Incentive-Based Selection and Composition of IoT Energy Services

TL;DR

This paper introduces an incentive-based framework for composing IoT energy services, optimizing rewards for providers through a novel scheduling approach, validated by extensive experiments with real user data.

Contribution

It presents a new incentive model and priority scheduling method for IoT energy service composition, improving reward maximization and efficiency.

Findings

The approach effectively maximizes provider rewards.

Experimental results demonstrate high efficiency.

The framework adapts to user behavior and context.

Abstract

We propose a novel incentive-based framework for composing energy service requests. An incentive model is designed that considers the context of the providers and consumers to determine rewards for sharing wireless energy. We propose a novel priority scheduling approach to compose energy service requests that maximizes the reward of the provider. A set of exhaustive experiments with a dataset and collected IoT users' behavior is conducted to evaluate the proposed approach. Experimental results prove the efficiency of the proposed approach.