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WISE: web of object architecture on IoT environment for smart home and building energy management

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Abstract

Fog computing extends cloud-based computing concept to the edge of the network, thus enabling a breed of services and applications. Previous research topics on fog computing have significantly focused on the concepts and fundamentals of fog computing and its importance in the context of Internet of things (IoT) and Web of object (WoO). Recently, inspired by IoT and WoO, the era of connecting all the things and people is coming. Unfortunately, various devices and objects in IoT environments hardly show the method for automatic connection and the cooperation applied to IoT applications and services. Firstly, in this paper we propose WoO based on the architecture which contains various devices and objects for providing Web base IoT services and applications. Secondly, various service overlay network concepts for providing mashup by service federation and composition are introduced. Also, we describe service deployment architecture over smart home IoT architecture on fog computing environment. Thirdly, we propose a new architecture for selecting optimal objects or things attributed from the metadata, resource and profiles by our WoO-based smart building energy prediction methodology.

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References

  1. Perera C, Liu C, Jayawardena S, Chen M (2014) A survey on internet of things from industrial market perspective. IEEE J Mag 2:1660–1679

    Google Scholar 

  2. Armbrust M, Fox A, Griffith R, Joseph A, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Mag Commun ACM 53(4):50–58

    Article  Google Scholar 

  3. Mell P, Grance T (2011) The NIST definition of cloud computing. National Institute of Standards and Technology (NIST), Special Publication 800-145

  4. Chen M, Qiu M, Liao L, Park J, Ma J (2011) Distributed multi-hop cooperative communication in dense wireless sensor networks. J Supercomput 56(3):353–369

    Article  Google Scholar 

  5. Faruque M, Vatanparvar K (2016) Energy management-as-a-service over Fog Computing platform. IEEE Internet Things J 3(2):161–169

    Article  Google Scholar 

  6. Park H, Lee C, Lee Y, Kim E-J (2016) Performance analysis for contention adaptation of M2M devices with directional antennas. J Supercomput 72(9):3387–3408

    Article  Google Scholar 

  7. 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, August 13–17. Helsinki, Finland, pp 13–16

  8. Bonomi F, Milito R, Natarajan P, Zhu J (2014) Fog Computing: a platform for internet of things and analytics. Big Data Internet Things Roadmap Smart Environ 546:169–186

    Article  Google Scholar 

  9. Sarkar S, Misra S (2016) Theoretical modelling of Fog Computing: a green computing paradigm to support IoT applications. IET Netw 5(2):23–29

    Article  Google Scholar 

  10. Shang X, Zhang R, Zhu X, Zhou Q (2016) Design theory, modelling and the application for the internet of things service. Enterp Inf Syst 10(3):249–267

    Article  Google Scholar 

  11. Yu J, Bang H, Lee H, Lee Y (2016) Adaptive internet of things and web of things convergence platform for internet of reality services. J Supercomput 72(1):84–102

    Article  Google Scholar 

  12. Pang Z, Chen Q, Zheng L (2012) Value creation, sensor portfolio and information fusion of internet-of-things solutions for food supply Chains. Inf Syst Front. doi:10.1007/s10796-012-9374-9

  13. ITU-T Y.2060 (2012) Overview of internet of things. ITU-T

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

    Article  Google Scholar 

  15. Pang Z, Chen J, Sarmiento M, Zhang Z, Gao J, Chen Q, Zheng L (2009) Mobile and wide area deployable sensor system for networked services. In: Proceedings of IEEE Sensors Conference, Christchurch, New Zealand, pp 1396–1399. doi:10.1109/ICSENS.2009.5398428

  16. Zorzi M, Gluhak A, Lange S, Bassi A (2010) From today’s INTRAnet of things to a future INTERnet of things: a wireless-and mobility-related view. IEEE Wirel Commun 17(6):44–51

    Article  Google Scholar 

  17. Chen M, Wan J, Gonzalez S, Liao X, Leung C (2014) A survey of recent developments in home M2M networks. IEEE Commun Surv Tutor 16(1):98–114

    Article  Google Scholar 

  18. Lee N, Lee H (2014) Device objectification for internet of things services. In: Proceedings of the 18th IEEE international symposium on consumer electronics, JeJu Island, Republic of Korea, pp 1–2. doi:10.1109/ISCE.2014.6884496

  19. Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805

    Article  MATH  Google Scholar 

  20. Pang Z, Zheng L, Tian J, Kao-Walter S, Dubrova E, Chen Q (2013) Design of a terminal solution for integration of in-home health care devices and services towards the internet-of-things. Enterp Inf Syst 9(1):86–116

    Article  Google Scholar 

  21. Nixon T, Regnier A (2009) OASIS devices profile for web services (DPWS) Version 1.1. OASIS Technical Committee, 1 July 2009

  22. Cannata A, Gerosa M, Taisch M (2008) SOCRADES: a framework for developing intelligent systems in manufacturing. In: Proceedings of 2008 IEEE International Conference on Industrial Engineering and Engineering Management, December 8–11, Singapore, pp 1904–1908. doi:10.1109/IEEM.2008.4738203

  23. Kim Y, Jeon Y, Chong L (2012) Service overlay network platform and service composition method for M2M service. In: Korea Computer Congress, June 27–29. Republic of Korea, JeJu Island, pp 230–232

  24. Fatima I, Fahim M, Lee Y, Lee S (2013) Analysis and effects of smart home dataset characteristics for daily life activity recognition. J Supercomput 66(2):760–780

    Article  Google Scholar 

  25. Ricquebourg V, Menga D, Durand D, Marhic B, Delahoche L, Loge C (2006) The smart home concept: our immediate future. In: Proceedings of IEEE International Conference on E-learning in Industrial Electronics, Hammamet, Tunisia, pp 23–28. doi:10.1109/ICELIE.2006.347206

  26. Kim J, Park S (2015) U-health smart system architecture and ontology model. J Supercomput 71(6):2121–2137

    Article  Google Scholar 

  27. Sheng W, Matsuoka Y, Ou Y, Liu M, Mastrogiovanni F (2015) Guest editorial special section on home automation. IEEE Trans Autom Sci Eng 12(4):1155–1156

    Article  Google Scholar 

  28. Vaidya B, Makrakis D, Mouftah H (2013) Secure communication mechanism for ubiquitous Smart grid infrastructure. J Supercomput 64(2):435–455

    Article  Google Scholar 

  29. Chen G, Huang J, Cheng B, Chen J (2015) A social network basedApproach for IoT device management and service composition. In: 2015 IEEE World Congress on Services, June 27-July 2. IEEE, New York, pp 1–8

  30. Fredj S, Boussard M, Kofman D, Noirie L (2013) A scalable IoT service search based on clustering and aggregation. In: Proceedings of IEEE International Conference on and IEEE Cyber, Physical and Social Computing, August 20–23, Beijing, China, pp 403–410. doi:10.1109/GreenCom-iThings-CPSCom.2013.86

  31. Yu J, Lee B, Park D (2014) Real-time cooling load forecasting using a hierarchical multi-class SVDD. Multimed Tools Appl 71(1):293–307

    Article  Google Scholar 

  32. Xu Q, Aung K, Zhu Y, Yong K (2016) Building a large-scale object-based active storage platform for data analytics in the internet of things. J Supercomput 72(7):2796–2814

    Article  Google Scholar 

  33. Perera C, Zaslavsky A, Liu C, Compton M, Christen P, Georgakopoulos D (2014) Sensor search techniques for sensing as a service architecture for the internet of things. IEEE Sens J 14(2):406–420

    Article  Google Scholar 

  34. Song K, Baek Y, Hong D, Jang G (2005) Short-term load forecasting for the holidays using fuzzy linear regression method. IEEE Trans Power Syst 20(1):96–101

    Article  Google Scholar 

  35. Efendigil T, Onut S, Kahraman C (2009) A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: a comparative analysis. Expert Syst Appl 36(2):6697–6707

    Article  Google Scholar 

  36. Wagner A, Wright J, Ganesh A, Zhou Z, Mobahi H, Ma Y (2012) Toward a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Trans Pattern Anal Mach Intell 34(2):372–386

    Article  Google Scholar 

  37. Wright J, Yang A, Ganesh A, Sastry S, Ma Y (2008) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  38. Yang J, Wright J, Huang T, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873

    Article  MathSciNet  MATH  Google Scholar 

  39. Yu J, Lee H, Im Y, Kim M, Park D (2010) Real-time classification of internet application traffic using a hierarchical multi-class SVM. KSII Trans Internet Inf Syst 4(5):859–876

    Google Scholar 

  40. Han J, Kamber M, Pei J (2011) Data mining: concept and techniques, 3rd edn. Morgan Kaufmann Publishers, MA, USA

    MATH  Google Scholar 

  41. Oh I, Lee J, Moon B (2004) Hybrid genetic algorithms for feature selection. IEEE Trans Pattern Anal Mach Intell 26(11):1424–1437

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Research Council of Science & Technology (NST) Grant by the Korea government (MSIP) (No. CRC-15-05-ETRI).

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Authors and Affiliations

  1. Department of Self-Machine Learning Engine Research Section, ETRI, Daejeon, Republic of Korea

    Jaehak Yu

  2. Department of Trust Media Research Section, ETRI, Daejeon, Republic of Korea

    NamKyung Lee

  3. Department of KSB (Knowledge-Converged Super Brain) Convergence Research, ETRI, Daejeon, Republic of Korea

    Cheol-Sig Pyo

  4. Division of Convergence Computer and Media, Mokwon University, Daejeon, Republic of Korea

    Yang Sun Lee

Authors
  1. Jaehak Yu
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  2. NamKyung Lee
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  3. Cheol-Sig Pyo
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  4. Yang Sun Lee
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Corresponding author

Correspondence to Yang Sun Lee.

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Yu, J., Lee, N., Pyo, CS. et al. WISE: web of object architecture on IoT environment for smart home and building energy management. J Supercomput 74, 4403–4418 (2018). https://doi.org/10.1007/s11227-016-1921-6

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  • DOI: https://doi.org/10.1007/s11227-016-1921-6

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