# CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting
### Research Contributions  
Our major contributions in this paper are the following. 1. Literature survey: We provide an in-depth literature survey, comparing with other existing crowdsensing platforms, and a discussion on the benefits of CrowdPower. 2. Software architecture, design, and implementation of a crowdsensing-as-a-service platform: We provide a description of the end-to-end development of a cloud-based comprehensive crowdsensing platform that offers sensing services, which researchers can use to study various crowdsensing applications further. We discuss the following: 1. The software requirements and the client/server architectural components: The interface between the various components on the mobile application (client) and the MSaaS server is described, providing modularity and scalability. We also discuss the various operations of the system, explaining the flow of messages using sequence charts. Moreover, we describe the design and implementation of comprehensive RESTful Web services. By exposing its capabilities as Web Services, the CrowdPower platform allows flexible access to a wide range of crowdsensing-related functionalities consumed by mobile and Web clients, thus facilitating the flexible implementation and deployment of smart applications. 2. The design and implementation of a sensing mobile app: Existing solutions require specialized applications to showcase crowdsensing capabilities and need to develop different applications for each use case. Our platform, on the other hand, is the first to offer a generic sensing gateway application that enables both data collectors and data consumers to participate in sensing activities. It allows the collection of a large variety of sensory and user collected data on different frequencies and scales and supports both opportunistic and participatory sensing (both triggered and voluntary). Such an application would enable the collection of any crowdsensing data required (according to the specified criteria), thus combining the capabilities of multiple crowdsensing applications in one. 3. The design and implementation of an administrative Web portal: An administration portal was designed and fully implemented to enable easy administration and visibility over sensing activities and sessions. Furthermore, a customer's Web portal was provided to allow authorities (acting as data consumers) to have a full view of the data related to their geographic location, along with useful statistics and analytics. 4. Model for gathering good quality sensory data for efficient processing: We use a double-edged approach to ensure efficient processing of the sensory data. For a given sensing campaign, as an initial phase, we proactively make a participant selection by comparing the sensors on the participants' mobile phones to ensure the quality of the phone. This approach was proposed earlier [16]. We briefly explain this approach to provide the reader with an overall understanding of the whole system. The next phase, which we describe here in detail, is where our system relies on unsupervised machine learning combined with user reputations to evaluate the quality of sensory data received at the server. We preprocess data received to detect and filter anomalies in the data, and then we use clustering, which is a machine learning technique to discover natural groupings in the data automatically. Moreover, we also describe a model for benchmarking phone sensors to compare sensors of the same type on different phones and allocate them a relative quality score. 4. Empirical evaluation: We discuss the evaluation of the participants' selection model (first phase) and the data quality model (second phase). Our data quality estimation model was tested, and the results are analyzed and discussed to show how the data quality is determined, including determining participant reputation and filtering outliers.