论文标题
通过异构移动代理商的分布式服务匹配覆盖范围
A distributed service-matching coverage via heterogeneous mobile agents
论文作者
论文摘要
我们为一组移动代理提供了分布式部署解决方案，该解决方案应为一组密集的目标提供服务。从某种意义上说，这些代理是异质的，因为他们的服务质量（QoS）以空间高斯分布为模型。为了提供最佳服务，目的是部署代理，以使其集体QoS分布尽可能接近目标的密度分布。我们提出了一种基于分布式共识的期望最大化（EM）算法来估计目标密度分布，以高斯混合模型（GMM）建模。 GMM不仅给出了目标分布的估计，而且还将区域分配给子区域，每个区域都由GMM的高斯基地之一表示。我们使用Kullback-Leibler Divergence（KLD）来评估每个代理的QoS分布与每个高斯基础/子区域之间的相似性。然后，将分布式分配问题提出并解决为一个离散的最佳质量传输问题，该问题通过将KLD作为分配成本将每个代理分配给子区域。我们通过传感器部署进行事件检测来证明我们的结果，其中传感器的QoS被建模为各向异性高斯分布。
We propose a distributed deployment solution for a group of mobile agents that should provide a service for a dense set of targets. The agents are heterogeneous in a sense that their quality of service (QoS), modeled as a spatial Gaussian distribution, is different. To provide the best service, the objective is to deploy the agents such that their collective QoS distribution is as close as possible to the density distribution of the targets. We propose a distributed consensus-based expectation-maximization (EM) algorithm to estimate the target density distribution, modeled as a Gaussian mixture model (GMM). The GMM not only gives an estimate of the targets' distribution, but also partitions the area to subregions, each of which is represented by one of the GMM's Gaussian bases. We use the Kullback-Leibler divergence (KLD) to evaluate the similarity between the QoS distribution of each agent and each Gaussian basis/subregion. Then, a distributed assignment problem is formulated and solved as a discrete optimal mass transport problem that allocates each agent to a subregion by taking the KLD as the assignment cost. We demonstrate our results by a sensor deployment for event detection where the sensor's QoS is modeled as an anisotropic Gaussian distribution.
