Ubiquitous Computing and Databases

One of the major challenges in turning the ubiquitous computing vision into reality is the development of distributed system architectures that will support effectively and efficiently the ability to instrument the physical world (Estrin et al., 2002; National Research Council, 2001). Such architectures are being developed around two core concepts: self-organizing networks of embedded devices with wireless communication capabilities and data-centricity. To augment physical artifacts with computational and communications capabilities, it is necessary to enable miniaturized hardware components capable of wireless communication. However, these same characteristics that allow for instrumentation of physical objects also impose significant constraints. Systems architectures require significant changes due to the severely limited resources available on these devices. One possible solution is offered by the emergence of data-centric systems. In this context, datacentric refers to in-network processing and storage, carried out in a decentralized manner (Estrin et al., 2000). One objective of data-centricity is to let systems exploit the anticipated high node densities to achieve longer unsupervised operating lifetimes. Indeed, smaller form factor wireless sensor nodes have limited resources and often cannot afford to transfer all the collected data to the network edge and forward to centralized information processing systems. A practical example of the data-centric approach for network routing is directed diffusion (Intanagonwiwat, Govindan & Estrin, 2000). This mechanism employs in-network processing by routing data along aggregation paths, thus, removing the need for an address-centric architecture. It exploits data naming as the lowest level of system organization and supports flexible and efficient in-network processing. In summary, databases have a dual role to play in ubiquitous computing: in the short term, they need to provide the mapping between physical and virtual entities and space in a highly distributed and heterogeneous environment. In the longer term, database management systems need to provide the infrastructure for the development of data-centric systems. Each of the two phases is discussed in turn in the following sections.