Prediction based computation offloading and resource allocation for multi-access ISAC enabled IoT system

TL;DR

This paper proposes a prediction-based resource allocation framework for multi-access ISAC-enabled IoT systems, utilizing data attributes like trajectory and velocity to improve offloading efficiency.

Contribution

It introduces a novel system design and theoretical framework for prediction-driven resource management in dynamic ISAC IoT environments, including the implementation of the ClusterMan software.

Findings

Prediction accuracy up to 97% using strong clustering features

Effective resource allocation improves IoT system performance

System design integrates deployment management with prediction mechanisms

Abstract

In the new era of the Internet of Things (IoT), tasks are now being migrated to edge sites closer to data generators. Mobile devices inherently encounter limitations in terms of energy and computational processing capabilities. In high mobility paradigm, ISAC provides a promising foundation for integrating deployment management within dynamic spatial settings. We are interested in applying prediction mechanism to resource allocation management by extracting data attributes, focusing on ISAC related contexts of the trajectory and velocity and making the allocating decision. We present a system design, a theoretical framework and an implementation of the ClusterMan software package. The numerical suggests that the strong clustering subset of feature may yield high accuracy up to 97\% in the prediction results.