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Information-Centric Fog Computing enables a multitude of nodes near theend-users to provide storage, communication, and computing, rather than in thecloud. In a fog network, nodes connect with each other directly to get contentlocally whenever possible. As the topology of the network directly influencesthe nodes' connectivity, there has been some work to compute the graphcentrality of each node within that network topology. The centrality is thenused to distinguish nodes in the fog network, or to prioritize some nodes overothers to participate in the caching fog. We argue that, for anInformation-Centric Fog Computing approach, graph centrality is not anappropriate metric. Indeed, a node with low connectivity that caches a lot ofcontent may provide a very valuable role in the network.  To capture this, we introduce acontent-based centrality (CBC) metric whichtakes into account how well a node is connected to the content the network isdelivering, rather than to the other nodes in the network. To illustrate thevalidity of considering content-based centrality, we use this new metric for acollaborative caching algorithm. We compare the performance of the proposedcollaborative caching with typical centrality based, non-centrality based, andnon-collaborative caching mechanisms. Our simulation implements CBC on threeinstances of large scale realistic network topology comprising 2,896 nodes withthree content replication levels. Results shows that CBC outperforms benchmarkcaching schemes and yields a roughly 3x improvement for the average cache hitrate.

A Content-based Centrality Metric for Collaborative Caching in Information-Centric Fogs