A pervasive computing environment represents a resource-rich, data-intensive environment where users and devices, including handhelds, wearables, computers in vehicles, computers embedded in the physical infrastructure, and (nano)sensors, are mobile and continuously exchange data. In order to exploit the available resources, mobile devices must act as semi-autonomous, self-describing, highly interactive and adaptive peers that employ cross-layer interaction between their data management and communication layers for inferring and expressing information they need, and for obtaining and storing such information by pro-actively interacting with other devices in their vicinity using available short-range ad-hoc networking technologies. In order to address the data management challenges introduced by these data-intensive, highly dynamic, pervasive computing environments, this work focuses on three questions: (i) What is the necessary model that will allow a device to infer and express what information its user needs based on the current context? (ii) How should a device determine when a user will need the information? (iii) How should a device effectively obtain and store such information from other devices in its vicinity? One contribution of this work is the design of a conceptual model for data management in pervasive computing environments. The second contribution of this work is the design and implementation of a prototype of the conceptual model - the MoGATU framework. Through experimental validation of the implementation prototype, the work demonstrates that the conceptual model and its implementation are superior to existing data management techniques.