A programming model for context-aware applications in large-scale pervasive systems

In recent years, new business and research opportunities have been increasingly emerging in the field of large-scale context-aware pervasive systems (e.g. pervasive health-care, city traffic monitoring, environmental monitoring, smart grids). These large-scale pervasive systems are characterized by the need to employ large number of context sources, process massive amounts of real-time context data, provide services to numerous context-aware applications, and cope with higher volatility of the environment. This paper proposes the Origins Model - a programming model for context-aware applications in large-scale pervasive systems. In the Origins Model, an origin is an abstraction of any source of context information. Origins are universal, discoverable, composable, migratable, and replicable components that are associated with type and meta-information. They create an adequate foundation for the development of context-aware applications. Based on them, four processing operations are defined in the Origins Model: filter, infer, aggregate, and compose. As such, these operations provide a powerful mechanism to express a rich set of processing schemes in context-aware applications. Based on the Origins Model, we present the Origins Toolkit - a proof-of-concept implementation developed using the Scala programming language and the Akka toolkit to provide a distributed, scalable, and fault-tolerant solution.