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Determining Quality- and Energy-Aware Multiple Contexts in Pervasive Computing Environments

机译:在普适计算环境中确定质量和能源感知的多个上下文

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In pervasive computing environments, understanding the context of an entity is essential for adapting the application behavior to changing situations. In our view, context is a high-level representation of a user or entity's state and can capture location, activities, social relationships, capabilities, etc. Inherently, however, these high-level context metrics are difficult to capture using uni-modal sensors only and must therefore be inferred using multi-modal sensors. A key challenge in supporting context-aware pervasive computing is how to determine multiple high-level context metrics simultaneously and energy-efficiently using low-level sensor data streams collected from the environment and the entities present therein. A key challenge is addressing the fact that the algorithms that determine different high-level context metrics may compete for access to low-level sensors. In this paper, we first highlight the complexities of determining multiple context metrics as compared to a single context and then develop a novel framework and practical implementation for this problem. The proposed framework captures the tradeoff between the accuracy of estimating multiple context metrics and the overhead incurred in acquiring the necessary sensor data streams. In particular, we develop two variants of a heuristic algorithm for multi-context search that compute the optimal set of sensors contributing to the multi-context determination as well as the associated parameters of the sensing tasks (e.g., the frequency of data acquisition). Our goal is to satisfy the application requirements for a specified accuracy at a minimum cost. We compare the performance of our heuristics with a brute-force based approach for multi-context determination. Experimental results with SunSPOT, Shimmer and Smartphone sensors in smart home environments demonstrate the potential impact of the proposed framework.
机译:在普适计算环境中,了解实体的上下文对于使应用程序行为适应不断变化的情况至关重要。在我们看来,上下文是用户或实体状态的高级表示,可以捕获位置,活动,社会关系,能力等。但是,从本质上讲,使用单模式传感器很难捕获这些高级上下文指标仅适用于此,因此必须使用多模式传感器进行推断。支持上下文感知的普适计算的关键挑战是如何使用从环境及其中存在的实体收集的低级传感器数据流来同时高效地确定多个高级上下文度量。一个关键的挑战是解决这样一个事实,即确定不同高级上下文度量的算法可能会竞争访问低级传感器。在本文中,我们首先强调与单个上下文相比,确定多个上下文度量的复杂性,然后针对此问题开发新颖的框架和实际实现。所提出的框架在估计多个上下文度量的准确性与获取必要的传感器数据流所产生的开销之间取得了平衡。特别是,我们开发了用于多上下文搜索的启发式算法的两个变体,该变体计算了有助于多上下文确定的最佳传感器集以及感测任务的相关参数(例如,数据采集的频率)。我们的目标是以最小的成本满足指定精度的应用要求。我们将启发式算法的性能与基于蛮力的多上下文确定方法进行比较。在智能家居环境中使用SunSPOT,Shimmer和Smartphone传感器的实验结果证明了该框架的潜在影响。

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