Younghyun Ju

CoMon: cooperative ambience monitoring platform with continuity and benefit awareness

Mobile applications that sense continuously, such as location monitoring, are emerging. Despite their usefulness, their adoption in real-world deployment situations has been extremely slow. Many smartphone users are turned away by the drastic battery drain caused by continuous sensing and processing. Also, the extractable contexts from the phone are quite limited due to its position and sensing modalities. In this paper, we propose CoMon, a novel cooperative ambience monitoring platform, which newly addresses the energy problem through opportunistic cooperation among nearby mobile users. To maximize the benefit of cooperation, we develop two key techniques, (1) continuity-aware cooperator detection and (2) benefit-aware negotiation. The former employs heuristics to detect cooperators who will remain in the vicinity for a long period of time, while the latter automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Through continuity- and benefit-aware operation, CoMon enables applications to monitor the environment at much lower energy consumption. We implement and deploy a CoMon prototype and show that it provides significant benefit for mobile sensing applications.

SocioPhone: everyday face-to-face interaction monitoring platform using multi-phone sensor fusion

In this paper, we propose SocioPhone, a novel initiative to build a mobile platform for face-to-face interaction monitoring. Face-to-face interaction, especially conversation, is a fundamental part of everyday life. Interaction-aware applications aimed at facilitating group conversations have been proposed, but have not proliferated yet. Useful contexts to capture and support face-to-face interactions need to be explored more deeply. More important, recognizing delicate conversational contexts with commodity mobile devices requires solving a number of technical challenges. As a first step to address such challenges, we identify useful meta-linguistic contexts of conversation, such as turn-takings, prosodic features, a dominant participant, and pace. These serve as cornerstones for building a variety of interaction-aware applications. SocioPhone abstracts such useful meta-linguistic contexts as a set of intuitive APIs. Its runtime efficiently monitors registered contexts during in-progress conversations and notifies applications on-the-fly. Importantly, we have noticed that online turn monitoring is the basic building block for extracting diverse meta-linguistic contexts, and have devised a novel volume-topography-based method. We show the usefulness of SocioPhone with several interesting applications: SocioTherapist, SocioDigest, and Tug-of-War. Also, we show that our turn-monitoring technique is highly accurate and energy-efficient under diverse real-life situations.

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.

SymPhoney: a coordinated sensing flow execution engine for concurrent mobile sensing applications

Emerging mobile sensing applications are changing the characteristics of smartphone workloads. Whereas typical mobile applications run alone in the foreground interacting with users, sensing applications concurrently run in the background, providing unobtrusive monitoring services. Such concurrent sensing workloads raise a new challenge incurring severe resource contention among themselves and with other foreground applications. To address the challenge, we develop SymPhoney, a coordinated sensing flow execution engine to support concurrent sensing applications. As its key approach, we develop a novel sensing-flow-aware coordination. We first introduce the new concept of frame externalization i.e., to identify and externalize semantic structures embedded in otherwise flat sensing data streams. Leveraging the identified frame structures, SymPhoney develops frame-based coordination and scheduling mechanisms, which effectively coordinates the resource use of concurrent contending applications and maximize their utilities even under severe resource contention. We implemented several sensing applications on top of the SymPhoney engine and performed extensive experiments, showing effective coordination capability of SymPhoney.

An efficient dataflow execution method for mobile context monitoring applications

In this paper, we propose a novel efficient dataflow execution method for mobile context monitoring applications. As a key approach to minimize the execution overhead, we propose a new dataflow execution model, producer-oriented model. Compared to the conventional consumer-oriented model adopted in stream processing engines, our model significantly reduces execution overhead to process context monitoring dataflow reflecting unique characteristics of context monitoring. To realize the model, we develop DataBank, an execution container that takes charge of the management and delivery of the output data for the associated operator. We demonstrate the effectiveness of DataBank by implementing three useful applications and their dataflow graphs, i.e., MusicMap, FindMyPhone, and CalorieMonitor. Using the applications, we show that DataBank reduces the CPU utilization by more than 50%, compared to the methods based on the consumer-oriented model; DataBank enables more context monitoring applications to run concurrently.

MobiCon: Mobile context monitoring platform: Incorporating context-awareness to smartphone-centric personal sensor networks

In this demonstration, we will show MobiCon, a context monitoring platform; it runs over smartphones and sensor OSs, and facilitates development and deployment of everyday context-aware applications. For many years, lots of research efforts have been made in building low-cost, yet effective sensor networks for various application domains such as structural health monitoring of bridges, disaster recovery, automated ventilation of buildings. Integration of sensors into smartphones and the advent of wearable devices open a new opportunity for mobile applications to leverage in-situ user contexts such as his/her location, activity, social relationship, health status. In recent studies of mobile and pervasive computing, a number of useful mobile context-aware applications have been proposed, but their actual deployment is slow due to complexity of context processing and heavy resource and battery usage. To address such challenges, we have been building MobiCon for many years, upon which diverse context-aware applications are developed and deployed without concerns about complexity of context processing and resource optimization.

Demo: CoMon - resource-aware cooperative context monitoring system for smartphone-centric sensor-rich pans

System for Smartphone-centric Sensor-rich PANs Youngki Lee, Younghyun Ju, Chulhong Min, Seungwoo Kang, Yunseok Rhee, Junehwa Song KAIST & HUFS, Republic of Korea {youngki, yhju, chulhong, swkang, junesong}@nclab.kaist.ac.kr, rheeys@hufs.ac.kr ABSTRACT In this work, we demonstrate the operation of CoMon, a cooperative context monitoring platform, under various usage scenarios. Deployed over smartphones and sensor devices, CoMon monitors diverse contexts of mobile users and their surroundings, and provides applications with the contexts of interests. Especially, to support long-term monitoring of highly diverse user contexts, it enables a smartphone to collaborate with other smartphones as well as external sensor devices in a nearby space opportunistically. Through cooperation, it overcomes the limitation of a single smartphone-based context monitoring, i.e. inherent scarcity of sensing modalities (e.g. no environmental sensors) and positions (e.g. in a pocket) or potential shortage of computation resources and battery power. Utilizing CoMon, context monitoring applications need to simply delegate their context monitoring requests to CoMon and become capable of fully exploiting resources within nearby spaces. Instead, CoMon plans the resource use for concurrent applications in cooperation with other devices. Specifically, CoMon maximizes sharing of sensing and computing resources among multiple devices of nearby users. Also, it avoids unnecessary usage of energy and computing resources by removing repetitive sensing and context processing across multiple devices.

CoMon+: A Cooperative Context Monitoring System for Multi-Device Personal Sensing Environments

Continuous mobile sensing applications are emerging. Despite their usefulness, their real-world adoption has been slow. Many users are turned away by the drastic battery drain caused by continuous sensing and processing. In this paper, we propose CoMon+, a novel cooperative context monitoring system, which addresses the energy problem through opportunistic cooperation among nearby users. For effective cooperation, we develop a benefit-aware negotiation method to maximize the energy benefit of context sharing. CoMon+ employs heuristics to detect cooperators who are likely to remain in the vicinity for a long period of time, and the negotiation method automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Especially, CoMon+ improves the negotiation method proposed in our earlier work, CoMon [30], to exploit multiple processing plans enabled by various personal sensing devices; each plan can be alternatively used for cooperation, which in turn will maximize overall power saving. We implement a CoMon+ prototype and show that it provides significant benefit for mobile sensing applications, e.g., saving 27-71 percent of smartphone power consumption depending on cooperation cases. Also, our deployment study shows that CoMon+ saves an average 19.7 percent of battery under daily use of a prototype application compared to the case without CoMon+ running.

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.