An Edge-Based Resource Allocation Optimization for the Internet of Medical Things (IoMT)

TL;DR

This paper proposes an edge-based resource allocation framework for IoMT that optimizes task offloading considering multiple conflicting criteria like privacy, latency, and costs, improving healthcare application performance.

Contribution

It extends the Edgify framework to incorporate multi-criteria decision making for healthcare data offloading at the edge, addressing resource constraints and security concerns.

Findings

Effective optimization of healthcare task offloading

Improved privacy and latency management

Demonstrated framework's usefulness through experiments

Abstract

As the number of Internet of Medical Things (IoMT) increases, the need for performing on-premises tasks within hospitals or medical centers also increases. Many healthcare organizations are progressively embracing or adopting an edge computing paradigm such that computationally intensive tasks can be processed at the edge of the network in order to avoid latency and network reliability issues associated with offloading tasks to the cloud. The problem, however, hospitals or medical centers may not be equipped with sufficient computing resources that can process advanced ML or AI tasks efficiently. In addition, some tasks may not be easily offloadable or contain sensitive patient healthcare data which increases the risks of having malicious attacks. In this paper, we extend our Edgify resource provisioning framework to consider the task offloading of healthcare applications' involving patients' data as a multiple criteria decision making (MCDM) process that often involves multiple conflicting criteria (e.g. data privacy risks, costs, latency, network reliability, among others). We evaluate our proposed framework through a number of experiments which demonstrate the usefulness and effectiveness of employing our optimization approach within hospitals or medical centers.