# On Maximizing Task Throughput in IoT-enabled 5G Networks under Latency and Bandwidth Constraints

## TL;DR

This paper proposes a graph-coloring based algorithm to maximize task throughput in IoT-enabled 5G networks, effectively handling diverse latency, bandwidth constraints, and device preferences, validated through experiments and numerical analysis.

## Contribution

It introduces an efficient graph-coloring approach to optimize task throughput in heterogeneous 5G IoT networks with complex constraints, addressing an intractable problem.

## Key findings

- The proposed algorithm improves task throughput compared to existing methods.
- Numerical results show significant performance gains in simulated environments.
- Real-world experiments validate the algorithm's effectiveness in practical settings.

## Abstract

Fog computing in 5G networks has played a significant role in increasing the number of users in a given network. However, Internet-of-Things (IoT) has driven system designers towards designing heterogeneous networks to support diverse demands (tasks with different priority values) with different latency and data rate constraints. In this paper, our goal is to maximize the total number of tasks served by a heterogeneous network, labeled task throughput, in the presence of data rate and latency constraints and device preferences regarding computational needs. Since our original problem is intractable, we propose an efficient solution based on graph-coloring techniques. We demonstrate the effectiveness of our proposed algorithm using numerical results, real-world experiments on a laboratory testbed and comparing with the state-of-the-art algorithm.