  In this paper, we consider the IoT data discovery problem in very large and
growing scale networks. Through analysis, examples, and experimental studies,
we show the importance of peer-to-peer, unstructured routing for IoT data
discovery and point out the space efficiency issue that has been overlooked in
keyword-based routing algorithms in unstructured networks. Specifically, as the
first in the field, this paper investigates routing table designs and various
compression techniques to support effective and space-efficient IoT data
discovery routing. Novel summarization algorithms, including alphabetical,
hash, and meaning-based summarization and their corresponding coding schemes,
are proposed. We also consider routing table design to support summarization
without degrading lookup efficiency for discovery query routing. The issue of
potentially misleading routing due to summarization is also investigated.
Subsequently, we analyze the strategy of when to summarize to balance the
tradeoff between the routing table compression rate and the chance of causing
misleading routing. For the 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 a 2-5% increase in latency 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.
