Parallel Processing Simulator for Separate Sensor of WSN Simulator with GPU

Hyun-Woo Kim, Eun-Ha Song, J. Park, Y. Jeong
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引用次数: 3

Abstract

Recently wireless sensor networks have been used as the technology that is actively grafted onto industries and daily living. Sensors should have built-in routing functions and basic sensing functions for the self-configuration of topologies. The number of sensors necessary for using them in an actual observation ranges from tens to hundreds or thousands. When theses sensors are wrongly placed in an observation region, they can quickly run out of batteries or be disconnected. These incidents may result in huge losses in terms of sensing data from numerous sensors and their costs. Therefore a number of simulators have been developed as tools for effective design and verification before the actual arrangement of sensors. While a number of simulators have been developed, simulation results can be fairly limited and the execution speed can be markedly slow depending on the function of each simulator. To improve the performance of existing simulators, this paper aimed to develop a parallel processing simulator for separate sensor (P2S3) that enables users to selectively use the GPU mode. It enables parallel and independent operations by matching GPU with many cores in order to resolve the slowdown of the execution speed when numerous sensor nodes are used for simulations. Also, P2S3 include the analyzed of sensor nodes with log data and visualization. The P2S3 supports the GPU mode in an environment that allows the operation of compute unified device architecture (CUDA), and performs the parallel simulation processing of multiple sensors using the mode within a short period of time.
基于GPU的WSN独立传感器并行处理模拟器
近年来,无线传感器网络作为一种技术被积极地嫁接到工业和日常生活中。传感器应具有内置路由功能和基本感知功能,以实现拓扑的自配置。在实际观测中使用它们所需的传感器数量从数十到数百或数千不等。当这些传感器被错误地放置在观测区域时,它们很快就会耗尽电池或断开连接。这些事件可能会导致来自众多传感器的传感数据及其成本的巨大损失。因此,在实际布置传感器之前,已经开发了一些模拟器作为有效设计和验证的工具。虽然已经开发了许多模拟器,但根据每个模拟器的功能,模拟结果可能相当有限,执行速度可能显着缓慢。为了提高现有模拟器的性能,本文旨在开发一种独立传感器并行处理模拟器(P2S3),使用户能够有选择地使用GPU模式。它通过多核匹配GPU实现并行和独立操作,以解决使用多个传感器节点进行模拟时执行速度减慢的问题。此外,P2S3还包括对传感器节点的日志数据分析和可视化。P2S3支持在CUDA (compute unified device architecture)运行环境下的GPU模式,可在短时间内完成多个传感器的并行仿真处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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