{'fraction_non_alphanumeric': 0.021386430678466076, 'fraction_numerical': 0.0029498525073746312, 'mean_word_length': 5.253456221198157, '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}}
A Low Cost Decentralized Future Contacts Prediction Model Using Wi-Fi Traces

The ability to accurately predict human encounters can inspire a variety of promising applications, ranging from epidemiology to data forwarding in opportunistic networks. This work aims at designing a low cost, highly accurate human encounter prediction model based on Wi-Fi datasets. By leveraging the temporal dependency of human mobility, we propose the distributed human encounter prediction (DHEP) model, which uses the Wi-Fi access history and inferred contact information of only the person of interest to estimate future encounters of that person. We implement the proposed DHEP model using a recurrent neural network and a feed-forward neural network. An embedding model that learns the low-dimensional representation of a person's location is proposed to reduce the number of training parameters. The experimental results on two large Wi-Fi datasets show the proposed RNN-based DHEP model outperforms existing models, and achieves 87 to 91 percent accuracy based on University at Buffalo (UB) traces. We also compare DHEP with the centralized human encounter prediction (CHEP) model, which gathers the contact history of all people for predicting future encounters. Despite a slightly lower performance than CHEP, DHEP has a low overhead and can protect data privacy.