September2018 by Editor IJITCE

ISSN (ONLINE) : 2045 -8711 ISSN (PRINT) : 2045 -869X

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING

SEPTEMBER 2018 VOL- 8 NO - 9

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.9 SEPTEMBER 2018

UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: editor@ijitce.co.uk Phone: +44-773-043-0249 USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626 India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 66/2 East mada st, Thiruvanmiyur, Chennai -600041 Mobile: 91-7598208700

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.9 SEPTEMBER 2018

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International Journal of Innovative Technology & Creative Engineering Vol.8 No.9 September 2018

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From Editor's Desk Dear Researcher, Greetings! Research article in this issue discusses about motivational factor analysis. Let us review research around the world this month. They climbed a jungle mountain on the island of Oahu, swatting mosquitoes and skirting wallows of wild pigs. The two headed to the site where a patch of critically endangered Phyllostegia kaalaensis had been planted a few months earlier. What they found was dispiriting. All the plants were gone the two ecologists found only the red flags placed at the site of each planting, plus a few dead stalks. The plants, members of the mint family but without the menthol aroma, had most likely died of powdery mildew caused by Neoerysiphe galeopsidis. Today the white-flowered plants, native to Oahu, survive only in two government-managed greenhouses on the island. It nearly extinct is unclear, though both habitat loss and powdery mildew are potential explanations. The fuzzy fungal disease attacks the plants in greenhouses, and the researchers presume it has killed all the plants theyâ&#x20AC;&#x2122;ve attempted to reintroduce to the wild. Just like humans and other animals, plants have their own microbiomes, the bacteria, fungi and other microorganisms living on and in the plants. Some, like the mildew, attack; others are beneficial. A single leaf hosts millions of microbes, sometimes hundreds of different types. The ones living within the plantâ&#x20AC;&#x2122;s tissues are called endophytes. Plants acquire many of these microbes from the soil and air; some are passed from generation to generation through seeds.

It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue.

Thanks, Editorial Team IJITCE

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Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA.

Review Board Members Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY.

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Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE. Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore. Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India).

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Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.9 SEPTEMBER 2018 Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688

Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar)01332-000, São Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India

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RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 55812-3042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India. MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran. Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India. Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,GianiZail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukić Vice Dean for education,Virovitica College,MatijeGupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677

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Contents Procedure and Tables for Construction and Selection of Chain Sampling Plan with Zero – Inflated Poisson Distribution Dr. M. Latha, Mr.A.Palanisamy …………………………. [527]

Healthcare Services –IoT Enabling Technologies, Usage and Impact Varsha Dubey Upadhyay, Nitika Vats Doohan, Naresh Menaria …………………. [534]

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.9 SEPTEMBER 2018

Procedure and Tables for Construction and Selection of Chain Sampling Plan with Zero – Inflated Poisson Distribution Dr. M. Latha Principal (Red), Kamarajar Government Arts College, Surandai- 627859, Tamil Nadu, India. E-mail ID: mmmlatha.madan@gmail.com Mr.A.Palanisamy Research Scholar, Department of Statistics, Government Arts College, Udumalpet- 642126, Tamil Nadu, India. Email ID: palanisamystat@gmail.com

Abstract- ZIP Chain sampling plans (ChSP-1) allow significant reductions in sample size under conditions of a continuing succession of lots from a stable, trusted supplier. This paper presents procedures and tables for the construction of such plans and for selection of plans by specified properties. The performance of the chain sampling plan for variable fraction defective is also discussed by determining the operating characteristic function. This paper provides a method for the selection of ZIP chain sampling plan (ChSP-1) on the basis of different combinations of entry parameter, Tables for determining the associated AQL and AOQL are also given. Keywords- Acceptable Quality Level (AQL), Average

Outgoing Quality Limit (AOQL), Chain Sampling Plan (ChSP-1) with Zero – Inflated Poisson distribution. 1. INTRODUCTION Dodge (1955) has proposed Chain Sampling Plan in which Chain Sampling Plan allows significant reduction in sample size and the condition for a continuing succession of lots from a stable and trusted supplier. Soundararajan (1978) has studied Chain Sampling Plan –1 involving designing of Chain Sampling Plan indexed through AQL and AOQL. The main thrust of this paper is to account for the possibility of dependence among the items of a sample. The Zero-Inflated Poisson (ZIP) distribution can be used as the appropriate probability distribution to data consisting many over dispersed zeros. ZIP distribution has been used in a wide range of disciplines such as Agriculture, Epidemiology, Econometrics, Public health, Process control, Medicine, Manufacturing, etc. Some of the applications of ZIP distribution can be found in Bohning et al. (1999) and Lambert (1992). Construction of control charts using ZIP distribution are discussed in Sim and Lim (2008). Single sampling plans by attributes under the conditions of Zero – inflated Poisson distribution are determined by Loganathan and Shalini (2013), Suresh and Latha (2002) have given a procedure and tables for the

selection of Bayesian chain sampling plan-1.Palanisamy and Latha,(2018) have discuss about the Construction of Bayesian Single Sampling Plan by Attributes under the Conditions of Gamma Zero – Inflated Poisson Distribution. Palanisamy and Latha,(2018) have given the procedure for the “Construction And Selection Of Chain Sampling Plan With Zero – Inflated Poisson Distribution. A study of construction of chain sampling plan (ChSP-1), corresponding to given value of the acceptable quality level (AQL) and the overall average outgoing quality limit (AOQL) is presented. Comparison with ChSP-1plan with Poisson model for given AQL and AOQL values. . II CHAIN SAMPLING PLAN (ChSP-1) For situation in which testing is destructive or very expensive sampling plans with small sample sizes are usually selected. These small sample size plans often have acceptance number of zero. Plans with zero acceptance numbers are often undesirable, however, their OC curves are convex throughout, which means that the probability of lot acceptance begins to drop very rapidly as the lot fraction defective becomes greater than zero. This is often unfair to the producer, and in situations where rectifying inspection is used requires the consumer to screen a large number of lots which are essentially of acceptable quality. Dodge (1955) suggested an alternate procedure, known as chain sampling that might be a substitute for ordinary singlesampling plans with zero acceptance numbers in certain circumstances. Chain sampling plans make use of the cumulative results of several preceding lots. The conditions for application and the operating procedure for the ChSP-1 plan are given as follows: III CONDITIONS FOR APPLICATION OF CHSP-1 The cost of destructiveness of testing is such that a relatively small sample size is necessary, although other factors make a large sample desirable. The product to be inspected comprises a series of successive lots produced by a continuing process. Normally lots are expected to be of essentially the same quality.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.9 SEPTEMBER 2018

The consumer has faith in the integrity of the producer.

In this distribution, φ may be termed as the mixing

IV OPERATING PROCEDURE The plan is implemented in the following way: 1. For each lot, select a sample of n units and test each unit for conformance to the specified requirements. 2. Accept the lot if d (the observed number of defectives) is zero in the sample of n units, and reject if d > 1. 3. Accept the lot if d is equal to 1 and if no defectives are found in the immediately preceding i samples of size n. Dodge (1955) has given the operating characteristic function of ChSP-1 as Pa(p) = P0 + P1(P0)i Where Pi = probability of finding i nonconforming units in a sample of n units for i = 0.1. The Chain sampling Plan is characterized by the parameters n and i. When i= ∞ , the OC function of a ChSP -1 plan reduces to the OC function of the Single Sampling Plan with acceptance number zero and when i = 0, the OC function of ChSP-1 plan reduces to the OC function of the Single Sampling Plan with acceptance number 1. V OPERATING CHARACTERISTIC FUNCTION OF ZIP MODEL The OC function is defined as (1) Where p is the fraction defective The numbers of defects are zero for many samples may consider Zero – inflated Poisson probability distribution. The probability mass function of the ZIP ( distribution is given by Lambert (1992) and McLachlam and peel (2000) )=

 f(x) + (1-  )P(X=x |λ)

(2)

Where

proportion.  and λ are the parameters of the ZIP distribution. According to McLachlan and Peel (2000), a Zip distribution is a special kind of mixture distribution. The OC function of ZIP ( defined as

Pa ( p )    (1   ) e   

distribution can be

 (1   ) x 1

e   x , x!

x  0 ,   0 , 0    1).

(3)

Where λ = np VI CHAIN SAMPLING PLANS (CHSP-1) WITH ZEROINFLATED POISSON DISTRIBUTION The probability of acceptance for chain sampling plan of type ChSP- 1 based on Zero- inflated Poisson distribution

Pa ( p )  (  (1   ) e  np )  (  (1   ) e  np ) i 1  (1   ) e  np np (  (1   ) e  np ) i

( 4)

VII AVERAGE OUTGOING QUALITY LIMIT (AOQL) Average outgoing Quality, is the average quality of outgoing product including all accepted lots or batches, plus all rejected lots or batches after the rejected lots or batches have been effectively 100 percent inspected and all nonconformities replaced by non-defectives. The maximum value on the AOQ curve corresponds to the highest average percent defective or the lowest average quality for the sampling plan. It is called the Average outgoing quality limit (AOQL). Average Outgoing Quality (AOQ) is approximately obtained

AOQ  p P (p)

a by . For ChSP–1 with Zero – Inflated Poisson distribution

nAOQ (np  np(1  ) e np )  np(  (1  ) e np ) i1

f(x) = and

e   x P( X  x /  ) = x ! ,

when x =0, 1, 2, … The probability mass function can also be expresses as

  (1  ) e   e   x ( 1   ) ,  x! 

when x  0

(5)

Differentiating AOQ with respect to np and equating to 0, the value of Average Outgoing limit (AOQL) can be obtained by solving the equation.   (1   ) e  np ((   (1   ) e  np ) i  np (1   ) e  np

P( X  x | ,  ) = 

 (1  ) e np (np) 2 (  (1  ) e np ) i

(1  np  i )

(  (1   ) e  np ) i  1  ( np ) 2 e  np (1   ) 2

i (  (1   ) e  np  1 ))  np (1   ) e  np  0

when x  1, 2 , ...,0    1,   0

6 

From Equation (6) the values of np (=npm) can be

calculated for different values of  and i . Substituting npm in equation (5) nAOQL values are obtained.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN: 2045-8711) VOL.8 NO.9 SEPTEMBER 2018 Table 1: Chain Sampling Plan under Zero - Inflated Poisson



0.0001

0.001

0.01

0.05

0.09

i 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9

0.99 0.6343 0.4658 0.3774 0.3214 0.2822 0.2528 0.2299 0.2114 0.1962 0.6350 0.4663 0.3778 0.3218 0.2825 0.2531 0.2302 0.2117 0.1964 0.6424 0.4715 0.3819 0.3251 0.2854 0.2557 0.2325 0.2138 0.1983 0.6775 0.4961 0.4011 0.3411 0.2992 0.2679 0.2435 0.2238 0.2076 0.7169 0.5233 0.4224 0.3588 0.3144 0.2813 0.2555 0.2348 0.2177

0.95 0.6691 0.4937 0.4019 0.3438 0.3031 0.2727 0.2490 0.2299 0.2141 0.6699 0.4943 0.4023 0.3441 0.3034 0.2730 0.2492 0.2301 0.2143 0.6779 0.4998 0.4067 0.3478 0.3066 0.2758 0.2518 0.2324 0.2164 0.7156 0.5262 0.4274 0.3651 0.3216 0.2891 0.2638 0.2434 0.2266 0.7580 0.5556 0.4504 0.3843 0.3381 0.3038 0.2770 0.2555 0.2378

Pa(p) 0.50 1.2014 0.9494 0.8334 0.7711 0.7361 0.7163 0.7054 0.6995 0.6964 1.2033 0.9508 0.8346 0.7722 0.7370 0.7172 0.7063 0.7004 0.6973 1.2224 0.9646 0.8462 0.7827 0.7469 0.7268 0.7156 0.7097 0.7065 1.3158 1.0316 0.9022 0.8331 0.7943 0.7725 0.7605 0.7541 0.7507 1.4263 1.1095 0.9667 0.8910 0.8486 0.8248 0.8118 0.8047 0.8011

0.10 2.5486 2.3343 2.3067 2.3038 2.3035 2.3034 2.3034 2.3034 2.3034 2.5583 2.3426 2.3149 2.3119 2.3116 2.3116 2.3116 2.3116 2.3116 2.6612 2.4307 2.4013 2.3982 2.3979 2.3978 2.3978 2.3978 2.3978 3.3414 2.9914 2.9493 2.9449 2.9444 2.9444 2.9444 2.9444 2.9444 4.7573 4.6560 4.5253 4.5123 4.5110 4.5108 4.5108 4.5108 4.5108

0.05 3.1605 3.0074 2.9981 2.9976 2.9976 2.9976 2.9976 2.9976 2.9976 3.1795 3.0248 3.0154 3.0149 3.0149 3.0149 3.0149 3.0149 3.0149 3.3936 3.2197 3.2093 3.2088 3.2088 3.2088 3.2088 3.2088 3.2088 3.2978 3.1290 3.0636 3.0366 3.0258 3.0258 3.0258 3.0258 3.0258 4.3167 4.1290 4.0260 4.0235 4.0220 4.0220 4.0220 4.0220 4.0220

0.01 4.6676 4.6156 4.6151 4.6151 4.6151 4.6151 4.6151 4.6151 4.6151 4.7640 4.7101 4.7095 4.7095 4.7095 4.7095 4.7095 4.7095 4.7095 4.2822 4.1127 4.0475 4.0209 4.0102 4.0102 4.0102 4.0102 4.0102 4.3003 4.2067 4.0598 4.0305 4.0278 4.0265 4.0265 4.0265 4.0265 5.3193 5.1486 5.0817 5.0538 5.0424 5.0424 5.0424 5.0424 5.0424

Table: 2 are used to construct chain sampling plan with ZIP through two points. The two points generally selected are ( p1 , 1   ), ( p 2 ,  ) with  - Producer’s Risk,  - Consumer Risk. Table 2 in the column for the appropriate given

 and  that is equal to or just less than the desired ratio. Corresponding to the selected tabular values of p 2 / p1 are

np1 and i. the sample size is determined by dividing np1 by p1 and i is read directly. As an example, to find the chain sampling plan for p1 = 0,005,  =0.05 and p 2 = 0.10,  =0.10 compute p2/p1 = 0.10/0.005 = 20, enter the table 2 for   0.05 and 0.10 , and select the values of the ratio p2/p1 in the column for   0.05 and   0.10equal to or just less than 20.the values 18.969, which has associated with a values of np1 =0.2378 and the values of  = 0.09 ,i = 9.The sample size for the desired plan is then np1/p1 = 0.2378 / 0.005 = 47.56 which , when rounded off to the next higher integer, is 48.Hence the required plan (48,5)

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN: 2045-8711) VOL.8 NO.9 SEPTEMBER 2018 Table 2: Values of

np1 ,  and i for construction chain sampling plan, whose OC curve is required to pass Through the Two points (



0.0001

0.001

0.01

  0.05   0.10

p1 , 1   ) and ( p 2 ,  )

Value of (p2/p1)for



np1

  0.05   0.10

np1

3.809

4.724

6.976

0.669

8.694

11.635

14.540

0.276

4.728

6.092

9.349

0.494

9.523

12.743

15.926

0.252

5.739

7.460

11.483

0.402

10.318

13.807

17.256

0.232

6.701

8.719

13.424

0.344

11.080

14.828

18.531

0.216

7.600

9.890

15.226

0.303

4.669

4.608

6.009

0.716

8.447

10.992

16.924

0.273

5.685

5.946

7.994

0.526

9.251

12.039

18.535

0.249

6.901

7.168

9.499

0.427

10.019

13.039

20.074

0.230

8.066

8.317

11.039

0.365

10.759

14.001

21.556

0.214

9.155

9.409

12.524

0.322

3.819

4.746

7.112

0.670

10.185

10.466

13.928

0.289

4.739

6.119

9.529

0.494

11.161

11.470

15.263

0.264

5.754

7.495

11.706

0.402

12.097

12.431

16.543

0.243

6.719

8.762

13.686

0.344

12.994

13.353

17.769

0.227

7.619

9.937

15.522

0.303

6.276

5.695

7.018

0.758

8.467

11.044

17.251

0.273

8.380

7.432

9.267

0.556

9.276

12.098

18.898

0.249

10.047

8.939

11.283

0.450

10.046

13.103

20.467

0.230

11.742

10.470

13.151

0.384

10.787

14.069

21.976

0.214

13.342

11.896

14.914

0.338

3.926

5.006

6.317

0.678

14.848

13.239

16.598

0.304

4.863

6.442

8.229

0.500

16.284

14.520

18.204

0.277

5.904

7.891

9.952

0.407

17.655

15.742

19.735

0.256

6.895

9.226

11.561

0.348

18.969

16.913

21.204

0.238

7.821

10.466

13.080

0.307

0.05

0.09

Table 3: Certain Parametric values Chain Sampling Plan (ChSP-1) with ZIP Model



0.0001

np1

np 2

np m

nAOQL

p2 / p1

AOQL/

0.9726

0.2758

2.3978

0.9788

0.3899

8.6940

1.4137

0.9996

0.2518

2.3978

0.9822

0.3891

9.5226

1.5453

5.7395

1.0453

0.2324

2.3978

0.9983

0.3888

10.3176

1.6730

0.3860

6.7010

1.1227

0.2164

2.3978

1.0674

0.3887

11.0804

1.7962

0.3739

7.5998

1.2336

0.7156

3.3414

0.9822

0.7156

4.6694

1.0000

0.9696

0.3701

8.4466

1.3572

0.5262

2.9914

0.9291

0.5600

5.6849

1.0642

0.9880

0.3688

9.2506

1.4811

0.4274

2.9493

0.9563

0.5041

6.9006

1.1795

2.3034

0.9954

0.3683

10.0191

1.6020

0.3651

2.9449

0.9623

0.4939

8.0660

1.3528

0.2141

2.3034

0.9985

0.3682

10.7585

1.7198

0.3216

2.9444

0.9764

0.4922

9.1555

1.5305

0.6699

2.5583

0.8347

0.6519

3.8189

0.9731

0.2891

2.9444

0.9783

0.4919

10.1847

1.7015

0.4943

2.3426

0.7097

0.4945

4.7392

1.0004

0.2638

2.9444

0.9854

0.4918

11.1615

1.8643

np1

np 2

np m

nAOQL

p 2 / p1

0.6691

2.5486

0.8325

0.6508

3.8090

0.4937

2.3343

0.7070

0.4935

4.7282

0.4019

2.3067

0.7109

0.4201

0.3438

2.3038

0.8187

0.3031

2.3035

0.9234

0.2727

2.3034

0.2490

2.3034

0.2299

9 1 2

AOQL/

530



0.05

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0.001

0.01

0.4023

2.3149

0.7153

0.4212

5.7542

1.0470

0.2434

2.9444

0.9930

0.4916

12.0970

2.0197

0.3441

2.3119

0.8259

0.3874

6.7187

1.1258

0.2266

2.9444

1.0620

0.4912

12.9938

2.1677

0.3034

2.3116

0.9306

0.3738

7.6190

1.2320

0.7580

4.7573

0.9973

0.7877

6.2761

1.0392

0.2730

2.3116

0.9761

0.3678

8.4674

1.3473

0.5556

4.6560

0.9224

0.6255

8.3801

1.1258

0.2492

2.3116

0.9941

0.3652

9.2761

1.4655

0.4504

4.5253

0.9374

0.5667

10.0473

1.2582

0.2301

2.3116

0.9963

0.3641

10.0461

1.5824

0.3843

4.5123

0.9418

0.5437

11.7416

1.4148

0.2143

2.3116

1.0044

0.3636

10.7867

1.6967

0.3381

4.5110

0.9592

0.5412

13.3422

1.6007

0.6779

2.6612

0.8571

0.6624

3.9257

0.9771

0.3038

4.5108

0.9750

0.5410

14.8479

1.7808

0.4998

2.4307

0.7382

0.5048

4.8633

1.0100

0.2770

4.5108

0.9882

0.5408

16.2845

1.9523

0.4067

2.4013

0.7643

0.4328

5.9044

1.0642

0.2555

4.5108

0.9989

0.5405

17.6548

2.1155

0.3478

2.3982

0.9047

0.4022

6.8953

1.1564

0.2378

4.5108

1.0079

0.5400

18.9689

2.2708

0.3066

2.3979

0.9346

0.3927

7.8209

1.2808

0.09

AOQL. For example, when AOQL =1%,  =0.001,i=1,then plans can be (651,1),(494,2), (420,2),(386,4),(374,5),(370,6),(369,7),(368,8) or (368) one of which may be chosen according to the requirement of inspection

VIII SELECTION PROCEDURE of ZIP CHAIN SAMPLING PLAN-1 FOR GIVEN AOQL AND  Table 4 is constructed for the selection of a ZIP chain sampling plan (ChSP-1) with given  , the parameter of Zero inflated Poisson distribution and for the required AOQL. Such table can be extended for any value of  and

Table 4: Value of sample size for given AOQL,

 and i

AOQL in Percent



0.0001

0.001

0.01

0.10

0.25

0.50

0.75

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

6.0

7.0

8.0

9.0

10.0

651

260

130

494

197

420

168

386

154

374

150

370

148

369

148

368

147

368

147

652

261

130

495

198

421

168

387

155

374

150

368

147

365

146

364

146

364

145

662

265

132

505

202

101

433

173

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0.05

0.09

402

161

393

157

390

156

389

156

389

156

389

155

716

286

143

560

224

112

504

202

101

494

198

492

197

492

197

492

197

492

197

491

196

788

315

158

105

626

250

125

567

227

113

544

217

109

541

216

108

541

216

108

541

216

108

541

216

108

540

216

108

plans are (492,8) and (541,8), for  = 0.05,0.09

respectively. It is observed that for small values of  , the

IX SELECTION PROCEDURE BASED OF ZIP CHAIN SAMPLING PLAN-1 BASED ON AOQL AND AQL Table 5 is constructed for the selection of ZIP chain sampling plan-1 for the given value of AOQL and AQL. For given values AQL and AOQL the ratio AOQL/AQL is obtained. The sample size n is obtained and hence a

optimum sample size is less and increases as  increases. The number of proceeding sample is more compared with the ChSP -1 with Poisson model. This is much favourable to the consumer.

XI CONSTRUCTION OF AQL/ AOQL TABLE In the Table 3, values of np1 have been calculated for p1 defined as AQL such that Pa(p) = 0.95. Also nAOQL values and the ratio AOQL/AQL are given .Given that AQL=0.15percent and AOQL is 0.25 percent, then (AOQL/AQL) = 1.6666. From table 3, the values closer to this is 1.6020 which corresponding to a value of i=8. with i = 8, np1 = 0.2299. Hence np1 / p1 = 0.0015 = 153.26,i.e., about 0.15. Thus the ChSP-1 corresponding to given AQL=0.15 percent and AOQL is 0.25 percent is given by n = 156,

combination (  ,n,i) for given AOQL and AQL for the ZIP chain sampling plan is obtained.For example, when AOQL =0.1 and AQL=0.05, the table values closer to the ratio AOQL/AQL=2 is obtained as 2.1155 for which

(  .i) = (0.09,8)and 2.2708, for which (  , i) = (0.09,9). Similarly more combination of (  ,i) can be formed as per the inspection

 = 0.01 and i = 8. In similar manner, chain sampling plan

X COMPARISON WITH ChSP-1 PLAN WITH POISSON MODEL The parameters of ZIP chain sampling plan- may be compared with the parameters of ChSP-1 given by Soundararajan (1978) for given AOQL and AQL. When AOQL=0.10 and AQL=0.05 percent the optimum plan is (504,1).In case of ChSP-1 with passion model. For the same combination of AOQL and AQL the appropriate

have been calculated for a wide range of AQL and AOQL values given the table.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN: 2045-8711) VOL.8 NO.9 SEPTEMBER 2018 [6] Sim and Lim (2008).Attributes charts for zero – Table 5: Selection procedure based on AQL and inflated processes, Communication in statistics – AOQL simulation and computation 37: 1440-1452. AOQL % [7] Soundararajan V Procedure and tables for AQL in % 0.1 0.25 construction and selection of chain sampling plan  n, i n, i (ChSP-1) part-1,Journal of Quality and Technology 0.05 492,8 0.05 10(2) (1978). 56-60. 0.09 541,8 [8] Soundararajan V Procedure and tables for 0.0001 construction and selection of chain sampling plan 370,6 (ChSP-1) part-2, Journal of Quality and Technology 0.001 368,6 0.075 (1978). 99-103. 0.01 393,5 [9] Suresh K K and Latha M Construction and evaluation 0.05 494,4 of performance measures for Bayesian chain 0.0001 494,2 sampling plan (BChSP-1) Far East Journal of 0.001 495,2 Theoretical Statistics (2002) 129-139. 0.10 0.01 505,2 [10] Palanisamy A and Latha M ,(2018) “Construction of 0.05 Bayesian Single Sampling Plan by Attributes under 716,1 0.09 the Conditions of Gamma Zero – Inflated Poisson 788,1 Distribution,” International Research Journal of 0.0001 146,8 Advanced Engineering and Science, Volume 3, Issue 0.001 147,8 0.15 1, pp. 67-71, 20. 0.01 156,8 [11] Palanisamy A and Latha M,(2018) “Construction 0.09 217,5 And Selection Of Chain Sampling Plan With Zero – Inflated Poisson Distribution ” International Journal XII CONCLUSION of Engineering Research and Modern Education , The quality level and quality interval sampling plan Volume 3, Issue 1, pp. 46-50. possesses wider potential applicable in industry ensuring higher standard of quality attainment for product or process. Thus quality interval and quality level are good measure for defining and Designing for acceptance sampling plan which are readymade use to industrial shopfloor situations. A zero inflated model is the appropriate probability distribution to the number of non-conformities per product manufactured in such production process. The Chain sampling plan gives more pressure on the producer if the quality deteriorates. The complete Chain sampling plan gives more pressure on the producer if the quality deteriorates. These plans provide consumer an assurance regarding the outgoing quality or the quality of the lot after the inspection. Hence one can recommend this type of sampling plans for better quality control practice.

[1]

[2] [3]

[4]

[5]

REFERENCES Bohning, D., Dietz, E.,Schlattmann, P. (1999). The zero-inflated Poisson model and the decayed missing and filled teeth index in dental epidemiology. Journal of Royal Statistical Society, Series A 162:195–209. Dodge H.F, Chain sampling inspection plans, Industrial Quality Control II(4) (1955), 10-13 . Lambert, D. (1992). Zero-inflated Poisson regression with an application to defects in manufacturing. Technometrics 34:1–14. Loganathan, A and Shalini, K., (2013). Determination of Single sampling plans by attributes under the conditions of Zero – inflated Poisson distribution. Communications in Statistics – Simulation and Computation 43:3, 538-548. McLachan,G.,Peel,D.(2000).FiniteMixtureModel.Ne wYork:John Willey & Sons

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Healthcare Services â&#x20AC;&#x201C; IoT Enabling Technologies, Usage and Impact Varsha Dubey Upadhyay Faculty of Computer Science, PAHER University, Udaipur, India E-mail ID:dubeyvarsha46@gmail.com Nitika Vats Doohan Computer Science Department, Medi-Caps University, Indore, India E-mail ID:nitika.doohan@gmail.com Naresh Menaria Department of Mathematics, PAHER University, Udaipur, India E-mail ID: naresh.menaria14@gmail.com

Abstract- The concept of integrating physical world integrating with digital world is possible with Internet of Things (IoT) technology. It has paved way for plethora of use cases which were not imaginable earlier. The unprecedented possibilities in distributed environments with IoT bring certain issues and implications with it. At the same time, healthcare industry in the contemporary world needs integration with IoT to reap benefits of the ultimate technology. The technologies associated with IoT such as Radio Frequency Identification (RFID), networks with sensors and actuators, wearable devices, Service Oriented Architecture (SOA), smart mobile devices make the integration with healthcare units possible. The existing research found in the literature shows the need for further focus on this technology usage in healthcare domain with comprehensive research framework. In this paper we present a comprehensive conceptual framework containing the combination of research methods such as quantitative, qualitative and case study. Key areas of Research covered are IoT integration with healthcare unit and patient-centric healthcare services, unprecedented growth in service quality, technology innovations, technology barriers, security issues, and data analytics for business intelligence. This paper summarise the usage, impact and issues of IoT integration with healthcare units besides testing hypothesis, giving conclusions and recommendations. Keywords - Internet of Things (IoT), healthcare units, technology barriers, security, standards, usage, impact .

1. INTRODUCTION This study is about the integration of IoT technology with healthcare units for covering usage, impact, privacy and security issues involved when IoT is integrated with healthcare infrastructure. IoT is a definitive innovation that exploits inter-disciplinary technologies to realize a dream network of things that connect physical world with digital world for maximizing benefits which could not be imagined

otherwise. Xu et al. [16] opined that the use of IoT in healthcare and other domains result in exponential growth of data. Managing such data and having ubiquitous approach to data access is challenging. Using Radio-Frequency Identification (RFID) physical objects can have ability to have identity and participate in integration with digital world. Lee & Lee [8] stated that there are five technologies that are essential for realizing IoT. They include RFID, Wireless Sensor Network (WSN), IoT Middleware, cloud computing and IoT application software. Catarinucci et al. [3] proposed IoT-aware smart architecture for monitoring and tracking medical devices, patients, and personnel automatically. In their architecture RFID, Wireless Sensor Network (WSN) and smart mobiles play an important role. Gandy [6] Health map is an application that can monitor disease trends across the globe. It can provide outbreaks in current location or any location in which people would like to know. It gives appropriate message to the users on diverse diseases and their severity. With IoT integration, these services can be made real time and improve quality and accuracy. Ahmad et al. [2] proposed the concept of smart cyber society (SCS) as a platform with different communication layers. They focused on Cyber Physical System (CPS), Web of Things (WoT) and with the use of Internet of things to realize SCS. They investigated the system with different things in mind including emergency, healthcare, safety and security. They opined that the proposed system can improve healthcare services. They also found that technologies like Web Services provide interoperability and Machine to Machine (M2M) interaction. They could also identify the role of wearable devices in healthcare when integrated with CPS. Sakr et al. [14] proposed a CPS for comprehensive data analytics and healthcare monitoring. In the context of big data produced by healthcare integrated IoT systems, their study assumes importance. They focus on big data analytics, IoT, cloud computing. Rathore et al.[12] proposed an IoT based system for integrating smart cities, IoT and big data analytics. Especially the focus was on IoT applications for healthcare units. Wan et al. [15] opined the use of Mobile Cloud Computing in healthcare. They opined that incorporation of mobile cloud computing in conjunction with body area

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.9 SEPTEMBER 2018

networks where body wearable equipments receive health data of patient as and when required in a given point in time. Service Oriented Architecture supports in the incorporation of assorted applications with interoperability. This is highly recommended technology for realizing IoT in distributed environment. Addo et al. [1] studied privacy and security in IoT applications. They proposed a reference architecture that can be used to build cloud-based IoT applications in the real world. They stated that privacy and security are essential part for maintainable IoT development in healthcare domain. Popescul & Radu [11] explored security related issues such as hacking, loss of boundary, messy complexity and hyper connectivity in case of smart cities involved in IoT integrated healthcare. . Roman et al. [13] explored that distributed character of IoT along with associated technology propel security and privacy concerns. Kamoun et al. [7] studied data breaches in healthcare domain. Data breaches are related to misuse of health information which leads to identity theft, fake health insurance, financial identity theft, medical identity fraud, and privacy violations. . IoT and its security concerns are also studied by Li [9]. They listed top 10 vulnerabilities of IoT such as poor physical security, insecure software, insecure configuration, insecure mobile interface, insecure cloud interface, privacy concerns, lack of transport encryption, insecure network services, authentication inefficiency, authorization inefficiency, and insecure web interface. 2. KEY AREAS OF RESEARCH

Fig.1 Key areas of Research 1. Investigates the effect of IoT integration with healthcare units on promoting patient-centric healthcare services. Since patient-centric healthcare services can improve service quality, it is considered for the study.

2. Investigates the effect of IoT integration with healthcare units in achieving unprecedented quality of service (QoS). 3. Investigates effect of technology innovations. It is considered as technology plays vital role in implementation of IoT in healthcare units. 4. Security issues and technology barriers. Since IoT is the conglomeration of many standards and technologies, it play vital role in probing the facts. 5. The effect of data analytics in healthcare units for improvement of quality of service 3. METHODOLOGIES The research methodology used in this study includes multiple approaches. They include secondary research, case study and primary research methods such as structured survey and interview. The results of these approaches are interpreted in order to have conclusions on the application of IoT in healthcare units and the impact. The review of literature provides useful insights to ascertain the present state-of-theart and gain knowledge to frame research questions or hypothesis for the completion of the study. Besides it leads to survey questions and interview questions that are used in the primary research. The secondary research is made on wide variety of technologies such as RFID, WSN, and EPC and so on that are required for the realization of IoT and leverage of quality services in healthcare units. In this research two types of data collection method are used. Questionnaire as structured survey using online survey tool Surveymonkey.com. Interview as a qualitative method using GoTo Meeting as video conferencing medium. The sample size for structured survey is 150. It does mean that around 150 people participate in the survey. Respondents industry association with medical industry is in the range of >1 year to more than 10 years. Respondentâ&#x20AC;&#x2122;s age is in the range of 25 to 60 years. Random sampling method has been used to collect sample data. Responses have been gathered from healthcare experts such as technicians, physicians and other stakeholders from various representative healthcare units. Sample size for Interview method is 25. In this research data analysis is done using two approaches as quantitative and qualitative. Quantitative analysis is computed using SPSS tool and Minitab (Chi-square test) which is meant for statistical analysis. Qualitative analysis is made to interpret the results of interview. 4.RESULT To compute and validate results, Minitab software tool has been used to conduct statistical analysis on the basis of collected data. Minitab offers the opportunity to run various Hypothesis statistical tests. Case Processing Summary Number of cases: Valid = 150, Excluded = 0, Total=150 The industry association or Experience of respondents with medical industry revealed that, 38% of

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respondents have 1-5 years of experience and equal share of 38% have less than 1 year of experience and 10 % people got rich experience i.e. more than 10 years. The age of the respondents indicating that, 38% of respondents are aged between 25-30 i.e. very young generation people, and 40 % are 31-40 years category followed by 10 % & 11% each in 4150 and 51-60 years category. Chi-square test for association, Pearson Chi square statistic:

H03: No significant relation between Industry association and their opinion of IoT associated with healthcare services can help improving patient experience thereby improving QoS. Pearson Chi-Square Value =138.784, DF=1, P Value = 0.000 Likelihood Ratio Value =140.289, DF=1, P Value = 0.000

Likelihood ratio Chi square statistic:

H05: No significant association among Age of respondents and their opinion of Patients health records are to be handled, accessed with high level security, with IoT in place Pearson Chi-Square Value =138.784, DF=1, P Value = 0.000 Likelihood Ratio Value =140.289, DF=1, P Value = 0.000

Here, Oij is observed frequency, Eij is expected frequency, DF equal to {(r â&#x20AC;&#x201C; 1) * (c â&#x20AC;&#x201C; 1)}. Here r represents number of rows and c represent number of columns Expected cell count:

Here, ni+ means number of observations from ith row, n+j means number of observations from jth column, n++ means total number of observations Contribution to Chi-square statistic:

Here, Oij is observed frequency, Eij is expected frequency Summary of Hypothesis Tested The null hypotheses defined for the present study has been tested using Chi-square analysis. A set of methods such as Observed value, Expected value, Contribution to Chisquare, Pearson, Likelihood ratio, degree of freedom are used. Finally significance is computed. The computed significant value and the standard significance value (P-Value - 0.05) is shown in Result column in Table 1. To conduct Chi-square test for association, collected data has been arranged to put into Minitab worksheet. Minitab command Stat > Tables > Chi-square test for association has been run. H01: No significant association among Age of respondents and their opinion of RFID, EPC can eliminate mistakes in Identification and tracking in healthcare services Pearson Chi-Square Value =133.656, DF=1, P Value = 0.000 Likelihood Ratio Value =135.026, DF=1, P Value = 0.000 H02: No significant association among Age of respondents and their opinion of GPS can help to improve locating ambulances and medical equipment in healthcare services Pearson Chi-Square Value =150.000, DF=1, P Value = 0.000 Likelihood Ratio Value =155.502, DF=1, P Value = 0.000

H04: No significant association among Age of respondents and their opinion of Privacy and Secure health information sharing is required in the context of integration with IoT Pearson Chi-Square Value =138.784, DF=1, P Value = 0.000 Likelihood Ratio Value =140.289, DF=1, P Value = 0.000

H06: No significant relation between Industry association and their opinion of Cloud computing technology can enable storage and data analytics in efficient health information system Pearson Chi-Square Value =144.458, DF=1, P Value = 0.000 Likelihood Ratio Value =151.483, DF=1, P Value = 0.000 H07: No significant relation between Industry association and their opinion of Big data analytics can improve IoT integration with healthcare services Pearson Chi-Square Value =150.000, DF=1, P Value = 0.000 Likelihood Ratio Value =160.565, DF=1, P Value = 0.000 H08: No significant association among Age of respondents and their opinion of MCC enable healthcare centres in monitoring Patient's health Pearson Chi-Square Value =150.000, DF=1, P Value = 0.000 Likelihood Ratio Value =155.502, DF=1, P Value = 0.000 H09: No significant relation between Industry association and their opinion of CPS can help IoT integration with healthcare services Pearson Chi-Square Value =150.000, DF=1, P Value = 0.000 Likelihood Ratio Value =160.565, DF=1, P Value = 0.000 H10: No significant relation between Industry association and their opinion of SOA play a crucial role in realization of IoT Pearson Chi-Square Value =133.253, DF=1, P Value = 0.000 Likelihood Ratio Value =131.361, DF=1, P Value = 0.000 Table 1 Explain hypothesis, result and derived interpretation of chi-square test

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Table 1 Chi Square Analysis Results Hypothesis Result Interpretation H01: No significant Computed As per expert’s opinion, association among significant EPC and RFID usage in Age of respondents value .000. medical field can and their opinion of (Standard sig. eliminate mistakes in RFID , EPC can value is <0.05) tracking and locating eliminate mistakes in Reject null medicines, patients, Identification and hypothesis doctors, and wearable tracking in healthcare devices used by patients services as part of IoT. H02: No significant Computed As per medical expert’s association among significant opinion, the role of GPS Age of respondents value .000 will improve the locating and their opinion of (Standard sig. medical equipment and GPS can help to value is <0.05) vehicles associated with improve locating Reject null healthcare. ambulances and hypothesis medical equipment in healthcare services H03: No significant Computed As per doctor’s opinions, relation between significant patient centric approach Industry association value .000 with IoT associated with and their opinion of (Standard sig. healthcare services can IoT associated with value is <0.05) reduce costs, improve healthcare services Reject null treatment results, real time can help improving hypothesis disease monitoring, patient experience minimize errors and thereby improving improve patient QoS experience in terms of improved Quality of Service (QoS). H04: No significant Computed As per expert’s opinion, association among significant there is need for privacy Age of respondents value .000 preserving and secure data and their opinion of (Standard sig. sharing in healthcare units Privacy and Secure value is <0.05) as they are integrated with health information Reject null IoT. sharing is required in hypothesis the context of integration with IoT H05: No significant Computed As per age wise medical association among significant experts, with IoT in place, Age of respondents value .000 patient health records are and their opinion of (Standard sig. to be handled, accessed Patients health value is <0.05) with high level of security records are to be Reject null handled, accessed hypothesis with high level security, with IoT in place H06: No significant Computed As per medical expert’s relation between significant opinion, Cloud computing Industry association value .000 technology can enable and their opinion of (Standard sig. storage and data analytics Cloud computing value is <0.05) in efficient health technology can Reject null information system enable storage and hypothesis data analytics in efficient health information system H07: No significant Computed As per medical expert’s relation between significant opinion, Big data Industry association value .000 analytics can improve IoT and their opinion of (Standard sig. integration with Big data analytics can value is <0.05) healthcare services improve IoT Reject null integration with hypothesis

healthcare services H08: No significant association among Age of respondents and their opinion of MCC enable healthcare centres in monitoring Patient's health

Computed significant value .000 (Standard sig. value is <0.05) Reject null hypothesis

As per medical experts opinion, (MCC) enables healthcare centres to monitor patients’ health through smart and wearable devices to provide patient centric services.

H09: No significant relation between Industry association and their opinion of CPS can help IoT integration with healthcare services H10: No significant relation between Industry association and their opinion of SOA play a crucial role in realization of IoT

Computed significant value .000 (Standard sig. value is <0.05) Reject null hypothesis Computed significant value .000 (Standard sig. value is <0.05) Reject null hypothesis

As per medical expert’s opinion, CPS can help IoT integration with healthcare services. CPS role is to protect digital infrastructure from cyber attacks As per medical expert’s opinion, SOA play an essential part in comprehension of IoT. SOA can help in interoperability among different platforms and technologies in the context of enhancing QoS in healthcare centers

Table 2 Comparative Study of Results Findings from Findings from Current Previous Research(s) Research RFID implementation EPC & RFID usage help in help in improving eliminating errors in patient safety – 70% locating, tracking of respondents agreed. Patients, medicines - 82 % RFID & EPC enable respondents agreed availability of information in an automated way which helps in required medication to patients. GPS Help in Patient Help in locating medical emergency situations equipment and healthcare through location vehicles 84 % tracking, informing respondents agreed nurse or doctor for quick rescue. Efficiency, Technology help in Patient centric approach Performance automating processes can reduce costs, improve reduced task time and treatment results, real time enhanced quality of disease monitoring, service delivery. minimize errors, and improve patient experience - 83 % respondents agreed Privacy and Identified the Patient health information Security importance of study should be provided with at around Security and most level of security. Privacy challenges from Necessity of data access distributed approach of policies with controls for IoT integration. providing electronic health records - 75 % respondents agreed Data Possibility to capture Healthcare units produce Analytics vast individual health huge amount of data and data and embedding that needs data analytics Business analytics tools and cloud for sustainable into IoT devices for real utilization 75 % time decision making. respondents agreed Studied Parameters RFID

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CPS

CPS integrates with multifunctional sensors and help in continuously monitoring physical environment (Healthcare) for its improvement.

CPS helps integrate healthcare systems, GPS, bio-sensors, Internet of vehicles and smart devices in the context of IoT. CPS plays a significant role in protecting digital infrastructure from cyber attacks. - 75 % respondents agreed

90 70 70 50 30 Previous Reseach

Current Research

Agreed Respondents %

Fig 2: Graphical representation of % Respondents agreed that RFID usage and implementation help in IoT

100 90 80 70 60 50 40 30

GPS

Data Analytics

CPS

Agreed Respondents %

Fig 3: Graphical representation of % Respondents agreed that GPS, Data Analytics and CPS play vital role in IoT integration with healthcare unit for improvement

5. DISCUSSION Several studies (Dlodlo, 2013; Madanian, 2016) opined that RFID and EPC contribute in achieving Internet of things and its integration with healthcare. There is 82% respondents agreed on the proposition that is EPC and RFID usage in medical field can eliminate mistakes in tracking and locating medicines, patients and doctors. A few researchers (Dlodlo, Mofolo, & Kagarura 2013; Wan, 2016) explored that the use of GPS technology with integration of IoT in healthcare services will improve the quality of life. When an accident happens, immediate family, a doctor, or a nurse are immediately informed, and they attempt rescue according to the GPS location. Also, if a patient falls seriously ill, they can conveniently request help. This proposition has 84% support which reflect the role of GPS in locating medical equipment and vehicles associated with healthcare. Dlodlo [5] explored potential applications of IoT including patient centric services in healthcare domain. This proposition has 83% support that is patient centric approach

with IoT associated with healthcare services can reduce costs, improve treatment results, real time disease monitoring, minimize errors, and improve patient experience in terms of improved QoS. Addo et al. [1] stated that privacy and security are essential part for maintainable IoT development in healthcare domain. It has 77% support in the research .It reveals that there is need for privacy preserving and secure data sharing in healthcare units as they are integrated with IoT. Roman et al. [13] explored that distributed character of IoT along with associated technology propel security and privacy concerns. This proposition has 75% support. That reveals privacy challenges. Lee & Lee [8] stated that cloud computing technology is essential for realizing IoT. This Preposition has 78% support. It reflect that healthcare units produce huge amount of data and that needs data analytics and cloud for sustainable utilization .Exponential development of data is likely to be formed by IoT Xu et al. [16] there is 82% support for this proposition. It exposes the connotation of correlating IoT integrate healthcare unit with big data processing by means of distributed programming frameworks and data analytics can improve healthcare services. Wan et al. [15] explored the usage of MCC in healthcare services. They stated that integration of MCC with body area networks where wearable devices get health information of patient in real time .The responses towards this were encouraging as 76% of the participants supported the proposition that is MCC enables healthcare centres to monitor patientsâ&#x20AC;&#x2122; health through smart and wearable devices to provide patient centric services. Ahmad et al. [2] explored on CPS and WoT in development of the usage of IoT to comprehend smart cyber society and healthcare monitor. This preposition has 77% support. It reveals that CPS can help IoT integration with healthcare services Chen, Hsu and Leu[4] SOA is an architecture that helps in integration of heterogeneous applications with interoperability and SOA and AJAX technologies can help achieve seamless integration among applications and devices. This preposition has 76% support which reflects SOA helps in integration of heterogeneous applications with interoperability. The research provides insight that standard identification technologies like RFID can help improving healthcare services. EPC and RFID usage in medical field can eliminate mistakes in tracking and locating medicines, patients, doctors and wearable devices used by patients as part of IoT. It is revealed from the research that role of GPS, wearable sensor devices, patient centric approach for improved QoS, data privacy concerns, handling exponential growth of data using cloud computing and the usage of distributed programming frameworks for data analytics are to

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be considered in the knowledge sharing and training programs to bring awareness among the stakeholders. The research provides the interesting information that Utility of big data science, need for high level of security, utility of MCC are to be considered for further awareness. It has been revealed in the research that SOA, use of cloud computing technology, increased utility of CPS, mobility based healthcare, privacy preserving and secure health information sharing, big data analytics and unimaginable impact of IoT in healthcare units are crucial aspects in IoT integration with healthcare unit for improvement. 6 .CONCLUSION AND FUTURE WORK The aim of this research is to investigate the application of IoT and related technologies to improve healthcare services besides issues or challenges involved. The primary data collection with survey and the results interpretation provided ample evidence that IoT integration with healthcare units can bring about plethora of benefits such as real time healthcare, patient centric services and unprecedented improvement in QoS. The results of survey showed problems faced in the integration barriers such as lack of standards, legacy systems and interoperability, uncertainty on IoT benefits, nonavailability of well defined workflows, and technologies are still immature. Security issues such as cyber attacks, ransomware like WannaCry (recent outburst of malady), data confidentiality and eavesdropping, and identity threats are raised in the interviews. Need for technology standards, interoperability improvement, and protection of sensors are other concerns. The insights of the research methods revealed that IoT integration with healthcare units should provided security, standards, data privacy and business intelligence concerns are taken care of. Technologies i.e. RFID, EPC, GPS, MCC, CPS, Cloud computing and Big data analytics are key IoT levers to improve Healthcare services. In future we intend to focus on the research based on the feasibility of remote health services in particular with IoT.

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