Quality of Experience in Crowdsourced Energy Services

We propose a novel Quality of Experience (QoE) metric as a key criterion for optimizing the composition of energy services within a crowdsourced IoT environment. Two novel composition approaches, namely, Importance-based and Heuristic-based, are proposed to ensure the highest QoE for consumers. The Importance-based approach prioritizes time slots based on their significance. The Heuristic-based approach considers the importance of time slots and the availability of services to maximize QoE while minimizing service provisioning costs. We conduct extensive experiments using real-world datasets to evaluate the effectiveness and efficiency of the proposed approaches. The results demonstrate that both approaches enhance consumer satisfaction by optimizing energy allocation, with the Heuristic-based approach outperforming the Importance-based method in minimizing rewards.