Fingerprint-based indoor localization has become one of the most attractive and promising techniques, and however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially for the large-scale and long-term deployment. In this paper, focusing on room localization, we first analyze the effect of different factors on indoor location accuracy, then propose an integrity check algorithm and a fuzzy map mechanism in response to network changes accurately and timely, and then design an accuracy check algorithm and a regional adaptive periodic update approach to update the fingerprint database effectively when an environment changes. Meanwhile, we design the active static data collecting mechanism and the active pin data collecting mechanism to ensure the accuracy and reliability of fingerprints newly captured and reduce the overhead on mobile devices significantly. The experimental results demonstrate that the proposed solution improves the performance of updating the fingerprint database in real time and robustness and maintains the location accuracy over 95% no matter how the indoor environmental changes; meanwhile, it also reduces energy efficiency imposed on the mobile phones by over 50%.
Inferring abnormal glucose events such as hyperglycemia and hypoglycemia is crucial for the health of both diabetic patients and non-diabetic people. However, regular blood glucose monitoring can be invasive and inconvenient in everyday life. We present SugarMate, a first smartphone-based blood glucose inference system as a temporary alternative to continuous blood glucose monitors (CGM) when they are uncomfortable or inconvenient to wear. In addition to the records of food, drug and insulin intake, it leverages smartphone sensors to measure physical activities and sleep quality automatically. Provided with the imbalanced and often limited measurements, a challenge of SugarMate is the inference of blood glucose levels at a fine-grained time resolution. We propose Md3RNN, an efficient learning paradigm to make full use of the available blood glucose information. Specifically, the newly designed grouped input layers, together with the adoption of a deep RNN model, offer an opportunity to build blood glucose models for the general public based on limited personal measurements from single-user and grouped-users perspectives. Evaluations on 112 users demonstrate that Md3RNN yields an average accuracy of 82.14%, significantly outperforming previous learning methods those are either shallow, generically structured, or oblivious to grouped behaviors. Also, a user study with the 112 participants shows that SugarMate is acceptable for practical usage.
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