Energy Efficient Sampling Policies for Edge Computing Feedback Systems

TL;DR

This paper develops energy-efficient sampling policies for edge feedback systems, balancing event detection delay and energy costs, with solutions for specific distributions and practical applications demonstrating significant savings.

Contribution

It introduces a novel convex optimization framework for sampling policy design, including initial offset optimization, applicable to real-world edge systems.

Findings

Achieved up to 36% energy savings over existing schemes.

Proved convexity of the optimization problem using novel techniques.

Applicable to exponential and Rayleigh event distributions.

Abstract

We study the problem of finding efficient sampling policies in an edge-based feedback system, where sensor samples are offloaded to a back-end server that processes them and generates feedback to a user. Sampling the system at maximum frequency results in the detection of events of interest with minimum delay but incurs higher energy costs due to the communication and processing of redundant samples. On the other hand, lower sampling frequency results in higher delay in detecting the event, thus increasing the idle energy usage and degrading the quality of experience. We quantify this trade-off as a weighted function between the number of samples and the sampling interval. We solve the minimisation problem for exponential and Rayleigh distributions, for the random time to the event of interest. We prove the convexity of the objective functions by using novel techniques, which can be of independent interest elsewhere. We argue that adding an initial offset to the periodic sampling can further reduce the energy consumption and jointly compute the optimum offset and sampling interval. We apply our framework to two practically relevant applications and show energy savings of up to 36% when compared to an existing periodic scheme.