Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia.
Jozef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana, Slovenia.
Sensors (Basel). 2023 Feb 26;23(5):2588. doi: 10.3390/s23052588.
Monitoring the presence and movements of individuals or crowds in a given area can provide valuable insight into actual behavior patterns and hidden trends. Therefore, it is crucial in areas such as public safety, transportation, urban planning, disaster and crisis management, and mass events organization, both for the adoption of appropriate policies and measures and for the development of advanced services and applications. In this paper, we propose a non-intrusive privacy-preserving detection of people's presence and movement patterns by tracking their carried WiFi-enabled personal devices, using the network management messages transmitted by these devices for their association with the available networks. However, due to privacy regulations, various randomization schemes have been implemented in network management messages to prevent easy discrimination between devices based on their addresses, sequence numbers of messages, data fields, and the amount of data contained in the messages. To this end, we proposed a novel de-randomization method that detects individual devices by grouping similar network management messages and corresponding radio channel characteristics using a novel clustering and matching procedure. The proposed method was first calibrated using a labeled publicly available dataset, which was validated by measurements in a controlled rural and a semi-controlled indoor environment, and finally tested in terms of scalability and accuracy in an uncontrolled crowded urban environment. The results show that the proposed de-randomization method is able to correctly detect more than 96% of the devices from the rural and indoor datasets when validated separately for each device. When the devices are grouped, the accuracy of the method decreases but is still above 70% for rural environments and 80% for indoor environments. The final verification of the non-intrusive, low-cost solution for analyzing the presence and movement patterns of people, which also provides information on clustered data that can be used to analyze the movements of individuals, in an urban environment confirmed the accuracy, scalability and robustness of the method. However, it also revealed some drawbacks in terms of exponential computational complexity and determination and fine-tuning of method parameters, which require further optimization and automation.
监测特定区域内个人或人群的存在和移动情况，可以深入了解实际行为模式和隐藏趋势。因此，在公共安全、交通、城市规划、灾害和危机管理以及大型活动组织等领域，这种监测对于采取适当的政策和措施以及开发先进的服务和应用都至关重要。在本文中，我们提出了一种非侵入式的隐私保护方法，通过跟踪携带 Wi-Fi 功能的个人设备来检测人员的存在和移动模式，使用这些设备传输的网络管理消息将其与可用网络相关联。然而，由于隐私法规的限制，网络管理消息中实施了各种随机化方案，以防止基于设备地址、消息序列号、数据字段和消息中包含的数据量等因素轻易识别设备。为此，我们提出了一种新颖的去随机化方法，通过使用新颖的聚类和匹配过程，根据相似的网络管理消息和相应的无线电通道特征来检测单个设备。该方法首先使用标记的公开可用数据集进行校准，然后在受控的农村和半受控的室内环境中进行验证，最后在不受控制的拥挤城市环境中进行可扩展性和准确性测试。结果表明，该去随机化方法能够正确检测农村和室内数据集的设备，单独验证每个设备时准确率超过 96%。当设备分组时，该方法的准确率会降低，但在农村环境中仍高于 70%，在室内环境中仍高于 80%。在城市环境中对非侵入性、低成本解决方案进行最终验证，该解决方案还提供了有关聚类数据的信息，可用于分析个人的运动情况，验证了该方法的准确性、可扩展性和鲁棒性。然而，该方法在指数级计算复杂度和方法参数的确定和微调方面也存在一些缺点，需要进一步优化和自动化。
