{'fraction_non_alphanumeric': 0.027417027417027416, 'fraction_numerical': 0.011544011544011544, 'mean_word_length': 5.164444444444444, '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}}
Predictive Association Algorithm for IEEE 802.11 WLANs

In the literature, many wireless local area network (WLAN) performance estimations are done with the assumption of uniformly distributed stations (STAs). On the contrary, in practice STAs are distributed unevenly among access points (APs) in the network, causing hot spots and under utilized APs (Bejerano, Han, and Li, 2004). Considering a WLAN is made up of multiple APs, having some APs carrying excessive loads (i.e. hot-spots) degrades both considered APs as well as the overall network performance. The system performance can be improved by associating incoming STAs effectively throughout the network, in a sense to balance the network load evenly between APs. Currently employed user association method in IEEE 802.11 WLANs considers only the received signal strength of APs at STAs, and associates STAs to the closest (in signal strength sense) AP ignoring its load. Novel user association algorithms are required in order to increase the network performance. In this work, a new association algorithm is proposed taking into consideration not only the received signal strength of the APs but also AP loadings. Proposed algorithm predicts the effective data rate of the connection and avoids the congestion. In hot-spot areas, system throughput improvement up to 90% is observed compared to current association algorithms