Enhancing emergency response systems through leveraging crowdsensing and heterogeneous data

Abu-Elkheir, Mervat; Hassanein, Hossam S.; Oteafy, Sharief M. A.

doi:10.1109/iwcmc.2016.7577055

Cited by 33 publications

(25 citation statements)

References 17 publications

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“…According to literature review, ICT is an essential component in smart city governance to gain necessary intelligence that provides basis for evidence‐based decision‐making. At the core of ICT based solutions are three main components: (i) data collection through sensors, Internet‐of‐Things, smart phones, remote sensing (e.g., satellite or in‐situ) and city databases; (ii) data processing and/or pre‐processing (e.g., filtering, data quality and format translations); (iii) data analysis using machine learning, data mining and other statistical algorithms to generate new knowledge in various cross‐thematic applications such as mobility (Docherty, Marsden, & Anable, ; Peters‐Anders et al, ; Rathore et al,), energy (Antonić, Marjanović, Pripužić, & Žarko, ; Carli, Albino, Dotoli, Mummolo, & Savino, ; Silva, Khan, & Han, ), health (Anisetti et al, ; Farahani et al, ), environment (Antonić et al, ), public services (Pérez‐González & Díaz‐Díaz, ; Zhang et al, ), economy (Chatfield & Reddick, ; Saggi & Jain, ; Zaman et al, ), waste management (Digiesi, Facchini, Mossa, Mummolo, & Verriello, ), social analysis (Kousiouris et al, ; Terroso‐Saenz, Gonzalez‐Vidal, Cuenca‐Jara, & Skarmeta, ), waste water management (Edmondson et al, ), urban planning (Eirinaki et al, ; Pettit et al, ; Rathore, Ahmad, Paul, & Rho, ), tourism and cultural heritage (Sun, Song, Jara, & Bie, ), buildings (Linder, Vionnet, Bacher, & Hennebert, ), agriculture (Kamilaris, Gao, Prenafeta‐Boldu, & Ali, ), emergency response (Abu‐Elkheir, Hassanein, & Oteafy, ), etc. The above three components—with some variations—are common among the most of smart city data analytics literature for example, Zhang et al (); Khan et al (); Rathore et al ().…”

Section: Big Data Analytics In Smart Citiesmentioning

confidence: 99%

Smart city big data analytics: An advanced review

Soomro

Bhutta

Khan

et al. 2019

WIREs Data Min & Knowl

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With the increasing role of ICT in enabling and supporting smart cities, the demand for big data analytics solutions is increasing. Various artificial intelligence, data mining, machine learning and statistical analysis-based solutions have been successfully applied in thematic domains like climate science, energy management, transport, air quality management and weather pattern analysis. In this paper, we present a systematic review of the literature on smart city big data analytics. We have searched a number of different repositories using specific keywords and followed a structured data mining methodology for selecting material for the review. We have also performed a technological and thematic analysis of the shortlisted literature, identified various data mining/machine learning techniques and presented the results. Based on this analysis we also present a classification model that studies four aspects of research in this domain. These include data models, computing models, security and privacy aspects and major market drivers in the smart cities domain. Moreover, we present a gap analysis and identify future directions for research. For the thematic analysis we identified the themes smart city governance, economy, environment, transport and energy. We present the major challenges in these themes, the major research work done in the field of data analytics to address these challenges and future research directions.

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Section: Big Data Analytics In Smart Citiesmentioning

confidence: 99%

Smart city big data analytics: An advanced review

Soomro

Bhutta

Khan

et al. 2019

WIREs Data Min & Knowl

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“…The 10 articles related to the response to emergencies focused on determining emergency situations [51], autonomous vehicles [22], intelligent management of emergency vehicles [23,52,53], and emergency management applications [24,[54][55][56][57].…”

Section: Response To Emergenciesmentioning

confidence: 99%

Smart Cities and Healthcare: A Systematic Review

et al. 2019

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Objectives: The study reported in this article aimed to identify: (i) the most relevant applications supported by smart city infrastructure with an impact on the provision of healthcare; (ii) the types of technologies being used; (iii) the maturity levels of the applications being reported; and (iv) major barriers for their dissemination. Methods: A systematic review was performed based on a literature search. Results: A total of 44 articles were retrieved. These studies reported on smart city applications to support population surveillance, active ageing, healthy lifestyles, disabled people, response to emergencies, care services organization, and socialization. Conclusions: Most of the included articles were either of a descriptive and conceptual nature or in an early stage of development, which means that a major barrier for their dissemination is their lack of concreteness.

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“…Provisioning techniques will dynamically configure communication capacity between the site(s) of an incident and locations where analytics will be done; and to dynamically provision the storage and computational resources needed to carry out the analytics. Analytics techniques will focus on learning the type, scope, severity and dynamic properties of incidents from heterogeneous data (Abu-Elkheir et al, 2016).…”

Section: Response Phasementioning

confidence: 99%

Coordinating emergency response by competent teams

Hasse¹,

Gauthier²,

Rolt³

et al. 2018

IJCSIS

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ICTs and social networks can contribute to the emergence of creating competent teams for emergency response. For example, during disasters such as Pakistan flood of 2010, Japan tsunami of 2011 and Thailand flood of 2011, On-Line Social Networks (OSNs) have served as a main technology for numerous people seeking to share information about their personal status, to request resources, or to report the status of their community. They can be used at least for three main functions: detecting emergencies, disseminating information, and managing emergencies. On the other hand, the combination of Internet and mobile technology generated smart devices with associated sensor technologies that are becoming crucial parts in delivering supports during disaster and emergency situation. However, such use of mobile devices usually requires a reliable support system from crowdsensing technologies and back-end intelligent systems. In this way, the mobile crowdsensing must be designed with focus on mechanisms to identify and localize survivors and first responders in an incident zone, as well as mechanisms for competence characterization provided by social networks to facilitate coordination for individuals as well as crowds. These main technologies, the crowdsensing and social networks combined with semantic web and ontologies can provide a complete emergency response system that is the purpose of the CO-SEMIWA platform. This platform permits an interaction among participants to create dynamically competent teams where all participants perform tasks and solve problems in a specific emergency context and situation.

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Enhancing emergency response systems through leveraging crowdsensing and heterogeneous data

Cited by 33 publications

References 17 publications

Smart city big data analytics: An advanced review

Smart city big data analytics: An advanced review

Smart Cities and Healthcare: A Systematic Review

Coordinating emergency response by competent teams

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