SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction on the Edge

TL;DR

SensorDrop employs reinforcement learning to dynamically select which IoT sensors should transmit data, significantly reducing communication overhead while maintaining high accuracy in object classification tasks.

Contribution

It introduces a novel A2C-based reinforcement learning framework for adaptive sensor data transmission in IoT systems, addressing communication costs.

Findings

Significant reduction in communication overhead achieved.

Marginal decrease in object classification accuracy.

Effective adaptation to changing data correlations.

Abstract

In IoT solutions, it is usually desirable to collect data from a large number of distributed IoT sensors at a central node in the cloud for further processing. One of the main design challenges of such solutions is the high communication overhead between the sensors and the central node (especially for multimedia data). In this paper, we aim to reduce the communication overhead and propose a method that is able to determine which sensors should send their data to the central node and which to drop data. The idea is that some sensors may have data which are correlated with others and some may have data that are not essential for the operation to be performed at the central node. As such decisions are application dependent and may change over time, they should be learned during the operation of the system, for that we propose a method based on Advantage Actor-Critic (A2C) reinforcement learning which gradually learns which sensor's data is cost-effective to be sent to the central node. The proposed approach has been evaluated on a multi-view multi-camera dataset, and we observe a significant reduction in communication overhead with marginal degradation in object classification accuracy.