Intelligent Communication Between IoT Devices on Edges in Retail Sector

  • M. SaravananEmail author
  • N. C. Srinidhi Srivatsan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 887)


In this paper, we have designed a custom communication model for IoT devices on its edges to induce intelligence in the retail sector. Wireless technologies, such as RFID (Radio Frequency Identification), NFC (Near Field Communication), needed BLE (Bluetooth Low Energy), and LPWAN (Low-Power Wide-Area Network), etc. were delegated to perform various tasks within the available services. We introduce Smart Shop (SmSH) architecture that integrates features of context-aware services, platform independent, edge computing and proximity sensing to establish seamless connectivity for the customer entrusted tasks. This architecture should encompass the M2M communication devices and gateways which are generally connected to the cloud environment to establish meaningful communication in commercial IoT applications. Our architecture has been proposed primarily for the retail sector and can be extended easily to other domains by altering the required functionalities, device types and services.


Device communications M2M-IoT SmSH architecture Commercial IoT Retail domain 


  1. 1.
    Millar, L.: Dead Malls: Half of America’s Shopping Centres Predicted to Close by 2030. ABC News, New York (2015)Google Scholar
  2. 2.
    Wong, B.: 6 Retail Metrics You Absolutely Need to Track for Your Store. Bindo POS, New York (2014)Google Scholar
  3. 3.
    Girish, D.: Beacon Analytics in Retail-4 Essential Metrics for Retailers. Beaconstac, New York (2015)Google Scholar
  4. 4.
    Wiechert, T., Schaller, A., Thiesse, F.: Near field communication use in retail stores: effects on the customer shopping process. In: Konferenz Mobile und Ubiquitäre Informationssysteme, Münster, Germany (2009)Google Scholar
  5. 5.
    Link Labs: A Comprehensive Look at Low Power, Wide Area Networks. Link Labs, New York (2016)Google Scholar
  6. 6.
    Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)CrossRefGoogle Scholar
  7. 7.
    Mulloni, A., Wagner, D., Barakonyi, I., Schmalstieg, D.: Indoor positioning and navigation with camera phones. IEEE Pervasive Comput. 8(2), 22 (2009)CrossRefGoogle Scholar
  8. 8.
    Cheung, K.C., Intille, S.S., Larson, K.: An Inexpensive Bluetooth-based indoor positioning hack. In: Eighth International Conference of Ubiquitous Computing (UbiComp2006), California (2006)Google Scholar
  9. 9.
    ARToolkit 6: Daqri. Accessed 31 Aug 2017
  10. 10.
    Studierstube Tracker: Accessed 31 Aug 2017
  11. 11.
    Gaskell, A.: Using AR to Help Disabled Shoppers, 28 August 2017. Accessed 31 Aug 2017
  12. 12.
    Zhu, W., Owen, C.B., Li, H., Lee, J.H.: Design of the PromoPad: An automated augmented reality shopping assistant. In: 12th Americas Conference on Information Systems, AMCIS 2006, Acapulco, Mexico (2006)Google Scholar
  13. 13.
    Brody, A.B., Gottsman, E.J.: Pocket Bargain finder: a handheld device for augmented commerce. In: 1st international symposium on Handheld and Ubiquitous Computing, London (1999)CrossRefGoogle Scholar
  14. 14.
    Ruiz, J., Li, Y.: DoubleFlip: a motion gesture delimiter for mobile interaction. In: CHI 2011 Session: Interaction on Mobile Devices, Vancouver, Canada (2011)Google Scholar
  15. 15.
    Bekkelien, A.: Bluetooth indoor positioning. In: 8th International Conference “Informatics for Environmental Protection”, Geneva (2012)Google Scholar
  16. 16.
    Hong, P.M.: IOTivity: Cloud Native Architecture and the Internet of Open Source Things, White Paper, September 2015. Accessed 31 Aug 2017
  17. 17.
    Anavi, L., Coval, P.: Connected Tizen: Bringing Tizen to your connected devices using the Yocto Project. In: FOSDEM 2016, Brussels (2016)Google Scholar
  18. 18.
    Jaykumar, J., Blessy, A.: Secure smart environment using IOT based on RFID. Int. J. Comput. Sci. Inf. Technol. 5(2), 2493–2496 (2014)Google Scholar
  19. 19.
    Perera, C., Zaslavsky, A., Christen, P., Salehi, A., Georgakopoulos, D.: Capturing sensor data from mobile phones using Global Sensor Network middleware. In: IEEE 23rd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), Sydney, NSW, Australia (2012)Google Scholar
  20. 20.
    Libelium: Meshlium, Libelium. Accessed 31 Aug 2017
  21. 21.
    Ludovici, A., Calveras, A.: A proxy design to leverage the interconnection of CoAP wireless sensor networks with web applications. Sens. Smart Cities 15(1), 1217–1244 (2014)Google Scholar
  22. 22.
    Noronha, A., Moriarty, R., O’Connell, K., Villa, N.: Attaining IoT Value: How to Move from Connecting Things to Capturing Insights, White Paper. Cisco, California (2015)Google Scholar
  23. 23.
    Oen, H.M.: Interoperability at the Application Layern in the Internet of Things - Master Thesis, Trondheim. Norwegian Institute of Science and Technology, Norway (2015)Google Scholar
  24. 24.
    Data-Distribution Service Specification at OMG-Proven Data Connectivity Standard for the IoT. Accessed 31 Aug 2017
  25. 25.
    Saravanan, A.D.A.V.I.M.: Smart water grid management using LPWAN IoT technology. In: 2017 Global Internet of Things Summit, Geneva (2017)Google Scholar
  26. 26.
    Niemeyer, G.: Geohash: Wikipedia. Accessed 31 Aug 2017
  27. 27.
    AN11480: Quick Start-up Guide for EXPLORE-NFC working with, NXP (2016)Google Scholar
  28. 28.
    ERPINNEWS: How the Internet of Things is Reinventing Retail-A ComQi White Paper, 2 March 2016. Accessed 31 Aug 2017
  29. 29.
    Ericsson: Ericsson Radio Dot System. Accessed 31 Aug 2017

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Ericsson Research IndiaEricsson India Global Services Pvt. Ltd.ChennaiIndia
  2. 2.Global Logic India Pvt. Ltd.ChennaiIndia

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