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中文摘要：
集中研究了非结构化的数据存储和查询。为了在保证查询成功率的同时最小化总的能耗，分别在存储受限和不受限两种情况下，建立了MESQ（minimizing energy on successful query）优化问题模型，给出并证明了最优的复本和查询个数。在此基础上，还设计了一个实用的分布式数据分发算法：BubbleGeocast，其主要包含精确自适应快速分发和基于拒绝的均匀分发两个部分，其中前者用自适应分支的方法加速数据扩散，并精确控制总的复本个数；后者根据每个节点Voronoi单元面积，决定是否接受或拒绝这个报文。从而保证了复本和查询分发的精确性、实时性、均匀性、顽健性。最后，详细的理论分析和模拟实验进一步验证了其性能。分析和实验表明，同已有工作相比，在相同查询成功率时，BubbleGeocast能量有效性平均提高了约30％，复本分发的延迟平均缩短了约30％，成功查询的延迟平均缩短了约50％。
英文摘要：
This work, focuses on unstructured random data storage and query. Since the energy is one of the most precious resources, an MESQ optimization problem is formed whose aim is to select the optimum number of replicas and queries that minimize the total energy cost, subject to unrestrained or restrained storage. In order to make more practical, a localized data dissemination algorithm, called BubbleGeocast, is designed. It is made up of two components： adaptive accurate data diffusion in real-time and rejection-based uniform data diffusion. The first one can control the total number of replicas and branch adaptively to diffuse data as soon as possible. The second one can guarantee each node accepts a packet in the same probability according to the area of its own Voronoi cell. These two can diffuse replicas and queries accurately, fast, uniformly, and robust. Simulations show that BubbleGeocast provide reduced 30% communication costs, 30% delay on replicas diffusion, and 50% delay on query on average, within the bound of successful query.
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