SensePresence: Infrastructure-Less Occupancy Detection for Opportunistic Sensing Applications
SensePresence: Infrastructure-Less Occupancy Detection for Opportunistic Sensing Applications
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DOI：
10.1109/mdm.2015.41
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Md Abdullah Al Hafiz Khan;Sajjad Hossain;Nirmalya Roy
中科院分区：
文献类型：
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作者：
Md Abdullah Al Hafiz Khan;Sajjad Hossain;Nirmalya Roy
已经研究了在环境中预测与占用相关的信息，以满足各种不断发展的普遍性，无处不在，机会主义和参与性感应应用的无数要求。基础架构和基于环境传感器的技术已被利用，主要是为了确定产生大量部署和改造成本的环境的占用。在本文中，我们主张一种无基础架构的零配置多模式智能手机传感器的技术来检测细粒度的占用信息。我们建议在没有任何对话数据的情况下，在存在人类对话和运动传感器的情况下，在存在人类对话和运动传感器的情况下利用机会性智能手机的声传感器。我们基于对对话数据的无监督聚类的聚类，开发了一种新颖的演讲者估计算法，以确定拥挤的环境中的乘员数量。我们还设计了一种混合方法，将声学传感与机车模型相结合，以进一步提高占用率检测准确性。我们在不同情况下评估了我们的算法，即对话，沉默，并在10个国内使用者的存在下混合。我们对从自然环境中10位乘员收集的现实数据痕迹的实验结果表明，使用这种混合方法，我们可以平均达到约0.76误差数距离，以达到占用率检测准确性。
Predicting the occupancy related information in an environment has been investigated to satisfy the myriad requirements of various evolving pervasive, ubiquitous, opportunistic and participatory sensing applications. Infrastructure and ambient sensors based techniques have been leveraged largely to determine the occupancy of an environment incurring a significant deployment and retrofitting costs. In this paper, we advocate an infrastructure-less zero-configuration multimodal smartphone sensor-based techniques to detect fine-grained occupancy information. We propose to exploit opportunistically smartphones' acoustic sensors in presence of human conversation and motion sensors in absence of any conversational data. We develop a novel speaker estimation algorithm based on unsupervised clustering of overlapped and non-overlapped conversational data to determine the number of occupants in a crowded environment. We also design a hybrid approach combining acoustic sensing opportunistically with locomotive model to further improve the occupancy detection accuracy. We evaluate our algorithms in different contexts, conversational, silence and mixed in presence of 10 domestic users. Our experimental results on real-life data traces collected from 10 occupants in natural setting show that using this hybrid approach we can achieve approximately 0.76 error count distance for occupancy detection accuracy on average.