SmartSPEC: A framework to generate customizable, semantics-based smart space datasets

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Andrew Chio , Daokun Jiang , Peeyush Gupta , Georgios Bouloukakis , Roberto Yus , Sharad Mehrotra , Nalini Venkatasubramanian
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Abstract

This paper presents SmartSPEC, an approach to generate customizable synthetic smart space datasets using sensorized spaces in which people and events are embedded. Smart space datasets are critical to design, deploy and evaluate systems and applications under issues of heterogeneity, scalability and robustness, leading to cost-effective operation which improves the safety, comfort and convenience experienced by space occupants. However, many challenges exist in obtaining realistic smart space datasets for testing and validation, from a lack of fine-grained sensing to privacy/security concerns. SmartSPEC is a smart space simulator and data generator that leverages a semantic model augmented with user-defined constraints to represent important attributes, relationships, and external domain knowledge for a smart space. We employ machine learning (ML) approaches to extract relevant patterns from a sensorized space, which are used in an event-driven simulation strategy to generate realistic simulated data about the space (events, trajectories, sensor observation datasets, etc.). To evaluate the realism of the generated data, we develop a structured methodology and metrics to assess various aspects of smart space datasets, including trajectories of people and occupancy of spaces. Our experimental study looks at two real-world settings/datasets: an instrumented smart campus building and a city-wide GPS dataset. Our results show the realism of trajectories produced by SmartSPEC (1.4x to 4.4x more realistic than the best synthetic data baseline when compared to real-world data, depending on the scenario and configuration), as well as sensor data derived from such trajectories which adhere to the underlying semantics of the smart space as compared to synthetic sensor data baselines, even under hypothetical changes.

SmartSPEC:生成可定制的、基于语义的智能空间数据集的框架
本文介绍了SmartSPEC,这是一种使用嵌入人物和事件的传感空间生成可定制的合成智能空间数据集的方法。智能空间数据集对于在异构性、可扩展性和稳健性问题下设计、部署和评估系统和应用至关重要,从而实现经济高效的操作,从而提高空间居住者的安全性、舒适性和便利性。然而,在获得用于测试和验证的真实智能空间数据集方面存在许多挑战,从缺乏细粒度传感到隐私/安全问题。SmartSPEC是一个智能空间模拟器和数据生成器,它利用添加了用户定义约束的语义模型来表示智能空间的重要属性、关系和外部领域知识。我们采用机器学习(ML)方法从传感空间中提取相关模式,并将其用于事件驱动的模拟策略,以生成有关空间的真实模拟数据(事件、轨迹、传感器观测数据集等)。为了评估生成数据的真实性,我们开发了一种结构化的方法和指标来评估智能空间数据集的各个方面,包括人的轨迹和空间占用率。我们的实验研究着眼于两个真实世界的设置/数据集:一个装有仪器的智能校园建筑和一个全市范围的GPS数据集。我们的结果显示了SmartSPEC产生的轨迹的真实性(根据场景和配置,与真实世界数据相比,比最佳合成数据基线更真实1.4倍至4.4倍),以及与合成传感器数据基线相比,从符合智能空间底层语义的此类轨迹导出的传感器数据,即使在假设的变化下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Pervasive and Mobile Computing
Pervasive and Mobile Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
7.70
自引率
2.30%
发文量
80
审稿时长
68 days
期刊介绍: As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.
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