Abstract
As Internet of Things (IoT) becomes popular, different approaches to increasing its quality also so. One of the used paradigms to enhance these applications is fog computing. The fog intends to bring computational power closer to the users (edge). This paradigm is known to mitigate costs and energy consumption and also to benefit location-aware applications. As fog environments can cover small to medium areas, these can be used to increase location awareness. To make it possible, researchers have used cluster computing. However, in new scenarios, cluster formation can be a challenge since when manually set, geographical-location parameters can be biased. In this manner, this paper aimed to promote a cluster formation algorithm based on these geographical parameters. To evaluate our proposal, we compared our approach to the original using the standardized EUA dataset through iFogSim v2. The proposed algorithm was capable of creating clusters based on accepted node range and maximum nodes per cluster, operating similarly to the original dataset.
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References
Akbari Torkestani, J., Meybodi, M.: A mobility-based cluster formation algorithm for wireless mobile ad-hoc networks. Clust. Comput. 14, 311–324 (2011). https://doi.org/10.1007/s10586-011-0161-z
Basu, P., et al.: A mobility based metric for clustering in mobile ad hoc networks. In: International Conference on Distributed Computing Systems Workshops, pp. 413–418 (2001). https://doi.org/10.1109/CDCS.2001.918738
Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16 (2012). https://doi.org/10.1145/2342509.2342513
Puliafito, C., et al.: MobFogSim: simulation of mobility and migration for fog computing. Simul. Model. Pract. Theory. 101, 102062 (2020). ISSN 1569-190X, https://doi.org/10.1016/j.simpat.2019.102062
Badidi, E., Ragmani, A.: An architecture for QoS-aware fog service provisioning. Proc. Comput. Sci. 170, 411–418 (2020). ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.03.083
Guo, Y., Zhao, L., Wang, Y., Liu, Q., Qiu, J.: Fog-Enabled WLANs for indoor positioning. In: 2019 IEEE 89th Vehicular Technology Conference (VTC 2019-Spring), pp. 1-5 (2019)
Shamseddine, H., et al.: A novel federated fog architecture embedding intelligent formation. IEEE Netw. 35(3), 198–204 (2021). https://doi.org/10.1109/MNET.011.2000505
Lin, C., et al.: Adaptive clustering for mobile wireless networks. IEEE J. Sel. Areas Commun. 15, 1265–1275 (1997). https://doi.org/10.1109/49.622910
Alazab, M., Lakshmanna, K., Reddy, G.T., Pham, Q.-V., Reddy Maddikunta, P.K.: Multi-objective cluster head selection using fitness averaged rider optimization algorithm for IoT networks in smart cities, Sustain. Energy Technol. Assess. 43, 100973 (2021). ISSN 2213-1388, https://doi.org/10.1016/j.seta.2020.100973
Meunier, B., et al.: Image analysis to refine measurements of dairy cow behaviour from a real-time location system. Biosyst. Eng. 173, 32–44 (2018). https://www.sciencedirect.com/science/article/pii/S1537511017302179
Ren, M., Khoukhi, L., Labiod, H., Zhang, J., Veque, V.: A new mobility-based clustering algorithm for vehicular ad hoc networks (VANETs). NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium, pp. 1203–1208 (2016). https://doi.org/10.1109/NOMS.2016.7502988
Pešić, S., et al.: BLEMAT: data analytics and machine learning for smart building occupancy detection and prediction. Int. J. Artif. Intell. Tools 28, 1960005 (2019)
Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: iFogSim2: an extended iFogSim simulator for mobility, clustering, and microservice management in edge and fog computing environments. J. Syst. Softw. 190, 111351 (2022). https://doi.org/10.1016/j.jss.2022.111351
Vergis, S., Tsoumanis, G., Oikonomou, K.: A proposed multi -head clustering algorithm for VANET environments. In: 2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), pp. 1–8 (2021). https://doi.org/10.1109/SEEDA-CECNSM53056.2021.9566263
Sun, Z., et al.: An energy-efficient cross-layer-sensing clustering method based on intelligent fog computing in WSNs. IEEE Access 7, 144165–144177 (2019). https://doi.org/10.1109/ACCESS.2019.2944858
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Martins, V.B., de Macedo, D.D.J., Pioli, L., Immich, R. (2023). A Cluster Formation Algorithm for Fog Architectures Based on Mobility Parameters at a Geographically LAN Perspective. In: Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2022. Lecture Notes in Networks and Systems, vol 571. Springer, Cham. https://doi.org/10.1007/978-3-031-19945-5_3
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