Task-Driven Cooperative Internet of Robotic Things Crowdsourcing: From the Perspective of Hierarchical Game Theoretic

The rapid advancement in robotics technology has catalyzed the emergence of Internet of the Robotic Things crowdsourcing, a novel paradigm in the digital economy era. However, the escalating complexity of crowdsourcing tasks, coupled with the constrained resources of robot nodes and the diverse demands of stakeholders, has resulted in inefficiency, which poses a significant challenge to the burgeoning robot crowdsourcing market in Internet of Things. To address these challenges, we develop a comprehensive analytical model that encapsulates the interests of both task scheduler nodes (TNs) and robotic nodes (RNs) within Internet of Things, which integrates various factors, such as communication, computation, mobility, latency, and energy, thereby tailoring utility functions for different stakeholders. Second, we propose a task-driven robotic crowdsourcing strategy based on hierarchical game (TRC-HG), which conceptualizes TNs and RNs as rational entities with sequential actions. At the first layer, the interaction between participants is transformed into a Stackelberg game. This aids TNs in determining optimal pricing strategies while guiding RNs toward the most efficient task-completion strategies. Furthermore, we delve into the collaborative dynamics within the RNs, a cooperative coalition formation strategy at the second layer is established, which iteratively determines node responsibilities and coalition members. The Nash stability and optimality of coalition are ensured, thereby maximizing the utility of RNs. Finally, we validate the performance through a series of high-fidelity simulation experiments. These experiment results, benchmarked against classical methods, highlight significant improvements in terms of latency, energy consumption, and cooperative efficiency.