Analyse or Transmit: Utilising Correlation at the Edge with Deep Reinforcement Learning

TL;DR

This paper presents a DRL-based approach for energy-efficient data analysis and transmission at the edge, leveraging device correlation to optimize accuracy and energy consumption in energy-harvesting scenarios.

Contribution

It introduces a novel DRL policy that adapts to energy harvesting patterns and exploits data correlation to improve edge device performance.

Findings

Increases accuracy by 15% over baseline policies

Prevents system outages under energy constraints

Effectively adapts to time-varying energy availability

Abstract

Millions of sensors, cameras, meters, and other edge devices are deployed in networks to collect and analyse data. In many cases, such devices are powered only by Energy Harvesting(EH) and have limited energy available to analyse acquired data. When edge infrastructure is available, a device has a choice: to perform analysis locally or offload the task to other resource-rich devices such as cloudlet servers. However, such a choice carries a price in terms of consumed energy and accuracy. On the one hand, transmitting raw data can result in a higher energy cost in comparison to the required energy to process data locally. On the other hand, performing data analytics on servers can improve the task's accuracy. Additionally, due to the correlation between information sent by multiple devices, accuracy might not be affected if some edge devices decide to neither process nor send data and preserve energy instead. For such a scenario, we propose a Deep Reinforcement Learning (DRL) based solution capable of learning and adapting the policy to the time-varying energy arrival due to EH patterns. We leverage two datasets, one to model energy an EH device can collect and the other to model the correlation between cameras. Furthermore, we compare the proposed solution performance to three baseline policies. Our results show that we can increase accuracy by 15% in comparison to conventional approaches while preventing outages.