Optimizing intra-facility crowding in Wi-Fi environments using continuous-time Markov chains
Optimizing intra-facility crowding in Wi-Fi environments using continuous-time Markov chains
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使用连续时间马尔可夫链优化 Wi-Fi 环境中的设施内拥挤
DOI：
10.1007/s43926-022-00026-x
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发表时间：
2022-09-12
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采取各种措施减少拥挤，缓解疫情传播。在这项研究中，我们提出了一种基于Wi-Fi网络使用的减少设施内拥挤的方法。我们分析在不断扩展的无线网络环境中不断产生的大量Wi-Fi日志，计算节点之间的转移概率和每个节点的平均停留时间。随后，我们将这些数据建模为连续时间马尔可夫链，以确定平稳分布的方差，该方差用作设施内拥挤度的度量。因此，我们以停留率为参数来求解优化问题，并给出了最小化设施内拥挤度的数值解。优化结果表明，设施内拥挤减少了约30%。这种解决方案可以在不改变人们运动的情况下调整人们的停留时间，实际上可以减少设施内的拥挤。我们使用k-means方法将Wi-Fi用户分为一系列类别，并记录了每个类别的行为特征，以帮助实施针对类别的措施来减少设施内拥挤，从而使设施管理人员能够根据情况实施有效的应对拥挤的措施。我们详细描述了用于分析大量Wi-Fi日志的计算环境和工作流程。我们相信这项研究将对分析师和设施运营商有用，因为我们使用了通用数据进行分析。
Various measures have been devised to reduce crowdedness and alleviate the transmission of COVID-19. In this study, we propose a method for reducing intra-facility crowdedness based on the usage of Wi-Fi networks. We analyze Wi-Fi logs generated continually in vast quantities in the ever-expanding wireless network environment to calculate the transition probabilities between the nodes and the mean stay time at each node. Subsequently, we model this data as a continuous-time Markov chain to determine the variance of the stationary distribution, which is used as a metric of intra-facility crowdedness. Therefore, we solved the optimization problem by using stay rate as a parameter and developed a numerical solution to minimize the intra-facility crowdedness. The optimization results demonstrate that the intra-facility crowding is reduced by approximately 30%. This solution can practically reduce intra-facility crowdedness as it adjusts people’s stay times without making any changes to their movements. We categorized Wi-Fi users into a set of classes using the k-means method and documented the behavioral characteristics of each class to help implement class-specific measures to reduce intra-facility crowdedness, thus enabling facility managers to implement effective countermeasures against crowdedness based on the circumstances. We present a detailed description of our computing environment and workflow used for the basic analysis of vast quantities of Wi-Fi logs. We believe this research will be useful for analysts and facility operators because we have used general-purpose data for analysis.