Abstract
There are a series of nodes in a Smart Grid environment and to let them work efficiently, their tasks should be adequately scheduled. As for the scheduling methods, this study proposes two kinds of scenarios: use of the greedy algorithm or the Floyd-Warshall algorithm both of which have their own merits and demerits. The effectiveness of the scheduling algorithm becomes different depending on the number of nodes. Also, there are two kinds of nodes: mobile nodes and non-mobile nodes. One good example of a node that easily moves is a person. The performing a headcount for the people with their personal information such as their images or whereabouts is not an easy task due to ever strengthening civil rights. It is also difficult to select an effective scheduling algorithm due to the number of dynamic nods. Thus, to determine an efficient scheduling method, some meaningful correlations between the number of AP access, which can be regarded as the number of people, and the number of people in a certain space have been studied by using the AP access record of a Smart Device (Smart Phone, Tablet, etc.) always carried by most of the people these days instead of using personal information. This study then provides a direction of improving network operation by grasping an exact number of nodes in the smart grip service environment based on the correlations revealed.
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References
Farhangi, H.: The path of the smart grid. IEEE Power Energy Mag. 8(1), 18–28 (2010)
Huh, J.-H., Seo, K.: Blockchain-based mobile fingerprint verification and automatic log-in platform for future computing. J. Supercomput., 1–17 (2018)
Huh, J.-H., Otgonchimeg, S., Seo, K.: Advanced metering infrastructure design and test bed experiment using intelligent agents: focusing on the PLC network base technology for Smart Grid system. J. Supercomput. 72(5), 1862–1877 (2016)
Weaver, W.W., Krein, D.P.: Game-theoretic control of small-scale power systems. IEEE Trans. Power Deliv. 24(3), 1560–1567 (2009)
Kasbekar, G.S., Sarkar, S.: Pricing games among interconnected microgrids. In: Proceedings of Power and Energy Society General Meeting, pp. 1–8. IEEE (2012)
Mohsenian-Rad, A.H., Wong, V.W.S., Jatskevich, J., Schober, R., Leon-Garcia, A.: Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans. Smart Grid 1(3), 320–331 (2010)
Kshetri, N.: Blockchain’s roles in strengthening cybersecurity and protecting privacy. Telecommun. Policy 41, 1027–1038 (2017)
Levin, R.B., Waltz, P., LaCount, H.: Betting blockchain will change everything – SEC and CFTC regulation of blockchain technology. In: Handbook of Blockchain. Digital Finance, and Inclusion, vol. 2, pp. 187–212. Elsevier (2017)
Prybila, C., Schulte, S., Hochreiner, C., Webe, I.: Runtime verification for business processes utilizing the Bitcoin Blockchain. Futur. Gener. Comput. Syst., 1–11 (2017)
Sikorski, J.J., Haughton, J., Kraft, M.: Blockchain technology in the chemical industry: machine-to-machine electricity market. Appl. Energy 195, 234–246 (2017)
Aguayo, D., et al.: Link-level measurements from an 802.11b mesh network. ACM SIGCOMM Comput. Commun. Rev. 34(4), 121–132 (2004)
Je, S.-M., Huh, J.-H.: Nash equilibrium solution for communication in strategic competition adopted by Zigbee network for micro grid. In: Kim, K.J., Baek, N. (eds.) ICISA 2018. LNEE, vol. 514, pp. 595–606. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1056-0_58
Lee, S., Huh, J.-H.: An effective security measures for nuclear power plant using big data analysis approach. J. Supercomput., 1–28 (2018)
Bychkovsky, V., et al.: A measurement study of vehicular internet access using in situ Wi-Fi networks. In: Proceedings of the 12th Annual International Conference on Mobile Computing and Networking. ACM (2006)
Cheng, Y.-C., et al.: Accuracy characterization for metropolitan-scale Wi-Fi localization. In: Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services. ACM (2005)
Bahl, P., et al.: White space networking with Wi-Fi like connectivity. ACM SIGCOMM Comput. Commun. Rev. 39(4), 27–38 (2009)
Kellogg, B., et al.: Wi-Fi backscatter: internet connectivity for RF-powered devices. ACM SIGCOMM Comput. Commun. Rev. 44(4), 607–618 (2014)
Huawei: Shortest Path Bridging IEEE 802.1aq Overview (2011). https://meetings.apnic.net/31/ppt/APRICOT_SPB_Overview.ppt
Dorri, A., Kanhere, S.S., Jurdak, R.: Towards an optimized blockchain for IoT. In: Proceedings of the Second International Conference on Internet-of-Things Design and Implementation, pp. 173–178. ACM (2017)
Donelli, M., et al.: A planar electronically reconfigurable Wi-Fi band antenna based on a parasitic microstrip structure. IEEE Antennas Wirel. Propag. Lett. 6, 623–626 (2007)
Huh, J.-H.: PLC-based design of monitoring system for ICT-integrated vertical fish farm. Hum. Centric Comput. Inf. Sci. 7(20), 1–19 (2017)
Brachet, N., et al.: Method and system for selecting and providing a relevant subset of Wi-Fi location information to a mobile client device so the client device may estimate its position with efficient utilization of resources, U.S. Patent No. 8,369,264, USA, 5 February 2013
Tran, B.: Mesh network personal emergency response appliance, U.S. Patent No. 7,733,224, USA, 8 June 2010
Talla, V., et al.: Powering the next billion devices with Wi-Fi. In: Proceedings of the 11th ACM Conference on Emerging Networking Experiments and Technologies. ACM (2015)
Pass, R., Shi, E.: Fruitchains: a fair blockchain. In: Proceedings of the ACM Symposium on Principles of Distributed Computing, pp. 315–324. ACM (2017)
Huh, J.-H.: Implementation of lightweight intrusion detection model for security of smart green house and vertical farm. Int. J. Distrib. Sens. Netw. 14(4), 1–11 (2018)
Raniwala, A., Chiueh, T.-C.: Architecture and algorithms for an IEEE 802.11-based multi-channel wireless mesh network. In: Proceedings of the IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2005, vol. 3. IEEE (2005)
Sagari, S., et al.: Coordinated dynamic spectrum management of LTE-U and Wi-Fi networks. In: 2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). IEEE (2015)
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This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2017R1C1B5077157).
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Je, SM., Huh, JH. (2019). An Optimization Theory of Home Occupants’ Access Data for Determining Smart Grid Service. In: Park, J., Shen, H., Sung, Y., Tian, H. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2018. Communications in Computer and Information Science, vol 931. Springer, Singapore. https://doi.org/10.1007/978-981-13-5907-1_15
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DOI: https://doi.org/10.1007/978-981-13-5907-1_15
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