CAJ | 학술논문

为了减少采集的数据量，提出在物联网中引入“边采样边压缩”的新型采样方法--压缩感知。针对压缩感知理论中信号重建算法计算复杂度较高的问题，设计并实现了一个基于云平台和代码迁移的算法加速方案；该方案解决了代码并行化的自动翻译、算法向云端迁移、本地和云端执行同步等问题，对可并行化的算法，仅需要增加几个新定义的接口及插入一些描述性的注释，就可以利用云资源实现算法的加速；实验表明，该方案是可行的、有效的。该文还研究了基于物联网资源的云加速方法，提出了基于云加速方案、结合多核/多CPU方法和GPGPU方法，能充分利用已有物联网资源的混合压缩感知算法加速框架，并初步设计了理论运行流程。
위료감소채집적수거량，제출재물련망중인입“변채양변압축”적신형채양방법--압축감지。침대압축감지이론중신호중건산법계산복잡도교고적문제，설계병실현료일개기우운평태화대마천이적산법가속방안；해방안해결료대마병행화적자동번역、산법향운단천이、본지화운단집행동보등문제，대가병행화적산법，부수요증가궤개신정의적접구급삽입일사묘술성적주석，취가이이용운자원실현산법적가속；실험표명，해방안시가행적、유효적。해문환연구료기우물련망자원적운가속방법，제출료기우운가속방안、결합다핵/다CPU방법화GPGPU방법，능충분이용이유물련망자원적혼합압축감지산법가속광가，병초보설계료이론운행류정。
In order to reduce the amount of data collected, the compressed sensing (CS), a new sampling method, is used for data acquisition and processing in Internet of Things (IoT). To overcome high computational complexity of CS algorithms, an acceleration scheme based on cloud platform and code migration is introduced in this paper. The scheme solved the automatically translated problem of parallelization code, the migration problem of algorithm, and the synchronization problems of local and cloud. It can use the resources from cloud environment to speed up algorithms by adding several interfaces and inserting some comments. In addition, this paper studies the method of cloud acceleration based on computing resources in Internet of Things, and put forwards an acceleration framework, in combination with multi-CPUs/multi-cores CPU and GPGPU parallelization, to speed up CS algorithm based on the cloud acceleration scheme.