Video streaming has become a popular form of transferring video over the Internet. With the emergence of mobile computing needs, a successful video streaming solution demands 1) uninterrupted services even with the presence of mobility and 2) adaptive video delivery according to current link properties. We study the need and evaluate the performance of adaptive video streaming in vertical handoff scenarios. We use universal seamless handoff architecture (USHA) to create a seamless handoff environment, and use the video transfer protocol (VTP) to adapt video streaming rates according to "eligible rate estimates". Using testbed measurements experiments, we verify the importance of service adaptation, as well as show the improvement of user-perceived video quality, via adapting video streaming in the vertical handoffs.

Task computing ([R. Masuoka et al., (2003)]) enables a user to compose and execute complex tasks in application-, device- and service-rich environments. Task computing is possible through the availability of semantically described services that, can be composed on-the-fly by end-users into executable tasks. Through the use of a task computing client, users can not only compose tasks from available semantically described services, but discover, create, manage and manipulate services as well. The focus of this paper is the technologies used for dynamic service discovery, and creation, management and manipulation of semantically described services.

Indoor positioning systems that make use of received signal strength based location fingerprints and existing wireless local area network infrastructure have recently been the focus for supporting location-based services in indoor and campus areas. A knowledge and understanding of the properties of the location fingerprint can assist in improving design of algorithms and deployment of position location systems. However, most existing research work ignores the radio signal properties. This paper investigates the properties of the received signal strength reported by IEEE 802.11b wireless network interface cards. Analyses of the data are performed to understand the underlying features of location fingerprints. The performance of an indoor positioning system in terms of its precision is compared using measured data and a Gaussian model to see how closely a Gaussian model may fit the measured data.

A continuous nearest neighbor (CNN) search retrieves the nearest neighbors corresponding to every point in a given query line segment. It is important for location-based services such as vehicular navigation tools and tourist guides. It is infeasible to answer a CNN search by issuing a traditional nearest neighbor query at every point of the line segment due to the large number of queries generated and the large overhead on bandwidth. Algorithms have been proposed recently to support CNN search in the traditional client-server service model. In this paper, we conduct a pioneering study on CNN search in wireless data broadcast environments. We propose two air indexing techniques, namely, R-tree air index and Hilbert curve air index, and develop algorithms based on these two techniques to search CNNs on the air. A simulation is conducted to compare the proposed air indexing techniques with a naive broadcast approach. The result shows that both of the proposed methods outperform the naive approach significantly. The Hilbert Curve air index is superior for uniform data distributions, while the R-tree air index is a better choice for skewed data distributions.

A completely visible pervasive transaction environment where it is possible to link all related transactions of physical objects and trace their mobility through their entire life process, has been elusive. With the emergence of radio frequency identification (RFID) based tags, it is now practicable to automatically collect information pertaining to any object's place, time, transaction, etc. Based on the pervasive deployment of RFID tags, we propose A novel ubiquitous architecture followed by a protocol for tracking mobile objects in real-time. Our delay analysis and simulation results indicate that the delay incurred in the database update of current tag location is very low and is in the order of seconds, thus providing a very fine granularity in time for consistent location update requests.