  With the advance in mobile computing, Internet of Things, and ubiquitous
wireless connectivity, social sensing based edge computing (SSEC) has emerged
as a new computation paradigm where people and their personally owned devices
collect sensor measurements from the physical world and process them at the
edge of the network. This paper focuses on a privacy-aware task allocation
problem where the goal is to optimize the computation task allocation in SSEC
systems while respecting the users' customized privacy settings. It introduces
a novel Game-theoretic Privacy-aware Task Allocation (G-PATA) framework to
achieve the goal. G-PATA includes (i) a bottom-up game-theoretic model to
generate the maximum payoffs at end devices while satisfying the end user's
privacy settings; (ii) a top-down incentive scheme to adjust the rewards for
the tasks to ensure that the task allocation decisions made by end devices meet
the Quality of Service (QoS) requirements of the applications. Furthermore, the
framework incorporates an efficient load balancing and iteration reduction
component to adapt to the dynamic changes in status and privacy configurations
of end devices. The G-PATA framework was implemented on a real-world edge
computing platform that consists of heterogeneous end devices (Jetson TX1 and
TK1 boards, and Raspberry Pi3). We compare G-PATA with state-of-the-art task
allocation schemes through two real-world social sensing applications. The
results show that G-PATA significantly outperforms existing approaches under
various privacy settings (our scheme achieved as much as 47% improvements in
delay reduction for the application and 15% more payoffs for end devices
compared to the baselines.).
