# Efficient micro data centres deployment for mobile healthcare monitoring systems in IoT urban scenarios

arXiv: https://arxiv.org/abs/2302.10201 · HTML: https://mlsanity.com/paper/2302.10201/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 53 references that are in the map.
- First posted on arXiv 20 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments](https://mlsanity.com/paper/1606.02007/) (2016)
- [HealthFog: An Ensemble Deep Learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in Integrated IoT and Fog Computing Environments](https://mlsanity.com/paper/1911.06633/) (2019)
- [YAFS: A simulator for IoT scenarios in fog computing](https://mlsanity.com/paper/1902.01091/) (2019)
- [EmuFog: Extensible and Scalable Emulation of Large-Scale Fog Computing Infrastructures](https://mlsanity.com/paper/1709.07563/) (2017)

## Built on by (most cited first, 1 of 1)

- [Advanced simulation-based predictive modelling for solar irradiance sensor farms](https://mlsanity.com/paper/2404.15324/) (2024)

## Abstract (arXiv)

In the last decade, the Internet of Things paradigm has caused an exponential increase in the number of connected devices. This trend brings the Internet closer to everyday activities and enables data collection that can be used to create and improve a great variety of services and applications. Despite its great benefits, this paradigm also comes with several challenges. More powerful storage and processing capabilities are required to service all these devices. Additionally, the need to deploy and manage the infrastructure to efficiently support these resources continues to pose a challenge. Modeling and simulation can help to design and analyze these scenarios, providing flexible and powerful mechanisms to study and compare different strategies and infrastructures. In this scenario, Micro Data Centers (MDCs) can be used as an effective way of reducing overwhelmed Cloud Data Center infrastructures. This paper explores an M&S methodology to study the overall power consumption of a healthcare IoT scenario. The patients wear non-intrusive monitoring devices that periodically generate tasks to be executed in MDCs. We extract the layout of existing urban infrastructures, simulate the monitored population's behavior, and compare the power consumption of several data center configurations.