Janko Vito, Luštrek Mitja
Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana 1000, Slovenia.
Jožef Stefan International Postgraduate School, Ljubljana 1000, Slovenia.
Sensors (Basel). 2017 Dec 29;18(1):80. doi: 10.3390/s18010080.
The recognition of the user's context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system's energy expenditure and the system's accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.
利用可穿戴传感系统识别用户情境是普适计算中的一个常见问题。然而，此类系统通常配备的小电池往往使得持续识别不切实际。如果传感器设置能适应每种情境，电池的负担就可以减轻。我们提出一种方法，能为每种情境高效找到接近最优的传感器设置。该方法使用马尔可夫链来模拟系统在不同配置下的行为，并使用多目标遗传算法来找到一组良好的非支配配置。该方法在三个真实数据集上进行了评估，发现在系统能耗和系统准确性之间取得了良好的权衡。例如，其中一种解决方案消耗的能量比默认方案少五倍，而仅牺牲了两个百分点的准确性。
