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| 基于蓝牙动态特征的移动情境感知 |
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陈益强1,2, 李秋实1,3, 刘军发1,2, 胡琨1,4, 陈振宇1,3
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1.中国科学院 计算技术研究所,北京 100190;2.移动计算与新型终端北京市重点实验室,北京 100190;3.中国科学院 研究生院,北京 100190;4.湘潭大学 信息工程学院,湖南 湘潭 411105
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| 摘要: |
| 传统的情境感知系统多基于定位技术,以识别出重要的地点,但无法直观地描述用户所处的动态语义情境.提出了一种仅仅基于环境中动态蓝牙信息即可对情境进行准确感知的方法,即通过观察周围蓝牙设备的出现规律,提取多维动态特性,用以建立短时情境分类模型,并进一步将此模型运用到分析连续蓝牙轨迹,推断真实生活中的长时语义情境.针对实际环境中的6 种典型情境的实验,其结果表明,仅基于动态蓝牙信息,提取的动态情景特征能够有效体现各类移动情景特点,且情景决策树模型对于短时情景的平均识别准确率可达86.8%,优于传统的其他几种模型方法.同时,基于短时情景的识别结果,综合推断出用户所处的长时间情境,其正确率可达92%. |
| 关键词: 情境感知 蓝牙 动态信息 分类模型 情景决策树 |
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| 基金项目:国家自然科学基金(61173066, 61070110); 北京市自然科学基金(4112056); 北京市教育委员会共建项目 |
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| Sensing Surrounding Contexts using Dynamic Bluetooth Information |
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CHEN Yi-Qiang1,2, LI Qiu-Shi1,3, LIU Jun-Fa1,2, HU Kun1,4, CHEN Zhen-Yu1,3
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1.Institute of Computing Technology, The Chinese Academy of Sciences, Beijing 100190, China;2.Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing 100190, China;3.Graduate University, The Chinese Academy of Sciences, Beijing 100190, China;4.College of Information Engineering, Xiangtan University, Xiangtan 411105, China
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| Abstract: |
| Traditional context-aware systems on mobile platform mainly focus on utilizing various localization based technologies to detect and recognize significantly meaningful places. However, they cannot intuitively describe the dynamic semantic context of the surroundings. In this paper, a novel context sensing approach is proposed to distinguish typical context based on dynamic Bluetooth information. The study builts a context classification model through observing the occurrence of ambient Bluetooth devices and dynamic statistical features extraction and further applied the model into inferring semantic social context based on Bluetooth traces from real-world personal lives. Evaluation results show, just based on dynamic Bluetooth information, the proposed feature extraction methods and DT (Decision Tree) can achieve an average accuracy of 86.8% for recognizing six representative short time-length contexts, which outperforms several traditional machine learning methods. In addition, the accuracy of long time-length context inferring can also reach 92% without any additional information but Bluetooth. |
| Key words: context-aware bluetooth, dynamic classification context decision tree |
