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.

翻译：在物联网新时代，任务正迁移至更靠近数据生成源的边缘站点。移动设备在能量和计算处理能力方面存在固有局限。在高移动性场景下，ISAC为动态空间环境中的部署管理集成提供了前景广阔的基础框架。我们致力于通过提取数据属性、聚焦轨迹与速度等ISAC相关上下文特征，将预测机制应用于资源分配管理并制定分配决策。本文提出了系统设计方案、理论框架及ClusterMan软件包的实现。数值实验表明，特征中的强聚类子集可使预测结果达到高达97%的准确率。