Abstract
Mobile crowdsourcing has emerged as a promising collaboration paradigm in which each spatial task requires a set of mobile workers in near vicinity to the target location. Considering the desired privacy of the participating mobile devices, trust is considered to be an important factor to enable effective collaboration in mobile crowdsourcing. The main impediment to the success of mobile crowdsourcing is the allocation of trustworthy mobile workers to nearby spatial tasks for collaboration. This process becomes substantially more challenging for large-scale online spatial task allocations in uncertain mobile crowdsourcing systems. The uncertainty can mislead the task allocation, resulting in performance degradation. Moreover, the large-scale nature of real-world crowdsourcing poses a considerable challenge to spatial task allocation in uncertain environments. To address the aforementioned challenges, first, an optimization problem of mobile crowdsourcing task allocation is formulated to maximize the trustworthiness of workers and minimize movement distance costs. Second, for the uncertain crowdsourcing scenario, a Markov decision process-based mobile crowdsourcing model (MCMDP) is formulated to illustrate the dynamic trust-aware task allocation problem. Third, to solve large-scale MCMDP problems in a stable manner, this study proposes an improved deep Q-learning-based trust-aware task allocation (ImprovedDQL-TTA) algorithm that combines trust-aware task allocation and deep Q-learning as an improvement over the uncertain mobile crowdsourcing systems. Finally, experimental results illustrate that the ImprovedDQL-TTA algorithm can stably converge in a number of training iterations. Compared with the reference algorithm, our proposed algorithm achieves effective solutions on the experimental data sets.
Highlights
With the advancing technology of mobile devices with numerous built-in sensors, mobile crowdsourcing has recently emerged as a new collaboration paradigm in numerous intelligent mobile information systems [1]
To adapt to changes in large-scale mobile crowdsourcing systems, we propose a deep Q-learning-based trust-aware task allocation (DQL-TTA) algorithm that is a combination of advances in deep neural network and Q-learning techniques
Computer simulations are conducted to illustrate the performance of the proposed ImprovedDQL-TTA algorithm in mobile crowdsourcing systems
Summary
With the advancing technology of mobile devices with numerous built-in sensors, mobile crowdsourcing has recently emerged as a new collaboration paradigm in numerous intelligent mobile information systems [1]. Trust‐aware task allocation with deep Q‐learning The majority of TTA optimization approaches require prior knowledge, but such approaches are not applicable in dynamic mobile crowdsourcing environments, where the availability of mobile workers is subject to frequent and unpredictable changes [2, 6]. We propose an improved deep Q-learning-based trust-aware task allocation (ImprovedDQL-TTA) algorithm by combining trust crowdsourcing optimization and deep Q-learning, which enables the learning agent to solve large-scale MCMDP problems in an uncertain scenario.
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