Xie Yu, Zhang Kuilin, Kou Huaizhen, Mokarram Mohammad Jafar
Chengdu University of Information Technology, Chengdu, China.
Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, China.
J Cloud Comput (Heidelb). 2022;11(1):38. doi: 10.1186/s13677-022-00300-x. Epub 2022 Sep 5.
With the continuous spread of COVID-19 virus, how to guarantee the healthy living of people especially the students who are of relative weak physique is becoming a key research issue of significant values. Specifically, precise recognition of the anomaly in student health conditions is beneficial to the quick discovery of potential patients. However, there are so many students in each school that the education managers cannot know about the health conditions of students in a real-time manner and accurately recognize the possible anomaly among students quickly. Fortunately, the quick development of mobile cloud computing technologies and wearable sensors has provided a promising way to monitor the real-time health conditions of students and find out the anomalies timely. However, two challenges are present in the above anomaly detection issue. First, the health data monitored by massive wearable sensors are often massive and updated frequently, which probably leads to high sensor-cloud transmission cost for anomaly detection. Second, the health data of students are often sensitive enough, which probably impedes the integration of health data in cloud environment even renders the health data-based anomaly detection infeasible. In view of these challenges, we propose a time-efficient and privacy-aware anomaly detection solution for students with wearable sensors in mobile cloud computing environment. At last, we validate the effectiveness and efficiency of our work via a set of simulated experiments.
随着新冠病毒的持续传播，如何保障人们尤其是体质相对较弱的学生的健康生活，正成为一个具有重大价值的关键研究问题。具体而言，精准识别学生健康状况异常，有利于快速发现潜在患者。然而，每所学校的学生数量众多，教育管理者无法实时了解学生的健康状况，也难以迅速准确地识别学生中可能存在的异常情况。幸运的是，移动云计算技术和可穿戴传感器的快速发展，为监测学生实时健康状况并及时发现异常提供了一条可行之路。然而，上述异常检测问题存在两个挑战。其一，大量可穿戴传感器监测到的健康数据通常规模庞大且更新频繁，这可能导致用于异常检测的传感器到云端的传输成本高昂。其二，学生的健康数据往往足够敏感，这可能会阻碍健康数据在云环境中的整合，甚至使基于健康数据的异常检测变得不可行。鉴于这些挑战，我们提出了一种在移动云计算环境中针对佩戴可穿戴传感器的学生的高效且注重隐私的异常检测解决方案。最后，我们通过一组模拟实验验证了我们工作的有效性和效率。
