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Time-aware activities are characterized by a set of timerelated aspects, independently from the involved application domain. For example, an activity may need to reason on facts that are held to be true in specific time intervals, or it may need to be executed at precise time instants. In this paper we present a temporal model capturing these concepts and their relations. The model is described by means of an UML formalization, enriched with OCL constraints where needed. The model turns into a set of architectural abstractions that makes timerelated concepts visible at the application level. This eases the analysis and implementation of time-aware systems and enables adaptivity so that temporal constraints may be dynamically met.

The paper presents a layered architecture that improves software modularity and reduces computational and communication overhead for systems requiring data from sensors in order to perform domain-related elaborations (e.g., tracking and surveillance systems). Each layer manages hypotheses that are abductions related to objects modeling the "real world" at a specific abstraction level, from raw data up to domain concepts. Each layer, by analyzing hypotheses coming from the lower layer, abduces new hypotheses regarding objects at a higher level of abstraction (e.g., from image blobs to identified people) and formulates timed previsions about objects. The failure of a prevision causes a hypothesis to flow upstream. In turn, previsions can flow downstream, so that their verification is delegated to the lower layers. The proposed architectural patterns have been reified in a Java framework, which is being exploited in an experimental multi-camera tracking system.