Yunseok Rhee

MobiCon: a mobile context-monitoring platform

User context is defined by data generated through everyday physical activity in sensor-rich, resource-limited mobile environments.

Orchestrator: An active resource orchestration framework for mobile context monitoring in sensor-rich mobile environments

In this paper, we present Orchestrator, an active resource orchestration framework for mobile context monitoring. Emerging pervasive environments will introduce a PAN-scale sensor-rich mobile platform consisting of a mobile device and many wearable and space-embedded sensors. In such environments, it is challenging to enable multiple context-aware applications requiring continuous context monitoring to simultaneously run and share highly scarce and dynamic resources. Orchestrator enables multiple applications to effectively share the resources while exploiting the full capacity of overall system resources and providing high-quality service to users. For effective orchestration, we propose an active resource use orchestration approach that actively finds appropriate resource uses for applications and flexibly utilizes them depending on dynamic system conditions. Orchestrator is built upon a prototype platform that consists of off-the-shelf mobile devices and sensor motes. We present the detailed design, implementation, and evaluation of Orchestrator. The evaluation results show that Orchestrator enables applications in a resource-efficient way.

Running or gaming

We developed Exertainer, a sensor-enabled, interactive running entertainment system to support advanced exercise applications. We designed Exertainer to be used in urban environments where outdoor running is often not convenient or practical; as such, Exertainer and Exertainer running applications represent an attractive alternative to traditional treadmill running. Exertainer effectively creates a robust design space around treadmill running. Developers can leverage Exertainers components, an advanced treadmill called Interactive Treadmill, Sensor Bracelet and the PSD game platform, to design interactive and immersive running games and other advanced running applications. We also developed Swan Boat, a multiplayer team racing game making the treadmill running an exciting social activity, and conducted a user study.

Swan boat: pervasive social game to enhance treadmill running

We designed and implemented a pervasive game called Swan Boat that targets the bland and tedious nature of running on a treadmill, making it fun through social interaction and immersive game play. We developed Swan Boat on top of PSD, a platform for pervasive games, and using the Interactive Treadmill hardware. We conducted a user study to evaluate our game.

Mobiiscape: Middleware support for scalable mobility pattern monitoring of moving objects in a large-scale city

With the explosive proliferation of mobile devices such as smartphones, tablets, and sensor nodes, location-based services are getting even more attention than before, considered as one of the killer applications in the upcoming mobile computing era. Developing location-based services necessarily requires an effective and scalable location data processing technology. In this paper, we present Mobiiscape, a novel location monitoring system that collectively monitors mobility patterns of a large number of moving objects in a large-scale city to support city-wide mobility-aware applications. Mobiiscape provides an SQL-like query language named Moving Object Monitoring Query Language (MQL) that allows applications to intuitively specify Mobility Pattern Monitoring Queries (MPQs). Further, Mobiiscape provides a set of scalable location monitoring techniques to efficiently process a large number of MPQs over a large number of location streams. The scalable processing techniques include a (1) Place Border Index, a spatial index for quickly searching for relevant queries upon receiving location streams, (2) Place-Based Window, a spatial-purpose window for efficiently detecting primitive mobility patterns, (3) Shared NFA, a shared query processing technique for efficiently matching complex mobility patterns, and (4) Attribute Pre-matching Bitmap, an in-memory data structure for efficiently filtering out moving objects based on their attributes. We have implemented a Mobiiscape prototype system. Then, we show the usefulness of the system by implementing promising location-based applications based on it such as a ubiquitous taxicab service and a location-based advertising. Also, we demonstrate the performance benefit of the system through extensive evaluation and comparison.

High-performance composite event monitoring system supporting large numbers of queries and sources

This paper presents a novel data structure, called Event-centric Composable Queue (ECQ), a basic building block of a new scalable composite event monitoring (CEM) framework, SCEMon. In particular, we focus on the scalability issues when large numbers of CEM queries and event sources exist in upcoming CEM environments. To address these challenges effectively, we take an event-centric sharing approach rather than dealing with queries and sources separately. ECQ is a shared queue, which stores incoming event instances of a primitive event class. ECQs are designed to facilitate efficient shared evaluations of multiple queries over very large volumes of event streams from numerous event sources. ECQs are composable and form a single shared network within which multiple queries are simultaneously evaluated. In this paper, we present efficient shared processing techniques operating on top of the proposed shared ECQ network. The performance evaluation shows that the proposed approach achieves a high level of scalability compared to conventional separate processing approaches in large-scale CEM environments.

Towards trajectory-based experience sharing in a city

As location-aware mobile devices such as smartphones have now become prevalent, people are able to easily record their trajectories in daily lives. Such personal trajectories are a very promising means to share their daily life experiences, since important contextual information such as significant locations and activities can be extracted from the raw trajectories. In this paper, we propose MetroScope, a trajectory-based real-time and on-the-go experience sharing system in a metropolitan city. MetroScope allows people to share their daily life experiences through trajectories, and enables them to refer to other peoples diverse and interesting experiences in a city. Eventually, MetroScope aims to satisfy users ever-changing interest in their social environments and enrich their life experiences in a city. To achieve real-time, on-the-go, and personalized recommendation, we propose an approach of monitoring activity patterns over peoples location streams.

SATI: A scalable and traffic-efficient data delivery infrastructure for real-time sensing applications

Upcoming ubiquitous technologies are expediting the advent of many real-time applications. Examples of such applications include physical world browsing, RFID-based supply chain management, city-wide road traffic monitoring, weather forecasting, and air pollution monitoring. These applications show different scales and characteristics in terms of sensing data delivery demands. They commonly demand a deep understanding on real-time data delivery from widely distributed data sources. Also, they have highly individualized and fine-grained delivery demands in terms of data and delay specifications, e.g., data value ranges of interest, spatial and temporal resolutions, and tolerable delay, etc. Due to the remarkable scale and complexity, however, existing data delivery systems cannot support such applications effectively. We present SATI (scalable and traffic-efficient data delivery infrastructure), a novel Internet-based sensing data delivery infrastructure that provides a common platform for data providers and consumers. Basically, it is comprised of a collection of proxy nodes forming an overlay network, where each proxy node conducts an in-network processing and efficient data delivery. It allows applications to specify their delivery requirements with intuitive and comprehensive delivery semantics. For scalable and efficient data delivery, SATI develops a novel delivery path management scheme based on an incremental relaxation method. The scheme enables SATI to construct and maintain efficient delivery paths satisfying a large number of delivery requests of high diversity. It fully exploits the diversity of delivery demands on both data and delay requirements, thus achieving a high level of service satisfaction and efficiency at the same time. The result from a large-scale simulation shows that SATI achieves a high level of scalability and bandwidth efficiency.

A Scalable and Efficient Path Management Scheme for Internet-Based Sensor Data Delivery Infrastructure

In upcoming ubiquitous environment, many real-time sensing applications will emerge. These applications show different scale and characteristics on delivery demands. The applications commonly depend on real-time understanding on data from widely distributed data sources. Also, they have highly diverse and complex delivery demands in terms of data and delay, e.g., data value ranges of interest, spatial and temporal resolution and tolerable delay, etc. Due to the remarkable scale and complexity, however, existing data delivery schemes cannot support the applications effectively. We present a novel data delivery scheme to support real-time sensing applications. Our scheme provides efficient delivery paths over Internet-based delivery infrastructure, which is comprised of a collection of dispersed nodes forming an overlay network. It fully exploits the diversity of delivery demands on both data and delay requirements, thus achieving high level of service satisfaction and efficiency at the same time. Also, it distributes path management overhead to multiple nodes by adopting a hierarchical path management approach. We evaluate our scheme through a large-scale simulation. The results show that it achieves a high level of scalability and bandwidth efficiency.

Broadcast directory: a scalable cache coherent architecture for mesh-connected multiprocessors

Abstract Large-scale shared memory multiprocessors favor a directory-based cache coherence scheme for its scalability. The directory space needed to record the information for sharers has a complexity of Θ(N2) when a full-mapped vector is used for an N-node system. Although this overhead can be reduced by limiting the directory size assuming that the sharing degree is small, it will experience significant inefficiency when data is widely shared. In this paper, we propose a new directory scheme and a cache coherence scheme based on it for a mesh interconnection. Deterministic and wormhole routing enables a pointer to represent a set of nodes. Also a message traversing on the mesh performs a broadcast mission to a set of nodes without extra traffic, which can be utilized for the cache coherence protocol. Only a slight change on a typical router is needed to implement our scheme. This scheme is also applicable to any k-ary n-cube networks including a mesh. The Splash-2 parallel program suite is used in the simulation study, where our scheme is compared with other directory-based schemes. Our scheme is proved to generate much less traffic for cache coherence while the space complexity is more scalable Θ(N 3/2 log N) .