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for sharing information in highly distributed and dynamic systems such as sensor networks. One of the many extensions to the basic Linda model that have been proposed over the past couple of decades is Lime, which, among other extensions, adds the concept of a reaction, which is a method to be executed when a tuple matching a particular pattern is found among the contents of the tuple space. An overview, classification, and analysis of approaches to embed reactive processing in shared dataspaces can be found in [ 111. Finally, demand-driven programming-also known as reduction programming-is a third paradigm where the demand for a value triggers the computation that is responsible for producing the value. That computation may in turn require values that lead to more computations and so on. Functional programming with lazy evaluation is an example of the demand-driven program flow mechanism. In reduction programs, there is typically no concept of a storage location that can be read and written. All program structures are expressions. When a program is expressed as a function whose arguments in turn can be functions themselves, the programmer is describing the solution space without specifying the exact sequence of instruction execution required to arrive at a solution. Regiment [42] is a functional language based on Haskell that exploits the declarative nature of functional programming to simplify the task of collaborative computing in networked sensor systems.
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2.2.7.2 Why data-driven The individual sensor node will typically have a traditional, sequentially programmable von Neumann or Harvard architecture, along with support for one or more control-driven, imperative languages such as C. At the system level, which is the domain of macroprogramming, there are different ways of modeling the collection of von Neumann architectures that forms the overall computing substrate. One approach is to (a) extend the node-level programming paradigm to encompass the entire system and (b) model the sensor network as a single processing element and a single centralized memory [23]. The von Neumann model can also be abandoned at the system level altogether, and the macroprogramming language can be based upon an alternate paradigm such as functional programming [42]. ATaG explores the dataflow paradigm for the following reasons.
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Reactive processing. A sensor network application can be intuitively modeled as a set of node-level or sy stem-level responses to node-level or system-level events. Events will be defined by the application developer at desired levels of semantic abstraction, based on the application domain. An event could indicate the occurrence of phenomena in the physical environment (physical event) or the execution of a particular phase of processing in the network
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(computational event). In addition to denoting occurrence, the event could also carry information about (a) the phenomena in the former case and (b) the results of intermediate computation in the latter. Similarly, a reaction to an event could involve a sequence of computation and communication involving one or more nodes of the network. Data-driven programming is especially suited for expressing reactive applications. A data-driven program consists of a set of tasks with well-defined input and output interfaces. In the pure data-driven model, a task is executed only when all of its inputs are available. However variants of the basic model (including our variant in ATaG) allow the definition of firing rules that can be used to define triggering condition of a task. For instance, a task could be triggered when a specific input is available, or when any one of its inputs is available, or when a certain fraction of its inputs are available. These basic rules can be used to define complex behaviors, as will be illustrated in Section 2.5. Also, tasks are disjoint from each other in the sense that all interaction between tasks is indirect-through the production and consumption of data items. Since tasks are decoupled, a given task can defined to use data items at the desired level of semantic abstraction without having to worrying about how they are produced. This supports application-level macroprogramming .
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Reusability and composability. Modularity, reusability, and composability are important nonfunctional requirements for sensor network applications. Ultimately, we envision our programming model to be integrated into an application synthesis framework similar to the vision of service-oriented program composition [36]. Macroprograms will be generated automatically from a high-level declarative specification and in turn compiled into node-level specifications. Modularity and composability enables the creation of libraries of commonly encountered behaviors and allows existing applications to be suitably reused as subsets of larger functionalities. In control-driven distributed programming using message passing or other communication libraries, tasks explicitly invoke each other s services. Since this requires a task to have information about other task it communicates with, any modification to a task is likely to affect other tasks in the program. Also, if a new task (functionality) is added to the program, all tasks that are to take advantage of that functionality must be modified to incorporate the suitable calls to the newly added task. This tight coupling of task interfaces restricts the reusability of code and composability of programs. In data-driven programming however, task interfaces are specified as Task A reads data item Temperature and produces data item Alarm or Task B reads data item Temperature and produces data item Maximum. Suppose
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