A Real-Time and Non-Cooperative Task Allocation Framework for Social Sensing Applications in Edge Computing Systems

Social sensing has emerged as a new sensing application paradigm where measurements about the physical world are collected from humans or devices on their behalf. A key limitation in the current social sensing solution space is that data processing and analytics are often done in a "backend" mode (e.g., on dedicated servers or commercial cloud platforms). Such mode ignores the rich processing capability of increasingly powerful edge devices (e.g., mobile phones and nodes in the Internet of Things). Exploiting such edge devices in the social sensing setting introduces new challenges to real-time resource management. In this work, we develop a Bottom-up Game-theoretic Task Allocation (BGTA) framework to solve the critical problem of allocating real-time social sensing tasks to self-aware and non-cooperative edge computing nodes. In particular, we address two important challenges in solving this problem. The first one is "conflicting interest" where the objectives of applications and edge nodes may be at odds with each other. The second challenge is "asymmetric and incomplete information" where the application is often unaware of the detailed status (e.g., energy profile, utilization, CPU frequency) and compliance level of the edge nodes. To address these challenges, we first design a non-cooperative task allocation game model to address the conflicting objectives of the applications and edge nodes. We then develop a decentralized Fictitious Play scheme to allow each edge node to make its own decision on which task to execute in a non-cooperative context. Finally, we design a dynamic incentive mechanism to ensure the decisions made by the edge nodes meet objectives of the application. We implement a system prototype deployed on Nvidia Jetson TX1 and Jetson TK1 boards and evaluate our task allocation framework using two real-world social sensing applications. The results show that our scheme can well satisfy Quality of Service (QoS) requirement of the applications while providing optimized payoffs to edge nodes compared to the state-of-the-art baselines.