Robot Subset Selection for Swarm Lifetime Maximization in Computation Offloading with Correlated Data Sources

TL;DR

This paper proposes a graph-based subset selection method for robot swarms to maximize lifetime by efficiently exploiting data correlation, using a novel LDIP algorithm for optimal partitioning and data transmission.

Contribution

It introduces a least-degree iterative partitioning algorithm to optimally select robot subsets for data offloading based on correlation modeling.

Findings

Maximized swarm lifetime through subset selection.

Optimal partitioning achieved in AWGN channels.

Enhanced performance with max-min principle in fading channels.

Abstract

Consider robot swarm wireless networks where mobile robots offload their computing tasks to a computing server located at the mobile edge. Our aim is to maximize the swarm lifetime through efficient exploitation of the correlation between distributed data sources. The optimization problem is handled by selecting appropriate robot subsets to send their sensed data to the server. In this work, the data correlation between distributed robot subsets is modelled as an undirected graph. A least-degree iterative partitioning (LDIP) algorithm is proposed to partition the graph into a set of subgraphs. Each subgraph has at least one vertex (i.e., subset), termed representative vertex (R-Vertex), which shares edges with and only with all other vertices within the subgraph; only R-Vertices are selected for data transmissions. When the number of subgraphs is maximized, the proposed subset selection approach is shown to be optimum in the AWGN channel. For independent fading channels, the max-min principle can be incorporated into the proposed approach to achieve the best performance.