Towards a Trust Ecosystem for Crowdsourcing IoT Services: A Macro Perspective

Trust plays a crucial role in crowdsourcing Internet of Things (IoT), as it can be used to select trustworthy participants to improve the quality of crowdsourced services and strengthen system security. While traditional research has focused on micro-level aspects, including trust computation and propagation, a comprehensive macro-level trust analysis remains underexplored. In this paper, we propose a macroscopic trust ecosystem analysis framework for crowdsourcing IoT services. We first construct a Trust Ecosystem Model (TEM), where trust clusters serve as an abstraction to capture and quantify overall trust characteristics based on their size and structure. To analyze the dynamic evolution of TEM, we propose a Percolation-based Trust Ecosystem Analysis Model (P-TEAM), which maps the formation of trust clusters to a joint site-bond percolation process. Thus, the study of TEM evolution can be reframed into an investigation of how trust clusters evolve as users' trust attributes change. Through P-TEAM, we identify the critical thresholds associated with trust attributes that trigger trust phase transitions in crowdsourcing IoT services, which act as key metrics for evaluating the ecosystem's robustness macroscopically. Finally, we further evaluate the trust ecosystem beyond these thresholds by calculating the proportions of trusted giant components. We validate our approach on directed networks, using both synthetic and real-world datasets. The experimental results further substantiate our findings and provide valuable insights into constructing a healthy and sustainable trust ecosystem for crowdsourcing IoT services. Codes are available at https://github.com/sd-sclab/MacroTrust.