通过无监督集群在边缘进行自动乘客计数.
Giorgio Delzanno1, Luca Caputo1, Daniele D'Agostino1
1DIBRIS, University of Genoa, via Dodecaneso, 35, 16146 Genoa, Italy.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一个实时的,基于边缘的乘客计数系统,使用低成本的WiFi扫描仪. 该系统有效地处理MAC地址随机化,并快速处理数据,以准确检测乘客.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 运输系统 运输系统
背景情况:
- 准确的实时乘客计数对于运输管理和效率至关重要.
- 现有的方法可能会面临设备隐私功能 (如MAC地址随机化) 的挑战.
- 边缘计算为现场实时数据处理提供了一个有前途的方法.
研究的目的:
- 开发和介绍一种基于设备和网络的新型解决方案,用于自动计数乘客.
- 在边缘实现这个解决方案以实现实时操作.
- 为了应对基于WiFi的跟踪中MAC地址随机化的挑战.
主要方法:
- 使用了一个低成本的WiFi扫描器设备,能够捕获802.11探测请求.
- 开发定制算法来管理MAC地址随机化.
- 实现了一个Python数据处理管道,使用轻量级的DBSCAN算法进行即时分析.
- 采用多线程和多处理来提高计算速度.
主要成果:
- 拟议的解决方案在各种移动设备上展示了有希望的实验结果.
- 该系统有效地捕获和分析乘客身份识别的WiFi探测请求.
- 边缘计算方法可以实现实时,现场数据处理.
结论:
- 提出的边缘计算解决方案为自动乘客计数提供了一种有效且低成本的方法.
- 该系统的模块化设计允许未来的扩展和改进.
- 该方法成功克服了技术障碍,例如MAC地址随机化,以提高准确性.
相关概念视频
Cluster Sampling Method
12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K
Probability Histograms
11.8K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.8K
Aggregates Classification
350
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
350


