物联网中适用于内容搜索的实体状态匹配预测方法

对实体匹配用户内容搜索的状态进行预测可显著提高物联网搜索的效率，降低搜索过程的通信开销。该文提出等时距与周期内实体状态预测方法，估计实体在用户查询时刻的状态；设计了适用于内容搜索的有序验证方法，依据实体匹配用户查询内容的概率对实体进行排序验证，以保证用户搜索结果的可靠性。结果表明，所提实体状态预测方法具有较高的精度，结合所提预测方法与匹配验证方法的搜索机制具有较低的通信开销。

An Entity State Matching Prediction Method for Content-based Search in the Internet of Things

Matching prediction with high accuracy of entity state can cansignificantly improve the efficiency of content-based search in the Internet of Things and reduce communication overhead while searching. The equal-interval and during the period entity state prediction methods are proposed, which are applied to the estimation of the entity state at the moment of querying. Moreover, the ordered verification approach is designed to verify the entities in sequence based on the degree of compliance with the searching content, for the sake of enhancing the reliability of searching results. Numerical results show that the proposed entity state prediction approachescan achieve high accuracy, which combines with the ordered verification approach to dramatically improve the performance of communication overheadduring the searching process.