Mew

Abstract

Mobile CrowdSensing (MCS) applications rely on the availability of multiple mobile devices for collecting sensor data on a large scale. These applications are gaining popularity and are used in several domains such as environmental monitoring and traffic monitoring. Several frameworks have been proposed to facilitate the development of these applications which deal with specific issues such as energy harvesting and ensuring the privacy of users. An important challenge that remains unaddressed is the allocation of sensing tasks to users as per the application requirements. Existing frameworks impose pre-built task allocation schemes to automatically allocate tasks to a subset of participants which limits their applicability to a small set of applications. However, as the number of MCS applications is growing, there is a need to provide a flexible environment to the developers to support a larger set of task allocation algorithms. Towards this, we propose our framework - Mew - which offers plug-n-play functionality for implementing custom task allocation algorithms to allow developers to reach out to the required set of participants while solving other challenges associated with MCS application development (e.g., reliable communication and secure data exchange) in a black-box manner. We describe the design choices of Mew required to support plug-n-play functionality and demonstrate its usability by developing three proof-of-concept applications using different task allocation algorithms.