Heterogeneous attachment strategies optimize the topology of dynamic wireless networks

TL;DR

This paper explores how different attachment strategies in dynamic wireless networks affect their topology, finding that a mix of local and random connections yields optimal network performance across various measures.

Contribution

It introduces a model of heterogeneous attachment strategies and demonstrates that combining local and random connections optimizes network topology.

Findings

Optimal topologies involve a mix of local and random connections.

Purely local or purely random strategies are less effective.

The model provides insights into designing robust, efficient wireless networks.

Abstract

In optimizing the topology of wireless networks built of a dynamic set of spatially embedded agents, there are many trade-offs to be dealt with. The network should preferably be as small (in the sense that the average, or maximal, pathlength is short) as possible, it should be robust to failures, not consume too much power, and so on. In this paper, we investigate simple models of how agents can choose their neighbors in such an environment. In our model of attachment, we can tune from one situation where agents prefer to attach to others in closest proximity, to a situation where distance is ignored (and thus attachments can be made to agents further away). We evaluate this scenario with several performance measures and find that the optimal topologies, for most of the quantities, is obtained for strategies resulting in a mix of most local and a few random connections.