Archive

Abstract:

The oxygen reduction reaction (ORR) in proton exchange membrane fuel cell (PEMFC) having sluggish kinetics at cathode side requires highly active and low cost catalyst. Conventionally, platinum (Pt) is considered to be the most feasible and active catalyst for ORR at cathode, however,it’s far expensive to meet the demand for commercialization. Herein novel non platinum group metal (N-PGM) nickel (Ni) nanotubes were prepared by using solvothermal method using transition metal precursor forming Ni ZeoliticImidazolateFramework (Ni/ZIF). Ni nanotubes were obtained after carbonizing Ni/ZIF at 850oC under inert nitrogen atmosphere. The morphology and motif were extensively studied by conducting XRD and SEM. The electro-catalytic performance of Pt/C catalyst, pristine Ni/ZIF and Ni nanotubes were investigated by Linear Sweep Voltammetry (LSV) performed with Rotating Disk Electrode (RDE) in alkaline medium. The Ni/ZIF shows a current density of -1.4 mA/cm2and Ni nanotubes depicts current density of -2.7mA.cm-2 and an over potential of -0.27V Vs Saturated Calomel Electrode (SCE). RDE Results were obtained at 400, 800, 1200 and 1600 rpm in 0.1M KOH solution. The evaluation shows that Ni nanotubes own extraordinary electro-catalytic behavior towards ORR activity and Ni nanotubes has potential to be used for PEMFC application.

Keywords:

Ni ZeoliticImidazolateFramework(Ni/ZIF),Ni nanotubes,Oxygen Reduction Reaction,Linear Sweep Voltammetry,

Refference:

I.Cheng, F., & Chen, J. (2012). Metal–air batteries: from oxygen reduction electrochemistry to cathode catalysts.Chemical Society Reviews,41(6), 2172-2192.

II.Fekete, M., Hocking, R. K., Chang, S. L., Italiano, C., Patti, A. F., Arena, F., &Spiccia, L. (2013). Highly active screen-printed electrocatalysts for water oxidation based on β-manganese oxide.Energy & Environmental Science,6(7), 2222-2232.

III.Gorlin, Y., & Jaramillo, T. F. (2010). A bifunctional nonprecious metal catalyst for oxygen reduction and water oxidation.Journal of the American Chemical Society,132(39), 13612-13614.

IV.Haoran, Y., Lifang, D., Tao, L., & Yong, C. (2014). Hydrothermal synthesis of nanostructured manganese oxide as cathodic catalyst in a microbial fuel cell fed with leachate.The Scientific World Journal,2014.

V.He, G., Qiao, M., Li, W., Lu, Y., Zhao, T., Zou, R., &Parkin, I. P. (2017). S, N‐Co‐Doped Graphene‐Nickel Cobalt Sulfide Aerogel: Improved Energy Storage and Electrocatalytic Performance.Advanced Science,4(1), 1600214.

VI.Iyer, A., Del-Pilar, J., King’ondu, C. K., Kissel, E., Garces, H. F., Huang, H., &Suib, S. L. (2012). Water oxidation catalysis using amorphous manganese oxides, octahedral molecular sieves (OMS-2), and octahedral layered (OL-1) manganese oxide structures.The Journal of Physical Chemistry C,116(10), 6474-6483.

VII.Kjaergaard, C. H., Rossmeisl, J., &Nørskov, J. K. (2010). Enzymatic versus inorganic oxygen reduction catalysts: Comparison of the energy levels in a free-energy scheme.Inorganic chemistry,49(8), 3567-3572.

VIII.Kundu, S., Nagaiah, T. C., Xia, W., Wang, Y., Dommele, S. V., Bitter, J. H., &Muhler, M. (2009). Electrocatalytic activity and stability of nitrogen-containing carbon nanotubes in the oxygen reduction reaction.The Journal of Physical Chemistry C,113(32), 14302-14310.

IX.Liao, L., Zhang, Q., Su, Z., Zhao, Z., Wang, Y., Li, Y., &Cai, X. (2014). Efficient solar water-splitting using a nanocrystallineCoOphotocatalyst.Nature nanotechnology,9(1), 69.

X.Mukerjee, S., &Srinivasan, S. (1993). Enhanced electrocatalysis of oxygen reduction on platinum alloys in proton exchange membrane fuel cells.Journal of Electroanalytical Chemistry,357(1-2), 201-224.

XI.Shi, X., Iqbal, N., Kunwar, S. S., Wahab, G., Kasat, H. A., &Kannan, A. M. (2018). PtCo@ NCNTs cathode catalyst using ZIF-67 for proton exchange membrane fuel cell.International Journal of Hydrogen Energy,43(6), 3520-3526.

XII.Shinozaki, K., Zack, J. W., Richards, R. M., Pivovar, B. S., &Kocha, S. S. (2015). Oxygen reduction reaction measurements on platinum electrocatalystsutilizing rotating disk electrode technique I. Impact of impurities, measurement protocols and applied corrections.Journal of The Electrochemical Society,162(10), F1144-F115

XIII.Su, B., Hatay, I., Trojánek, A., Samec, Z., Khoury, T., Gros, C. P.,&Girault, H. H. (2010). Molecular electrocatalysis for oxygen reduction by cobalt porphyrins adsorbed at liquid/liquid interfaces.Journal of the American Chemical Society,132(8), 2655-2662.

XIV.Song, E., Shi, C., & Anson, F. C. (1998). Comparison of the behavior of several cobalt porphyrins as electrocatalysts for the reduction of O2 at graphite electrodes.Langmuir,14(15), 4315-4321.

XV.Xia, B. Y., Yan, Y., Li, N., Wu, H. B., Lou, X. W. D., & Wang, X. (2016). A metal–organic framework-derived bifunctional oxygen electrocatalyst.Nature Energy,1(1), 15006.

XVI.Yang, J., Sun, H., Liang, H., Ji, H., Song, L., Gao, C., &Xu, H. (2016). A highly efficient metal‐free oxygen reduction electrocatalyst assembled from carbon nanotubes and graphene.Advanced Materials,28(23), 4606-4613.

XVII.Zhang, W., Shaikh, A. U., Tsui, E. Y., &Swager, T. M. (2009). Cobalt porphyrin functionalized carbon nanotubes for oxygen reduction.Chemistry of Materials,21(14), 3234-3241.

XVIII.Zhang, X., Chen, Y., Wang, J., &Zhong, Q. (2016). Nitrogen and fluorine dual‐doped carbon black as an efficient cathode catalyst for oxygen reduction reaction in neutral medium.ChemistrySelect,1(4), 696-702.

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Abstract:

The Porous silica nanoparticles deposited on the glass as well as bare silicon wafer substrate to obtain super hydrophilicity and antireflectivity. The coating is performed by using aerosol impact deposition system using silane, air and helium as precursor gases. The desired coating thickness over the substrate surface is achieved by tuning the RF power, pressures ratio of reaction to deposition chamber and maneuvering of silane flow rate, helium and air mixture. Scanning electron microscopy reveals the particle size of 12.6 nm, whereas, atomic force microscopy (AFM) is deployed to study the coated film surface topology. This indicates outstanding antifogging and super-hydrophilic properties due to surface roughness and nano-porosity. Moreover, the coated surface graded index increases the transmissivity from 90% to 99.2%. Such enhancements are much favorable for the solar PV applications.

Keywords:

Nanoparticles, Antireflectivity,erosol deposition,Solar PV,SEM,AFM,

Refference:

I.Cebeci, F.Ç., Wu, Z., Zhai, L., Cohen, R.E. and Rubner, M.F., 2006. Nanoporosity-driven superhydrophilicity: a means to create multifunctional antifogging coatings. Langmuir, 22(6), pp.2856-2862.

II.Chen, D., 2001. Anti-reflection (AR) coatings made by sol–gel processes: a review. Solar Energy Materials and Solar Cells, 68(3-4), pp.313-336.

III.Deubener, J., Helsch, G., Moiseev, A. and Bornhöft, H., 2009. Glasses for solar energy conversion systems. Journal of the European Ceramic Society, 29(7), pp.1203-1210.

IV.Eshaghi, A., Aghaei, A.A., Zabolian, H., Jannesari, M.O.H.A.M.M.A.D. and Firoozifar, A.L.I.R.E.Z.A., 2013. Transparent superhydrophilic SiO2/TiO2/SiO2 tri-layer nanostructured antifogging thin film. Ceram–Silikaty, 57, pp.210-214.

V.Faustini, M, Nicole, L., Boissi ere, C., Innocenzi, P., Sanchez, C., Grosso, D., 2010. Hydrophobic, antireflective, self-Cleaning, and antifogging sol–gel coatings: an example of multifunctional nano structured materials for photovoltaic cells, Chemistry of Materials 22 (2010) 4406–4413.

VI.Granqvist, C.G., 2007. Transparent conductors as solar energy materials: A panoramic review. Solar energy materials and solar cells, 91(17), pp.1529-1598

VII.Guillemot, F., Brunet-Bruneau, A., Bourgeat-Lami, E., Gacoin, T., Barthel, E. andBoilot, J.P., 2010. Latex-templated silica films: tailoring porosity to get a stable low-refractive index. Chemistry of Materials, 22(9), pp.2822-2828.

VIII.Hassan, A. H., Rahoma, U. A., Elminir, H. K., and Fathy, A. M., 2005, “Effect of airborne dust concentration on the performance of PV modules,” J AstronSoc Egypt, 13, pp. 24-38.

IX.Karasiński, P., Jaglarz, J., Reben, M., Skoczek, E. and Mazur, J., 2011. Porous silica xerogel films as antireflective coatings–Fabrication and characterization. Optical Materials, 13(12), pp.1989-1994.

X.Lee, S., Cho, L. S., Lee, J. H., Kim, D. H., Kim, D. W., Kim, J. Y., Shin, H, Lee, J. k., Jung, H. S., Park, N. G., Kim, K.,, M.J.Ko, K.S.Hong, 2010. Two-step sol–gel method-based TiO2 nanoparticles with uniform morphology and size for efficient photo-energy conversiondevices. Chemistry of Materials, 22, pp.1958–1965.

XI.Li, D., Liu, Z., Wang, Y., Shan, Y. and Huang, F., 2015. Efficiency Enhancement of Cu (In, Ga) Se 2 Solar Cells by Applying SiO 2–PEG/PVP Antireflection Coatings. Journal of Materials Science & Technology, 31(2), pp.229-234.

XII.Li, X. and He, J., 2016. Synthesis of raspberry-like SiO2–TiO2 nanoparticles toward antireflective and self-cleaning coatings. ACS applied materials & interfaces, 5(11), pp.5282-5290.

XIII.Li, Y., Zhang, J., Zhu, S., Dong, H., Jia, F., Wang, Z., Sun, Z., Zhang, L., Li, Y., Li, H. and Xu, W., 2009. Biomimetic surfaces for high‐performance optics. Advanced Materials, 21(46), pp.4731-4734.Yang, Adv. Mater. 21 (2009) 4731.

XIV.Prado, R., Beobide, G., Marcaide, A., Goikoetxea, J. and Aranzabe, A., 2016. Development of multifunctional sol–gel coatings: Anti-reflection coatings with enhanced self-cleaning capacity. Solar Energy Materials and Solar Cells, 94(6), pp.1081-1088.

XV.Prosser, J. H., Brugarolas, T., Lee, S., Nolte, A. J., Lee, D., 2012. Avoiding Cracks in Nanoparticle Films. Nano Lett., 12, pp.5287− 5291.

XVI.Raut, H.K., Ganesh, V.A., Nair, A.S. and Ramakrishna, S., 2011. Anti-reflective coatings: A critical, in-depth review. Energy & Environmental Science, 4(10), pp.3779-3804.

XVII.Singh, K. B., Tirumkudulu, M. S., Cracking in Drying Colloidal Films, 2007. Phys. Rev. Lett., 98, pp.218302.

XVIII.Tanesab, J., Parlevliet, D., Whale, J., Urmee, T. and Pryor, T., 2015. The contribution of dust to performance degradation of PV modulesin a temperate climate zone. Solar Energy, 120, pp.147-157.

XIX.Verma, L.K., Sakhuja, M., Son, J., Danner, A.J., Yang, H., Zeng, H.C. and Bhatia, C.S., 2011. Self-cleaning and antireflective packaging glass for solar modules. Renewable Energy, 36(9), pp.2489-2493.

XX.Xu, G., Jin, P., Tazawa, M. and Yoshimura, K., 2004. Optimization of antireflection coating for VO2-based energy efficient window. Solar Energy Materials andSolar Cells, 83(1), pp.29-37.

XXI.Zhang, X.P., Lan, P.J., Lu, Y.H., Li, J., Xu, H., Zhang, J., Lee, Y., Rhee, J. Y., Choy, K. L., Song, W. J., 2014. Multifunctional antireflection coatings based on novel hollow silica-silica nanocomposites, ACS Appl. Mater. Interfaces, 6, pp.1415–1423.

XXII.Zhang, X.X., Xia, B.B., Ye, H.P., Zhang, Y.L., Xiao, B., Yan, L.H., Lv, H.B. and Jiang, B., 2012. One-step sol–gel preparation of PDMS–silica ORMOSILs as environment-resistant and crack-free thick antireflective coatings. Journal of Materials Chemistry, 22(26), pp.13132-13140.

XXIII.Zhang, L., Qiao, Z.A., Zheng, M., Huo, Q. and Sun, J., 2010. Rapid and substrate-independent layer-by-layer fabrication of antireflection-and antifogging-integrated coatings. Journal of Materials Chemistry, 20(29), pp.6125-6130.

XXIV.Zhang, X., Fujishima, A., Jin, M., Emeline, A.V. and Murakami, T., 2006. Double-layered TiO2− SiO2 nanostructured films with self-cleaning and antireflective properties. The Journal of Physical Chemistry B, 110(50), pp.25142-25148.

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Abstract:

In order to fulfill all demands of the customers, related to energy, the capacity of the Grid is designed in such a way that, it satisfies not only the average power demand, but also the peak power demand. In this paper, Demand Side Management (DSM),programs which play a vital role in near future Smart Grid (SG) has been considered.DSM aims either at reducing or shifting consumption to shape users’ energy consumption profile. In the energy zone, the country is facing severe short fall from the last two decades, and hence effecting not only the economic growth, but also the industrial development. The main goal of DSM is usually to encourage the consumers to use less energy during peak hours or to move the time to use energy in the off-peak hours.

Keywords:

Smart Grid (SG),Demand Side Management (DSM),Peak-to Average Ratio (PAR),

Refference:

I. Fang, Xi, SatyajayantMisra, GuoliangXue, and Dejun Yang. “Smart grid—The new and improved power grid: A survey.” IEEE communications surveys & tutorials 14, no. 4 (2012): 944-980.

II. Hussain, Hafiz Majid, NadeemJavaid, Sohail Iqbal, QadeerUlHasan, Khursheed Aurangzeb, and MusaedAlhussein. “An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid.” Energies 11, no. 1 (2018): 190.

III. Jacquot, Paulin, Olivier Beaude, StéphaneGaubert, and Nadia Oudjane. “Demand side management in the smart grid: an efficiency and fairness tradeoff.” In Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2017 IEEE PES, pp. 1-6.IEEE, 2017.

IV. Kim, Sungwook. “An adaptive smart grid management scheme based on the coopetition game model.” ETRI journal36, no. 1 (2014): 80-88.

V. Latifi, Milad, AzamKhalili, Amir Rastegarnia, SajadZandi, and Wael M. Bazzi. “A distributed algorithm fordemand-side management: Selling back to the grid.” Heliyon 3, no. 11 (2017): e00457.

VI. Liu, Yi, Chau Yuen, Shisheng Huang, NaveedUl Hassan, Xiumin Wang, and ShengliXie. “Peakto-average ratio constrained demand-side management with consumer’s preference in residential smart grid.” IEEE Journal of Selected Topics in Signal Processing 8, no. 6 (2014): 1084-1097.

VII. Longe, O. M., K. Ouahada, H. C. Ferreira, and S. Rimer. “Consumer preference electricity usage plan for demand side management in the smart grid.” SAIEE Africa Research Journal 108, no. 4 (2017): 174-183.

VIII. Longe, Omowunmi Mary, and KhmaiesOuahada. “Mitigating Household Energy Poverty through Energy Expenditure Affordability Algorithm in a Smart Grid.” Energies 11, no. 4 (2018): 947.

IX. Mahmood, Anzar, M. N. Ullah, S. Razzaq, Abdul Basit, U. Mustafa, M. Naeem, and NadeemJavaid. “A new scheme for demand side management in future smart grid networks.”ProcediaComputer Science 32 (2014): 477-484.

X. Safdarian, Amir, Mahmud Fotuhi-Firuzabad, and MattiLehtonen. “Optimal residential load managementin smart grids: A decentralized framework.” IEEE Transactions on Smart Grid 7, no. 4 (2016): 1836-1845.

XI. Touzene, Abderezak, Sultan Al Yahyai, and Amar Oukil. “Smart Grid Resources Optimization Using Service Oriented Middleware.” International Journal of Computer Applications in Technology (2018): 1.

XII. Ullah, M. N., NadeemJavaid, I. Khan, AnzarMahmood, and M. U. Farooq. “Residential energy consumption controlling techniques to enable autonomous demand side management in future smart grid communications.” In Broadband and Wireless Computing, Communication and Applications (BWCCA), 2013 Eighth International Conference on, pp. 545-550. IEEE, 2013

XIII. Wijaya, Tri Kurniawan, Thanasis G. Papaioannou, Xin Liu, and Karl Aberer. “Effective consumption scheduling for demand-side management in the smart grid using non-uniform participation rate.” In Sustainable Internet and ICT for Sustainability (SustainIT), 2013, pp. 1-8. IEEE, 2013.

XIV. Yu, Mengmeng, and Seung Ho Hong. “A Real-Time Demand-Response Algorithm for Smart Grids: A Stackelberg Game Approach.” IEEE Trans. Smart Grid 7, no. 2 (2016): 879-888.

XV. Yu, Yixin, Yanli Liu, and Chao Qin. “Basicideas of the smart grid.” Engineering 1, no. 4 (2016): 405-408.

XVI. Zahoor, Saman, NadeemJavaid, Asif Khan, F. J. Muhammad, M. Zahid, and M. Guizani. “A cloud-fog-based smart grid model for efficient resource utilization.”In 14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC-2018). 2018.

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Abstract:

This article presents the effectiveness of Chip Mental Arithmetic Kit in teaching and learning of topic addition and subtraction. The methods used in the research weredescriptive analysis. The sample consist of two hundred and forty-six(246)standard one students of 3 primary school in total in the state ofSelangor and Perak. Samples were divided into groups and the Chip Mental Arithmetic Kit was distributed to each group.The researcher applied the Chip Mental Arithmetic Kit in the classroom during their teaching and learning process of topic addition and subtraction in primary education. The instrument used by researcher is questionnaire. The questionnaire contained 21 questions which include the figure, the reliability, the endurance of Chip Mental Arithmetic Kit. The student’s answers wereanalyzed. The result showed that Chip Mental Arithmetic Kit had a significant positive impact among the students, and the latter preferred Chip Mental Arithmetic Kit rather than using the traditional method of teaching addition and subtraction in standardone. The findings of the current study encouraged teachers and students to use Chip Mental Arithmetic Kit in their teaching and learning process.

Keywords:

addition and subtraction,Chip Mental Arithmetic Kit,teaching manipulative,

Refference:

I.C.Allen, “An Action Based Research Study on How Using Manipulatives Will Increase Students’ Achievement in Mathematics “, Chicago: MarygroveCollege, 2007.

II.C. J. Ross, “The Effect Of Mathematical Manipulative Materials on Third Grade Students’ Participation, Engagement, And Academic Performance”, Master’s Thesis. University of Central Florida, Orlando, 2008.

III.D. H. Clements, “Concrete Manipulatives, Concrete Ideas”,Contemporary Issues in Early Childhood, Volume:1, Issue: 1, pp: 45 -60, 1999.

IV.D. George, M. Mallery, “SPSS for Windows Step by Step: A Simple Guide and Reference”, 17.0 update (10a ed.) Boston: Pearson, 2010.

V.F. Nisih, “Does the Japanese Abacus Improve Underachieving Children’s Performance inMathematics? ” Proceedings of the British Society for Research into Learning Mathematics, pp: 13-18,2014.

VI.J. C. Nunnally, McGraw-Hill series in psychology. Psychometric theory. New York, NY, US:McGraw-Hill, 1967

VII.J. M. Shaw, “Manipulatives Enhance the Learning of Mathematics”, (C. Y. R. N. Farah, Ed.) Retrieved (May 8, 2017), [Online] Available: http://www. eduplace.com/state/author/shaw.pdf, 2002.

VIII.K. P. Hinzman, “Use of Manipulatives in Mathematics at The Middle School Level and Their Effects on Students’ Grades and Attitudes”(Degree’s Thesis). Salem-Teikyo University, Salem, 1997.

IX.M. Siegel, R. Borasi, and J. Fonzi, “Supporting Students’ Mathematical Inquiries through Reading”, Journal for Research in Mathematics Education,Volume: 29, Issue: 4, pp: 378 –413, 1998.

X.Md. Yunus, A. Suraya and Wan Ali, Wan Zah, “Metacognition and motivation in mathematicalproblem solving “, The International Journal of Learning, Volume: 15, Issue: 3, pp: 121-132, 2008.

XI.R. N. Farah, N. Bahirah, “Chip Mental Arithmetic Kit as A New Teaching Aids in Teaching andLearning 21stCentury”, Journal of Advanced Research in Dynamical and Control Systems, Volume: 9, pp: 348 –352, 2018.

XII.R. N. Farah, N. Bahirah, R. L. Zuraida, “Manipulative Kit Used in Teaching and Learning TopicAddition and Subtraction in 21stCentury”, International Journal of Recent Scientific Research, Volume: 9, pp: 29508 –29513, 2018.

XIII.Y. Liu, “Tangram Race Mathematical Game: Combining Wearable Technology and Traditional Games for Enhancing Mathematics Learning “, Massachusetts: Worcester Polytechnic Institute, 2014

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Abstract:

Nosql database is also known as not only sql database. For real time web application and for large set of distributed data, nosql database gaining popularity to handle big data. Nosql database has enormous function to handle big data. In contrast to this, nosql database also supports auto sharding, auto replication and many other feature making it suitable to be used as storage mechanism. Nosql database is used to store data in an unstructured way, when more attention is paid to Performance and real time access rather than consistency, then nosql databases seems to be more appropriate. However, research on the security of nosql database is very limited. Although, there are many research benefit in nosql database like scalability, faster data access and availability as compare to rdbms. But nosql database has some security issues. The experimental testing on advance nosql injections is performed. The demonstration of advance nosql injection attack against a nosql database is performed with php and JavaScript. It shows the client’s data. Method are discussed to prevent this type of security problems from happening again. This paper also shows how to create a security layer of nosql application to prevent nosql injection. In this paper, we will demonstrate, advance nosql injection attack and propose defense method to secure the nosql database. Hence nosql database programmer be aware of the nosql injection attack mechanism and build a more secure database to store huge data.

Keywords:

Nosql,MongoDB,Injection,Attack,Consistency,Vulnerability,Scalability,

Refference:

I.Abramova, Veronika, and Jorge Bernardino”NoSQL databases: MongoDB vs Cassandra.” Proceedings of the International C* Conference on Computer Science and Software Engineering10 Jul: 14-22, 2013.

II.Ahmed M. Eassa , Hazem M. El-Bakry“NoSQL Racket: A Testing Tool for Detecting NoSQL Injection Attacks in Web Applications”International Journal of Advanced Computer Science and Applications, Vol. 8, No. 11, 2017.

III.Aviv Ron,Alexandra Shulman-Peleg,Emanuel Bronshtein “No SQL, No Injection? Examining NoSQL Security Examining NoSQL Security” In proceedings of the 9thworkshop on web 2.0 security and privacy (W2SP) 2015.

IV.BoyuHou,Kai Qian “MongoDBNoSQL injection Analysis and detection” 2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing.

V.BoyuHou,yongshi“ Towards analyzing MongoDBNoSQL security and designing injection defense solution” ieee3rd international conference on big data security on cloud (bigdatasecurity), ieee international conference on high performance and smart computing (hpsc), and ieee international conference on intelligent data and security (ids), 26-28 may 2017.

VI.Chickerur, Satyadhyan, AnoopGoudar, and AnkitaKinnerkar”Comparison of Relational Database with Document-Oriented Database (MongoDB) for Big Data Applications.” 28th International Conference on Advanced Software Engineering & Its Applications (ASEA) 25 Nov. 2015: 41-47.

VII.Changlin He,“Survey on nosql database technology”,journal of applied science and engineering innovation vol. 2 no. 2,2015.

VIII.EbrahimSahafizadeh, Mohammad Ali Nematbakhsh“A Survey on Security Issues in Big Data and NoSQL” ACSIJ Advances in ComputerScience: an International Journal, Vol. 4, Issue 4, No.16 , July 2015 ISSN : 2322-5157.

IX.https://www.mongodb.org

X.Jing Han,haihongE,GuanLe,JianDu,“survey on nosql database”, 2011 IEEE.

XI.Kadebu, Prudence, and Innocent Mapanga, “A Security Requirements Perspective towards a Secured NOSQL Database Environment.” International Conference of Advance Research and Innovation, 2014.

XII.ManovegSaxena,ZakirAli, Vinod Kumar Singh,“NOSQL database –analysis,Techniques and classification” journal of advanced database management &system,volume 1 issue 2,2014.

XIII.Noiumkar, Preecha, and TawatchaiChomsiri,”A Comparison the Level of Security on Top 5 Open Source NoSQL Databases.” The 9th International Conference on Information Technology and Applications (ICITA2014).

XIV.“No SQL Injection in MongoDB” https://zanon.io/posts/nosql-injection-in-mongodb.

XV.Okman, Lior et al, “Security issues in nosql databases.” 2011 IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications 16 Nov. 2011:541-547.

XVI.Pokorny, Jaroslav,”NoSQL databases: a step to database scalability in web environment.” International Journal of Web Information Systems9.1 :69-82,2013.

XVII.RoshniBajpayee,Sonalipriya Sinha,Vinod Kumar ,“Big data :A brief investigation on NOSQL database”,International journal of innovations & advancement in computer science,volume 4, issue 1 January 2015.

XVIII.S.Priyadharshini, R. Rajmohan“Analysis on data base security model against nosql injection” 2017 International Journal of Scientific Research in Computer Science, Engineering and Information Technology , Volume 2 , Issue 2 ,2017,ISSN : 2456-3307

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Abstract:

MEMS, macroscopic devices posses characteristic length of less than 1μm and integrate mechanical and electronic components on a single chip. Sensitivity is the major concern in existing MEMS/NEMS devices which are mostly made of elastic cantilever beam. In this work porous MEMS cantilevers are designed using Silicon dioxide, Polysilicon, Silicon nitride & Aluminium. The designed cantilevers are in the micrometer range with optimized dimension as l=120, w=10 and t=1.5 (all are in micrometers). Sensitivity is measured on Silicon dioxide based cantiliver with different type of hole on fixed end as rectangle, circle and ellipse. The ellipse hole gives better result (maximum resultant stress 1767.5 N/m2) in terms of sensitivity of the device. Futher elliptical hole parameters (position, number and dimension) are varied in order to achieve maximum stress and in response maximum deflection of microcantilevers. The optimized design achieved is implemented with two more materials viz. polysilicon and silicon nitride for comparison.

Keywords:

MEMS,Microcantilevers,COMSOL Multiphysics,

Refference:

I.Ansari, M.Z., Cho, C., Kim, J.,Bang, B. Comparison between deflection and vibration characteristics of rectangular and trapezoidal profile microcantilevers. Sensors2009, 9, 2706–2718.

II.Anuj Kumar Goel. Analytical modeling and simulation of microcantilever based MEMS devices. Wulfenia, 2017, vol. 24, No.1, pp.79-91.

III.Anuj Kumar Goel, Kuldip Kumar, Dushyant Gupta. Design and Simulation of Microcantilevrs for Sensing applications. International Journal of applied engineering research, 2016, Vol. 11 No. 1 pp 501-503.

IV.ChivukulaV,Wang M,Ji HF,KhaliqA,FangJ&VarahramyanK. Simulation of SiO based piezoresistive microcantilevers. Sensors andActuatorsA, 2006; vol.125: pp.526-533.

V.Fernando, S., Austin, M., Chaffey, J. Improved cantilever profiles for sensor elements. J. Phys. D: Appl. Phys.2007, 40, 7652–7655.

VI.Madhu Santosh Ku Mutyala, Deepika Bandhanadham, Liu Pan, Vijaya Rohini Pendyala, Hai-Feng Ji. Mechanical and electronic approaches to improve the sensitivity of microcantilever sensors. Acta Mechanica Sinica, 2009, Vol. 25. No. 1. pp 1-12.

VII.Mansour Abtahi, Gholamreza Vossoughi, Ali Meghdari. Full Operational Range Dynamic Modeling of Microcantilever Beams. Journal of Microelectromechanical systems, 2013,Vol. 22. No. 5.

VIII.Mansour Abtahi, GholamrezaVossoughi, Ali Meghdari. Full Operational Range Dynamic Modeling of Microcantilever Beams. Journal of Microelectromechanicalsystems,.2013, Vol. 22. No. 5.

IX.Naeli, K., Brand, O.Cantilever sensor with stress-concentrating piezoresistor design, Sensors, 2005 IEEE, pp. 592–595.

X.RasmussenPA,HansenO&Boisen A. Cantilever surface stress sensors with single crystalline silicon piezoresistors. Applied physics letter, 2005; Vol. 86.

XI.Shahriar Kouravand. esign and modeling of some sensing and actuating mechanisms for MEMS applications, Applied Mathematical Modelling. Elsevier,2011,Vol. 35. pp 5173–5181.

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Abstract:

In the context of Supply Chain Management (SCM), Internet of Things (IoT) and Big Data Analytics (BDA) play a significant role in the evolution and success of a data intensive application and its respective security concerns. The aim of the research is to assess the suitability of IoT and BDA to strengthen and enhance SCM network. There have been independent research on IoT and Big Data in this domain; however no conclusive systematic study has been done to gather knowledge and expertise in analysis of SCM with respect to a combined application of IoT and big data analytics. The research in the literature has been put to documentation and recommendations for practitioners of SCM in industry have been addressed for future scope of IoT and data analysis.

Keywords:

SCM, Internet of Things (IoT),Data Analytics,

Refference:

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II.Analytics: The real-world use of big data in financial services -IBM.” https://www.ibm.com/services/multimedia/Analytics_The_real_world_use_of_big_data_in_Financial_services_Mai_2013.pdf.‖

III.”Analysing the interaction of supply chain synchronization and material‖ https://www.tandfonline.com/doi/abs/10.1080/13675567.2016.1174202.

IV.”A Framework of Sustainable Service Supply Chain Management -MDPI.” 12 Mar. 2017, http://www.mdpi.com/2071-1050/9/3/421/pdf.

V.”Big Data computing and clouds: Trends and future -The CLOUDS Lab.” 27 Aug. 2014, http://www.cloudbus.org/papers/BDC-Trends-JPDC.pdf.

VI.”Big Data -Related Technologies, Challenges and Future Prospects” https://www.springer.com/gp/book/9783319062440.

VII.”Big data analytics for supply chain management: A literature review.” http://fossowambasamuel.com/wp-content/uploads/2016/10/Big-data-analytics-for-supply-chain-management-A-literature-review-and-research-agenda.pdf. .

VIII.”Big data analytics in supply chain-Research Gate.” https://www.researchgate.net/publication/318300929_Big_data_analytics_in_supply_chain_management_A_state-of-the-art_literature_review.

IX.”Beyond the hype: Big data concepts, methods, and analytics.” http://psycnet.apa.org/record/2015-06483-002.

X.”Big data analytics in supply chain –Research Gate.” https://www.researchgate.net/publication/318300929_Big_data_analytics_in_supply_chain_management_A_state-of-the-art_literature_review.

XI.”Big data analytics in supply chain-Research Gate.” https://www.researchgate.net/publication/318300929_Big_data_analytics_in_supply_chain_management_A_state-of-the-art_literature_review.

XII.”Benefits of implementing RFID in Supply Chain Management -RFID” 14 Nov. 2013, http://www.rfidarena.com/2013/11/14/benefits-of-implementing-rfid-in-supply-chain-management.aspx.

XIII.”Big data analytics for supply chain management.” 4 Jul. 2018, https://zapdf.com/big-data-analytics-for-supply-chain-management.html.

XIV.”Big data analytics for supply chain management” http://www.academia.edu/33699458/Big_data_analytics_for_supply_chain_management.

XV.Big data analytics in supply chain.” https://www.researchgate.net/publication/318300929_Big_data_analytics_in_supply_chain_management_A_state-of-the-art_literature_review.

XVI.”Big data and predictive analytics for supply chain and organizational” https://www.sciencedirect.com/science/article/pii/S014829631630491X.

XVII.”Big data analytics in logistics and supply chain management” https://www.emeraldinsight.com/doi/full/10.1108/IJLM-02-2018-0026.

XVIII.”Deloitte’s 2018 global block chain survey.” https://www2.deloitte.com/content/dam/Deloitte/cz/Documents/financial-services/cz-2018-deloitte-global-blockchain-survey.pdf.

XIX.”Data Science, Predictive Analytics, and Big Data in Supply Chain” http://www.logisticsexpert.org/top_articles/2016/2016-Research-JBL Data Science, Predictive Analytics, and Big Data in Supply Chain Managementl.pdf.

XX.”Gross domestic product (GDP) growth rate in India 2022 -Statistic.”https://www.statista.com/statistics/263617/gross-domestic-product-gdp-growth-rate-in-india/.

XXI.”GitHub -car2go/ AnyMaps: Easily switch between Google, Baidu‖ https://github.com/car2go/AnyMaps.

XXII.”How IoT Will Impact The Supply Chain -Forbes.” 9 Jan. 2018, https://www.forbes.com/sites/danielnewman/2018/01/09/how-iot-will-impact-the-supply-chain/. .

XXIII.”How ‘big data’ can make big impact: findings from a systematic review‖ http://ro.uow.edu.au/buspapers/725/. XXIV.https://pdfs.semanticscholar.org/dd31/accf431497cf6a774880280280bf5f48c48e.pdf. XXV.https://www.researchgate.net/publication/261418480_RiskVis_Supply_chain_visualization_with_risk_management_and_real-time_monitoring. XXVI.https://www.researchgate.net/publication/269107422_Big_Data_Analytics_for_Supply_Chain_Management. XXVII.”IoT in Supply Chain Management, How to Leverage IoT Benefits” 28 Feb. 2018, https://www.embitel.com/blog/ecommerce-blog/how-modern-retailers-can-leverage-the-iot-benefits-in-their-supply-chain-management. .XXVIII.”Supply Chain GameChangers —Mega, Nano, and Virtual Trends ” 25 Jun. 2014, https://www.ssrn.com/abstract=2458168.

XXIX.”The 5 Vs of Big Data -Watson Health Perspectives -IBM.” 17 Sep. 2016, https://www.ibm.com/blogs/watson-health/the-5-vs-of-big-data/. .

XXX.”The Internet of Things: Today and Tomorrow -Aruba Networks.” https://www.arubanetworks.com/assets/eo/HPE_Aruba_IoT_Research_Report.pdf.

XXXI.”Understandable Big Data: A survey | Request PDF –Research Gate.” 30 Apr. 2018, https://www.researchgate.net/publication/278160976_Understandable_Big_Data_A_survey.

XXXII.”White Paper: Big Data for Development: Opportunities & Challenges” https://www.unglobalpulse.org/projects/BigDataforDevelopment.

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Abstract:

Internet of things (IOT) enables electronic gadgets to communicate with the server and each other, enabling them to share crucial information. With the advancement in the technology, more and more devices are added to the network of IOT every day. In the era of smart cities, the amount of data being transmitted is immense. While transferring such a huge amount of data, the system has to prioritize the data being sent based on the importance, such as medical and fire safety information. Lack of efficient scheduling algorithms leads to inappropriate delivery of emergency packets, thus rupturing the functionality of the system. Also, the data sent over the network has to guardagainst attacks over the channel. To overcome these drawbacks, a scheduling algorithm named Efficient data emergency aware packet scheduling scheme (EARS), enhanced with data security using Elliptic curve cryptography is proposed in this paper. In EARS, each packet has a description of its priority and the deadline before which it has to reach the sink. This enables easy identification of the emergency nodes. Further, in order to reduce the total number of transmissions in the network, the normal data packets can be network-coded and sent to the destination. This will reduce the congestion in the network. The proposed method is compared with the existing state of the art techniques and the results produced outperformed the exciting methods.

Keywords:

network of IOT,efficient scheduling algorithms, Elliptic curve cryptography,emergency nodes, transmissions in the network,

Refference:

I.A. T Hashemet al., “The role of big data in smart city,” Int. J. Inf. Manage., vol. 36, no. 5, pp. 748–758, 2016.

II.F. Yang and I. Aug ́e-Blum, “Delivery ratio-maximized wakeup scheduling for ultra-low duty-cycled WSN under real-time constraints,” Comput.Netw., vol. 55, no. 3, pp. 497–513, 2011.

III.G. Lu and B. Krishnamachari, “Minimum latency joint scheduling and routing in wireless sensornetworks,” Ad Hoc Netw., vol. 5, no. 6, pp. 832–843, 2007.

IV.K.-H. Phung, B. Lemmens,M. Goossens, A. Nowe, L. Tran, and K. Steenhaut, “Schedule-based multi-channel communication in wireless sensor networks: A complete design and performance evaluation,” Ad Hoc Netw., vol. 26, pp. 88–102, 2015.

V.M. Nitti, R. Girau, and L. Atzori, “Trustworthiness management in the social internet of things,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 5, pp. 1253–1266, May 2014.

VI.M. V. Moreno et al., “Applicability of big data techniques to smart cities deployments,” IEEE Trans. Ind. Informat., vol. 13, no. 2, pp. 800–809, Apr. 2017.

VII.P. Guo, T. Jiang, Q. Zhang, and K. Zhang, “Sleep scheduling for critical event monitoring in wireless sensor networks,” IEEE Trans. ParallelDistrib. Syst., vol. 23, no. 2, pp. 345–352, Feb. 2012.

VIII.R. Gomathi and N. Mahendran, “An efficient data packet scheduling schemes in wireless sensor networks,” in Proc. Int. Conf. Electron. Commun.Syst., Feb. 26–27, 2015, pp. 542–547.

IX.T.Qiu,K. Zheng, H. Song, M. Han, and B.Kantarci, “A local-optimization emergency scheduling scheme with self-recovery for smart grid,” IEEETrans. Ind. Inf, doi: 10.1109/TII.2017.2715844.

X.T. Qiu, R. Qiao, and D. Wu, “EABS: An event-aware backpressure scheduling scheme for emergency internet of things,” IEEE Trans. MobileComput., doi: 10.1109/TMC.2017.2702670.

XI.U. Jang, S. Lee, and S. Yoo, “Optimal wake-up scheduling of data gathering trees for wireless sensor networks,” J. Parallel Distrib. Comput., vol. 72, no. 4,pp. 536–546, 2012.

XII.V. Chang, “Towards a big data system disaster recovery in a private cloud,” Ad Hoc Netw., vol. 35, pp. 65–82, 2015.

XIII.X. Shen, C. Bo, J. Zhang, S. Tang, X. Mao, and G. Dai, “EFCon: Energy flow control for sustainable wireless sensor networks,” Ad Hoc Netw., vol. 11, no. 4, pp. 1421–1431, 2013.

XIV.Xue, B. Ramamurthy, and M. C. Vuran, “SDRCS: A servicedifferentiated real-time communication scheme for event sensing in wireless sensor networks,” Comput. Netw., vol. 55, no. 15, pp. 3287–3302, 2011.

XV.X. Xu, X. Li, andM. Song, “Distributed scheduling for real-time data collection in wireless sensor networks,” in Proc. IEEE Global Telecommun.Conf., Dec. 9–13, 2013, pp. 426–431.

 

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Abstract:

The wireless sensor network is the application oriented network which performs task of monitoring and object tracking. The wireless sensor node has the architecture which involves wireless interface for the communication. The design of the wireless sensor network depends upon the significant of application, cost and type of hardware. The architecture of WSN is dynamic due to which security and energy consumption are the major constraints. The Sybil attack is the attack which is possible in wireless sensor networks and it affect network performance. The attacker node generates multiple identities to attract network traffic and leads to denial of service in the network. In this research work, two step verification technique is proposed for the detection of malicious nodes from the network. In the two step verification technique, the cluster heads detect the node as untrusted if its energy consumption is abnormal. The extra observer nodes are deployed in the network, which observe network traffic. On the basis of network traffic observations, the node is declared as trusted or untrusted. When the cluster head and observer node both declare on node as untrusted node, then that sensor node will be considered as malicious node. The experiment is conducted is NS2 by considering certain simulation parameters. It is analyzed that two step verification technique detect malicious nodes successfully and it also leads to improve network performance in terms of Delay, PDR and Packetloss.

Keywords:

wireless sensor network,sensor node,Sybil attack,malicious nodes,observer node,network performance,

Refference:

I.Akyildiz, I.F., Su, W., Sankarasubramaniam, y., Cyirci, E., ‘Wireless sensor networks: a survey. Computer Networks’, Vol. 38 no.4: p. 393-422, 2002.

II.Abirami, K., Santhi, B. (2013). ‘ Sybil attack in wireless sensor network’, International Journal of Engineering and Technology, 5 (2), pp. 620-623.

III.Abirami, K., Santhi, B. , ‘Sybil attack in wireless sensor network’. International Journal of Engineering and Technology, 5 (2), pp. 620-623.

IV.Alsaedi N, Hashim F, and Sali A. ‘Energy Trust System for Detecting Sybil Attack in Clustered Wireless Sensor Networks’. IEEE 12th Malaysia International Conference on Communications (MICC), Kuching, Malaysia, Nov 2015.

V.Cheng, C., Qian, Y., & Zhang, D. , ‘An Approach Based on Chain Key Predistribution against Sybil Attack in Wireless Sensor Networks’. International Journal of Distributed Sensor Networks, 2013.

VI.Cheikhrouhou O., ‘Secure Group Communication in Wireless Sensor Networks: A survey’, Journal of Network and Computer Applications, Feb. 2016, vol. 61, pp. 115–132.

VII.Douceur J. R., ‘The sybil attack’, in Proc. 1st Int. Workshop Peerto-Peer Syst., London, UK, Mar., 2002, pp. 252−260.

VIII.Demirbas Murat, Song Youngwhan, ‘An RSSI-based Scheme for Sybil Attack Detection in Wireless Sensor Networks’, Proceedings of WoWMoM 2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks, 2006. 5 pp. –570.

IX.Di Pietro R., Mancini L. V., Soriente C., Spognardi A.,

X.Dhanalakshmi T.G., Bharathi Dr.N., Monisha M., ‘Safety concerns of Sybil attack in WSN’, IEEE 2014.

XI.Demirbas M. and Song Y., ‘An RSSI-based scheme for sybil attack detection in wireless sensor networks’, Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks, 2006, pp.564-570.

XII.Eschenauer L., ‘On Trust Establishment in Mobile Ad-hoc Networks’, in Department of Electrical and Computer Engineering, vol. Master of Science: University of Maryland, College Park, 2002, pp. 45.

XIII.Ganeriwal S., Balzano L. K. and Srivastava M. B., ‘Reputation-based Framework for High Integrity Sensor Networks’, ACM Transactions on Sensor Networks, vol. v, 2007.

XIV.Hsu, K., Leung, M. K., & Su, B., ‘Security Analysis on Defenses against Sybil Attacks in Wireless Sensor Networks’. IEEE Journal.

XV.Karlof, C., Wagner, D., ‘Secure routing in wireless sensor networks: Attacks and Countermeasures’, Ad hoc Networks Journal (Elsevier) 1(2–3) (2003) 293–315.

XVI.Kavitha T.,Sridharan D., ‘Security vulnerabilities in wireless sensor networks: a survey’, J. Inform. Assurance Security , 2010, vol. 5, pp. 31–44.

XVII.Kaschel H., Mardones J., and Quezada G., ‘Safety in wireless sensor networks: types of attacks and solutions’, Stud. Informatics Control, Sept., 2013, vol. 22, no. 3, pp. 323−329.

XVIII.Liu Z., Joy A. W. and Thompson R. A., ‘A Dynamic Trust Model for Mobile Ad-hoc Networks’, in The 10th IEEE International Workshop on Future Trends of Distributed Computing Systems (FTDCS ’04), 2004.

XIX.Levine B. N., Shields C., and Margolin N. B., ‘A survey of solutions to the Sybil attack’,University of Massachusetts Amherst, Amherst, MA,2006.

XX.Leopold M., ‘Sensor network motes: portability and performance’,Ph.D. dissertation, Dept. Comput. Sci., Copenhagen Univ.,Denmark, 2008.

XXI.Muraleedharan R., Yan Y., and Osadciw L. A., ‘Detecting sybil attacks in image senor network using cognitive intelligence’, Proceedings of the First ACM workshop on Sensor and actor networks, 2007, pp. 59-60.

XXII.Prasanna S., Rao S., ‘An Overview of Wireless Sensor Networks Applications and Security’, IJSCE,vol-2(2), May 2012, ISSN: 2231–2307.

XXIII.Putra G.D., Sulistyo S, ‘Trust Based Approach in Adjacent Vehicles to Mitigate Sybil Attacks in VANET’, Proceedings of the 2017 International Conference on Software and e-Business, (ICSEB ‘17) 2017, pp. 117-122.

XXIV.PaulA, Sinha S, and Pal S. ‘An Efficient Method to Detect Sybil Attack using Trust based Model’. Proc. of Int. Conf. on Advances in Computer Science, AETACS, Elsevier, 2013.

XXV.Rathod V., Mehta M., ‘Security in wireless sensor network: a survey’, Ganpat University Journal of Engineering & Technology, vol. 1, pp. 35–44, 2011.

XXVI.Rakesh G.V., Rangaswamy S., Hegde V., Shoba G., ‘A Survey of techniques to defend against Sybil attacks in Social Networks’, IJARSCCE, 2014.

XXVII.Raghunathan V., Schurgers C., Park. S, and Srivastava M. B., ‘Energy-aware wireless microsensor networks’. IEEE Signal Processing Magazine 2002, Volume: 19 Issue: 2, Page(s): 40 –50.

XXVIII.Rashidibajgan S., ‘A trust structure for detection of sybil attacks in opportunistic networks’, 11th International Conference for Internet Technology and Secured Transactions (ICITST) 2016.

XXIX.Sujatha V., Mary Anita E.A., ‘An efficient trust based method for Sybil node detection in mobile wireless sensor network’, Proceedings of the 3rdInternational Conference on Applied Science and Technology (ICAST’18) AIP Conference Proceedings, 2018.

XXX.Singh, Kumar Shio, Singh M. P., and Singh D. K., ‘A survey on network security and attack defense mechanism for wireless sensor networks’, International journal of computer trends and technology 2011, Vol1, no. 2, pp. 9-17.

XXXI.Singh R., Singh J., and Singh R., ‘A novel sybil attack detection technique for wireless sensor networks’, Advances in Computational Sciences and Technology 2017, vol. 10, pp. 185−202.

XXXII.Tsudik G., ‘Data security in unattended wireless sensor networks’,IEEE Trans. Comput., Nov., 2009, vol. 58, no. 11, pp. 1500−1511.

XXXIII.Wang Q., Balasingham I., ‘Wireless Sensor Networks –An Introduction, Wireless Sensor Networks: Application-Centric Design’, 2010.

XXXIV.Wang G, Musau F, Guo S, and Abdullahi M B. ‘Neighbor Similarity Trust against Sybil Attack in P2P E-Commerce’. IEEE Transactions o Parallel and Distributed Systems, December 2013.

XXXV.. Younis, O., &Fahmy, S. ‘HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks’. Mobile Computing, IEEE Transactions on 2004, Vol 3(4), pp. 366-379.

XXXVI.Zhang H, Xu C, and Zhang J. ‘Exploiting Trust and Distrust Information to Combat Sybil Attack in Online Social Networks’. 8th IFIP WG 11.11 International Conference, IFIPTM 2014 Singapore, July 7-10, 2014.

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Abstract:

4G LTE / 5G is the high speed communication system developed for smart phones and other mobile devices in the recent era. The current level of mobile device usage and data exchange over the internet has raised the need for such a fast and secure communication system. One of the important feature in an LTE system is the use of OFDM technique, owing to its advantage namely robustness to multipath fading and interference. This paper proposes an improved OFDM based 4G LTE system fused with turbo code encoding technique to further reduce the bit error rate over noisy real-time channels. The proposed turbo codes system has a hybrid two stage interleaver which is a combination of 3GP interleaver and block interleaver. This interleaver reduces the time required for interleaving processing while maintaining the BER criteria up to the levels. The traditional decoder has been replaced with a threshold-log-MAP algorithm based interleaver for improved noise tolerance. The proposed system has been tested over various channels like Rayleigh, rician and nakagami channels. The experimental results prove that the performance of the stem has improved in comparison by the addition of turbo codes.

Keywords:

turbocode,interleaver,ofdm,decoder,performance of the stem,

Refference:

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IV.Berrou, C.; Glavieux,A.; Thitimajshima, P.Near Shannon limit error-correcting coding and decoding: Turbo-codes.In Proceedings of ICC ’93—IEEE International Conference on Communications, Geneva, Switzerland,23–26May 1993; pp. 1064–1070.

V.Bose, R.C.; Ray-Chaudhuri, D.K. On a class of error correcting binary group codes. Inf. Control 1960, 3, 68–79.

VI.Divsalar, D.; Jin, H.;McEliece, R.J. Coding theorems for ―turbo-like‖ codes. In Proceedings of the 36th Annual Allerton Conference on Communication Control and Computing,Monticello, IL, USA, 23–25 September 1998;pp. 201–210.

VII.Dolinar, S., Divsalar, D.: Weight distribution for turbo codes using random and nonrandom permutations. TDA Progress Report 42–122, pp. 56–65 (1995)

VIII.D. Wiegandt, Z. Wu, C. R. Nassar, “High Capacity HighPerformance Low PAPR OFDM via Carrier Interferometry”, IEEE Transactions on Communications, vol. 51, no. 7, pp. 1123-1134, July 2003.

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XII.Houshou Chen, Kuo-Chen Chung, ―A Low Complexity PTS Technique Using Minimal Trellis in OFDM Systems‖, IEEE Trans. onVehicular Technology, vo. 67, no. 1, pp. 817 –821, Jan. 2018.

XIII.Jan, YH. Wireless Pers Commun, ―Iterative Joint Channel Estimation and Signal Detection for OFDM System in Double Selective Channels‖, Wireless Personal Communications, vol. 99, no. 3, pp 1279–1294, Apr. 2018.

XIV.Jason B. Coder, Yao Ma, ―On the susceptibility of coded OFDM to interference: A simulation study‖,United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Jan. 2018.

XV.Khandani, A.K.: Design of turbo-code interleaver using hungarian method. Electron. Lett. 34(1), 63–65 (1998)

XVI.M. Rajani Devi, K. Ramanjaneyulu, B.T. Krishna, “Design of Cascaded Hybrid Interleaver for Fast Turbo Coding and Decoding”, Springer Nature Singapore Pte Ltd, 2017.

XVII.M. Rajani Devi, K. Ramanjaneyulu, B.T. Krishna, ―Performance analysis of sub-interleaver based turbo codes‖, B.T. Cluster Comput (2018).

XVIII.N. Michailow et al., “Generalized frequency division multiplexing for 5th generation cellular networks”, IEEE Trans. Commun., vol. 62, no. 9, pp. 3045-3061, Sep. 2014.

XIX.Peterson, L.L.; Davie, B.S. Computer Networks: A Systems Approach; Elsevier: Amsterdam, The Netherlands, 2007.

XX.Reed, I.S.; Solomon, G. Polynomial codes over certain finite fields. J. Soc. Ind. Appl. Math. 1960, 8, 300–304.

XXI.R. v. Nee and R. Prasad, ―OFDM for Wireless Multimedia Communications‖, Artech House, 2000.

XXII.R. van Nee, A. de Wild, “Reducing the peak-to-average power ratio of OFDM”, Proc. IEEE Vehicular Technology Conf. (VTC’98), pp. 2072-2076, 1998-May.

XXIII.Subramanium, P. & Raut, R.D. J Supercomput, ―AI-enabled turbo-coded OFDM system for improved BER performance‖, The Journal of Supercomputing, pp 1–11, April, 2018.

XXIV.Singh, N., Kaur, G.: Performance analysis of serially concatenated convolutional codes using different generator polynomial and constraint length. Adv. R. Electr. Electron. Eng. 2(1), 73–76 (2014).

XXV.Toshiaki Koike-Akino, Congzhe Cao, Ye Wang, ―Turbo Product Codes with Irregular Polar Coding for High-Throughput Parallel Decoding in Wireless OFDM Transmission‖, 2018 IEEE International Conference on Communications (ICC), May. 2018.

XXVI.Wael Abd-Alaziz, Martin Johnston and Stephane Le Goff, ―Non-binary trellis codes on the synthetic statistical MIMO power line channel‖, IEEE InternationalSymposium on Power Line Communications and its Applications (ISPLC), Apr. 2018.

XXVII.Wang, C.; Sklar, D.; Johnson, D. Forward error-correction coding. Crosslink 2001, 3, 26–29.

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