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With the ever-increasing data processing capabilities of edge computing devices and the growing acceptance of running social sensing applications on such cloud-edge systems, effectively allocating processing tasks between the server and the edge devices has emerged as a critical undertaking for maximizing the performance of such systems. Task allocation in such an environment faces several unique challenges: (i) the objectives of applications and edge devices may be inconsistent or even conflicting with each other, and (ii) edge devices may only be partially collaborative in finishing the computation tasks due to the "rational actor" nature and trust constraints of these devices, and (iii) an edge device's availability to participate in computation can change over time and the application is often unaware of such availability dynamics. Many social sensing applications are also delay-sensitive, which further …