Visible to the public I(FIB)F: Iterated bloom filters for routing in named data networks

TitleI(FIB)F: Iterated bloom filters for routing in named data networks
Publication TypeConference Paper
Year of Publication2017
AuthorsMuñoz, C., Wang, L., Solana, E., Crowcroft, J.
Conference Name2017 International Conference on Networked Systems (NetSys)
Keywordsclean slate, clean-slate redesign, Collaboration, Constrained Devices, content distribution efficiency, cryptography, data structures, energy supply, forwarding information base, forwarding strategy, Future Internet, hierarchical names, Human Behavior, human factor, human factors, Information filters, information-centric networking, Internet, Internet of Things, iterated bloom filters, iterative hashes, Memory management, Metrics, named data networking, named data networks, policy governance, Policy-Governed Secure Collaboration, pubcrawl, resilience, Resiliency, Routing, Standards

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 optimized. 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 optimize 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.

Citation Keymunoz_ifibf:_2017