In-laboratory mobile device data gathering is useful to support fields of study that rely on data derived from mobile devices as elementary research input. Particularly, Dew Computing, a sub-area of mobile distributed computing, aims at scavenging idle computing resources from mobile devices at the edge. To produce repeatable experiments for developed Dew approaches, simulation of relevant mobile device aspects is an acceptable practice, being battery behavior one of such aspects. Our recently-proposed DewSim simulation toolkit uses a trace-based approach to model battery behavior realistically. However, to generically characterize the impact of different device components –e.g., CPU at different usages– on battery behavior, it is necessary to easily capture battery traces, and run benchmarks to quantify computing capabilities. Considering that traces are captured during long charging or discharging cycles, such data gathering duty is tedious and time-consuming and no tool has been proposed yet to automate it. To fill this gap, we propose a platform that leverages common IoT hardware to control battery state of devices subject to pre-configured profiling/benchmarking plans. The platform has a server-side component to manage benchmark/profiling executions using one out of two possible operation modes (exclusive or shared), and an extensible Android application that implements the benchmark and profiling logic to be run on devices. We conclude that the operation modes represent a clear trade-off between benchmark/profile execution time and IoT hardware cost. From validation experiments, we also conclude that using our platform to run a benchmark does not introduce a considerable performance and energy footprint compared to running the same benchmark as a plain Android application.

实验室内移动设备数据收集对于支持依赖于从移动设备获得的数据作为基础研究输入的研究领域很有用。特别地，Dew Computing是移动分布式计算的一个子区域，旨在从边缘移动设备清除空闲的计算资源。为了针对已开发的Dew方法产生可重复的实验，相关移动设备方面的模拟是可以接受的做法，因为电池行为就是此类方面之一。我们最近提出的DewSim仿真工具包使用基于跟踪的方法对电池行为进行实际建模。但是，要概括地描述不同设备组件（例如，不同使用情况下的CPU）对电池行为的影响，必须轻松捕获电池痕迹并运行基准以量化计算能力。考虑到在较长的充电或放电周期中捕获了迹线，因此这种数据收集工作既繁琐又耗时，因此尚未提出任何工具来使它自动化。为了填补这一空白，我们提出了一个平台，该平台利用常见的IoT硬件来控制受预先配置的性能分析/基准测试计划约束的设备的电池状态。该平台具有一个服务器端组件，可以使用两种可能的操作模式（独占或共享）中的一种来管理基准测试/性能分析执行，以及一个可扩展的Android应用程序，该应用程序实现要在设备上运行的基准测试和性能分析逻辑。我们得出结论，操作模式代表了基准/配置文件执行时间与物联网硬件成本之间的明显权衡。根据验证实验，