What we published

FM-based Indoor Localization - Accepted at IEEE TMC

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.

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 with only about 6 and 10 meters of errors on average, respectively.
[ 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 - Accepted at IEEE TMC

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.

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. 


Delayed Offloading Benefit? - Presented at ACM MobiHoc

What if a mobile device can put downloading of volume data such as video, apps, and system updates in the background and continue downloading them whenever WiFi connection becomes available? If this can be unawarely while a user is moving, it will be possible for users to save a lot of cellular data cost. How much can be saved through what process is first in-depth studided!

ACM MobiHoc (Acceptance ratio < 12%) is one of flagship ACM conferences on networking, which is run by rigorous papers of 10 pages.

Smart mobile devices are generating a tremendous amount of data traffic that is putting stress on even the most advanced cellular networks. Delayed offloading has recently been proposed as an efficient mechanism to substantially alleviate this stress. The idea is simple. It allows a mobile device to delay transmission of data packets for a certain amount of time, while it searches WiFi (or similarly femtocell) networks to offload the data during the time. When the time expires, it completes the remaining portion of the delayed transmission through the cellular network that is available at the moment. In this paper, we develop an analytical framework using an embedded Markov process for the delayed offloading system, which provides a closed-form expression for estimating how much data generated by the users can be offloaded to WiFi networks from cellular networks even when there are non- Markovian data arrivals and service interruptions. We provide extensive numerical studies with various ranges of delay, service interruption time, arrived data, and service rate. These numerical studies show that the current deployment of WiFi networks measured from a metropolitan city is capable of offloading about 80% of the generated data with 30 minutes of delay and 1 Mbps of WiFi data rate, but increasing the data rate does not help improve the amount of offloading. Further studies using this framework on two new deployment strategies of WiFi networks in the near future give guidance on how to upgrade WiFi networks by revealing that the amount of offloading for 30 minutes of delay and 1 Mbps of data rate can be drastically improved to about 90% or 98% according to the strategy. 


PhonePool like CarPool? - Presented at IEEE SECON

CarPool is efficient in a metropolitan city. In a similar manner, can PhonePool will improve the networking performance of nearby mobile devices? We focused on the fact that devices in proximity are sometime subscribing diverse cellular carriers and this can extremely enhance the networking experience, especially when being located in a high speed train (e.g., KTX, TGV, Shinkansen) or in an express bus.

IEEE SECON (Conference on Sensing, Communication, and Networking, Acceptance ratio < 29%) is one of IEEE ComSoc’s quality conferences, which is run by rigorous papers of 9 pages.

Energy consumption for cellular communication is increasingly gaining importance in smartphone battery lifetime as the bandwidth of wireless communication and the demand for mobile traffic increase. For energy-efficient cellular communica- tion, we tackle two energy characteristics of cellular networks: (1) transmission energy highly varies upon channel condition, and (2) transmission of a packet accompanies unnecessary tail energy waste. Under the objective of transmitting packets when the best channel is provided as well as a number of packets are accumulated, we propose a new mobile collaboration framework “PhonePool” that aggregates smart devices across multiple cellular providers. Compared to the standalone operation, even without a buffering delay, PhonePool allows better channel and reduces more tail energy in a statistical point of view. To maximize the energy benefit while maintaining the fairness among the nodes in collaboration, we further develop a dynamic programming framework providing the optimal algorithm of PhonePool and its approximated heuristic. Trace-driven simulations on our experi- mental HSPA/EVDO/LTE network traces show that PhonePool of 5 devices achieves up to 42% of energy reduction.