20,319 research outputs found
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Smartphone apps usage patterns as a predictor of perceived stress levels at workplace
Explosion of number of smartphone apps and their diversity has created a
fertile ground to study behaviour of smartphone users. Patterns of app usage,
specifically types of apps and their duration are influenced by the state of
the user and this information can be correlated with the self-reported state of
the users. The work in this paper is along the line of understanding patterns
of app usage and investigating relationship of these patterns with the
perceived stress level within the workplace context. Our results show that
using a subject-centric behaviour model we can predict stress levels based on
smartphone app usage. The results we have achieved, of average accuracy of 75%
and precision of 85.7%, can be used as an indicator of overall stress levels in
work environments and in turn inform stress reduction organisational policies,
especially when considering interrelation between stress and productivity of
workers
On the Feature Discovery for App Usage Prediction in Smartphones
With the increasing number of mobile Apps developed, they are now closely
integrated into daily life. In this paper, we develop a framework to predict
mobile Apps that are most likely to be used regarding the current device status
of a smartphone. Such an Apps usage prediction framework is a crucial
prerequisite for fast App launching, intelligent user experience, and power
management of smartphones. By analyzing real App usage log data, we discover
two kinds of features: The Explicit Feature (EF) from sensing readings of
built-in sensors, and the Implicit Feature (IF) from App usage relations. The
IF feature is derived by constructing the proposed App Usage Graph (abbreviated
as AUG) that models App usage transitions. In light of AUG, we are able to
discover usage relations among Apps. Since users may have different usage
behaviors on their smartphones, we further propose one personalized feature
selection algorithm. We explore minimum description length (MDL) from the
training data and select those features which need less length to describe the
training data. The personalized feature selection can successfully reduce the
log size and the prediction time. Finally, we adopt the kNN classification
model to predict Apps usage. Note that through the features selected by the
proposed personalized feature selection algorithm, we only need to keep these
features, which in turn reduces the prediction time and avoids the curse of
dimensionality when using the kNN classifier. We conduct a comprehensive
experimental study based on a real mobile App usage dataset. The results
demonstrate the effectiveness of the proposed framework and show the predictive
capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape
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iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings
Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs
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