QoS Aware Task Management Strategies for Mobile Crowdsensing Applications

Abstract

Mobile Crowdsensing (MCS) systems are under rapid development due to the popularization of smart mobile devices with various sensing abilities. MCS systems are useful in applications that require large scale spatial data collecting. To provide satisfactory service, the MCS system is expected to contain effective and efficient task management strategies, considering worker qualification, and possible worker moving. Most existing works design moving trajectory for each worker, in order to improve task accomplishment ratio (TAR) for QoS consideration. Other than great real time operation cost, it is also not practical to control workers’ moving due to privacy issue. In this work, we propose a MCS model with comprehensive integrated parameters to support the design of effective QoS aware task assignment strategies. We design strategies to perform task assignment based on worker qualification, using gradient to represent worker moving probability. We develop the accumulated gradient based reward allocation (AGRA) that improves QoS by motivating worker moving. Our experiments show that the proposed QoS MCS management strategies improve task accomplishment ratio significantly.