{'fraction_non_alphanumeric': 0.022787028921998246, 'fraction_numerical': 0.006134969325153374, 'mean_word_length': 5.488636363636363, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Identifying wireless user groups and association patterns in a campus

Our goal is to understand the movement pat- terns of wireless users and producing models that describe client associations. This can be beneficial in many domains, including coverage planning, resource reservation, support- ing location-dependent applications and applications with real-time constraints, and produce models for simulations. We conducted a 77-day measurement study of wireless users and their association patterns on a major university campus using the IEEE 802.11 wireless infrastructure. We cluster wireless clients based on their inter-building mobility and the characteristics of their movement while they are continuously connected to the infrastructure. We further distinguish wireless clients based on their inter- building mobility, their visits to access points (APs), and the features of their continuous walks in the wireless infrastruc- ture. Such measurements can benefit protocols and algo- rithms that aim to improve the performance of the wireless infrastructures by load balancing, admission control, and resource reservation across APs.