International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 9, Issue 8 (August 2022), Pages: 49-54

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 Original Research Paper

Effect of data aggregation on the delay of demand-response communications within a Wi-Fi home area network with and without other traffic

 Author(s): N. S. Weerakoon 1, *, K. M. Liyanage 2

 Affiliation(s):

 1Department of Computing, Rajarata University of Sri Lanka, Mihintale, Sri Lanka
 2Department of Electrical and Electronics Engineering, University of Peradeniya, Peradeniya, Sri Lanka

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-2711-0926

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.08.006

 Abstract:

Home area networks play an important role in the demand-response function of smart grids. It is responsible for responding to requests received from the grid by controlling the devices in the home in a predefined manner. Communication within a Home Area Network should be efficient in terms of both delay and energy. Delay matters since the devices need to respond to the request within the stipulated delay. Energy matters since thousands of Home Area Networks are likely to create a significant energy footprint on the global level. In order to reduce energy consumption, the number of communications needs to be reduced and data aggregation can achieve this goal. However, data aggregation introduces a prolonged delay and may thus render the system unfit for its purpose. Therefore, it is required to determine the variation of delay when data aggregation is performed at different levels. This paper presents algorithms for data aggregation and device clustering optimization. Finally, the delay distribution was studied in a simulation environment with one level of data aggregation. The results show that an existing Wi-Fi network can be used for Smart Grid communications with in-network data aggregation provided that there is a spare (unused) bandwidth of 3 Mbit/s in the network.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Smart grids, Demand-response, Home area networks, Data aggregation, Energy efficiency, Delay

 Article History: Received 26 January 2022, Received in revised form 29 April 2022, Accepted 16 May 2022

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Weerakoon NS and Liyanage KM (2022). Effect of data aggregation on the delay of demand-response communications within a Wi-Fi home area network with and without other traffic. International Journal of Advanced and Applied Sciences, 9(8): 49-54

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 Figures

 Fig. 1 Fig. 2 

 Tables

 Table 1 Table 2 Table 3 Table 4

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 References (15)

  1. Akyildiz IF, Su W, and Sankarasubramaniam Y and Cayirci E (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8): 102–114. https://doi.org/10.1109/MCOM.2002.1024422   [Google Scholar]
  2. Dijkstra EW (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1: 269–271. https://doi.org/10.1007/BF01386390   [Google Scholar]
  3. DOE (2008). The SMART GRID: An introduction. United States Department of Energy, Washington, USA.   [Google Scholar]
  4. Gopstein A, Nguyen C, O'Fallon C, Hastings N, and Wollman D (2021). NIST framework and roadmap for smart grid interoperability standards, release 4.0. NIST SP 1108r4, National Institute of Standards and Technology, Gaithersburg, USA. https://doi.org/10.6028/NIST.SP.1108r4   [Google Scholar]
  5. Herberg U, Mashima D, Jetcheva JG, and Mirzazad-Barijough S (2014). OpenADR 2.0 deployment architectures: Options and implications. In the 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), IEEE, Venice, Italy:  782-787. https://doi.org/10.1109/SmartGridComm.2014.7007743   [Google Scholar]
  6. Hu F, May C, and Cao X (2006). Data aggregation in distributed sensor networks: Towards an adaptive timing control. In the 3rd International Conference on Information Technology: New Generations, IEEE, Las Vegas, USA: 256-261. https://doi.org/10.1109/ITNG.2006.46   [Google Scholar]
  7. Krishnamachari L, Estrin D, and Wicker S (2002). The impact of data aggregation in wireless sensor networks. In the 22nd International Conference on Distributed Computing Systems Workshops, IEEE, Vienna, Austria: 575-578. https://doi.org/10.1109/ICDCSW.2002.1030829   [Google Scholar]
  8. Li L and Halpern JY (2001). Minimum-energy mobile wireless networks revisited. In the ICC 2001, IEEE International Conference on Communications, Conference Record (Cat. No. 01CH37240), IEEE, Helsinki, Finland, 1: 278–283. https://doi.org/10.1109/ICC.2001.936317   [Google Scholar]
  9. Momoh JA (2018). Energy processing and smart grid. John Wiley and Sons, Hoboken, USA. https://doi.org/10.1002/9781119521129   [Google Scholar]
  10. Pottie GJ and Kaiser WJ (2000). Wireless integrated network sensors. Communications of the ACM, 43(5): 51-58. https://doi.org/10.1145/332833.332838   [Google Scholar]
  11. Rajagopalan R and Varshney PK (2006). Data aggregation techniques in sensor networks: A survey. IEEE Communications Surveys Tutorials, 8: 48–63. https://doi.org/10.1109/COMST.2006.283821   [Google Scholar]
  12. Rappaport TS (1996). Wireless communications: Principles and practice. Prentice-Hall, Upper Saddle River, USA.   [Google Scholar]
  13. Shih E, Cho SH, Ickes N, Min R, Sinha A, Wang A, and Chandrakasan A (2001). Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks. In the 7th Annual International Conference on Mobile Computing and Networking, Association for Computing Machinery, Rome, Italy: 272-287. https://doi.org/10.1145/381677.381703   [Google Scholar]
  14. Weerakoon NS and Liyanage KM (2020). Delay characteristics of smart grid traffic in Wi-Fi home area networks with and without other traffic. In the 4th World Conference on Smart Trends in Systems, Security and Sustainability, IEEE, London, UK: 484-489. https://doi.org/10.1109/WorldS450073.2020.9210352   [Google Scholar]
  15. Zheng J and Jamalipour A (2009). Wireless sensor networks: A networking perspective. John Wiley and Sons, Hoboken, USA. https://doi.org/10.1002/9780470443521   [Google Scholar]