Microsystem Technologies

, Volume 25, Issue 1, pp 83–96 | Cite as

Unified framework for IoT and smartphone based different smart city related applications

  • Joy Dutta
  • Sarbani RoyEmail author
  • Chandreyee Chowdhury
Technical Paper


By embracing the potential of IoT and smartphones, traditional cities can be transformed to smart cities. The success of such smart city mission is firmly vested in populace and thus it should have a bottom-up nature, initiated by the citizens. This paper focuses on the design and development of a unified framework, which can provide a platform to empower all the applications across different dimensions of urban life in a smart city. The aim of this framework is to connect citizens, data, knowledge and services related to IoT as well as smartphone based applications. Here, we categorize all the applications for the smart city in three representative types, viz. IoT based, IoT and smartphone based and smartphone as IoT based applications. We have also developed and tested one prototype following this architecture for each of these three representative category type, i.e, IoT based smart classroom, IoT and smartphone based air quality monitoring system and only smartphone based noise monitoring system to demonstrate the effectiveness of the proposed framework for the smart city scenario.



The research work of the first author is supported by Visvesvaraya PhD Scheme, Ministry of Communications and IT, Government of India.


  1. Air Pollution in the World, Realtime Air Quality Index. Accessed 30 Apr 2018
  2. Antonic A, Roankovic K, Marjanovic M, Pripuic K, Zarko IP (2014) A mobile crowdsensing ecosystem enabled by a cloud-based publish/subscribe middleware. In: International conference on future internet of things and cloud (FiCloud), pp 107–114Google Scholar
  3. Castellani AP, Bui N, Casari P, Rossi M, Shelby Z, Zorzi M (2010) Architecture and protocols for the Internet of Things: a case study. In: 8th annual IEEE international conference on pervasive computing and communications, pp 678–683Google Scholar
  4. Datta SK, Ferreira da Costa RP, Bonnet C, Hrri J (2016) oneM2M architecture based IoT framework for mobile crowd sensing in smart cities. In: 2016 European conference on networks and communications (EuCNC), pp 168–173Google Scholar
  5. Dutta J, Pramanick P, Roy S (2018) Energy-efficient GPS usage in location-based applications. In: Satapathy S, Tavares J, Bhateja V, Mohanty J (eds) Information and decision sciences. Advances in intelligent systems and computing, vol 701. Springer, Singapore. Google Scholar
  6. Dutta J, Chowdhury C, Roy S, Middya AI, Gazi F (2017) Towards smart city: sensing air quality in city based on opportunistic crowd-sensing. In: Proceedings of the 18th international conference on distributed computing and networking (ICDCN ’17). ACM, Article 42, pp 1–6Google Scholar
  7. Dutta J, Gazi F, Roy S, Chowdhury C, AirSense: Opportunistic crowd-sensing based air quality monitoring system for smart city. In: 2016 IEEE sensors. Orlando, FL, USA, 2016, pp 1–3.
  8. Dutta J, Pramanick P, Roy S (2017) NoiseSense: crowdsourced context aware sensing for real time noise pollution monitoring of the city. In: Proceedings of the IEEE international conference on advanced networks and telecommunications systems (ANTS), Bhubaneswar, pp 1–6Google Scholar
  9. Dutta J, Roy S (2017) IoT-fog-cloud based architecture for smart city: prototype of a smart building. In: Proceedings of the 7th international conference on cloud computing, data science and engineering (confluence), Noida, pp 237–242.
  10. Farkas K, Feher G, Benczur A, Sidlo C (2015) Crowdsending based public transport information service in smart cities. IEEE Commun Mag 53(8):158–165CrossRefGoogle Scholar
  11. Ganti RK, Fan Y, Hui L (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 2011:32–39CrossRefGoogle Scholar
  12. Geoffrey CF, Kamburugamuve S, Hartman RD (2012) Architecture and measured characteristics of a cloud based internet of things API. in: International conference on collaboration technologies and systems (CTS), pp 6–12Google Scholar
  13. Ghayvat H, Liu J, Mukhopadhyay SC, Gui X (2015) Wellness sensor networks: a proposal and implementation for smart home for assisted living. IEEE Sens J 15(12):7341–7348CrossRefGoogle Scholar
  14. Ghosh S, Dutta J, Roy S (2018) SenseDcity: a participatory sensing based approach. In: Proceedings of the workshop program of the 19th international conference on distributed computing and networking (Workshops ICDCN ’18). ACM, New York, NY, USA, Article 16.
  15. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): a vision, architectural elements, and future directions. Future Gen Comput Syst 29(7):1645–1660CrossRefGoogle Scholar
  16. Hariri F, Daher G, Sibai H, Frenn K, Doniguian S, Dawy Z (2013) Towards a silent mobile sensing framework for smart cities. In: Wireless World Research Forum (WWRF 30)Google Scholar
  17. Jayaraman PP, Sinha A, Sherchan W, Krishnaswamy S, Zaslavsky A, Haghighi PD, Loke S, Do MT (2012) Here-n-now: a framework for context-aware mobile crowdsensing. In: Proceedings of the tenth international conference on pervasive computing, pp 1–4Google Scholar
  18. Jin J, Jin J, Marusic Gubbi S, Palaniswami M (2014) An information framework for creating a smart city through internet of things. IEEE Internet Things J 1(2):112–121CrossRefGoogle Scholar
  19. Lee YT, Hsiao WH, Huang CM, Chou SCT (2016) An integrated cloud-based smart home management system with community hierarchy. IEEE Trans Consum Electron 62(1):1–9CrossRefGoogle Scholar
  20. Louta M, Mpanti K, Karetsos G, Lagkas T (2016) Mobile crowd sensing architectural frameworks: a comprehensive survey. In: 2016 7th international conference on information intelligence systems and applications (IISA), pp 1–7Google Scholar
  21. Nastic S, Sehic S, Le DH, Truong HL, Dustdar S (2014) Provisioning software-defined IoT cloud systems. In: 2014 international conference on future internet of things and cloud, 2014, pp 288–295Google Scholar
  22. Openshift Platform Accessed 30 Apr 2018
  23. Ra MR (2012) Medusa: a programming framework for crowd-sensing applications. In: Proceedings of the 10th international conference on mobile systems, applications, and services, 2012, pp 337–350Google Scholar
  24. Zhao J (1998) Applying slicing technique to software architectures. In: Proceedings of the 4th IEEE international conference on engineering of complex computer systems, pp 87–98Google Scholar
  25. Zheng Y, Liu F, Hsieh HP (2013) U-Air: when urban air quality inference meets big data. In: Proceedings of ACM conference knowledge discovery and data mining, pp 1436–1444Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

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