Abstract
Resource allocation of the availability of certain departments for dealing with emergency recovery is of high importance in municipalities. Efficient planning for facing possible disasters in the coverage area of a municipality provides reassurance for citizens. Citizens can assist with such malfunctions by acting as human sensors at the edge of an infrastructure to provide instant feedback to the appropriate departments fixing the problems. However, municipalities have limited department resources to handle upcoming emergency events. In this study, we propose a smartphone crowdsensing system that is based on citizens’ reactions as human sensors at the edge of a municipality infrastructure to supplement malfunctions exploiting environmental crowdsourcing location-allocation capabilities. A long short-term memory (LSTM) neural network is incorporated to learn the occurrence of such emergencies. The LSTM is able to stochastically predict future emergency situations, acting as an early warning component of the system. Such a mechanism may be used to provide adequate department resource allocation to treat future emergencies.
Highlights
Human habitation is heading in the direction of smart cities—or cities 2.0—made possible due to technological advancements based on the Internet of things (IoT) [1,2,3]
We studied the municipality of Papagos–Cholargos, a smart city located in Athens, Greece
We ran the experiments for the period of one year with real data provided by the Citify municipality ability of the long short-term memory (LSTM) neural network classifier to predict which department
Summary
Human habitation is heading in the direction of smart cities—or cities 2.0—made possible due to technological advancements based on the Internet of things (IoT) [1,2,3]. When a report arrives to the municipality control center the system allocates certain department to serve the problem. Since incidents are served by a certain number of departments with limited resources, the early planning and allocation of a department’s resources before the incident emerges is of crucial significance. To handle such situations, we used an inference engine model that is based on Sensors 2020, 20, 3966; doi:10.3390/s20143966 www.mdpi.com/journal/sensors
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
