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.
@article{arxiv.1903.02294,
title = {Energy-Efficient Edge-Facilitated Wireless Collaborative Computing using Map-Reduce},
author = {Antoine Paris and Hamed Mirghasemi and Ivan Stupia and Luc Vandendorpe},
journal= {arXiv preprint arXiv:1903.02294},
year = {2019}
}