相关数据源计算卸载中最大化群体生存时间的机器人子集选择
Robot Subset Selection for Swarm Lifetime Maximization in Computation Offloading with Correlated Data Sources
摘要：
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，最大限度地提高群体的生命周期。通过选择合适的机器人子集将其感测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。每个子图具有至少一个顶点（即，子集），称为代表顶点（R-顶点），其与并且仅与子图内的所有其他顶点共享边;仅R-顶点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在AWGN信道中是最优的。对于独立衰落信道，最大-最小原则可以被纳入到所提出的方法，以实现最佳性能。
英文摘要：
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.
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