SociableSense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing
SociableSense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing
复制标题
DOI：
10.1145/2030613.2030623
复制
发表时间：
2011-09
期刊：
影响因子：
--
通讯作者：
K. Rachuri;C. Mascolo;Mirco Musolesi;P. Rentfrow
中科院分区：
文献类型：
--
作者：
K. Rachuri;C. Mascolo;Mirco Musolesi;P. Rentfrow
工作场所用户之间的互动和社会关系已经被许多代社会心理学家研究过。有证据表明，在工作场所互动更多的用户群体工作效率更高。然而，社会科学家仍然很难捕捉到关于这类现象的细粒度数据，并找到促进互动的正确方法。用户也很难跟踪自己与同事的社交水平。虽然手机为获取长期和细粒度数据提供了一个极好的平台，但它们也带来了挑战：电池电量有限，需要权衡传感器读数的准确性和数据传输，而处理计算密集型任务的能源成本很高。在本文中，我们提出了一个基于智能手机的平台Social Sense，它可以捕捉用户在办公室环境中的行为，同时为用户提供他们的社交能力和同事社交能力的量化衡量。我们解决了构建这样一个工具的技术挑战：该系统提供了一种自适应采样机制以及模型，以决定是否在本地或远程执行任务计算，如执行分类和推理算法。我们执行了几个微基准测试来微调和评估这些机制的性能，我们表明自适应采样和计算分布方案在精确度、能量、延迟和数据流量之间进行了权衡。最后，通过对10名参与者进行为期两周的社会心理学研究，我们证明了Social Sense促进了参与者之间的互动，并有助于增强他们的社交能力。
The interactions and social relations among users in workplaces have been studied by many generations of social psychologists. There is evidence that groups of users that interact more in workplaces are more productive. However, it is still hard for social scientists to capture fine-grained data about phenomena of this kind and to find the right means to facilitate interaction. It is also difficult for users to keep track of their level of sociability with colleagues. While mobile phones offer a fantastic platform for harvesting long term and fine grained data, they also pose challenges: battery power is limited and needs to be traded-off for sensor reading accuracy and data transmission, while energy costs in processing computationally intensive tasks are high. In this paper, we propose SociableSense, a smart phones based platform that captures user behavior in office environments, while providing the users with a quantitative measure of their sociability and that of colleagues. We tackle the technical challenges of building such a tool: the system provides an adaptive sampling mechanism as well as models to decide whether to perform computation of tasks, such as the execution of classification and inference algorithms, locally or remotely. We perform several micro-benchmark tests to fine-tune and evaluate the performance of these mechanisms and we show that the adaptive sampling and computation distribution schemes balance trade-offs among accuracy, energy, latency, and data traffic. Finally, by means of a social psychological study with ten participants for two working weeks, we demonstrate that SociableSense fosters interactions among the participants and helps in enhancing their sociability.