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A framework for IoT service selection

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Abstract

IoT is getting popular as it makes human life comfortable. The industry giants such as IBM, Microsoft, Cisco and Amazon have started offering IoT assistance in form of services. Numerous IoT applications exist today with different roles to play in day-to-day life. Because of application diversity and a good number of IoT service providers, it is difficult for IoT users to select the best one as per the requirement and expected quality of service, QoS. To address this, QoS metrics related to major IoT components, i.e., communication, computing and things, are designed to assess the alternative services. IoT users can express their requirements regarding QoS, while service providers exhibit their offerings. Because of three major IoT components, service selection is considered as multi-criteria group decision-making (MCGDM) problem. This work proposes a new MCGDM framework to rank the IoT services that considers rank reversal problem, judgments of decision makers in linguistic term and the uncertainty and risk-attitudinal characteristics in human decision-making. The proposed framework is validated by comparing it with an existing MCGDM model. A case study on IoT health-care application is provided besides the sensitivity analysis to demonstrate the effectiveness of the proposed framework.

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References

  1. internet of things: Global Internet of Things market to hit $1.29 trillion by 2020: Report, Telecom News, ET Telecom (2017). https://telecom.economictimes.indiatimes.com/news/global-internet-of-things-market-to-hit-1-29-trillion-by-2020-report/61053782. Accessed 5 Feb 2018

  2. Internet of Things Market by Software Solution & Platform—2022 | MarketsandMarkets (2017). https://www.marketsandmarkets.com/Market-Reports/internet-of-things-market-573.html. Accessed 5 Feb 2018

  3. Cees Links (2017) Evolution of the IoT as a service | 2017-05-15 | Microwave Journal. http://www.microwavejournal.com/articles/28301-evolution-of-the-iot-as-a-service. Accessed 5 Feb 2018

  4. Freight Farms| Xively by LogMeIn (2018). https://www.xively.com/customers/freight-farms. Accessed 5 Feb 2018

  5. Xively (2018). https://www.xively.com/. Accessed 5 Feb 2018

  6. Internet of Things (2018). https://www.happiestminds.com/services/internet-of-things/. Accessed 5 Feb 2018

  7. Kim S (2017) R-learning-based team game model for Internet of things quality-of-service control scheme. Int J Distrib Sens Netw 13(1):1550147716687558

    Google Scholar 

  8. Junior FRL, Osiro L, Carpinetti LCR (2014) A comparison between fuzzy AHP and fuzzy TOPSIS methods to supplier selection. Appl Soft Comput 21:194–209

    Article  Google Scholar 

  9. Hatami-Marbini A, Tavana M (2011) An extension of the Electre I method for group decision-making under a fuzzy environment. Omega 39(4):373–386

    Article  MATH  Google Scholar 

  10. Jin X, Chun S, Jung J, Lee KH (2014) IoT service selection based on physical service model and absolute dominance relationship. In: Proceedings—IEEE 7th International Conference on Service-Oriented Computing and Applications, SOCA 2014, pp 65–72

  11. Jin X, Chun S, Jung J, Lee K-H (2016) A fast and scalable approach for IoT service selection based on a physical service model. Inf Syst Front 19(6):1357–1372

    Article  Google Scholar 

  12. Khanouche ME, Amirat Y, Chibani A, Kerkar M, Yachir A (2016) Energy-centered and QoS-aware services selection for Internet of things. IEEE Trans Autom Sci Eng 13(3):1256–1269

    Article  Google Scholar 

  13. Perera C, Zaslavsky A, Christen P, Compton M, Georgakopoulos D (2013) Context-aware sensor search, selection and ranking model for internet of things middleware. In: Proceedings—IEEE International Conference on Mobile Data Management, vol 1, pp 314–322

  14. Liu J et al (2013) A cooperative evolution for QoS-driven IOT service composition. Autom J Control Meas Electron Comput Commun 54(4):438–447

    Google Scholar 

  15. Qi L, Dai P, Yu J, Zhou Z, Xu Y (2017) “Time–Location–Frequency”–aware Internet of things service selection based on historical records. Int J Distrib Sens Netw 13(1):1550147716688696

    Article  Google Scholar 

  16. Giacobbe M, Di Pietro R, Zaia A, Puliafito A (2017) The internet of things in oil and gas industry : a multi criteria decision making brokerage strategy. In: Proceedings-4th International Conference on Automation, Control Engineering and Computer Science, vol 21, pp 47–52

  17. Singla C, Mahajan N, Kaushal S, Verma A, Sangaiah AK (2018) Modelling and analysis of multi-objective service selection scheme in IoT-cloud environment. In: Lecture Notes on Data Engineering and Communications Technologies, pp 63–77

  18. Ai Y, Peng M, Zhang K (2017) Edge cloud computing technologies for internet of things: a primer. Digit Commun Netw 4(2):77–86

    Article  Google Scholar 

  19. Bonomi F, Milito R, Zhu J, Addepalli S (2012) “Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp 13–16

  20. Baranwal G, Vidyarthi DP (2014) A framework for selection of best cloud service provider using ranked voting method. In: Souvenir of the 2014 IEEE International Advance Computing Conference, IACC 2014, pp 831–837

  21. Tripathi A, Pathak I, Vidyarthi DP (2017) Integration of analytic network process with service measurement index framework for cloud service provider selection. Concurr Comput 29(12):e4144

    Article  Google Scholar 

  22. Naik N (2017) Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP. In: 2017 IEEE International Symposium on Systems Engineering, ISSE 2017—Proceedings

  23. Garg SK, Versteeg S, Buyya R (2013) A framework for ranking of cloud computing services. Futur Gener Comput Syst 29(4):1012–1023

    Article  Google Scholar 

  24. Baranwal G, Vidyarthi DP (2016) A cloud service selection model using improved ranked voting method. Concurr Comput 28(13):3540–3567

    Article  Google Scholar 

  25. P. Persson and O. Angelsmark, “Calvin – Merging Cloud and IoT,” Procedia Comput. Sci., vol. 52, no. Ant, pp. 210–217, 2015

  26. Miorandi D, Sicari S, De Pellegrini F, Chlamtac I (2012) Internet of things: vision, applications and research challenges. Ad Hoc Netw 10(7):1497–1516

    Article  Google Scholar 

  27. Wu J, Ping L, Ge X, Ya W, Fu J (2010) Cloud storage as the infrastructure of cloud computing. In: Proceedings—2010 International Conference on Intelligent Computing and Cognitive Informatics, ICICCI 2010, pp 380–383

  28. Azure IoT Hub high availability and disaster recovery | Microsoft Docs (2017). https://docs.microsoft.com/en-us/azure/iot-hub/iot-hub-ha-dr. Accessed 5 Feb 2018

  29. Sidhu J, Singh S (2017) Improved TOPSIS method based trust evaluation framework for determining trustworthiness of cloud service providers. J Grid Comput 15(1):81–105

    Article  Google Scholar 

  30. Heer T, Garcia-Morchon O, Hummen R, Keoh SL, Kumar SS, Wehrle K (2011) Security challenges in the IP-based Internet of Things. Wirel Pers Commun 61(3):527–542

    Article  Google Scholar 

  31. Información general sobre AWS IoT Core—Amazon Web Services (2018). https://aws.amazon.com/iot-core/pricing/. Accessed 5 Feb 2018

  32. Pricing | Google Cloud Internet of Things Core | Google Cloud Platform (2018). https://cloud.google.com/iot/pricing. Accessed 5 Feb 2018

  33. Senouci MA, Mushtaq MS, Hoceini S, Mellouk A (2016) TOPSIS-based dynamic approach for mobile network interface selection. Comput Netw 107:304–314

    Article  Google Scholar 

  34. Bari F, Leung V (2007) Automated network selection in a heterogeneous wireless network environment. IEEE Netw 21(1):34–40

    Article  Google Scholar 

  35. Network Delays and Losses (2018). https://www.d.umn.edu/~gshute/net/delays-losses.xhtml. Accessed 5 Feb 2018

  36. Gerber A (2017) Connecting all the things in the Internet of Things. https://www.ibm.com/developerworks/library/iot-lp101-connectivity-network-protocols/index.html. Accessed 5 Feb 2018

  37. Conti M, Dehghantanha A, Franke K, Watson S (2018) Internet of Things security and forensics: challenges and opportunities. Futur Gener Comput Syst 78:544–546

    Article  Google Scholar 

  38. Weber RH (2010) Internet of Things–New security and privacy challenges. Comput Law Secur Rev 26(1):23–30

    Article  MathSciNet  Google Scholar 

  39. Gridelli S (2014) How to calculate network availability | NetBeez. https://netbeez.net/blog/how-to-calculate-network-availability/. Accessed 5 Feb 2018

  40. Islam K, Shen W, Wang X (2012) Wireless sensor network reliability and security in factory automation: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):1243–1256

    Article  Google Scholar 

  41. Xiao Q, Xu K, Wang D, Li L, Zhong Y (2014) TCP performance over mobile networks in high-speed mobility scenarios. In: Proceedings—International Conference on Network Protocols, ICNP, pp 281–286

  42. Henry Menke (2018) How do I choose the right sensor? | AUTOMATION INSIGHTS. https://automation-insights.blog/2010/02/24/hello-world/. Accessed 5 Feb 2018

  43. Sensor Selection Guide (2018). https://w3.siemens.com.br/buildingtechnologies/br/pt/automacao-predial/dc/documents/sensores.pdf.[Accessed 30 Oct 19

  44. Sensor Selection Guide (2018). http://www.intlsensor.com/pdf/sensorSelectionGuide.pdf. Accessed 30 Oct 19

  45. Sensor Selection Guide | MaxBotix Inc. (2018). https://www.maxbotix.com/SelectionGuide/Selection-Guide.htm. Accessed 30 Oct 19

  46. Shieh J, Huber JE, Fleck NA, Ashby MF (2001) The selection of sensors. Prog Mater Sci 46(3–4):461–504

    Article  Google Scholar 

  47. “IEC 60529,” 2013. [Online]. Available: https://webstore.iec.ch/publication/2452#additionalinfo. [Accessed: 05-Feb-2018]

  48. NEMA (2018). https://www.nema.org/pages/default.aspx. Accessed 5 Feb 2018

  49. Carolyn Mathas (2012) Sensor reliability challenges and improvements | DigiKey. https://www.digikey.in/en/articles/techzone/2012/sep/sensor-reliability-challenges-and-improvements. Accessed 5 Feb 2018

  50. Papetti A, Capitanelli A, Cavalieri L, Ceccacci S, Gullà F, Germani M (2016) Consumers vs Internet of Things: a systematic evaluation process to drive users in the smart world. Procedia CIRP 50:541–546

    Article  Google Scholar 

  51. Futek (2018) Sensor Reliability. http://www.futek.com/files/Pdf/TechnicalDocuments/Sensor Reliability.pdf. Accessed 5 Feb 2018

  52. Kalantar-zadeh K (2013) Sensors: an introductory course. Springer, New York

    Book  Google Scholar 

  53. Hwang C-L, Yoon K (1981) Methods for multiple attribute decision making. In: Multiple Attribute Decision Making: Methods and Applications A State-of-the-Art Survey, pp 58–191

  54. García-Cascales MS, Lamata MT (2012) On rank reversal and TOPSIS method. Math Comput Model 56(5–6):123–132

    Article  MathSciNet  MATH  Google Scholar 

  55. Yager RR (1988) On ordered weighted averaging aggregation operators in multi criteria decision making. IEEE Trans Syst Man Cybern 18(1):183–190

    Article  MATH  Google Scholar 

  56. Zarghami M (2011) Soft computing of the Borda count by fuzzy linguistic quantifiers. Appl Soft Comput J 11(1):1067–1073

    Article  Google Scholar 

  57. Chen CT (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst 114(1):1–9

    Article  MATH  Google Scholar 

  58. Ghosh A, Sarkar S (2015) Pricing for profit in internet of things. In: IEEE International Symposium on Information Theory—Proceedings, 2015–June, pp 2211–2215

  59. Hossain MS, Muhammad G (2015) Cloud-assisted Industrial Internet of Things (IIoT)—Enabled framework for health monitoring. Comput Netw 101:192–202

    Article  Google Scholar 

  60. Garg H, Agarwal N, Tripathi A (2015) Entropy based multi-criteria decision making method under fuzzy environment and unknown attribute weights. Glob J Technol Optim 6(3):13–20

    Google Scholar 

  61. Pajer S, Streit M, Torsney-Weir T, Spechtenhauser F, Möller T, Piringer H (2016) Weightlifter: visual weight space exploration for multi-criteria decision making. IEEE Trans Vis Comput Graph 23(1):611–620

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Banaras Hindu University, Varanasi, India

    Gaurav Baranwal

  2. Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India

    Manisha Singh

  3. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

    Deo Prakash Vidyarthi

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  1. Gaurav Baranwal
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  2. Manisha Singh
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  3. Deo Prakash Vidyarthi
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Correspondence to Gaurav Baranwal.

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Baranwal, G., Singh, M. & Vidyarthi, D.P. A framework for IoT service selection. J Supercomput 76, 2777–2814 (2020). https://doi.org/10.1007/s11227-019-03076-1

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