Martinez David Alejandro, Mojica-Nava Eduardo, Watson Kym, Uslander Thomas
IEEE Trans Cybern. 2022 Jul;52(7):5744-5755. doi: 10.1109/TCYB.2020.3035783. Epub 2022 Jul 4.
The constant development of sensing applications using innovative and affordable measurement devices has increased the amount of data transmitted through networks, carrying in many cases, redundant information that requires more time to be analyzed or larger storage centers. This redundancy is mainly present because the network nodes do not recognize environmental variations requiring exploration, which causes a repetitive data collection in a set of limited locations. In this work, we propose a multiagent learning framework that uses the Gaussian process regression (GPR) to allow the agents to predict the environmental behavior by means of the neighborhood measurements, and the rate distortion function to establish a border in which the environmental information is neither misunderstood nor redundant. We apply this framework to a mobile sensor network and demonstrate that the nodes can tune the parameter s of the Blahut-Arimoto algorithm in order to adjust the gathered environment information and to become more or less exploratory within a sensing area.
使用创新且经济实惠的测量设备的传感应用的不断发展，增加了通过网络传输的数据量，在许多情况下，这些数据携带了冗余信息，需要更多时间来分析或更大的存储中心。这种冗余主要是因为网络节点无法识别需要探索的环境变化，这导致在一组有限的位置进行重复的数据收集。在这项工作中，我们提出了一个多智能体学习框架，该框架使用高斯过程回归（GPR）让智能体通过邻域测量来预测环境行为，并使用率失真函数来建立一个边界，在这个边界内环境信息既不会被误解也不会冗余。我们将这个框架应用于移动传感器网络，并证明节点可以调整Blahut-Arimoto算法的参数，以便调整收集到的环境信息，并在传感区域内或多或少地进行探索。
