Flow-Based Energy Services Composition

TL;DR

This paper introduces a novel framework for composing IoT energy services using a maximum flow matching algorithm, enabling efficient and flexible energy provisioning through partial service overlaps.

Contribution

It presents a new spatio-temporal service composition model and algorithms tailored for wearable-based energy and wireless power transfer technologies.

Findings

EnergyFlowComp efficiently provisions multiple energy requests.

PartialFlowComp improves service utilization by considering partial overlaps.

Extensive experiments validate the framework's effectiveness.

Abstract

We propose a novel spatio-temporal service composition framework for crowdsourcing multiple IoT energy services to cater to multiple energy requests. We define a new energy service model to leverage the wearable-based energy and wireless power transfer technologies. We reformulate the problem of spatio-temporal service composition to provision multiple energy requests as a matching problem. We leverage the fragmented nature of energy to offer partial services to maximize the utilization of energy services. We propose EnergyFlowComp, a modified Maximum Flow matching algorithm that efficiently provisions IoT energy services to accommodate multiple energy requests. Moreover, we propose PartialFlowComp, an extension of the EnergyFlowComp approach that considers the partial-temporal overlap between services and requests in provisioning. We conduct an extensive set of experiments to assess the effectiveness and efficiency of the proposed framework.