Network as a Sensor for Smart Crowd Analysis and Service Improvement
Network as a Sensor for Smart Crowd Analysis and Service Improvement
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DOI：
10.1109/mnet.001.2200345
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发表时间：
2023-03
期刊：
影响因子：
9.3
通讯作者：
Mu Mu-Mu
中科院分区：
文献类型：
--
作者：
Mu Mu-Mu
随着数据处理和机器学习基础设施的日益普及，人群分析正在成为应对智能社区经济、社会和环境挑战的重要工具。物联网解决方案捕获的异构人群移动数据可以为政策制定和对社区事件或事件的快速响应提供信息。然而，使用摄像机和面部识别的传统人群监控技术会干扰日常生活。本文介绍了一种新颖的非侵入式人群监控解决方案，该解决方案使用 1,500 多个软件定义网络 (SDN) 辅助 WiFi 接入点作为 24/7 传感器来监控和分析人群信息。使用在大学校园捕获的 9 亿多条 WiFi 记录开发了原型和人群行为模型。我们使用一系列数据可视化和时间序列数据分析工具来揭示大规模人群数据中复杂且动态的模式。研究结果可以极大地有利于智慧社区中的组织和个人进行数据驱动的服务改进。
With the growing availability of data processing and machine learning infrastructures, crowd analysis is becoming an important tool to tackle economic, social, and environmental challenges in smart communities. The heterogeneous crowd movement data captured by IoT solutions can inform policy-making and quick responses to community events or incidents. However, conventional crowd-monitoring techniques using video cameras and facial recognition are intrusive to everyday life. This article introduces a novel non-intrusive crowd monitoring solution which uses 1,500+ software-defined networks (SDN) assisted WiFi access points as 24/7 sensors to monitor and analyze crowd information. Prototypes and crowd behavior models have been developed using over 900 million WiFi records captured on a university campus. We use a range of data visualization and time-series data analysis tools to uncover complex and dynamic patterns in large-scale crowd data. The results can greatly benefit organizations and individuals in smart communities for data-driven service improvement.