中文摘要：

传感器网络广泛地在许多应用程序被使用联合地从物理环境收集信息。在这些应用，关系的探索和在多重区域以内察觉到数据的连接能被在这些区域加入元组自然地表示。然而，高度分布式并且网络的资源限制自然使 join 成为挑战性的询问。在这篇论文，我们处理在传感器网络日益增多地并且精力高效地在 diffeerent 区域之中处理连接查询的问题。建议算法 PEJA (进步精力有效的连接算法) 采用事件驱动策略尽快输出加入的结果，并且在在里面网络节点减轻存储缺乏问题。在早处理阶段修剪不能匹敌的元组也在加入的区域安装过滤器，节省大量不必要的传播。合成、真实的世界数据集合的广泛的实验显示 PEJA 计划超过另外的连接算法，并且它在在处理的 join 期间减少传播的数字和质问结果的延期是有效的。这篇文章的联机版本(做 i：10.1007/s11390-008-9191-2 ) 包含增补材料，它对授权用户可得到。

英文摘要：

Sensor networks are widely used in many applications to collaboratively collect information from the physical environment. In these applications, the exploration of the relationship and linkage of sensing data within multiple regions can be naturally expressed by joining tuples in these regions. However, the highly distributed and resource-constraint nature of the network makes join a challenging query. In this paper, we address the problem of processing join query among different regions progressively and energy-efficiently in sensor networks. The proposed algorithm PEJA （Progressive Energy-efficient Join Algorithm） adopts an event-driven strategy to output the joining results as soon as possible, and alleviates the storage shortage problem in the in-network nodes. It also installs filters in the joining regions to prune unmatchable tuples in the early processing phase, saving lots of unnecessary transmissions. Extensive experiments on both synthetic and real world data sets indicate that the PEJA scheme outperforms other join algorithms, and it is effective in reducing the number of transmissions and the delay of query results during the join processing.