Robot Subset Selection for Swarm Lifetime Maximization in Computation Offloading with Correlated Data Sources
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 leastdegree 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 (RVertex), which shares edges with and only with all other vertices within the subgraph; only RVertices 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 maxmin principle can be incorporated into the proposed approach to achieve the best performance.
 Publication:

arXiv eprints
 Pub Date:
 January 2023
 DOI:
 10.48550/arXiv.2301.10522
 arXiv:
 arXiv:2301.10522
 Bibcode:
 2023arXiv230110522Z
 Keywords:

 Electrical Engineering and Systems Science  Systems and Control;
 Computer Science  Distributed;
 Parallel;
 and Cluster Computing
 EPrint:
 7 pages, 3 figures, ICC 2023