Dynamic and flexible selection of a reputation mechanism for heterogeneous environments

Current trust and reputation management approaches usually offer rigid and inflexible mechanisms to compute reputation scores, which hinder their dynamic adaptation to the current circumstances in the system where they are deployed. At most, they provide certain parameters which are configurable or tunable. Yet, this is not enough for such heterogeneous and dynamic environments as the ones introduced by Internet of Things (IoT). In this paper we propose a rupture with this old philosophy and have therefore designed and prototyped a flexible mechanism to select the most suitable trust and reputation model to apply on-the-fly, amongst a pool of predefined ones, considering both the current system conditions and the selected performance measurements, which, to the best of our knowledge, is missing nowadays. Additionally, this mechanism guarantees a smooth transition between different computation engines avoiding abrupt changes in the computed reputation scores. Conducted experiments prove that our solution is able to identify and start up the most suitable trust and reputation model depending on the current system conditions (number of users, allocated resources, etc.) and expected performance measurements (accuracy, scalability, robustness, etc.). Design a flexible mechanism to select the most suitable trust and reputation model to apply on-the-fly.Selection of the reputation model based on current system conditions and expected performance measurements.The approach guarantees a smooth and automatic transition between the reputation computation engines.Experiments conducted to validate the feasibility of the approach.Focus on heterogeneous Internet of Things environments.