer Networks, vol. 54, no. 4, pp. 589–604, Mar. 2010.

[4] M. Conti and M. Kumar, “Opportunities in Opportunistic Computing,” Computer, vol. 43, no. 1, pp. 42–50, Jan. 2010.

[5] S. Milgram and J. Travers, “The Small World Problem - An Experimental Study of the Small World Problem,” Psychology Today, vol. 2, no. 4, pp. 60–67, 1967.

[6] D. Karamshuk, C. Boldrini, M. Conti, and A. Passarella, “Human mobility models for opportunistic networks,” IEEE Communications Magazine, vol. 49, no. 12, pp. 157–165, Dec. 2011.

[7] H. Anh Nguyen and S. Giordano, “Routing in Opportunistic Networks,” in Ubiquitous Developments in Ambient Computing and Intelligence, K. Curran, Ed. IGI Global, 2011, pp. 179– 193.

[8] P. Hui, “People are the network: experimental design and evaluation of social-based forwarding algorithms,” Cambridge, 2008.

[9] H. A. Nguyen and S. Giordano, “Context information prediction for social-based routing in opportunistic networks,” Ad Hoc Networks, vol. 10, no. 8, pp. 1557–1569, Nov. 2012.

[10] C. Boldrini, J. Y. Le Boudec, A. Chaintreau, and M. Conti, “Deliverable 2.2: Final specification of forwarding paradigms in Haggle,” 2008.

[11] D. J. Watts and S. H. Strogatz, “Collective dynamics of ‘small-world’ networks.,” Nature, vol. 393, no.

6684, pp. 440–2, Jun. 1998.

[12] B. J. Prettejohn, M. J. Berryman, and M. D. McDonnell, “Methods for generating complex networks with selected structural properties for simulations: a review and tutorial for neuroscientists.,” Frontiers in computational neuroscience, vol. 5, p. 11, Jan. 2011.

[13] D. Simard, L. Nadeau, and H. Kröger, “Fastest learning in small-world neural networks,” Physics Letters A, vol. 336, no. 1, pp. 8–15, Feb. 2005.

[14] M. Orlinski and N. Filer, “The rise and fall of spatio-temporal clusters in mobile ad hoc networks,” Ad Hoc Networks, Mar. 2013

Citation Count – 5

ACCELERATION DETECTION OF LARGE (PROBABLY) PRIME NUMBERS Dragan Vidakovic1, Olivera Nikolic1 and Dusko Parezanovic2, 1Singidunum University, Belgrade, Serbia and 2 INFOMAT Ivanjica, Serbia

ABSTRACT In order to avoid unnecessary applications of Miller-Rabin algorithm to the number in question, we resort to trial division by a few initial prime numbers, since such a division take less time. How far we should go with such a division is the that we are trying to answer in this paper?For the theory of the matter is fully resolved. However, that in practice we do not have much use.Therefore, we present a solution that is probably irrelevant to theorists, but it is very useful to people who have spent many nights to produce large (probably) prime numbers using its own software.

KEYWORDS Kryptography, Digital Signatures, RSA, Miller-Rabin, (Large, probably) Prime Numbers

FOR MORE DETAILS: http://airccse.org/journal/iju/papers/4113iju01.pdf VOLUME LINK: http://www.airccse.org/journal/iju/vol4.html

REFERENCES [1] D. Basin, J. Doser, and T. Lodderstedt,.”Model Driven Security: from UML Models to Access Control Infrastructures”, Interactive Objects Software GmbH Freiburg, September 2005.

[2] SecureUML: A UML-Based Modeling Language for Model-Driven Security Torsten Lodderstedt, David Basin, and Jürgen Doser, Institute for Computer Science, University of Freiburg, Germany, 2005.

[3] D. Vidakovic, D. Simic :A Novel Approach to Building Secure Systems, Second International Conference on Availability, Reliability and Security, In 1th IEEE International Workshop on Secure Software Engineering (SecSE 2007), Vienna, 2007., Austria, pp 1074-1081 [4] A. Menezes, P.C. van Oorschot, S. Vanstone, Handbook of Applied Cryptography, CRC Press, New York, 1997.

[5] D. Vidakovic, “Analysis and implementation of asymmetric algorithms for data secrecy and integrity protection”, Master Thesis (mentor J. Golic), Faculty of Electrical Engineering, Belgrade, Serbia, 1999.