# CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting
###### Abstract  
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; sensing-as-a-service; incident reporting; smart city application; data quality; data reliability +
Footnote †: journal: Applied  
Sensing-as-a-Service (MSaaS). In MSaaS, mobile phones are perceived as data collectors, and their users participate in the sensing process offering their phones' sensory data collection capabilities as services. In addition to the enhanced data processing and validation capabilities achieved on smartphones, motivations for using MSaaS include: the increased sensing coverage area due to devices/users mobility, higher uptime of sensors as the users ensure to charge their phones, the easy on-demand deployment of a network of sensors in an area of interest, the addition of a social dimension to sensing, and the saving of time and cost in comparison to traditionally deployed specialized WSN infrastructures [4]. A variety of application areas have been suggested and studied in the literature for MSaaS, such as traffic monitoring [5; 6], environment monitoring [7; 8], location tagging [9], and localization [10; 11]. Such MSaaS applications are categorized as either participatory (with the active involvement of users that contribute data) or opportunistic (data collection without direct user interaction) [12]. The source of this data is mobile users who share data from their sensor-rich smartphones. There are many challenges in realizing an MSaaS platform, including the time-space constrained nature of crowdsensing tasks, combined with participants' mobility, the diverse types and quality of sensors on the devices, effective power utilization of the devices, user acceptance, and unpredictable behavior that affects the sensory data [13]. Moreover, in participatory sensing, the user should be motivated to participate in the process, while in opportunistic sensing, the phone's context and the availability of the required sensors are difficult to predict [14; 15]. In this context, we recently proposed a comprehensive MSaaS participant recruitment model that proactively ensures the quality and reliability of the smartphone sensors before it contributes to a sensing campaign hosted on the MSaaS platform [16].