{'fraction_non_alphanumeric': 0.02, 'fraction_numerical': 0.0, 'mean_word_length': 5.596899224806202, '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}}
Opportunistic crowd sensing in WiFi-enabled indoor areas

Crowd sensing in indoor areas is becoming more and more fundamental for flow management, security and surveillance, or building usage statistics. This paper deals with a simple crowd sensing approach, which opportunistically exploits the already deployed WiFi networks, thus avoiding dedicated wiring and installations. The proposed algorithm is based on a two-step procedure that first applies a Wavelet decomposition of the signal strength data and then exploits the obtained coefficients to learn the unknown relation between crowd presence and signal changes. To this end, a customized learning-by-example (LBE) algorithm is trained for successive real-time crowd detection. The results of the experimental validation are presented to assess system potentialities and current limitations.