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中文摘要：
RFID数据采集过程中漏读现象频频发生，降低了RFID（radio frequency identification）应用中查询结果的准确性．目前解决漏读问题的算法主要是以RFID原始读数为粒度，并基于标签自身历史读数进行窗口平滑，这种作法会填补许多与查询无关的冗余数据，并且在多逻辑区域参与的复杂应用中，填补准确率较差．为解决上述问题，首次将RFID数据从数据层抽象到逻辑区域层作为处理的粒度，提出3种基于动态概率路径事件模型的数据填补算法，通过挖掘已知的区域事件的顺序相关性来对后续发生的事件进行判断和填补．进一步，增加对时间因素的考虑，对概率路径事件模型进行扩展．大量实验证明，提出的各个算法在不同的情况下有着不同的性能优势，并且在精简性和准确性上要高于现有的策略．
英文摘要：
Missing reads occur frequently during RFID （radio frequency identification） data collection, which will reduce the accuracy of query results in RFID applications. To solve this problem, the existing algorithms mainly take primitive RFID readings as granularity and adopt window smooth strategy based on tag historical readings, which may interpolate data that the query doesn＇t care about and incur inaccuracy when multiple logic areas are involved. In this paper, data are transformed from data level to logic area level as the interpolation granularity. Then three data interpolating algorithms based on the probabilistic path-event model are proposed, where the incoming events are judged and interpolated by mining the sequence correlation of known area events. Furthermore, the factor of time is considered, and thus probabilistic path-event model is developed. Abundant experiments prove the proposed algorithms have different performance advantages in different conditions and are predominant over the existing strategy in redundancy and accuracy.
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同项目期刊论文
Research on Parallel Real-time Scheduling Algorithm of Hybrid Parameter Tasks on Multi-core Platform
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