Crowdsourcing Upon Learning: Energy-Aware Dispatch With Guarantee for Video Analytics

Abstract

Over the last decade, the mobile crowdsourcing has become a paradigm to conduct the manual annotation and further analytics by recruited workers, with their rewards depending on the result quality. Existing dispatchers cannot precisely capture the resource-quality trade-off for video analytics, because the configurations supported by recruited workers are limited, and workers' availability changes over time. To determine the most suitable configurations as well as workers for video analytics, we formulate a non-linear mixed program in long term, maximizing the crowdsourcing profit. Based on previous results under various configurations and workers, we design an algorithm via a series of subproblems to decide the configurations adaptively upon the prediction of workers' feedbacks. Such prediction is based on volatile multi-armed bandit to capture workers' availability and stochastic changes on resource uses. Furthermore, we extend the proposed algorithms to the multi-worker selection scenario where the platform needs to determine a candidate worker set instead of a single worker for video analytics. Via rigorous proof, the regret is ensured upon the Lyapunov optimization and the bandit, measuring the gap between the online decisions and the offline optimum. Extensive trace-driven experiments show that our proposed algorithm improves the profit by 37% compared with other algorithms.