Task Selective and Comfort-Aware User Recruitment with Incentives in Mobile Crowd-Sensing

Abstract

With the significant improvement in IoT technology and smart devices, data collection and distributed computation have led a foundation for Mobile crowd-sensing (MCS). MCS utilizes the capabilities of embedded sensors in smart devices for gathering data. MCS benefits both data provider (participant/user), and data requester, i.e. data providers via incentives/rewards, data requesters by delivering required data. Apart from the benefits gained through acquiring data, confronting challenges such as participant privacy, data trustworthiness, malicious attacks (from illegitimate users) need to be addressed to build robust and reliable data solicitation. In addition to that, it is necessary to consider user motivation and user preference, comfort during its engagement in crowd-sensing. User preferences/constraints can be due to privacy concerns in terms of location, the sensitivity of data or energy usage and many more. With this in mind, the main contributions of the thesis can be listed as follows. 1) We design user selective trustworthy data acquisition frameworks. We introduce a variety of user selection criteria to form participant communities based on participants reliability and income. To evaluate the trustworthiness of our selective reputation-based data acquisition, we consider malicious users in the environment and calculate the total rewards given to malicious users. Simulations results show that community formation based on the acquired income of participants ended up with a substantial loss to the cloud platform as well as participants. Contrary to that, reputation-based community formation has shown nearly equal platform utility (profit), negligible loss of user utility compared to benchmark Non-selective data acquisition with 7% malicious probability. 2) Moreover, we attempt to enable users to modify (allow/deny access to) their builtin sensor set according to their comfort levels. We formulate three comfort levels high (only allow access to sensors that would not directly reveal personal identity such as accelerometer, light sensor, etc.), moderate (obstruct access to sensitive data, e.g. camera), zero comfort (allow access to all users). We introduce Static modification, where users pre-arrange their sensor set before the start of data collection. Our feasibility study shows that pre-arrangement of the sensor set favours user comfort, user utility at the cost of loss in platform utility and performs better than selective reputation-based recruitment for the considered settings. 3) We apply Adaptive sensor modification on top of pre-arrangement of sensor set through which participants are authorized to re-arrange their sensor availability based on reliability scores. Simulation results show that the Adaptive comfort-aware approach performed better than static in terms of platform utility and achieved comparatively better user comfort with reasonable loss in user utility.