Pervasive computing enables computers to interact with the real world in a ubiquitous and natural manner. Quality of service (QoS), related to transmission delay, bandwidth, or packet loss, has been studied in various building blocks in pervasive computing, e.g., different QoS mechanisms are presented for wireless or wired networks; the notion of computational QoS is used for parallel processing. The emerging pervasive computing, however, is application-driven and mission-critical and the existing QoS notions to do not really match. Quality of Information (QoI) or Information Quality (IQ) of sensor-originated information relates to the fitness of the information for a sensor-enabled application. Harnessing and optimizing QoI of information derived from sensor networks will be key to bringing together information acquisition and processing systems that support the on-demand information needs of a broad spectrum of smart, sensor-enabled applications such as remote real-time habitat monitoring, utility grid monitoring, environmental control, supply-chain management, health care, machinery control, intelligent highways, military intelligence, reconnaissance and surveillance (ISR), border control, and hazardous material monitoring, just to mention a few.

QoI touches every part of the end-to-end flow of sensor-derived information, from the sensors themselves and the observation data they produce to the various fusion layers that process these data and eventually to the applications (and their users) that use them. For example, sensor-generated information is used as the basis for determining context at varying levels of accuracy and fidelity, in a hierarchical fashion, with lower-layer context effectively serving as a virtual sensor stream for higher-layer context determination. The effectiveness of actions taken by the applications using this information serves as the ultimate assessor of the quality and value-add provided by the entire sensor-enabled application. For example, an action may be highly effective achieving all its anticipated goals, partially effective, or entirely ineffective. Complementing 'traditional' provisioning of QoS with QoI for pervasive computing is challenging and difficult due to the resource-constrained, dynamic and distributed nature of the system, the weakness under security attacks, and the lack of a design approach that takes into account the different types of resources and their inter-dependencies. Novel mechanisms are required in pervasive computing which should integrate QoI, network QoS, computational QoS, security, and a user's Quality of Experience (QoE), which will be influence by the application goals and the pervasive environment in which the application is utilized. Such advances require inter-disciplinary activities at the intersection of pervasive computing, human-computer interfaces, intent modeling, sensor fusion, machine learning, and information theory.