Fragmented-Iterated Bloom Filters for Routing in Distributed Event-Based Sensor Networks

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.

A New Bloom Filter Architecture for FIB Lookup in Named Data Networking

“…The I(FIB)F structure [12] uses iterated Bloom filters, and can complete the search procedure without accessing the off-chip memory since an I(FIB)F is constructed for each output face, as opposed to a single FIB that defines the next-hop. An I(FIB)F consists of d iterated Bloom filters (IBF) [29] with iterated hash functions. In order to construct an I(FIB)F, a m-bit standard BF is split in d IBFs of m/d bits.…”

I(FIB)F: Iterated bloom filters for routing in named data networks

“…We note that by computing h 2 using the second strategy computing resources are saved because we profit from the previous computed hash. Iterative Bloom Filters (IBFs) [4] take advantage of the properties of iterative hash functions. The strategy followed by IBFs is to split the m bit-positions of a Standard BF to save the same number of elements n. Then, a Standard BF [5] may be split in d IBFs of m/d bit-positions and n elements.…”