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Consider robot swarm wireless networks where mobile robots offload theircomputing tasks to a computing server located at the mobile edge. Our aim is tomaximize the swarm lifetime through efficient exploitation of the correlationbetween distributed data sources. The optimization problem is handled byselecting appropriate robot subsets to send their sensed data to the server. Inthis work, the data correlation between distributed robot subsets is modelledas an undirected graph. A least-degree iterative partitioning (LDIP) algorithmis proposed to partition the graph into a set of subgraphs. Each subgraph hasat 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 ofsubgraphs is maximized, the proposed subset selection approach is shown to beoptimum in the AWGN channel. For independent fading channels, the max-minprinciple can be incorporated into the proposed approach to achieve the bestperformance.

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