In this paper, we consider the IoT data discovery data objects to specific nodes in the network. They are very problem in very large and growing scale networks. Specifically, we investigate in depth the routing table summarization techniques to support effective and space-efficient IoT data discovery routing. Novel summarization algorithms, including alphabetical based, hash based, and meaning based summarization and their corresponding coding schemes are proposed. The issue of potentially misleading routing due to summarization is also investigated. Subsequently, we analyze the strategy of when to summarize in order to balance the tradeoff especially in handling MAA based lookups. between the routing table compression rate and the chance of Unstructured discovery routing approaches, such as [4] [5], causing misleading routing. For experimental study, we have collected 100K IoT data streams from various IoT databases as the input dataset. Experimental results show that our summarization solution can reduce the routing table size by 20 to 30 folds with 2-5% increase in latency when compared with similar peer-to-peer discovery routing algorithms without summarization. Also, our approach outperforms DHT based approaches by 2 to 6 folds in terms of latency and traffic.

中文翻译：

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物联网数据发现路由的汇总技术

在本文中，我们考虑到网络中特定节点的物联网数据发现数据对象。它们在规模非常大且规模不断扩大的网络中是非常有问题的。具体来说，我们深入研究了路由表汇总技术，以支持有效且节省空间的物联网数据发现路由。提出了新的摘要算法，包括基于字母的、基于哈希的和基于意义的摘要及其相应的编码方案。还研究了由于汇总而导致潜在误导性路由的问题。随后，我们分析了何时进行汇总以平衡权衡的策略，尤其是在处理基于 MAA 的查找时。路由表压缩率和非结构化发现路由方法（例如 [4] [5]）的机会之间，导致误导性路由。为了进行实验研究，我们从各种物联网数据库中收集了 10 万个物联网数据流作为输入数据集。实验结果表明，与类似的没有汇总的点对点发现路由算法相比，我们的汇总解决方案可以将路由表大小减少 20 到 30 倍，延迟增加 2-5%。此外，我们的方法在延迟和流量方面优于基于 DHT 的方法 2 到 6 倍。