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With the advance in mobile computing, Internet of Things, and ubiquitouswireless connectivity, social sensing based edge computing (SSEC) has emergedas a new computation paradigm where people and their personally owned devicescollect sensor measurements from the physical world and process them at theedge of the network. This paper focuses on a privacy-aware task allocationproblem where the goal is to optimize the computation task allocation in SSECsystems while respecting the users' customized privacy settings. It introducesa novel Game-theoretic Privacy-aware Task Allocation (G-PATA) framework toachieve the goal. G-PATA includes (i) a bottom-up game-theoretic model togenerate the maximum payoffs at end devices while satisfying the end user'sprivacy settings; (ii) a top-down incentive scheme to adjust the rewards forthe tasks to ensure that the task allocation decisions made by end devices meetthe Quality of Service (QoS) requirements of the applications. Furthermore, theframework incorporates an efficient load balancing and iteration reductioncomponent to adapt to the dynamic changes in status and privacy configurationsof end devices. The G-PATA framework was implemented on a real-world edgecomputing platform that consists of heterogeneous end devices (Jetson TX1 andTK1 boards, and Raspberry Pi3). We compare G-PATA with state-of-the-art taskallocation schemes through two real-world social sensing applications. Theresults show that G-PATA significantly outperforms existing approaches undervarious privacy settings (our scheme achieved as much as 47% improvements indelay reduction for the application and 15% more payoffs for end devicescompared to the baselines.).

Towards Privacy-aware Task Allocation in Social Sensing based Edge Computing Systems