| Summary: | 碩士 === 國立東華大學 === 資訊工程學系 === 107 === With the rapid development of mobile device technology, mobile phones are equipped with a variety of sensors and have powerful computing power, and because of this, you can always know the information of users and their various situations. However, most of the time mobile phones and these sensors are idle, so some people have proposed Mobile Crowd Sensing and Computing (MCSC) technology, which uses users' ubiquitous and idle mobile phone sensors to sense. The data in the real world is around, and the user's query of these materials is promptly answered. Since the time and space characteristics of the target are determined first when the MCSC query request is processed, then the query condition is formed and Task Assignment is performed, and an appropriate user group is found to sense and integrate the reply information to become a result. The efficiency of such query processing is obviously not easy to improve. Therefore, we propose a method to improve the sensing performance of the masses by using cache, and design several data replacement strategies to make the entire cache space more efficient. Since the data sensed by the action masses is data in the real world, these materials may also change over time, invalidating the data in the cache. In order to ensure the effectiveness of the cache, we have designed a mechanism to dynamically adjust the effective time of the data and determine whether the cached data value needs to be retested. This mechanism can not only solve the problem of data value changes, but also improve the cache hit. rate.
Due to the limited size of the cache, only the data that has been queried recently can be saved, so there will be a miss for a long time without querying or new data. We also designed a predictive cache mechanism, using data and query characteristics to analyze whether the data has sufficient value for predictive query, and store the results of the sensing in the predictive cache to compensate for the general cache. Insufficient.
We used MongoDB to implement our proposed cache and query processing mechanism on the Hadoop cluster and conducted a number of experiments. The experimental results confirm that the proposed mechanism can effectively improve the cache hit rate and query performance. The effective time maintenance and retest probability adjustment mechanism can effectively reduce unnecessary repeated queries. The hit rate after adding the predictive cache will increase by 2~. 10%, with the query characteristics and predictive cache take up the size of the entire cache space, on average, the ratio of the general and predictive cache to 6:4 is the best.
Keyword: Resensing probability, Valid Time, Caching Replacement Policy, Predictive Caching
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