Performance Evaluation of Distributed k-WTA on Dynamic Undirected Connected Graphs and Its Application to Task Allocation
Kexin Liu, Yinyan Zhang · 2023
The k-winners-take-all (k-WTA) network is a model based on competition. The extant literature on k-WTA models only deals with static undirected connected graphs. In the actual application scenario, the static undirected connection graph cannot adapt to the complex and changing communication conditions. Thus, in this paper we find a distributed k-WTA network that can be applied to dynamic undirected connected graphs. Our experimental simulation results demonstrate that the network efficiently identify the top k largest inputs from n inputs in dynamic undirected connected graphs. Furthermore, we apply this network to a distributed multi-robot target tracking task assignment scenario. Our proposed algorithm shows promising results in this application, indicating its potential in real-world scenarios.