SMARTCOM: SMART CONSUMPTION MANAGEMENT ARCHITECTURE FOR PROVIDING A USER-FRIENDLY SMART HOME BASED ON METERING AND COMPUTATIONAL INTELLIGENCE

Authors

  • Edvar da L. Oliveira
  • Rodrigo D. Alfaia
  • Anderson V. F. Souto
  • Marcelino S. Silva
  • Carlos Renato L. Francês
  • N. L. Vijaykumar

DOI:

https://doi.org/10.1590/2179-10742017v16i3965

Keywords:

device software platforms, Internet of Things, middleware, Smart Home

Abstract

With advances in wellness information technology, Smart Home-based solutions associated with the Internet of Things (IoT) have gained importance and have become accepted as an alternative as a means to save energy based on HEMS - Home Energy Management Systems. This paper defines a modern architecture (SmartCoM), which is implemented to monitor and to manage residences by using of IoT technologies. Firstly, essential parameters are established for making possible the interoperability between measurement and management elements, and layers of data communication, which are the characteristics necessary for the development of hardware for monitoring and measurement. In addition, an interface is defined by a middleware layer to integrate the management of external installations and the visualization of data by means of a cloud service. The SmartCoM end-to-end architecture is defined in detail in the point of view of consumer optimization strategies for both the end customer and the utility. The main advantages of using SmartCoM are confirmed by numerical results obtained from the proposed architecture. At the end, this paper shows the current stage of SmartCoM as well as the next steps of this research.

References

[1] N. Komninos, E. Philippou, and A. Pitsillides, “Survey in Smart Grid and Smart Home Security: Issues, Challenges
andCountermeasures,” IEEE Commun. Surv. Tutorials, vol. 16, no. 4, pp. 1933–1954, 2014.
[2] Nist, N. S. Publication, and National Institute of Standards and Technology, “NIST Special Publication 1108 NIST
Framework and Roadmap for Smart Grid Interoperability Standards,” Nist Spec. Publ., vol. 0, pp. 1–90, Oct. 2010.
[3] T. L. Nelson and G. J. FitzPatrick, “NIST role in the interoperable Smart Grid,” in 2011 IEEE Power and Energy
Society General Meeting, 2011, pp. 1–3.
[4] B. L. Risteska Stojkoska and K. V. Trivodaliev, “A review of Internet of Things for smart home: Challenges and
solutions,” J. Clean. Prod., vol. 140, pp. 1454–1464, 2017.
[5] IEEE Guide for Smart Grid Interoperability of Energy Technology and Information Technology Operation with the
Electric Power System (EPS), End-Use Applications, and Loads, IEEE Std 2030-2011, setembro. 2011.
[6] M. Beaudin and H. Zareipour, “Home energy management systems: A review of modelling and complexity,” Renew.
Sustain. Energy Rev., vol. 45, pp. 318–335, May 2015.
[7] A. Ahmed, S. Razzaq, A. Khan, and F. Khursheed, “HEMSs and enabled demand response in electricity market : An
overview,” Renew. Sustain. Energy Rev., vol. 42, pp. 773–785, 2015.
[8] J. Han et al., “Smart home energy management system including renewable energy based on ZigBee and PLC,” IEEE
Trans. Consumer Electron., vol. 60, No. 2, pp. 198-202, May. 2014.
[9] H. Lee, W.-K. Park, and I.-W. Lee, “A home energy management system for energy-efficient smart homes,” in
Computational Science and Computational Intelligence (CSCI), 2014 International Conference on, vol. 2, 2014, pp.
142–145.
[10] T. Gabriele, L. Pantoli, V. Stornelli, D. Chiulli, and M. Muttillo, “Smart power management system for home
appliances and wellness based on wireless sensors network and mobile technology,” in 2015 XVIII AISEM Annual
Conference, 2015, pp. 1–4.
[11] B. Walek, J. Zacek, M. Janosek, and R. Farana, “Adaptive fuzzy control of thermal comfort in smart houses,” in
Proceedings of the 2014 15th International Carpathian Control Conference (ICCC), 2014, pp. 675–678.
[12] M.-R. Haghifam, A. Mohsenzadeh, and M. H. Shariatkhah, “Applying fuzzy techniques to model customer comfort in a
smart home control system,” in 22nd International Conference and Exhibition on Electricity Distribution (CIRED
2013), 2013, no. 1164, pp. 1164–1164
[13] A. Keshtkar et al. “Smart residential load reduction via fuzzy logic, wireless sensors, and smart grid incentives.” Energy
and buildings, v. 104, p. 165-180, 2015.
[14] A. Patel and T. A. Champaneria. “Fuzzy logic based algorithm for Context Awareness in IoT for Smart home
environment.” In: Region 10 Conference (TENCON), 2016 IEEE. IEEE, 2016. p. 1057-1060.
[15] T. Shoji, W. Hirohashi, Y. Fujimoto, and Y. Hayashi, “Home energy management based on Bayesian network
considering resident convenience,” in 2014 International Conference on Probabilistic Methods Applied to Power
Systems (PMAPS), 2014, pp. 1–6.;
[16] M. Soliman, T. Abiodun, T. Hamouda, J. Zhou, and C. Lung, “Smart home: integrating internet of things with web
services and cloud computing,” in Proc. IEEE International Conference on Cloud Computing Technology and Science,
Bristol, pp. 317 - 320, Dec. 2013.
[17] P. Patel, M. Patel, and V. Panchal, “Home Automation Using Internet of Things,” Imp. J., pp. 1–6, 2016.
[18] X. Li, L. Nie, S. Chen, D. Zhan, and X. Xu, “An IoT Service Framework For Smart Home: Case Study On HEM,”
IEEE Softw., vol. 32, no. 3, pp. 6–6, 2015.
[19] J. Chhabra, “IoT based Smart Home Design using Power and Security Management,” no. Iciccs, pp. 6–10, 2016.
[20] V. Patchava, H. B. Kandala and P. R. A. Babu. “Smart Home Automation technique with Raspberry Pi using IoT.” In:
Smart Sensors and Systems (IC-SSS), International Conference on. IEEE, 2015. p. 1-4.
[21] M. A. Rashid and X. han. “Gesture control of ZigBee connected smart home Internet of Things.” In: Informatics,
Electronics and Vision (ICIEV), 2016 5th International Conference on. IEEE, 2016. p. 667-670.
[22] B. Marc et al. “IoT-Cloud Service Optimization in Next Generation Smart Environments.” IEEE Journal on Selected
Areas in Communications, v. 34, n. 12, pp. 4077-4090, 2016.
[23] S. Kraijak and P. Tuwanut, “A survey on IoT architectures, protocols, applications, security, privacy, real-world
implementation and future trends,” in proc. of the 11th International Conference on Wireless Communications,
Networking and Mobile Computing (WiCOM 2015), September 2015.
[24] A. Zaslavsky et al., "Sensing as a service and big data." arXiv preprint arXiv:1301.0159 (2013).
[25] S. Nepal, S. Chen, J. Yao, and D. Thilakanathan, “Diaas: Data integrity as a service in the cloud,” in Proceedings of the
2011 IEEE 4th International Conference on Cloud Computing, ser. CLOUD ’11. IEEE Computer Society, 2011, pp.
308–315.
[26] Y. Januzaj et al., "DBMS as a Cloud service: Advantages and Disadvantages." Procedia-Social and Behavioral
Sciences 195 (2015): 1851-1859.
[27] A. Keshtkar and S. Arzanpour, “An adaptive fuzzy logic system for residential energy management in smart grid
environments,” Appl. Energy, vol. 186, pp. 68–81, 2016.
[28] T. G. A. Council, "GridWise Interoperability context - Setting Framework," GridWise Architecture Council and
Battelle Memorial Institute, Mar. 2008.

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Published

2017-08-01

How to Cite

Edvar da L. Oliveira, Rodrigo D. Alfaia, Anderson V. F. Souto, Marcelino S. Silva, Carlos Renato L. Francês, & N. L. Vijaykumar. (2017). SMARTCOM: SMART CONSUMPTION MANAGEMENT ARCHITECTURE FOR PROVIDING A USER-FRIENDLY SMART HOME BASED ON METERING AND COMPUTATIONAL INTELLIGENCE. Journal of Microwaves, Optoelectronics and Electromagnetic Applications (JMOe), 16(3), 736-755. https://doi.org/10.1590/2179-10742017v16i3965

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Regular Papers