Browsing by Subject "Mobile communication"
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Item AARPA: Combining Mobile and Power-line Sensing for Fine-grained Appliance Usage and Energy Monitoring(IEEE, 2015-09-14) Roy, Nirmalya; Pathak, Nilavra; Misra, ArchanTo promote energy-efficient operations in residential and office buildings, non-intrusive load monitoring (NILM) techniques have been proposed to infer the fine-grained power consumption and usage patterns of appliances from power-line measurement data. Fine-grained monitoring of everyday appliances (such as toasters and coffee makers) can not only promote energy-efficient building operations, but also provide unique insights into the context and activities of individuals. Current building-level NILM techniques are unable to identify the consumption characteristics of relatively low-load appliances, whereas smart-plug based solutions incur significant deployment and maintenance costs. In this paper, we investigate an intermediate architecture, where smart circuit breakers provide measurements of aggregate power consumption at room (or section) level granularity. We then investigate techniques to identify the usage and energy consumption of individual appliances from such measurements. We first develop a novel correlation-based approach called CBPA to identify individual appliances based on both their unique transient and steady-state power signatures. While promising, CBPA fails when the set of candidate appliances is too large. To further improve the accuracy of appliance level usage estimation, we then propose a hybrid system called AARPA, which uses mobile sensing to first infer high-level activities of daily living (ADLs), and then uses knowledge of such ADLs to effectively reduce the set of candidate appliances that potentially contribute to the aggregate readings at any point. We evaluate two variants of this algorithm, and show, using real-life data traces gathered from 10 domestic users, that our fusion of mobile and power-line sensing is very promising: it identified all devices that were used in each data trace, and it identified the usage duration and energy consumption of low-load consumer appliances with 87% accuracy.Item App behavioral analysis using system calls(IEEE, 2017-05-01) Das, Prajit Kumar; Joshi, Anupam; Finin, TimSystem calls provide an interface to the services made available by an operating system. As a result, any functionality provided by a software application eventually reduces to a set of fixed system calls. Since system calls have been used in literature, to analyze program behavior we made an assumption that analyzing the patterns in calls made by a mobile application would provide us insight into its behavior. In this paper, we present our preliminary study conducted with 534 mobile applications and the system calls made by them. Due to a rising trend of mobile applications providing multiple functionalities, our study concluded, mapping system calls to functional behavior of a mobile application was not straightforward. We use Weka tool with manually annotated application behavior classes and system call features in our experiments, to show that using such features achieves mediocre F1-measure at best. Thus leading to the conclusion that system calls were not sufficient features for mobile application behavior classification.