Abstract
During disasters, individuals witness various dreadful scenes and situations. These scenes and situations generate a sense of panic and cause panic attacks to individuals. Panic attacks cause various disorders like Panic Disorder, Post Traumatic Stress Disorder, and Anxiety Disorder, etc. On the other side, smart cities are becoming the mainstay for urbanization. Hence, the increasing incidents of disruptions due to disasters require the smart cities to adopt emergency response and resilience as the most critical dimension for its design so that the disaster-related risks can be prevented and controlled. This dimension of smart city design helps in minimizing the disruption, human, and socio-economic loss through Information and Communication Technologies (ICT) and called smart disaster management. In this paper, a Fog-Cloud centric Internet of Things (IoT)-based cyber physical framework is proposed, which prioritizes the evacuation of the panicked stranded individuals and provides timely medical support. The physical subsystem of the framework acquires data from stranded individuals and disaster-affected environment and provides various information services to the respective stakeholders (evacuation personnel and stranded individuals). Whereas, the cyber subsystem of the proposed framework, initially at the Fog layer classifies the Panic Health Status (PHS) of the stranded individuals in real-time based on the acquired health data and analyzes the novelty of the data for avoiding unnecessary data traffic to Cloud. After PHS diagnosis, the cyber subsystem uses Bayesian Belief Network (BBN) to monitor the panic health sensitivity of the stranded panicked individuals using disaster-related health and environmental data, at the Cloud layer. This subsystem also builds the evacuation map using acquired disaster-related environmental data at the Cloud layer. Based on the evacuation map and monitored panic health sensitivity of the individuals, the subsystem prepares evacuation strategy, which prioritizes the evacuation of the stranded individuals. The vital points of this proposed framework are the immediate panic-related diagnostic and curative alert generation to the mobile devices of the stranded individuals from the Fog layer, and the preparation of the evacuation strategy based on the evacuation map and panic health sensitivity monitoring of the stranded individuals, at the Cloud layer. The experimental evaluation of the proposed framework depicts the classification efficiency of Support Vector Machine (SVM) for classifying the PHS of the stranded individuals in real-time, and efficiency of Data Novelty Analysis (DNA) for avoiding unnecessary data traffic, at the Fog layer. The experimental evaluation also acknowledges the efficiency of evacuation map building using Unmanned Aerial Vehicles (UAVs), and panic health sensitivity monitoring using BBN at the Cloud layer.
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References
Abeywickrama H V, Jayawickrama B A, He Y, Dutkiewicz E (2018) Empirical power consumption model for uavs. In: 2018 IEEE 88th Vehicular technology conference (VTC-Fall). IEEE, pp 1–5
Alazawi Z, Alani O, Abdljabar M B, Altowaijri S, Mehmood R (2014) A smart disaster management system for future cities. In: Proceedings of the 2014 ACM international workshop on wireless and mobile technologies for smart cities, pp 1–10, ACM
Albahri O, Albahri A, Mohammed K, Zaidan A, Zaidan B, Hashim M, Salman O H (2018) Systematic review of real-time remote health monitoring system in triage and priority-based sensor technology: taxonomy, open challenges, motivation and recommendations. J Med Syst 42(5):80–106
Association AP et al (2013) Diagnostic and statistical manual of mental disorders (DSM-5®;) American Psychiatric Pub
Athukorala PC, Resosudarmo BP (2005) The Indian ocean tsunami: economic impact, disaster management, and lessons. Asian Econ Pap 4(1):1–39
Baker T, Asim M, Tawfik H, Aldawsari B, Buyya R (2017) An energy-aware service composition algorithm for multiple cloud-based iot applications. J Netw Comput Appl 89:96–108
Bariya M, Nyein H Y Y, Javey A (2018) Wearable sweat sensors. Nat Electron 1(3):160–171
Below R, Wallemacq P (2018) Natural disasters 2017. Tech. rep., Center for Research on the Epidemiology of Disasters. https://cred.be/sites/default/files/adsr_2017.pdf. Accessed: 2020 Jan 29
Ben Arbia D, Alam M, Kadri A, Ben Hamida E, Attia R (2017) Enhanced iot-based end-to-end emergency and disaster relief system. J Sens Actuator Netw 6(3):19–36
Bhatia M, Sood S K (2016) Temporal informative analysis in smart-icu monitoring: M-healthcare perspective. J Med Syst 40(8):190–204
Botta A, De Donato W, Persico V, Pescapé A (2016) Integration of cloud computing and internet of things: a survey. Future Gener Comput Syst 56:684–700
California Wildfires (2018) https://en.wikipedia.org/wiki/2018_California_wildfires#cite_note-LargeIncidentReport-4. Accessed: 2020 Jan 29
Chaitra M, Sivakumar B (2017) Disaster debris detection and management system using wsn & iot. Int J Adv Netw Appl 9(1):3306–3310
Cherney K (2020) Effects of anxiety on the body. https://www.healthline.com/health/anxiety/effects-on-body#1. Accessed: 2020 Jan 29
Chiang M, Zhang T (2016) Fog and iot: an overview of research opportunities. IEEE Internet Things J 3(6):854–864
Clarke M, Schluter P, Reinhold B, Reinhold B (2015) Designing robust and reliable timestamps for remote patient monitoring. IEEE J Biomed Health Inf 19(5):1718–1723
CupCarbon (2020) Cupcarbon u-one 3.8. http://www.cupcarbon.com/. Accessed: 2020 Jan 29
da Costa T D, Vara MDFF, Cristino CS, Zanella TZ, Neto GNN, Nohama P (2019) Breathing monitoring and pattern recognition with wearable sensors. In: Wearable Devices. IntechOpen
Deng T, Zeng J, Wang S, Yan S, Chen A (2019) An optical fire detector with enhanced response sensitivities for black smoke based on the polarized light scattering. Meas Sci Technol 30(11):115203
Drake R (2009) The hierarchy of emergency preparedness. In: Safeguarding homeland security. Springer, pp 31–40
Drenckhan I, Glöckner-Rist A, Rist F, Richter J, Gloster A T, Fehm L, Lang T, Alpers G W, Hamm A O, Fydrich T et al (2015) Dimensional structure of bodily panic attack symptoms and their specific connections to panic cognitions, anxiety sensitivity and claustrophobic fears. Psychol Med 45 (8):1675–1685
Facts + statistics: Global catastrophes (2020). https://www.iii.org/fact-statistic/facts-statistics-global-catastrophes. Accessed: 2020 Jan 29
FHWA (2020) Emergency transportation operations. https://ops.fhwa.dot.gov/eto_tim_pse/index.htm. Accessed: 2020 Jan 29
Freire R C, Zugliani M M, Garcia R F, Nardi A E (2016) Treatment–resistant panic disorder: a systematic review. Expert Opin Pharmacother 17(2):159–168
Grimaldi G, Manto M (2010) Neurological tremor: sensors, signal processing and emerging applications. Sensors 10(2):1399–1422
Haiti Earthquake PDNA (2010) Assessment of damage, losses, general and sectoral needs. Tech. rep., World Bank. https://siteresources.worldbank.org/INTLAC/Resources/PDNA_Haiti-2010_Working_Document_EN.pdf. Accessed: 2020 Jan 29
Han Y, Liu H, Moore P (2017) Extended route choice model based on available evacuation route set and its application in crowd evacuation simulation. Simul Model Pract Theory 75:1–16
He X, Zhu Y, Epstein A, Mo Y (2018) Statistical variances of diffusional properties from ab initio molecular dynamics simulations. NPJ Comput Mater 4(1):18–26
HMS (2008) Anxiety and physical illness. https://www.health.harvard.edu/staying-healthy/anxiety_and_physical_illness. Accessed: 2020 Jan 29
Huang C, Nie S, Guo L, Fan Y (2017) Inexact fuzzy stochastic chance constraint programming for emergency evacuation in qinshan nuclear power plant under uncertainty. J Environ Inform 30(1):63–78
Iskander J, Attia M, Saleh K, Nahavandi D, Abobakr A, Mohamed S, Asadi H, Khosravi A, Lim C P, Hossny M (2019) From car sickness to autonomous car sickness: a review. Transp Res Part F: Traffic Psychol Behav 62:716–726
Ji Z, Anwen Q (2010) The application of internet of things (iot) in emergency management system in China. In: 2010 IEEE International conference on technologies for homeland security (HST). IEEE, pp 139–142
Kaur H, Sood S K (2019) Adaptive neuro fuzzy inference system (anfis) based wildfire risk assessment. J Exp Theoret Artif Intel 31(4):599–619
Kotas R, Janc M, Kamiński M, Marciniak P, Zamysłowska-Szmytke E, Tylman W (2019) Evaluation of agreement between static posturography methods employing tensometers and inertial sensors. IEEE Access 7:164120–164126
Li P, Miyazaki T, Wang K, Guo S, Zhuang W (2017) Vehicle-assist resilient information and network system for disaster management. IEEE Trans Emerg Top Comput 5(3):438–448
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. Appl Soft Comput 68:360–376
Lujak M, Billhardt H, Dunkel J, Fernández A, Hermoso R, Ossowski S (2017) A distributed architecture for real-time evacuation guidance in large smart buildings. Comput Sci Inf Syst 14(1):257–282
Ma X, Wang Z, Zhou S, Wen H, Zhang Y (2018) Intelligent healthcare systems assisted by data analytics and mobile computing. https://doi.org/10.1155/2018/3928080
Mahdavinejad M S, Rezvan M, Barekatain M, Adibi P, Barnaghi P, Sheth A P (2018) Machine learning for internet of things data analysis: a survey. Digit Commun Netw 4(3):161–175
Mayo Clinic (2016) Chronic stress puts your health at risk. https://www.mayoclinic.org/healthy-lifestyle/stress-management/in-depth/stress/art-20046037. Accessed: 2020 Jan 29
Mehmood Y, Ahmad F, Yaqoob I, Adnane A, Imran M, Guizani S (2017) Internet-of-things-based smart cities: recent advances and challenges. IEEE Commun Mag 55(9):16–24
Modenesi A P, Braga A P (2009) Analysis of time series novelty detection strategies for synthetic and real data. Neural Process Lett 30(1):1
Motlagh N H, Bagaa M, Taleb T (2017) Uav-based iot platform: a crowd surveillance use case. IEEE Commun Mag 55(2):128– 134
Mousa M, Zhang X, Claudel C (2016) Flash flood detection in urban cities using ultrasonic and infrared sensors. IEEE Sens J 16(19):7204–7216
Mumtaz Z, Ullah S, Ilyas Z, Aslam N, Iqbal S, Liu S, Meo J, Madni H (2018) An automation system for controlling streetlights and monitoring objects using arduino. Sensors 18(10):3178–3191
Neelam S, Sood S K (2020) A scientometric review of global research on smart disaster management. https://doi.org/10.1109/TEM.2020.2972288
NIMH (2020) Anxiety disorders. https://www.nimh.nih.gov/health/topics/anxiety-disorders/index.shtml#part1
Norio O, Ye T, Kajitani Y, Shi P, Tatano H (2011) The 2011 eastern Japan great earthquake disaster: overview and comments. Int J Disaster Risk Sci 2(1):34–42
Oksuz M K, Satoglu S I (2020) A two-stage stochastic model for location planning of temporary medical centers for disaster response. Int J Disaster Risk Reduct 44(101):426
Oubbati O S, Chaib N, Lakas A, Lorenz P, Rachedi A (2019a) Uav-assisted supporting services connectivity in urban vanets. IEEE Trans Veh Technol 68(4):3944–3951
Oubbati O S, Lakas A, Lorenz P, Atiquzzaman M, Jamalipour A (2019b) Leveraging communicating uavs for emergency vehicle guidance in urban areas IEEE Trans Emerg Top Comput. https://doi.org/10.1109/TETC.2019.2930124
Pace P, Aloi G, Gravina R, Caliciuri G, Fortino G, Liotta A (2018) An edge-based architecture to support efficient applications for healthcare industry 4.0. IEEE Trans Ind Inform 15(1):481– 489
Panic—meaning in the Cambridge English dictionary (2020). https://dictionary.cambridge.org/dictionary/english/panic. Accessed: 2020 Jan 29
Raw R S, Kumar A, Kadam A, Singh N et al (2016) Analysis of message propagation for intelligent disaster management through vehicular cloud network. In: Proceedings of the second international conference on information and communication technology for competitive strategies. ACM. https://doi.org/10.1145/2905055.2905252
Ray P P, Mukherjee M, Shu L (2017) Internet of things for disaster management: state-of-the-art and prospects. IEEE Access 5:18818–18835
Ray P P, Dash D, De D (2019) A systematic review and implementation of iot-based pervasive sensor-enabled tracking system for dementia patients. J Med Syst 43(9):287–307
Rego A, Garcia L, Sendra S, Lloret J (2018) Software defined network-based control system for an efficient traffic management for emergency situations in smart cities. Future Gener Comput Syst 88:243–253
Sacchi L, Larizza C, Combi C, Bellazzi R (2007) Data mining with temporal abstractions: learning rules from time series. Data Min Knowl Discov 15(2):217–247
Sahil, Sood SK (2019) Smart vehicular traffic management: An edge cloud centric iot based framework. Internet Things 100140. https://doi.org/10.1016/j.iot.2019.100140
Sahil, Sood SK (2020) Bibliometric monitoring of research performance in ict-based disaster management literature. Qual Quant 1–30. https://doi.org/10.1007/s11135-020-00991-x
Sarkar S, Chatterjee S, Misra S (2015) Assessment of the suitability of fog computing in the context of internet of things. IEEE Trans Cloud Comput 6(1):46–59
Shen C L, Huang T H, Hsu P C, Ko Y C, Chen F L, Wang W C, Kao T, Chan C T (2017) Respiratory rate estimation by using ecg, impedance, and motion sensing in smart clothing. J Med Biol Eng 37(6):826–842
Smola A J, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14 (3):199–222
Sood S K, Mahajan I (2018a) A fog-based healthcare framework for chikungunya. IEEE Internet Things J 5(2):794–801
Sood S K, Mahajan I (2018b) Iot-fog-based healthcare framework to identify and control hypertension attack. IEEE Internet Things J 6(2):1920–1927
Sood S K, Sandhu R, Singla K, Chang V (2018) Iot, big data and hpc based smart flood management framework. Sustain Comput: Inform Syst 20:102–117
Sood SK, Sood V, Mahajan I, Sahil (2020) Fog–cloud assisted IoT-based hierarchical approach for controlling dengue infection. Comput J. https://doi.org/10.1093/comjnl/bxaa005. Bxaa005
Šoštarić D, Mester G, Dorner S (2019) Mobile ecg and spo2 chest pain subjective indicators of patient with gps location in smart cities. Interdiscip Descr Complex Syst: INDECS 17(3-B):629– 639
Tang B, Chen Z, Hefferman G, Pei S, Wei T, He H, Yang Q (2017) Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Trans Ind Inform 13(5):2140–2150
Temimi M, Fonseca R M, Nelli N R, Valappil V K, Weston M J, Thota M S, Wehbe Y, Yousef L (2020) On the analysis of ground-based microwave radiometer data during fog conditions. https://doi.org/10.1016/j.atmosres.2019.104652
Trono E M, Fujimoto M, Suwa H, Arakawa Y, Yasumoto K (2017) Generating pedestrian maps of disaster areas through ad-hoc deployment of computing resources across a dtn. Comput Commun 100:129–142
Tsai P H, Lin C L, Liu J N (2016) On-the-fly nearest-shelter computation in event-dependent spatial networks in disasters. IEEE Trans Veh Technol 65(3):1109–1120
Ukkusuri S V, Hasan S, Luong B, Doan K, Zhan X, Murray-Tuite P, Yin W (2017) A-rescue: an agent based regional evacuation simulator coupled with user enriched behavior. Netw Spat Econ 17 (1):197–223
Vasconez K C, Kehrli M (2010) Highway evacuations in selected metropolitan regions: Assessment of impediments. Tech rep
Verma P, Sood S K (2018) Fog assisted-iot enabled patient health monitoring in smart homes. IEEE Internet Things J 5(3):1789–1796
Wang H, Dong L, Wei W, Zhao W S, Xu K, Wang G (2017) The wsn monitoring system for large outdoor advertising boards based on zigbee and mems sensor. IEEE Sens J 18(3):1314–1323
WebMD (2018). What is cortisol? https://www.webmd.com/a-to-z-guides/what-is-cortisol#1
Weka 3 (2020) Machine learning software in java. https://www.cs.waikato.ac.nz/~ml/weka/. Accessed: 2020 Jan 29
Zhai Y, Chen K, Zhou J X, Cao J, Lyu Z, Jin X, Shen G Q, Lu W, Huang G Q (2019) An internet of things-enabled bim platform for modular integrated construction: a case study in Hong Kong. Adv Eng Inform 42. https://doi.org/10.1016/j.aei.2019.100997
Zhou Y, Cheng N, Lu N, Shen X S (2015) Multi-uav-aided networks: aerial-ground cooperative vehicular networking architecture. IEEE Veh Technol Mag 10(4):36–44
Zhou L, Wu D, Chen J, Dong Z (2017) When computation hugs intelligence: content-aware data processing for industrial iot. IEEE Internet Things J 5(3):1657–1666
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Sahil, Sood, S.K. Fog-Cloud centric IoT-based cyber physical framework for panic oriented disaster evacuation in smart cities. Earth Sci Inform 15, 1449–1470 (2022). https://doi.org/10.1007/s12145-020-00481-6
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DOI: https://doi.org/10.1007/s12145-020-00481-6