Source of Publication
Applied Sciences (Switzerland)
Crowdsensing using mobile phones is a novel addition to the Internet of Things applications suite. However, there are many challenges related to crowdsensing, including (1) the ability to manage a large number of mobile users with varying devices’ capabilities; (2) recruiting reliable users available in the location of interest at the right time; (3) handling various sensory data collected with different requirements and at different frequencies and scales; (4) brokering the relationship between data collectors and consumers in an efficient and scalable manner; and (5) automatically generating intelligence reports after processing the collected sensory data. No comprehensive end-to-end crowdsensing platform has been proposed despite a few attempts to address these challenges. In this work, we aim at filling this gap by proposing and describing the practical implementation of an end-to-end crowdsensing-as-a-service system dubbed CrowdPower. Our platform offers a standard interface for the management and brokerage of sensory data, enabling the transformation of raw sensory data into valuable smart city intelligence. Our solution includes a model for selecting participants for sensing campaigns based on the reliability and quality of sensors on users’ devices, then subsequently analysing the quality of the data provided using a clustering approach to predict user reputation and identify outliers. The platform also has an elaborate administration web portal developed to manage and visualize sensing activities. In addition to the architecture, design, and implementation of the backend platform capabilities, we also explain the creation of CrowdPower’s sensing mobile application that enables data collectors and consumers to participate in various sensing activities.
crowdsensing, data quality, data reliability, incident reporting, sensing-as-a-service, smart city application
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Mathew, Sujith Samuel; El Barachi, May; and Kuhail, Mohammad Amin, "CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting" (2022). All Works. 5477.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series