Context-Aware Sensing via Dynamic Programming for Edge-Assisted Wearable Systems
Context-Aware Sensing via Dynamic Programming for Edge-Assisted Wearable Systems
复制标题
DOI：
10.1145/3351286
复制
发表时间：
2020-03
期刊：
影响因子：
--
通讯作者：
Delaram Amiri;A. Anzanpour;I. Azimi;M. Levorato;P. Liljeberg;N. Dutt;A. Rahmani
中科院分区：
文献类型：
--
作者：
Delaram Amiri;A. Anzanpour;I. Azimi;M. Levorato;P. Liljeberg;N. Dutt;A. Rahmani
物联网支持的医疗保健应用程序可以在日常设置中对患者进行个性化监控。此类应用通常由电池供电的传感器与边缘层的智能网关耦合组成。智能网关提供多种本地计算和存储服务（例如，数据聚合、压缩、本地决策），并且还提供了实现传感器层不同参数的本地闭环优化的机会，特别是能耗。为了实现有效的优化方法，需要考虑有关患者的环境和状态的信息，以便找到调整能量的机会，以满足所需的准确性。边缘辅助优化可以管理传感器层的能量消耗，但也可能对感测数据的质量产生不利影响，从而影响对健康恶化风险因素的可靠检测。在本文中，我们提出了两种方法：近视和马尔可夫决策过程(mdp) -考虑能源约束和风险因素要求，以实现双重目标：节约能源，同时满足患者生命体征异常检测的准确性要求。从光容积图信号中提取包括心率、呼吸率和氧饱和度在内的生命体征，并将提取的特征的误差与建模为高斯分布的真实值进行比较。我们控制传感器的传感能量，以尽量减少功耗，同时满足满意的检测性能的期望水平。我们在物联网系统中使用可重构光体积脉搏图传感器进行了实际案例研究，结果表明，与非自适应方法相比，在24小时健康监测系统中，近视的传感能耗平均降低16.9%，异常误检的最大概率为0.17。此外，经过4周的监测，我们证明MDP策略与近视方法相比，在实现相同的平均误检概率的情况下，平均延长电池寿命2倍以上。我们比较了近视、MDP和非适应性方法在1个月内监测14名受试者的结果。
Healthcare applications supported by the Internet of Things enable personalized monitoring of a patient in everyday settings. Such applications often consist of battery-powered sensors coupled to smart gateways at the edge layer. Smart gateways offer several local computing and storage services (e.g., data aggregation, compression, local decision making), and also provide an opportunity for implementing local closed-loop optimization of different parameters of the sensor layer, particularly energy consumption. To implement efficient optimization methods, information regarding the context and state of patients need to be considered to find opportunities to adjust energy to demanded accuracy. Edge-assisted optimization can manage energy consumption of the sensor layer but may also adversely affect the quality of sensed data, which could compromise the reliable detection of health deterioration risk factors. In this article, we propose two approaches: myopic and Markov decision processes (MDPs)—to consider both energy constraints and risk factor requirements for achieving a twofold goal: energy savings while satisfying accuracy requirements of abnormality detection in a patient’s vital signs. Vital signs, including heart rate, respiration rate, and oxygen saturation, are extracted from a photoplethysmogram signal and errors of extracted features are compared to a ground truth that is modeled as a Gaussian distribution. We control the sensor’s sensing energy to minimize the power consumption while meeting a desired level of satisfactory detection performance. We present experimental results on realistic case studies using a reconfigurable photoplethysmogram sensor in an IoT system, and show that compared to nonadaptive methods, myopic reduces an average of 16.9% in sensing energy consumption with the maximum probability of abnormality misdetection on the order of 0.17 in a 24-hour health monitoring system. In addition, over 4 weeks of monitoring, we demonstrate that our MDP policy can extend the battery life on average of more than 2x while fulfilling the same average probability of misdetection compared to the myopic method. We illustrate results comparing myopic, MDP, and nonadaptive methods to monitor 14 subjects over 1 month.