Three methods of selecting a smart meter for data concentration in the automatic meter reading last mile network

• Autorzy: Kiedrowski Piotr

Identyfikatory

DOI

10.24425/bpasts.2023.146476

• Języki publikacji: EN

Abstrakty

EN

This paper proposes three methods of the optimal smart meter selection for acting as a data concentrator in the automatic meter reading last mile network. The study explains the reasons why the selected smart meter should also act as a data concentrator, in addition to its basic role. To select the smart meter, either the reliability of communication or the speed of the automatic meter reading process was considered. Graph theory is employed to analyse the last mile network, described as sets of nodes and unreliable links. The frame error ratio was used to assess the unreliability whilst the number of hops was used to describe the speed of the reading process. The input data for the analysis are qualitative parameters determined based on observations in the real, operated last mile networks as well as their typical topological arrangements. The results of the research can be useful in the last mile network migration process, which uses concentrators to the networks without them, or during the process of newer last mile network implementation, where data concentrators are no longer applicable. The efficiency of the proposed methods is assessed measurably.

Słowa kluczowe

EN

Smart Meter; AMR; network optimization; Industrial IoT; Industrial Internet of Things; last mile networks

PL

AMR; sieć ostatniej mili; optymalizacja sieci; inteligentny licznik; przemysłowy Internet rzeczy

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2024
• Tom: Vol. 72, nr 1
• Strony: art. no. e146476
• Opis fizyczny: Bibliogr. 28 poz., rys., tab.

Twórcy

autor

Kiedrowski Piotr

• Institute of Telecommunications and Computer Science, Bydgoszcz University of Science and Technology, Al. Prof. S. Kaliskiego 7, 85-796 Bydgoszcz, Poland

• https://orcid.org/0000-0002-3180-3098

Bibliografia

• [1] B. Adebisi et al., “IP-centric high rate narrowband PLC for smart grid applications,” IEEE Commun. Mag., vol. 49, no. 12, pp. 46–54, Dec. 2011, doi: 10.1109/MCOM.2011.6094005.
• [2] Y.M. Chung, “Overview and Characteristics of IoT PLC,” 2020 International Conference on Electronics, Information, and Communication (ICEIC), 2020, pp. 1–3, doi: 10.1109/ICEIC49074.2020.9051002.
• [3] P. Pal et al., “IoT-Based Real Time Energy Management of Virtual Power Plant Using PLC for Transactive Energy Framework,” IEEE Access, vol. 9, pp. 97643–97660, 2021, doi: 10.1109/ACCESS.2021.3093111.
• [4] A. Duarte dos Santos, R. Moreira Bacurau, A. Vedoveto Martins, A. Dante, and E. Chagas Ferreira, “Simple and Low-Cost PLC Modem for IoT Applications,” IEEE Latin Am. Trans., vol. 20, no. 12, pp. 2455–2462, Dec. 2022, doi: 10.1109/TLA.2022.9905614.
• [5] S. Dodla, L. Mahendra, K. Jaganmohan, R.K.S. Kumar, and B.S. Bindhumadhava, “Wireless Real-time Meter Data Acquisition System,” TENCON 2019 – 2019 IEEE Region 10 Conference (TENCON), 2019, pp. 997–1002, doi: 10.1109/TENCON.2019.8929650.
• [6] H. Miao, G. Chen, Z. Zhao, and F. Zhang, “Evolutionary Aggregation Approach for Multihop Energy Metering in Smart Grid for Residential Energy Management,” IEEE Trans. Ind. Inf., vol. 17, no. 2, pp. 1058–1068, Feb. 2021, doi: 10.1109/TII.2020.3007318.
• [7] F.F. Jurado-Lasso, K. Clarke, and A. Nirmalathas, “Performance Analysis of Software-Defined Multihop Wireless Sensor Networks,” IEEE Syst. J., vol. 14, no. 4, pp. 4653–4662, Dec. 2020, doi: 10.1109/JSYST.2019.2948203.
• [8] D. Ari, M. Çibuk, and F. Ağgün, “The Comparison of Energy Consumption of Different Topologies in Multi-hop Wireless Sensor Networks,” 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018, pp. 1–5, doi: 10.1109/IDAP.2018.8620903.
• [9] I. Petruševski, M. Živanović, A. Rakić, and I. Popović, “Novel AMI .architecture for real-time Smart Metering,” 2014 22nd Telecommunications Forum Telfor (TELFOR), 2014, pp. 664–667, doi: 10.1109/TELFOR.2014.7034496.
• [10] T. Matt, M. Schappacher, and A. Sikora, “Development of a web-based monitoring device for the wired Metering Bus (M-Bus) as defined in EN13757-3,” 2015 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), 2015, pp. 187–194, doi: 10.1109/ICSGCE.2015.7454294.
• [11] EN 13757-3:2018, Communication systems for meters – Part 3: Application protocols, 04-Apr-2018
• [12] M.D. Hossain, H. Ochiai, T. Arisawa and Y. Kadobayashi, “Smart Meter Modbus RS-485 Spoofing Attack Detection by LSTM Deep Learning Approach,” 2022 9th Swiss Conference on Data Science (SDS), 2022, pp. 47–52, doi: 10.1109/SDS54800.2022.00015.
• [13] J. Zhou, R. Qingyang Hu, and Y. Qian, “Scalable Distributed Communication Architectures to Support Advanced Metering Infrastructure in Smart Grid,” IEEE Trans. Parallel Distrib. Syst., vol. 23, no. 9, pp. 1632–1642, Sept. 2012, doi: 10.1109/TPDS.2012.53.
• [14] M. Zeinali, J. Thompson, C. Khirallah, and N. Gupta, “Evolution of home energy management and smart metering communications towards 5G,” 2017 8th International Conference on the Network of the Future (NOF), 2017, pp. 85–90, doi: 10.1109/NOF.2017.8251225.
• [15] G.B. Gaggero, M. Marchese, A. Moheddine, and F. Patrone, “A Possible Smart Metering System Evolution for Rural and Remote Areas Employing Unmanned Aerial Vehicles and Internet of Things in Smart Grids,” Sensors, vol. 21, no. 5, p. 1627, 2021, doi: 10.3390/s21051627.
• [16] T. Craig, J.G. Polhill, I. Dent, C. Galan-Diaz, and S. Heslop, “The North East Scotland Energy Monitoring Project: Exploring relationships between household occupants and energy usage,” Energy Build., vol. 75, pp. 493–503, 2014, doi: 10.1016/j.enbuild.2014.02.038.
• [17] M. Ravider and V. Kulkarni, “Intrusion detection in smart meters data using machine learning algorithms: A research report,” Front. Energy Res., vol. 11, pp. 1–7, 2023, doi: 10.3389/fenrg.2023.1147431.
• [18] N. Deo, Graph Theory with Applications to Engineering and Computer Science. Dover: Dover Publications, 2016, p. 496.
• [19] J. Guo, X. Ding, and W. Wu, “A Blockchain-Enabled Ecosystem for Distributed Electricity Trading in Smart City,” IEEE Internet Things J., vol. 8, no. 3, pp. 2040–2050, 1 Feb.1, 2021, doi: 10.1109/JIOT.2020.3015980.
• [20] P. Kiedrowski, “Selection of the Optimal Smart Meter to Act as a Data Concentrator with the Use of Graph Theory,” Entropy, vol. 23, no. 6, p. 658, May 2021, doi: 10.3390/e23060658.
• [21] A.A. Amarsingh, H.A. Latchman, and D. Yang, “Narrowband Power Line Communications: Enabling the Smart Grid,” IEEE Potentials, vol. 33, no. 1, pp. 16–21, Jan.-Feb. 2014, doi: 10.1109/MPOT.2013.2249691.
• [22] J. Xu, H. Shi, and J. Wang, “Analysis of Frame Length and Frame Error Rate For the Lowest Energy Dissipation in Wireless Sensor Networks,” 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008, pp. 1–4, doi: 10.1109/WiCom.2008.958.
• [23] J.N. Al-Karaki and A.E. Kamal, “Routing techniques in wireless sensor networks: a survey,” IEEE Wirel. Commun., vol. 11, no. 6, pp. 6–28, Dec. 2004, doi: 10.1109/MWC.2004.1368893.
• [24] P. Kiedrowski, B. Dubalski, T. Marciniak, T. Riaz, and J. Gutierrez, “Energy Greedy Protocol Suite for Smart Grid Communication Systems Based on Short Range Devices,” in Image Processing and Communications Challenges 3. Advances in Intelligent and Soft Computing, vol. 102, Choraś, R.S. (ed), Springer, Berlin, Heidelberg. doi: 10.1007/978-3-642-23154-4_54.
• [25] CC1101 low-power sub-1 GHz RF transceiver datasheet, Chipcon products from Texas Instruments, 2009. [Online]. Available: https://www.ti.com/lit/ds/symlink/cc1101.pdf
• [26] P. Kiedrowski and Ł. Saganowski, “Method of Assessing the Efficiency of Electrical Power Circuit Separation with the Power Line Communication for Railway Signs Monitoring,” Transp. Telecommun. J., vol. 22, no.4, pp. 407–416, Nov. 2021. doi: 10.2478/ttj-2021-0031.
• [27] ITU-T Recommendation G.9904. Narrowband orthogonal frequency division multiplexing power line communication transceivers for PRIME networks, 2013.
• [28] ITU-T Recommendation G.9903. Narrowband orthogonal frequency division multiplexing power line communication transceivers for G3-PLC networks, 2017.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).