# Energy-Efficient Edge-Facilitated Wireless Collaborative Computing using Map-Reduce

## TL;DR

This paper proposes an optimal energy-efficient collaborative computing scheme for heterogeneous wireless devices using Map-Reduce, balancing energy consumption and latency constraints.

## Contribution

It introduces a novel optimal scheme that accounts for device capabilities and communication links, improving energy efficiency and latency trade-offs.

## Key findings

- Significant energy savings over non-collaborative approaches
- Enhanced latency performance compared to blind schemes
- Flexible energy-latency trade-off capability

## Abstract

In this work, a heterogeneous set of wireless devices sharing a common access point collaborates to perform a set of tasks. Using the Map-Reduce distributed computing framework, the tasks are optimally distributed amongst the nodes with the objective of minimizing the total energy consumption of the nodes while satisfying a latency constraint. The derived optimal collaborative-computing scheme takes into account both the computing capabilities of the nodes and the strength of their communication links. Numerical simulations illustrate the benefits of the proposed optimal collaborative-computing scheme over a blind collaborative-computing scheme and the non-collaborative scenario, both in term of energy savings and achievable latency. The proposed optimal scheme also exhibits the interesting feature of allowing to trade energy for latency, and vice versa.