Symbiotic Sensing for Energy-Intensive Tasks in Large-Scale Mobile Sensing Applications.
Symbiotic Sensing for Energy-Intensive Tasks in Large-Scale Mobile Sensing Applications.
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DOI：
10.3390/s17122763
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发表时间：
2017-11-29
期刊：
影响因子：
--
通讯作者：
Havinga PJM
中科院分区：
文献类型：
--
作者：
Le DV;Nguyen T;Scholten H;Havinga PJM
在开发移动传感应用时，能耗是一项关键的性能和用户体验指标，特别是随着近年来传感应用数量的显著增加。十年前提出的，当时移动应用程序还不流行，大多数移动操作系统都是单任务处理，传统的传感范例，如机会主义传感和参与式传感，并不探索能量密集型任务的并发应用之间的关系。受自然界生物之间社会关系的启发，我们提出了一种共生感知范式，它可以节约能量，同时保持与现有范式相同的性能。关键思想是感知应用程序应该协作地执行常见任务，以避免多次获取相同的资源。通过这样做，这种感测范例执行感测任务时只需很少的额外资源消耗，因此延长了电池寿命。为了评估和比较现有的共生感知范式，我们建立了关于完成概率和估计能量消耗的数学模型。从实际数据集获得的各种参数的定量评估结果表明，在大规模传感应用中，如路况监测、空气污染监测和城市噪声监测，共生传感比机会传感和参与式传感具有更好的性能。
Energy consumption is a critical performance and user experience metric when developing mobile sensing applications, especially with the significantly growing number of sensing applications in recent years. As proposed a decade ago when mobile applications were still not popular and most mobile operating systems were single-tasking, conventional sensing paradigms such as opportunistic sensing and participatory sensing do not explore the relationship among concurrent applications for energy-intensive tasks. In this paper, inspired by social relationships among living creatures in nature, we propose a symbiotic sensing paradigm that can conserve energy, while maintaining equivalent performance to existing paradigms. The key idea is that sensing applications should cooperatively perform common tasks to avoid acquiring the same resources multiple times. By doing so, this sensing paradigm executes sensing tasks with very little extra resource consumption and, consequently, extends battery life. To evaluate and compare the symbiotic sensing paradigm with the existing ones, we develop mathematical models in terms of the completion probability and estimated energy consumption. The quantitative evaluation results using various parameters obtained from real datasets indicate that symbiotic sensing performs better than opportunistic sensing and participatory sensing in large-scale sensing applications, such as road condition monitoring, air pollution monitoring, and city noise monitoring.
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DOI：
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发表时间：
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期刊：
Proceedings. IEEE Workshop on Mobile Computing Systems and Applications
影响因子：
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