Efficient micro data centres deployment for mobile healthcare monitoring systems in IoT urban scenarios

References

• Aazam, M., & Huh, E.-N. (2015). Fog computing micro datacenter based dynamic resource estimation and pricing model for iot. In 2015 IEEE 29th international conference on advanced information networking and. applications, 687–694. doi:10.1109/AINA.2015.254.
   Google Scholar
• Afuang, A. (2020). IoT growth demands rethink of long-term storage strategies (Tech. Rep.). International Data Corporation
   Google Scholar
• Ali, F., El-Sappagh, S., Islam, S. R., Kwak, D., Ali, A., Imran, M., & Kwak, K.-S. (2020). A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Information Fusion, 63, 208–222. doi:10.1016/j.inffus.2020.06.008.
   Web of Science ®Google Scholar
• Álvarez-Sánchez, C. (2018). Deployment and management of platforms based on fog computing within an interconnected city (Tech. Rep.). Universidad Complutense de Madrid.
   Google Scholar
• Barba, C. T., Mateos, M. A., Soto, P. R., Mezher, A. M., & Igartua, M. A. (2012). Smart city for vanets using warning messages, traffic statistics and intelligent traffic lights. In 2012 IEEE intelligent vehicles. symposium, 902–907. doi:10.1109/IVS.2012.6232229.
   Google Scholar
• Behrisch, M., Bieker, L., Erdmann, J., & Krajzewicz, D. (2011). Sumo–simulation of urban mobility: An overview. The Third International Conference on Advances in System Simulation (SIMUL 2011), October 23–29.
   Google Scholar
• Brogi, A., & Forti, S. (2017). QoS-aware deployment of iot applications through the fog. IEEE internet of Things Journal, 4(5), 1185–1192. doi:10.1109/JIOT.2017.2701408.
   Web of Science ®Google Scholar
• Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., & Buyya, R. (2011). Cloudsim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1), 23–50. doi:10.1002/spe.995.
   Web of Science ®Google Scholar
• Cárdenas, R., Arroba, P., Blanco, R., Malagón, P., Risco-Martín, J. L., & Moya, J. M. (2020). Mercury: A modeling, simulation, and optimization framework for data stream-oriented iotapplications. Simulation Modelling Practice and Theory, 101, 102037. doi:10.1016/j.simpat.2019.102037.
   Google Scholar
• Dey, A. K., Abowd, G. D., & Salber, D. (2000). A context-based infrastructure for smart environments. In Nixon, Paddy, Lacey, Gerard, Simon, Dobson (Eds,). Managing interactions in smart environments (pp. 114–128). Springer.
   Google Scholar
• Di, S., & Cappello, F. (2015). Gloudsim: Google trace based cloud simulator with virtual machines. Software: Practice and Experience, 45(11), 1571–1590. doi:10.1002/spe.2303.
   Web of Science ®Google Scholar
• Erdemir, A., Mulugeta, L., Ku, J. P., Drach, A., Horner, M., Morrison, T. M., and Myers, J. G. (2020). Credible practice of modeling and simulation in healthcare: Ten rules from a multidisciplinary perspective. Journal of Translational Medicine, 18(1), 1–18 doi:10.1186/s12967-020-02540-4.
   PubMed Web of Science ®Google Scholar
• Fox, A., Griffith, R., Joseph, A., Katz, R., Konwinski, A., & Lee, G. (2009). Above the Clouds: A Berkeley View of Cloud Computing, UCB/EECS-2009-28, University of California at Berkeley. https://www2.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdf
   Google Scholar
• Gaba, D. M. (2007). The future vision of simulation in healthcare. Simulation in Healthcare, 2(2), 126–135. https://doi.org/10.1097/01.SIH.0000258411.38212.32
   PubMedGoogle Scholar
• Giffin, N., Ruggiero, L., Lipton, R. B., Silberstein, S., Tvedskov, J., Olesen, J., … Macrae, A. (2003). Premonitory symptoms in migraine: An electronic diary study. Neurology, 60(6), 935–940. https://doi.org/10.1212/01.WNL.0000052998.58526.A9
   PubMed Web of Science ®Google Scholar
• Gupta, H., Vahid Dastjerdi, A., Ghosh, S. K., & Buyya, R. (2017). iFogSim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience, 47(9), 1275–1296. doi:10.1002/spe.2509.
   Web of Science ®Google Scholar
• Henares, K. (2021). MDCs optimization scenario repository. October 07,2021. https://github.com/khvilaboa/mdcsoptimization
   Google Scholar
• Jararweh, Y., Alsmirat, M., Al-Ayyoub, M., Benkhelifa, E., Darabseh, A., Gupta, B., & Doulat, A. (2017). Software-defined system support for enabling ubiquitous mobile edge computing. The Computer Journal, 60(10), 1443–1457. https://doi.org/10.1093/comjnl/bxx019
   Web of Science ®Google Scholar
• Kelman, L. (2006). The postdrome of the acute migraine attack. Cephalalgia, 26(2), 214–220. https://doi.org/10.1111/j.1468-2982.2005.01026.x
   Web of Science ®Google Scholar
• Kolbe, T. H., Gröger, G., & Plümer, L. (2005). CityGML: Interoperable Access to 3D City Models. In Oosterom, P., Zlatanova, S., Fendel, E. (Eds.), Geo-information for Disaster Management (pp. 883–899). Springer.
   Google Scholar
• Lera, I., Guerrero, C., & Juiz, C. (2019). Yafs: A simulator for iot scenarios in fog computing. IEEE Access, 7, 91745–91758. doi:10.1109/ACCESS.2019.2927895.
   Web of Science ®Google Scholar
• Linde, M., Gustavsson, A., Stovner, L. J., Steiner, T. J., Barré, J., & Katsarava, Z. (2012). The cost of headache disorders in Europe: The eurolight project. European Journal of Neurology, 19(5), 703–711. https://doi.org/10.1111/j.1468-1331.2011.03612.x
   PubMed Web of Science ®Google Scholar
• Liu, H., Xu, B., Lu, D., & Zhang, G. (2018). A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm. Applied Soft Computing, 68, 360–376. doi:10.1016/j.asoc.2018.04.015.
   Web of Science ®Google Scholar
• Masera, M., Bompard, E. F., Profumo, F., & Hadjsaid, N. (2018). Smart (electricity) grids for smart cities: Assessing roles and societal impacts. Proceedings of the IEEE, 106(4), 613–625. https://doi.org/10.1109/JPROC.2018.2812212
   Web of Science ®Google Scholar
• Mayer, R., Graser, L., Gupta, H., Saurez, E., & Ramachandran, U. (2017). Emufog: Extensible and scalable emulation of large-scale fog computing infrastructures, 2017 IEEE Fog World Congress (FWC), 30 Oct–1 Nov Santa Clara, CA, USA, 1–6. doi:10.1109/FWC.2017.8368525.
   Google Scholar
• Medina, A., Lakhina, A., Matta, I., & Byers, J. (2001). Brite: An approach to universal topology generation. MASCOTS 2001, Proceedings Ninth International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, 15-18 Aug., Cincinnati, OH, USA, 346–353. doi:10.1109/MASCOT.2001.948886.
   Google Scholar
• Mell, P., & Grance, T. (2011). The NIST definition of cloud computing (Tech. Rep.). National Institute of Standards and Technology.
   Google Scholar
• Mittal, S., Diallo, S., & Tolk, A. (2018). Emergent behavior in complex systems engineering: A modeling and simulation approach. John Wiley & Sons.
   Google Scholar
• Mutlag, A. A., Abd Ghani, M. K., Arunkumar, N. A., Mohammed, M. A., & Mohd, O. (2019). Enabling technologies for fog computing in healthcare iot systems. Future Generation Computer Systems, 90, 62–78. doi:10.1016/j.future.2018.07.049.
   Web of Science ®Google Scholar
• Núñez, A., Vázquez-Poletti, J. L., Caminero, A. C., Castañé, G. G., Carretero, J., & Llorente, I. M. (2012). icancloud: A flexible and scalable cloud infrastructure simulator. Journal of Grid Computing, 10(1), 185–209. https://doi.org/10.1007/s10723-012-9208-5
   Web of Science ®Google Scholar
• Olesen, J. (2004). The international classification of headache disorders. https://ichd-3.org/
   Google Scholar
• Pagán, J., De Orbe, M. I., Gago, A., Sobrado, M., Risco-Martín, J. L., Mora, J. V., … Ayala, L. (2015). Robust and accurate modeling approaches for migraine per-patient prediction from ambulatory data. Sensors, 15(7), 15419–15442. https://doi.org/10.3390/s150715419
   Web of Science ®Google Scholar
• Pagán, J., Risco-Martín, J. L., Moya, J. M., & Ayala, J. L. (2016). Grammatical evolutionary techniques for prompt migraine prediction. GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016, July 20–24, Denver Colorado USA, 973–980. doi:10.1145/2908812.2908897.
   Google Scholar
• Penas, I., Zapater, M., Risco-Martín, J. L., & Ayala, J. L. (2017). SFIDE: A simulation infrastructure for data centers. SummerSim '17: Proceedings of the Summer Simulation Multi-Conference, July 9–12, Bellevue Washington, 1–12. doi:10.5555/3140065.3140099.
   Google Scholar
• Qayyum, T., Malik, A. W., Khattak, M. A. K., Khalid, O., & Khan, S. U. (2018). FogNetSim++: A toolkit for modeling and simulation of distributed fog environment. IEEE Access, 6, 63570–63583. doi:10.1109/ACCESS.2018.2877696.
   Web of Science ®Google Scholar
• Reinsel, D., Gantz, J., & Rydning, J. (). Data age 2025: The digitization of the world from edge to core. Seagate, https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf.
   Google Scholar
• Saha, H. N., Auddy, S., Pal, S., Kumar, S., Pandey, S., Singh, R., and Saha, S. (2017). Health monitoring using Internet of Things (IoT). 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), 16–18 Aug, Bangkok, Thailand, 69–73. doi:10.1109/IEMECON.2017.8079564.
   Google Scholar
• Sahoo, P. K., Mohapatra, S. K., & Wu, S.-L. (2016). Analyzing healthcare big data with prediction for future health condition. IEEE Access, 4, 9786–9799. doi:10.1109/ACCESS.2016.2647619.
   Web of Science ®Google Scholar
• Schijve, L. (nd.). Pedestrian dynamics. RetrievedNovember 25,2020 https://www.incontrolsim.com/software/pedestrian-dynamics
   Google Scholar
• Sonmez, C., Ozgovde, A., & Ersoy, C. (2018). EdgeCloudSim: An environment for performance evaluation of edge computing systems. T ransactions on Emerging Telecomunications Technologies, 29(11), e3493. doi:10.1002/ett.3493.
   Web of Science ®Google Scholar
• Stovner, L. J., & Andree, C. (2010). Prevalence of headache in Europe: A review for the eurolight project. The journal of headache and pain, 11(4), 289. doi:10.1007/s10194-010-0217-0.
   PubMed Web of Science ®Google Scholar
• Tokody, D., Mezei, I. J., & Schuster, G. (2017). An overview of autonomous intelligent vehicle systems. In Jármai, K., Bolló, B.(Eds.), Vehicle and automotive engineering (pp. 287–307). Springer.
   Google Scholar
• Tripathi, G., Singh, K., & Vishwakarma, D. K. (2019). Convolutional neural networks for crowd behaviour analysis: A survey. The Visual Computer, 35(5), 753–776. https://doi.org/10.1007/s00371-018-1499-5
   Web of Science ®Google Scholar
• Tuli, S., Basumatary, N., Gill, S. S., Kahani, M., Arya, R. C., Wander, G. S., & Buyya, R. (2020). Healthfog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments. Future Generation Computer Systems, 104, 187–200. doi:10.1016/j.future.2019.10.043.
   Web of Science ®Google Scholar
• Voorsluys, W., Broberg, J., & Buyya, R. (2011). Introduction to Cloud Computing. In Buyya, R., Broberg, J., Goscinski, A. (Eds.), Cloud computing: Principles and Paradigms, 1–44. doi:10.1002/9780470940105.ch1.
   Google Scholar
• Wang, M., Li, Q., Hu, Q., & Zhou, M. (2013). Quality analysis of open street map data. 8th International Symposium on Spatial Data Quality, 30 May–1 June, Hong Kong. https://pdfs.semanticscholar.org/9653/93d713f4d068a25c78af2382b6d7c78b6c2b.pdf
   Google Scholar
• Wang, F., Wang, F., Ma, X., & Liu, J. (2019). Demystifying the crowd intelligence in last mile parcel delivery for smart cities. IEEE Network, 33(2), 23–29. https://doi.org/10.1109/MNET.2019.1800228
   Web of Science ®Google Scholar
• Zeng, X., Garg, S. K., Strazdins, P., Jayaraman, P. P., Georgakopoulos, D., & Ranjan, R. (2017). IOTSim: A simulator for analysing IoT applications. Journal of Systems Architecture, 72, 93–107. doi:10.1016/j.sysarc.2016.06.008.
   Web of Science ®Google Scholar
• Zhou, S., Chen, D., Cai, W., Luo, L., Low, M. Y. H., Tian, F., and Hamilton, B. D. (2010). Crowd modeling and simulation technologies. ACM Transactions on Modeling and Computer Simulation, 20(4), 1–35. doi:10.1145/1842722.1842725.
   Web of Science ®Google Scholar
• Zhou, B., Wang, X., & Tang, X. (2012). Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 16–21 June, Providence, RI, USA, 2871–2878. doi:10.1109/CVPR.2012.6248013.
   Google Scholar