Published April 30, 2020 | Version v1
Journal article Open

DEVELOPING A MULTILEVEL DISTRIBUITING CROWDSOURCING SYSTEM FOR AIDING AND RESCUING TO OVERCOME WIDESPREAD CRISES

Creators

  • 1. K.N. Toosi University of Technology Mirdamad, Arak University of Technology
  • 2. K.N. Toosi University of Technology Mirdamad

Description

Today, the management of different crises in urban areas is among the main challenges of societies due to their scope and limited resources. Using the crowd to solve these problems would be a proper solution. Crowdsourcing, due to a large number of people, the diversity of expertise, superficial dispersion and low cost, has long been considered. However, managing such a volume of people to restore the crisis situation has many problems that modern IT-based techniques in recent years have Facilitates the issue.

In this paper, a distributed geospatial system consisting of segments and different users is designed that can be used to manage the crowd to solve the problems of the urban crisis. The system consists of several subsystems and several user groups that operate on the basis of spatial crowdsourcing service.

The proposed new service is an atomic, consisting of a guiding section, an operational content, and a control segment. Operational content involves performing a simple activity. Solving complex issues involves the proper combination of simple services. After identifying the crisis environment with system elements, the system design a suitable combination of services for addressing regional issues and then allocate services to appropriate rescuers at the region level. The designed mechanism to allocate and combine services is based on a multidisciplinary agent environment.

In order to evaluate, in addition to designing software test scenarios, the system was tested during the Aqala flood of 2019 in Golestan province of Iran. The system accuracy in allocation was as well as its performance when the number of users increased. The system also considerably raised various quality indicators such as rescuer fatigue or mission latency. Furthermore, an innovated crowdsourcing evaluation method also announced the overall system success rate of 44.5 %

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