Poster abstract: Information-maximizing data collection in social sensing using named-data

Abstract

This poster describes the information funnel, a data collection protocol for social sensing that maximizes a measure of delivered information utility. We argue that information-centric networking (ICN), where data objects are named instead of hosts, is especially suited for utility-maximizing transport in resource-constrained environments, because data names can expose similarities between named objects that can be leveraged for minimizing redundancy, hence maximizing utility. We implement the funnel on the recently proposed named-data networking (NDN) stack, an instance of ICN. With proper name space design, a protocol prioritizes transmission of data items over bottlenecks to maximize information utility, with very weak assumptions on the utility function. This prioritization is achieved merely by comparing data names, without knowing application-level name semantics, which makes it generalizable across a wide range of applications. Evaluation results show the information funnel improves the utility of the collected data objects compared with state-of-the-art solutions.