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.

中文翻译：

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关联数据源计算卸载中群体寿命最大化的机器人子集选择

考虑机器人群无线网络，其中移动机器人将其计算任务卸载到位于移动边缘的计算服务器。我们的目标是通过有效利用分布式数据源之间的相关性来最大化集群的生命周期。通过选择合适的机器人子集将其感知数据发送到服务器来处理优化问题。在这项工作中，分布式机器人子集之间的数据相关性被建模为无向图。提出了一种最小度迭代划分 (LDIP) 算法，将图划分为一组子图。每个子图至少有一个顶点（即子集），称为代表性顶点（R-Vertex），它与子图中的所有其他顶点共享边；只有 R-Vertices 被选择用于数据传输。当子图的数量最大化时，所提出的子集选择方法显示在 AWGN 信道中是最佳的。对于独立的衰落信道，最大-最小原则可以结合到所提出的方法中以实现最佳性能。