Jinwon Lee

SeeMon: scalable and energy-efficient context monitoring framework for sensor-rich mobile environments

Proactively providing services to mobile individuals is essential for emerging ubiquitous applications. The major challenge in providing users with proactive services lies in continuously monitoring their contexts based on numerous sensors. The context monitoring with rich sensors imposes heavy workloads on mobile devices with limited computing and battery power. We present SeeMon, a scalable and energy-efficient context monitoring framework for sensor-rich, resource-limited mobile environments. Running on a personal mobile device, SeeMon effectively performs context monitoring involving numerous sensors and applications. On top of SeeMon, multiple applications on the device can proactively understand users contexts and react appropriately. This paper proposes a novel context monitoring approach that provides efficient processing and sensor control mechanisms. We implement and test a prototype system on two mobile devices: a UMPC and a wearable device with a diverse set of sensors. Example applications are also developed based on the implemented system. Experimental results show that SeeMon achieves a high level of scalability and energy efficiency.

E-Gesture: a collaborative architecture for energy-efficient gesture recognition with hand-worn sensor and mobile devices

Gesture is a promising mobile User Interface modality that enables eyes-free interaction without stopping or impeding movement. In this paper, we present the design, implementation, and evaluation of E-Gesture, an energy-efficient gesture recognition system using a hand-worn sensor device and a smartphone. E-gesture employs a novel gesture recognition architecture carefully crafted by studying sporadic occurrence patterns of gestures in continuous sensor data streams and analyzing the energy consumption characteristics of both sensors and smartphones. We developed a closed-loop collaborative segmentation architecture, that can (1) be implemented in resource-scarce sensor devices, (2) adaptively turn off power-hungry motion sensors without compromising recognition accuracy, and (3) reduce false segmentations generated from dynamic changes of body movement. We also developed a mobile gesture classification architecture for smartphones that enables HMM-based classification models to better fit multiple mobility situations.

A Scalable and Energy-Efficient Context Monitoring Framework for Mobile Personal Sensor Networks

The key feature of many emerging pervasive computing applications is to proactively provide services to mobile individuals. One major challenge in providing users with proactive services lies in continuously monitoring users context based on numerous sensors in their PAN/BAN environments. The context monitoring in such environments imposes heavy workloads on mobile devices and sensor nodes with limited computing and battery power. We present SeeMon, a scalable and energy-efficient context monitoring framework for sensor-rich, resource-limited mobile environments. Running on a personal mobile device, SeeMon effectively performs context monitoring involving numerous sensors and applications. On top of SeeMon, multiple applications on the mobile device can proactively understand users contexts and react appropriately. This paper proposes a novel context monitoring approach that provides efficient processing and sensor control mechanisms. We implement and test a prototype system on two mobile devices: a UMPC and a wearable device with a diverse set of sensors. Example applications are also developed based on the implemented system. Experimental results show that SeeMon achieves a high level of scalability and energy efficiency.

Demo: e-gesture - a collaborative architecture for energy-efficient gesture recognition with hand-worn sensor and mobile devices

We demonstrate E-Gesture, a collaborative architecture for energy-efficient gesture recognition on a hand-worn sensor device and an off-the-shelf smartphone that greatly reduces energy consumption while achieving high accuracy recognition under dynamic mobile situations. E-gesture employs a novel gesture segmentation and classification architecture carefully crafted by studying sporadic occurrence patterns of gestures in continuous sensor data streams and analyzing energy consumption characteristics in both sensor and smartphone.

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.

HiCon: a hierarchical context monitoring and composition framework for next-generation context-aware services

This article presents a hierarchical context monitoring and composition framework that effectively supports next-generation context-aware services. The upcoming ubiquitous space will be covered with innumerable sensors and tiny devices, which ceaselessly pump out a huge volume of data. This data gives us an opportunity for numerous proactive and intelligent services. The services require extensive understanding of rich and comprehensive contexts in real time. The framework provides three hierarchical abstractions: PocketMon (personal), HiperMon (regional), and EGI (global). The framework provides effective approaches to combining context from each level, thereby allowing us to create a rich set of applications, not possible otherwise. It deals with an extensively broad spectrum of contexts, from personal to worldwide in terms of scale, and from crude to highly processed in terms of complexity. It also facilitates efficient context monitoring and addresses the performance issues, achieving a high level of scalability. We have prototyped the proposed framework and several applications running on top of it in order to demonstrate its effectiveness.

CISS: An efficient object clustering framework for DHT-based peer-to-peer applications

In most DHT-based peer-to-peer systems, objects are totally declustered since such systems use a hash function to distribute objects evenly. However, such an object de-clustering can result in significant inefficiencies in advanced access operations such as multi-dimensional range queries, continuous updates, etc, which are common in many emerging peer-to-peer applications. In this paper, we propose CISS (Cooperative Information Sharing System), a framework that supports efficient object clustering for DHT-based peer-to-peer applications. CISS uses a Locality Preserving Function (LPF) instead of a hash function, thereby achieving a high level of clustering without requiring any changes to existing DHT implementations. To maximize the benefit of object clustering, CISS provides efficient routing protocols for multi-dimensional range queries and continuous updates. Furthermore, our cluster-preserving load balancing schemes distribute loads without hotspots while preserving the object clustering property. We demonstrate the performance benefits of CISS through extensive simulation.

Don't slow me down: Bringing energy efficiency to continuous gesture recognition

Gesture is a compelling user interaction modality for enabling truly on-the-go interactions. Unlike keyboard and touch screen interactions which require considerable visual attention and impose stringent constrains on the form factor of mobile devices, people can easily use hand gestures to perform simple actions (e.g. retrieve voice mail) without having to slow down. In this paper we present an efficient gesture recognition pipeline optimized for “continuous” recognition while minimizing processing overhead and enhancing usability by not requiring the user to delimit explicitly the start and end of gestures. The pipeline is constructed to allow for early filtering of unwanted sensor data with minimal processing cost, and limiting the invocation of processing intensive stages (i.e. HMM) to a limited subset of data (< 5% of sensor data). We also present our evaluation results from a 10 user experiment using 17 gestures and demonstrate that we can achieve considerable processing and power saving without impacting overall recognition accuracy.

CISS: an efficient object clustering framework for DHT-Based peer-to-peer applications

Distributed Hash Tables (DHTs) have been widely adopted in many Internet-scale P2P systems. Emerging P2P applications such as massively multi player online games (MMOGs) and P2P catalog systems frequently update data or issue multi-dimensional range queries, but existing DHT-based P2P systems can not support these applications efficiently due to object declustering. Object declustering can result in significant inefficiencies in data update and multi-dimensional range query routing. In this paper, we propose CISS, a framework that supports efficient object clustering for DHT-based P2P applications. While utilizing DHT as a basic lookup layer, CISS uses a Locality Preserving Function (LPF) instead of a hash function. Thus, CISS achieves a high level of clustering without requiring any changes to existing DHT implementations. Technically, we study LPF encoding function, efficient routing protocols for data updates and multi-dimensional range queries, and cluster-preserving load balancing. We demonstrate the performance benefits of CISS through simulation.

BMQ-Index: Shared and Incremental Processing of Border Monitoring Queries over Data Streams

Border Monitoring Query (BMQ) has different query semantic from conventional continuous range query. It monitors the values of data streams and reports them only when data streams cross the borders of its range. In this paper, we first emphasize the importance and usefulness of BMQ through attractive service scenarios. Then, we propose BMQ-Index, which is specialized to BMQ evaluation. It efficiently processes a large number of BMQs in a shared and incremental manner. For shared processing, BMQ-Index adopts a query indexing approach, thereby achieving a high level of scalability. For incremental processing, BMQ-Index employs an incremental access method. Thus, successive BMQ evaluations are significantly accelerated. We present an index structure and a search algorithm to support onedimensional as well as multi-dimensional BMQ. Lastly, we demonstrate the performance benefits of BMQ-Index through analysis and experiments.