We consider two closely related problems: anomaly detection in sensor networks and testing for infections in human populations. In both problems, we have $n$ nodes (sensors, humans), and each node exhibits an event of interest (anomaly, infection) with probability $p$. We want to keep track of the anomaly/infection status of all nodes at a central location. We develop a $group$ $updating$ scheme, akin to group testing, which updates a central location about the status of each member of the population by appropriately grouping their individual status. Unlike group testing, which uses the expected number of tests as a metric, in group updating, we use the expected age of information at the central location as a metric. We determine the optimal group size to minimize the age of information. We show that, when $p$ is small, the proposed group updating policy yields smaller age compared to a sequential updating policy.
翻译:我们考虑两个密切相关的问题:传感器网络中的异常点检测和对人体感染的检测。在这两个问题上,我们都有一美元节点(传感器、人类),而每个节点都显示一个感兴趣的事件(个别的,感染),概率为1美元。我们想跟踪一个中心点所有节点的异常/感染状况。我们开发了一个类似于群体测试的一组美元计划,它通过适当组合其个人状况,更新一个有关每个人口成员状况的中心点。与群体测试不同,在群体更新中,我们使用中央点的预期信息年龄作为衡量标准。我们确定最佳群体规模,以尽量减少信息年龄。我们表明,在小额时,拟议群体更新政策比按顺序更新政策降低年龄。