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Context-Aware Background Application Scheduling in Interactive Mobile Systems - Published at IEEE JSAC 2017

Today, lots of mobile users want to extend the battery life time of their smartphones. If we can preload and unload applications to the background with the knowledge about future launching pattern, can we reduce the battery consumption of the smartphones? We verified that battery efficiency can be improved and it is showed by real-world measurement.

IEEE JSAC (Journal on Selected Areas in Communications, Impact Factor: 3.672) is a most prestigious journal which covers the entire field of communications and networking.


[ ABSTRACT ]
Each individual’s usage behavior on mobile devices depends on a variety of factors, such as time, location, and previous actions. Hence, context-awareness provides great opportunities to make the networking and computing capabilities of mobile systems more personalized and more efficient in managing their resources. To this end, we first reveal new findings from our own Android user experiment: 1) the launching probabilities of applications follow Zipf’s law and 2) inter-running and running times of applications conform to log-normal distributions.We also find contextual dependencies between application usage patterns, for which we classify contexts autonomously with unsupervised learning methods. Using the knowledge acquired, we develop a context-aware application scheduling framework, context-aware application scheduler (CAS), that adaptively unloads and preloads background applications for a joint optimization in which the energy saving is maximized and the user discomfort from the scheduling is minimized. Our trace-driven simulations with 96 user traces demonstrate that the context-aware design of the CAS enables it to outperform existing process scheduling algorithms. Our implementation of the CAS over Android platforms and its end-to-end evaluations verify that its human-involved design indeed provides substantial user-experience gains in both energy and application launching latency.

CAS_overview
[ Overall architecture of CAS and its operations over time ]

FM-based Indoor Localization - Published at IEEE TMC 2015

Wireless signals from modern communication systems such as LTE and WiFi are operated in high frequencies (e.g., GHz). If we can utilize a lower frequency like FM radio which goes further and is readily available in most of the countries, can we build a pseudo universal indoor localization system? We have studied it and showed that it has a high potential.

* Many congratulations to YeoCheon Yun! This is YeoCheon Yun's first journal publication.

IEEE TMC (Transactions on Mobile Computing, Impact Factor: 2.912) is a top-notch mobile computing journal.

[ ABSTRACT ]
We present ACMI, an FM-based indoor localization system that does not require proactive site profiling. ACMI constructs the fingerprint database based on pure estimation of indoor RSS (received signal strength) distribution, where only the signals transmitted from commercial FM radio stations are used. Based on extensive field measurement study, we established our own signal propagation model that harnesses FM radio characteristics and open information of FM transmission towers in combination with the floor-plan of a building. Output of the model is an RSS fingerprint database. Using the fingerprint database as a knowledge base, ACMI refines a positioning result via the two-step process; parameter calibration and path match- ing, during its runtime. Without site profiling, our evaluation indicates that ACMI in 7 campus locations and 3 downtown buildings using 8 distinguished FM stations finds positions in only about 6 and 10 meters of errors on average, respectively.
TMC-ACMI
[ The indoor FM radio signal map estimated by our system with multiple levels (from left to right) of propagation modeling. ]

Time Guarantee for Info Spreading - Published at IEEE TMC 2015

So far, there have been no prior work that can answer how much confidence we can have for spreading information to a network (social or physical) over time. We have mathematically revealed it!

IEEE TMC (Transactions on Mobile Computing, Impact Factor: 2.912) is a top-notch mobile computing journal.

[ ABSTRACT ]
Predicting spreading patterns of information or virus has been a popular research topic for which various mathematical tools have been developed. These tools have mainly focused on estimating the average time of spread to a fraction (e.g., α) of the agents, i.e., so-called average α-completion time E(Tα). We claim that understanding stochastic confidence on the time Tα rather than only its average gives more comprehensive knowledge on the spread behavior and wider engineering choices. Obviously, the knowledge also enables us to effectively accelerate or decelerate a spread. To demonstrate the benefits of understanding the distribution of spread time, we introduce a new metric Gα,β that denotes the time required to guarantee α completion (i.e., penetration) with probability β. Also, we develop a new framework characterizing Gα,β for various spread parameters such as number of seeders, contact rates between agents, and heterogeneity in contact rates. We apply our technique to a large-scale experimental vehicular trace and show that it is possible to allocate resources for acceleration of spread in a far more elaborated way compared to conventional average-based mathematical tools. 

temporal_information_spread