Munir Md Shirajum, Kim Do Hyeon, Bairagi Anupam Kumar, Hong Choong Seon
Department of Computer Science and EngineeringKyung Hee University Yongin 17104 Republic of Korea.
IEEE Sens J. 2021 Mar 24;21(12):13858-13869. doi: 10.1109/JSEN.2021.3068782. eCollection 2021 Jun 15.
In this work, we propose a risk-aware physical distancing system to assure a private safety distance from others for reducing the chance of being affected by the COVID-19 or such kind of pandemic. In particular, we have formulated a physical distancing problem by capturing Conditional Value-at-Risk (CVaR) of a Bluetooth-enabled personal area network (PAN). To solve the formulated risk-aware physical distancing problem, we propose two stages solution approach by imposing control flow, linear model, and curve-fitting schemes. Notably, in the first stage, we determine a PAN creator's safe movement distance by proposing a probabilistic linear model. This scheme can effectively cope with a tail-risk from the probability distribution by satisfying the CVaR constraint for estimating safe movement distance. In the second stage, we design a Levenberg-Marquardt (LM)-based curve fitting algorithm upon the recommended safety distance and current distances between the PAN creator and others to find an optimal high-risk trajectory plan for the PAN creator. Finally, we have performed an extensive performance analysis using state-of-the-art Bluetooth data to establish the proposed risk-aware physical distancing system's effectiveness. Our experimental results show that the proposed solution approach can effectively reduce the risk of recommending safety distance towards ensuring private safety. In particular, for a 95% CVaR confidence, we can successfully deal with 45.11% of the risk for measuring the PAN creator's safe movement distance.
在这项工作中，我们提出了一种风险感知物理距离系统，以确保与他人保持私密安全距离，从而降低感染新冠病毒或此类大流行病的几率。具体而言，我们通过捕捉基于蓝牙的个人区域网络（PAN）的条件风险价值（CVaR），构建了一个物理距离问题。为解决所构建的风险感知物理距离问题，我们提出了一种两阶段解决方案，采用了控制流、线性模型和曲线拟合方案。值得注意的是，在第一阶段，我们通过提出一个概率线性模型来确定PAN创建者的安全移动距离。该方案通过满足估计安全移动距离的CVaR约束，能够有效应对概率分布中的尾部风险。在第二阶段，我们根据推荐的安全距离以及PAN创建者与其他人之间的当前距离，设计了一种基于列文伯格-马夸尔特（LM）的曲线拟合算法，以找到PAN创建者的最优高风险轨迹计划。最后，我们使用最先进的蓝牙数据进行了广泛的性能分析，以确定所提出的风险感知物理距离系统的有效性。我们的实验结果表明，所提出的解决方案能够有效降低推荐安全距离时的风险，以确保私密安全。特别是，对于95%的CVaR置信度，我们能够成功应对测量PAN创建者安全移动距离时风险的45.11%。
