Recent technological advances in mobile computing and wireless communication have made portable devices, such as PDA, laptops, and wireless modems to be very compact and affordable. On the other hand, wireless networks have gained such wide popularity that new network infrastructure is continually introduced. It is thus likely that many of the future portable devices will be equipped with multiple wireless modems such as Bluetooth and 802.11 WLAN, in order to increase device inter‐operability. The availability of multiple modems can leverage the performance of the communication traffic generated by the applications, for example Internet access. We envision a tool for managing the device connection through these modems. At the core of this tool is an optimization engine that splits packet traffic across a subset of the available transports so that user's performance metrics are maximized. This paper describes a mathematical model for such an optimization problem considering its applicability to small portable devices. Relevant quality of service (QoS) parameters such as bandwidth, average delay, and energy consumption are covered in the model. The mathematical formulation is validated using a simulated environment. The experimental results have demonstrated the effectiveness of our model and captured the inter‐relationship among the quality parameters. Copyright © 2006 John Wiley & Sons, Ltd.

All nations are facing many global problems, the solutions to which require efficient and effective sharing of distributed, heterogeneous data, knowledge and application systems. We present a way to capture organizations' multi-faceted knowledge by three popular rule types and rule structures, and wrap them as Web Services for their registration, discovery and invocation. Distributed data associated with events defined by these organizations are transmitted through a distributed event infrastructure to those sites that contain applicable rules. The processing of heterogeneous rules and application operations specified in rules may add to or modify the event data to produce a dynamic event data set that can be used to support collaborating organizations' decisionmaking and problem solving. A peer-to-peer architecture of a distributed system and the functions of its components are described. Issues and approaches related to event data evolution and distributed rule and trigger processing are also discussed.