32nd IEEE Conference on Local Computer Networks
A Ubiquitous Computing Network Framework for Assisting People in Urban Areas Minsoo Lee, Yoonsik Uhm, Zion Hwang, Yong Kim, Joohyung Jo and Sehyun Park School of Electrical and Electronics Engineering, Chung-Ang University, 156-756, 221 Huksuk-dong, Dongjak-gu, Seoul, Korea, email:{lemins, neocharisma, zhwang, ykim, jhjo}@wm.cau.ac.kr,
[email protected]
Abstract—This paper presents a framework to address the combination of ubiquitous computing interests as an Intelligent Assisting Location-Aware Service and development of its hardware and software systems. In our real testbed, the system can dynamically localize the areas of interest and adapt to changes in the ubiquitous intelligence landscape. Index Terms—context-aware, home network, middleware, mobility management, security, smart home, ubiquitous computing
I. I NTRODUCTION One of the key challenges in urban computing is the great diversity and density of people, devices, and built artifacts found in urban places. The future ubiquitous computing environment in urban areas will incorporate a wide variety of devices and services from different manufacturers and developers. Therefore, we should achieve platform and vendor independence, as well as architecture openness, before ubiquitous computing spaces become even more common. Another key issue in urban areas, including smart homes, is location-awareness based on the ubiquitous intelligence paradigm, whose goal is providing fine-grained persistent services following the roaming path of a moving user. Location is a crucial component of context, and much research in the past decade has focused on location-sensing technologies, including WiFi-based, ultrasound, RFID, vision-based, the middleware support, and location-based service (LBS) architectures. This paper presents a framework to address the combination of ubiquitous computing interests as an Intelligent Assisting Location-Aware Service and development of its hardware and software systems. The proposed location-aware service architecture provides the intelligent services and dynamically localizes the areas of interest. II. M IDDLEWARE FOR AUTONOMOUS S ERVICES Recent work in the u-city, context-aware system [1] [2] [3] [4]and agent-based system has focused on offering user-centric services, managing the services, and tracking the user movements. However, the middleware architecture in most of the Corresponding author: Sehyun Park (Tel: 82-2-822-5338 Fax: 82-2-8251584 E-mail:
[email protected]) This research was supported by the MIC (Ministry of Information and Communication), Korea, under the Chung-Ang University HNRC (Home Network Research Center)-ITRC support program supervised by the IITA(Institute of Information Technology Assessment).
0742-1303/07 $25.00 © 2007 IEEE DOI 10.1109/LCN.2007.54
Fig. 1.
Our system’s system level architecture
parts of u-city has not been adequately considered in respect of city information management, the correlation of each server, and service maintenance. We propose the UCASS (U-city Context-Aware Service Agent System) Architecture, as shown in Fig.1, for a ubiquitous-city to meet the location-aware computing requirements. Our architecture has the service-oriented systems including Home Servers, Knowledge Base systems, Apartment servers, Vehicular servers, Service Providers based on city information system, various networks, ontology, and agent-based systems [5] [6]. Location-Aware Service Agent : For location-aware intelligent services, we deploy LAS Agents with location tracking and prediction schemes in each room of a smart home. Knowledge Repository : It provides various contexts and
215
information, structured and unstructured data, and rules. It stores the static and dynamic situation patterns as knowledge. Using RuleML and JESS script language, a rule-based engine accesses and stores and deletes rules. Context Managing Agent : It has the function of collecting, filtering and managing context. Interface Agent : It performs the formulation of contextual data from MMI (Multimodal Interaction framework) or sensor agents, creating queries, and transforming and transmitting the result about the queries. Inference & Mining Agent : It is responsible for checking the class consistency and implying the inter-ontology relations. We implemented the learning mechanism of Data Miner using a Hierarchical Hidden Markov Model (HHMM). III. T ESTBED AND P ERFORMANCE E VALUATION
IV. C ONCLUSION In this paper, we have described a Ubiquitous city ContextAware Service Agent System (UCASS) architecture for providing context-aware services in an urban area and a smart
Fig. 2.
6000
The assistive service scenario for u-city
Without our Intelligent Agents With our Intelligent Agents Without Authorization
5000
Delay (ms)
We deployed our location-aware service system in an actual home setting. The overall area is about 162 square meters. To cover the large space and to get the user’s position in a room, 46 IR long distance measuring sensors and 13 pressure sensors were used. We deployed network cameras with motion detection and alarm functions. The detected images were remotely saved in the Vision-based Location Tracking Agent and then retrieved as needed for the location of a user. We also deployed the vision-based location tracking system, which consists of CCD cameras and a pan-tilt-zoom (PTZ) camera with a feature fusion-based people tracking algorithm. As a Portable Personal Device for Smart Home, we implemented a Ubiquitous Toy (UbiToy) which communicated with a homeserver or other UbiToys by Zigbee communication and sensed the signal generated by the user’s hand actions. We also implemented a Virtual Context monitoring Module (VCM) which integrated exiting wireless network technologies with ZigBee communication. VCM gathers the data from several sensors: temperature, humidity, CDS and etc. We evaluated our model using the assisting service scenario, as shown in Fig. 2, in which an elder receives an audio/video guiding service for home delivery. The elders have more opportunities to receive home delivery service. However, it is difficult for the elders to receive ordered goods in a safe way because usually family members do not stay in the elders’ houses during the daytime. By using our service framework, we could check the arrival time of a home delivery service and automatically monitor a deliveryman in the house. As shown in Fig. 3, we examine the trade off between contextawareness and service delay. Our intelligent assisting service with our agents shows about the same performance, and the average context reasoning delay was 1,097 ms by avoiding the additional delay of 3,501 ms introduced by location-aware pre-caching Web Crawling with LAS agents along the roaming path of the user.
4000
3000
2000
1000
Step 1 to 3
Fig. 3.
Step 4 to 6
Step 7 to 8
Step 9
Execution time of applications under the reasoning prototypes
home. We discussed the ways that the OCASS system affected the quality of life for inhabitants and their home network service members. R EFERENCES [1] H. Chen, F. Perich, T. W. Finin, and A. Joshi, “Soupa: Standard ontology for ubiquitous and pervasive applications.” in MobiQuitous, 2004, pp. 258–267. [2] J. Pascoe, N. Ryan, and D. R. Morse, “Issues in developing context-aware computing.” in HUC, 1999, pp. 208–221. [3] Hong and J. I., “An infrastructure approach to context-aware computing,” HCI, vol. 16. [4] S. BN, “A context-aware system architecture for mobile distributed computing,” in PhD thesis, Department of Computer Science, Columbia University. [5] J. Hightower and G. Borriello, “Location systems for ubiquitous computing.” IEEE Computer, vol. 34, no. 8, pp. 57–66, 2001. [6] A. J. F. P. D. C. Harry Chen, Tim Finin and L. Kagal, “Intelligent agents meet the semantic web in smart spaces.” IEEE Internet Computing, vol. 8, no. 6, pp. 69–79, 2004.
216
