A Low Cost Decentralized Future Contacts Prediction Model Using Wi-Fi Traces
| Autor: | Jangyoung Kim, Cong Binh Nguyen, Seokhoon Yoon, Chunming Qiao, Tan Quan Ngo, Nga Dao-Thi |
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| Rok vydání: | 2022 |
| Předmět: |
Information privacy
Dependency (UML) Artificial neural network Computer Networks and Communications Computer science business.industry Ranging Machine learning computer.software_genre Variety (cybernetics) Recurrent neural network Embedding Artificial intelligence Electrical and Electronic Engineering Representation (mathematics) business computer Software |
| Zdroj: | IEEE Transactions on Mobile Computing. 21:3807-3821 |
| ISSN: | 2161-9875 1536-1233 |
| Popis: | 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% accuracy based on University at Buffalo (UB) traces. We also compare DH P 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. |
| Databáze: | OpenAIRE |
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