Context-Aware Trustworthy IoT Energy Services Provisioning
Amani Abusafia, Athman Bouguettaya, Abdallah Lakhdari, Abdallah Lakhdari
TL;DR
The paper addresses QoE-aware provisioning of IoT energy services in microcells under dynamic provider behavior. It introduces a context-aware trust assessment framework that computes $\mathcal{P}_{Trust}$ from multiple attributes and filters history via a CSP-based context model. A two-phase energy service composition framework combines context-aware trust assessment with a trust-based selection and backup provisioning to maximize $QoE = \sum_{i=1}^n S_i.a \cdot P_{Trust_i} / Ed.d(t)$ under $Ed.d(t) > 0$. Experiments using real energy-sharing data and retail transactions demonstrate QoE gains and acceptable computation costs, illustrating the practicality of edge-enabled QoE-aware energy sharing.
Abstract
We propose an IoT energy service provisioning framework to ensure consumers' Quality of Experience (QoE). A novel context-aware trust assessment model is proposed to evaluate the trustworthiness of providers. Our model adapts to the dynamic nature of energy service providers to maintain QoE by selecting trustworthy providers. The proposed model evaluates providers' trustworthiness in various contexts, considering their behavior and energy provisioning history. Additionally, a trust-adaptive composition technique is presented for optimal energy allocation. Experimental results demonstrate the effectiveness and efficiency of the proposed approaches.