# Composing Energy Services in a Crowdsourced IoT Environment

arXiv: https://arxiv.org/abs/2111.07528 · HTML: https://mlsanity.com/paper/2111.07528/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2021.

## Built on by (most cited first, 3 of 3)

- [Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environments](https://mlsanity.com/paper/2506.11054/) (2025)
- [COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics](https://mlsanity.com/paper/2208.04704/) (2022)
- [Energy Loss Prediction in IoT Energy Services](https://mlsanity.com/paper/2305.10238/) (2023)

## Abstract (arXiv)

We propose a novel framework for composing crowdsourced wireless energy services to satisfy users' energy requirements in a crowdsourced Internet of Things (IoT) environment. A new energy service model is designed to transform the harvested energy from IoT devices into crowdsourced services. We propose a new energy service composability model that considers the spatio-temporal aspects and the usage patterns of the IoT devices. A multiple local knapsack-based approach is developed to select an optimal set of partial energy services based on the deliverable energy capacity of IoT devices. We propose a heuristic-based composition approach using the temporal and energy capacity distributions of services. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.