Abstract
When a disaster occurs, a huge amount of inconsistent victim or damage information data is received by many different sources. Disaster management systems achieve the completion of a significantly vital task, which is to reduce the number of victims or amount of damage caused by a disaster, with real-time information monitoring infrastructure. A fundamental role of these systems that could help rescue teams is to make a quick and accurate decision about the region that will be affected by the disaster and the possible effects of the tragedy. Employing IoT solutions in these systems provides the possibility of rapidly and precisely orienting rescue teams to be dispatched to the disaster area and also quickly receive specific information about the effects of the disaster. To achieve this purpose, we present a post-disaster framework using the IoT communication technologies for disaster management based on the proposed crowd sensing clustering algorithm in this paper. The proposed framework provides information about the damage status of buildings with crowd density data along with efficient real-time data collection, data aggregation, and the process of monitoring dissemination stages. This framework realizes clustering of resident density by using the cellular networks and Wi-Fi connections and calculating the damage status of buildings through the designed and specifically implemented IoT unit data. Furthermore, it employs a fuzzy logic-based decision support system to manage the resources. The proposed framework, on real base stations and access points dataset, has shown significant results for identifying crowd densities with the highlighting status of buildings in the disaster area.
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Abbreviations
- API:
-
Application programming interface
- SAR:
-
Search and rescue
- IoT:
-
Internet of things
- IDE:
-
Integrated development environment
- MQTT:
-
Message queue telemetry transport
- RFID:
-
Radio frequency identification
- Wi-Fi:
-
Wireless fidelity
- BS:
-
Base station
- AP:
-
Access point
- GPRS:
-
General packet radio
- GSM:
-
Global system for mobile communication
- 3G:
-
Third generation
- 4G:
-
Fourth generation
- HDFS:
-
Hadoop distributed file system
- TCP/IP:
-
Transmission control protocol/internet protocol
- CSS:
-
Cascading style sheets
- HTML5:
-
Hypertext markup language 5
- SENDROM:
-
Sensor networks for disaster relief operations management
- JDK:
-
Java development kit
- QoS:
-
Quality of service
- \(a\) :
-
Acceleration
- \(v\) :
-
Velocity
- \(s\) :
-
Distance
- \(SA\) :
-
Static acceleration vector length
- \(a_{x}\) :
-
The latitude component of the static acceleration vector
- \(a_{y}\) :
-
The longitude component of the static acceleration vector
- \(a_{z}\) :
-
The altitude component of the static acceleration vector
- \(DA\) :
-
Dynamic acceleration vector length
- \(b_{x}\) :
-
The latitude component of the dynamic acceleration vector
- \(b_{y}\) :
-
The longitude component of the dynamic acceleration vector
- \(b_{z}\) :
-
The altitude component of the dynamic acceleration vector
- \(G\) :
-
Gravity
- \(VA\) :
-
Vertical acceleration vector length
- \(\varTheta\) :
-
Angle of rotation of the building
- \(\vartheta_{y}\) :
-
The x-axis angle of rotation vector of the IoT-unit
- \(\vartheta_{y}\) :
-
The y-axis angle of rotation vector of the IoT-unit
- \(\vartheta_{z}\) :
-
The z-axis angle of rotation vector of the IoT-unit
- \(D^{BS}\) :
-
The base station data set
- \(D^{WF}\) :
-
The access point data set
- \(PN\) :
-
The subscriber’s phone number
- \(\varphi^{BS}\) :
-
The base station’s latitude
- \(\lambda^{BS}\) :
-
The base station’s longitude
- \(t^{BS}\) :
-
The subscriber connection time to the BS
- \(\Delta t^{BS}\) :
-
The duration time of the subscriber
- \(MAC\) :
-
The user’s MAC number
- \(\varphi^{WF}\) :
-
The access point’s latitude
- \(\lambda^{WF}\) :
-
The access point’s longitude
- \(t^{WF}\) :
-
The user connection time to the AP
- \(\Delta t^{WF}\) :
-
The duration time of the user
- \(d^{BS}\) :
-
The distance between the two BSs
- \(d^{WF}\) :
-
The distance between the two APs
- \(R_{e}\) :
-
The radius of the equator
- \(R_{p}\) :
-
The radius to the north pole
- \(\varphi_{c}\) :
-
The latitude of the cluster center
- \(\lambda_{c}\) :
-
The longitude of the cluster center
- \(\Delta T^{BS}\) :
-
Time difference between two base station data
- \(\Delta T^{WF}\) :
-
Time difference between two base station data
- \(user\_n_{c}\) :
-
The number of smart phone users attained to the BS
- \(T_{{BS_{MAX} }}\) :
-
The subscriber duration time threshold for the BS
- \(T_{{WF_{MAX} }}\) :
-
The user duration time threshold for the AP
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Acknowledgements
This study was supported by the Scientific Research Projects Committee of Sakarya University under Grant no. 2017-12-10-010. We are also thankful to the anonymous reviewers for their useful suggestions.
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Kucuk, K., Bayilmis, C., Sonmez, A.F. et al. Crowd sensing aware disaster framework design with IoT technologies. J Ambient Intell Human Comput 11, 1709–1725 (2020). https://doi.org/10.1007/s12652-019-01384-1
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DOI: https://doi.org/10.1007/s12652-019-01384-1