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A Robust and Space-Efficient Stack Management Method for Wireless Sensor Network OS with Scarce Hardware Resources

机译:硬件资源匮乏的无线传感器网络操作系统的一种鲁棒且节省空间的堆栈管理方法

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Due to such requirements as low power consumption and low cost, sensor nodes commonly do not include advanced H/W features. The absence of the features such as the memory management unit enforces several tasks to share a memory address domain on a small data memory space (1~16 KB). It exposes each task to the stack overflow causing the corruption of other memory areas. In this paper, we propose a robust and efficient stack memory management method (RESM) that dynamically assigns and releases a preestimated amount of stack memory to each function call at runtime. RESM maintains the stack memory usage with the similar amount of the stack usage that the system actually requires, and the stack memory area of each task is individually protected from corruption by the stack overflow. RESM can also anticipate a saturated condition in data memory at runtime. When the memory state is unsafe from the saturated condition, it conditionally allows function calls to operate tasks without any memory fault by using the proposed function call deferring algorithm. From the analytical and experiment results, it is proven that the performance of RESM outperforms the other mechanisms and RESM can provide more robust stack operation environment.
机译:由于诸如低功耗和低成本的要求,传感器节点通常不包括高级硬件功能。缺少诸如内存管理单元之类的功能会强制执行多个任务,以在较小的数据存储空间(1〜16 KB)上共享一个内存地址域。它将每个任务暴露给堆栈溢出,导致其他内存区域损坏。在本文中,我们提出了一种强大而有效的堆栈内存管理方法(RESM),该方法可在运行时动态分配并释放每个函数调用的预期堆栈内存量。 RESM维护的堆栈内存使用量与系统实际需要的堆栈使用量相似,并且每个任务的堆栈存储区都受到单独的保护,以防止堆栈溢出损坏。 RESM还可以在运行时预测数据存储器中的饱和状态。当内存状态在饱和状态下不安全时,通过使用建议的函数调用延迟算法,它有条件地允许函数调用操作任务而没有任何内存故障。从分析和实验结果可以证明,RESM的性能优于其他机制,并且RESM可以提供更强大的堆栈操作环境。

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