# D2D Data Offloading in Vehicular Environments with Optimal Delivery Time   Selection

**Authors:** Loreto Pescosolido, Marco Conti, Andrea Passarella

arXiv: 1901.01744 · 2022-05-31

## TL;DR

This paper introduces a Content Delivery Management System for vehicular networks that optimally schedules D2D content transmissions to minimize energy use amid highly dynamic topologies.

## Contribution

It develops an analytical framework for predicting energy consumption in D2D offloading, validated through simulations, and compares its performance with benchmark schemes.

## Key findings

- Reduced energy consumption compared to benchmarks
- Efficient spectrum utilization
- Effective in highly dynamic vehicular scenarios

## Abstract

Within the framework of a Device-to-Device (D2D) data offloading system for cellular networks, we propose a Content Delivery Management System (CDMS) in which the instant for transmitting a content to a requesting node, through a D2D communication, is selected to minimize the energy consumption required for transmission. The proposed system is particularly fit to highly dynamic scenarios, such as vehicular networks, where the network topology changes at a rate which is comparable with the order of magnitude of the delay tolerance. We present an analytical framework able to predict the system performance, in terms of energy consumption, using tools from the theory of point processes, validating it through simulations, and provide a thorough performance evaluation of the proposed CDMS, in terms of energy consumption and spectrum use. Our performance analysis compares the energy consumption and spectrum use obtained with the proposed scheme with the performance of two benchmark systems. The first one is a plain classic cellular scheme, the second is a D2D data offloading scheme (that we proposed in previous works) in which the D2D transmissions are performed as soon as there is a device with the required content within the maximum D2D transmission range...

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/1901.01744/full.md

## Figures

48 figures with captions in the complete paper: https://tomesphere.com/paper/1901.01744/full.md

## References

13 references — full list in the complete paper: https://tomesphere.com/paper/1901.01744/full.md

---
Source: https://tomesphere.com/paper/1901.01744