{'fraction_non_alphanumeric': 0.015587529976019185, 'fraction_numerical': 0.0, 'mean_word_length': 5.68, '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}}
Navigating through dynamic indoor environments using WIFI for smartphones

In recent days with a huge mass deployment of wireless networks, smartphones and various related services, different navigation techniques have been developed for indoor environments. Leveraging on these wireless networks, this paper proposes an indoor navigation model for a smartphone using WIFI. While a number of methods have been developed using received signal strength (RSS) based fingerprinting; only seldom approaches consider the dynamicity of the indoor environments. To tackle this limitation, we propose a positioning algorithm based on RSSI - fingerprinting and Manhattan distance which addresses navigation in a rapidly changing indoor environment. The model described is one of the efficient methods in terms of memory and battery consumption.