Into Summarization Techniques for IoT Data Discovery Routing

TL;DR

This paper introduces novel routing table summarization techniques for IoT data discovery in large networks, significantly reducing routing table size while maintaining low latency and outperforming existing methods.

Contribution

It proposes new summarization algorithms and strategies for IoT routing, balancing compression and accuracy, with extensive experimental validation.

Findings

Routing table size reduced by 20-30 times

Latency increased by only 2-5% compared to non-summarized methods

Outperforms DHT-based approaches by 2-6 times in latency and traffic

Abstract

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.