A Replication Strategy for Mobile Opportunistic Networks based on Utility Clustering

TL;DR

This paper proposes a utility clustering-based replication strategy for mobile opportunistic networks that reduces resource consumption while maintaining delivery performance, adaptable to various utility functions and social network characteristics.

Contribution

It introduces a clustering approach to improve dynamic replication by grouping nodes based on utility, reducing replicas and resource use without sacrificing delivery success.

Findings

Significantly reduces the number of replicas in diverse networks.

Maintains high delivery efficiency despite resource reduction.

Effective with multiple utility functions in social-aware networks.

Abstract

Dynamic replication is a wide-spread multi-copy routing approach for efficiently coping with the intermittent connectivity in mobile opportunistic networks. According to it, a node forwards a message replica to an encountered node based on a utility value that captures the latter's fitness for delivering the message to the destination. The popularity of the approach stems from its flexibility to effectively operate in networks with diverse characteristics without requiring special customization. Nonetheless, its drawback is the tendency to produce a high number of replicas that consume limited resources such as energy and storage. To tackle the problem we make the observation that network nodes can be grouped, based on their utility values, into clusters that portray different delivery capabilities. We exploit this finding to transform the basic forwarding strategy, which is to move a packet using nodes of increasing utility, and actually forward it through clusters of increasing delivery capability. The new strategy works in synergy with the basic dynamic replication algorithms and is fully configurable, in the sense that it can be used with virtually any utility function. We also extend our approach to work with two utility functions at the same time, a feature that is especially efficient in mobile networks that exhibit social characteristics. By conducting experiments in a wide set of real-life networks, we empirically show that our method is robust in reducing the overall number of replicas in networks with diverse connectivity characteristics without at the same time hindering delivery efficiency.