I(FIB)F: Iterated Bloom Filters for Routing in Named Data Networks

TL;DR

This paper introduces I(FIB)F, an efficient routing strategy for Named Data Networks using iterated Bloom filters, optimized for constrained IoT devices, reducing overhead, memory, and false positives.

Contribution

It proposes a novel routing structure using iterated Bloom filters with hierarchical name optimization for better performance in IoT scenarios.

Findings

I(FIB)F reduces packet overhead compared to existing solutions.

The approach lowers memory and complexity in forwarding strategies.

Optimizations decrease false positive probability in routing.

Abstract

Named Data Networks provide a clean-slate redesign of the Future Internet for efficient content distribution. Because Internet of Things are expected to compose a significant part of Future Internet, most content will be managed by constrained devices. Such devices are often equipped with limited CPU, memory, bandwidth, and energy supply. However, the current Named Data Networks design neglects the specific requirements of Internet of Things scenarios and many data structures need to be further optimised. The purpose of this research is to provide an efficient strategy to route in Named Data Networks by constructing a Forwarding Information Base using Iterated Bloom Filters defined as I(FIB)F. We propose the use of content names based on iterative hashes. This strategy leads to reduce the overhead of packets. Moreover, the memory and the complexity required in the forwarding strategy are lower than in current solutions. We compare our proposal with solutions based on hierarchical names and Standard Bloom Filters. We show how to further optimise I(FIB)F by exploiting the structure information contained in hierarchical content names. Finally, two strategies may be followed to reduce: (i) the overall memory for routing or (ii) the probability of false positives.