Into Summarization Techniques for IoT Data Discovery Routing
This addresses space-efficient data discovery for IoT networks, but it is incremental as it builds on existing routing methods with summarization improvements.
The paper tackles the IoT data discovery problem in large-scale networks by proposing novel routing table summarization techniques, achieving a 20-30 fold reduction in routing table size with only a 2-5% latency increase compared to non-summarized peer-to-peer algorithms.
In this paper, we consider the IoT data discovery 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 between the routing table compression rate and the chance of 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.