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a new functionality is to be added to this temperature monitoring program. The purpose of this new task is to corroborate the readings from a wider area around the node that produced the alarm and produce another verified alarm based on the results. In data-driven programming, all that is required is to simply define a new task as Task C reads data items Alarm and Temperature and produces data item VerifiedAlarm. The representation of the spatial aspect of this processing will be discussed in the next section, specifically the collection of data from the neighborhood. The emphasis here is on the fact that the addition of Task C does not change the existing tasks in any way. Also, Task C does not care about how the Alarm is produced by Task B. The new program is simply a concatenation of the three tasks, and their mutual dependency is implicit in their input and output interfaces defined in terms of data items.
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Imperative programming is a programming paradigm where computation is specified in terms of statements (commands) that are to be executed in sequence and that change the program state. Almost all processors are designed to execute imperative programs, and the program state at any given time is represented by the contents of the processor memory at that time. Since imperative programming requires the programmer to specify the how of computation in detail, the advantage of intimate control over program execution is offset by the programming complexity, especially for large-scale andor distributed systems. High-level procedural languages and object-oriented languages provide constructs such as objects that ease the task of writing complex imperative programs, but the basic paradigm remains unchanged. nesC [ 191 and Kairos [23]are examples of imperative programming languages for sensor network applications. Declarative programming, in contrast, focuses on the what of computation, leaving the how unspecified. A declarative program can be viewed as the description of a solution space where the sequence of steps to arrive at the solution is left to some underlying interpreter. Functional programming and logic programming are examples of declarative programming. The major advantage of declarative programming from an application development perspective is the reduced complexity of programming that is a result of delegating most of the selection and synthesis of underlying mechanisms to an unspecified interpreter, while the application developer focuses primarily on formulating the solution space. Regiment [42], TinyDB [38], and Semantic Streams [57] are examples of the declarative programming paradigm for sensor network applications.
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Now, the functional aspect of a sensor network application refers to the code (tasks) that runs on the individual sensor nodes and performs data processing. Examples of nonfunctional aspects are task placement and mechanisms for communication and coordination. Consider a simple application where a collector task running on a designated root node periodically receives and logs temperature readings from every node in the network. The functional aspects of this application are completely defined by the code that performs the sampling and the code that performs the logging. As long as there is a mechanism to (i) ensure the placement of one sampling task on each node of the network and one logging task on the root node, (ii) periodically execute the sampling task, and (iii) route the sampled data from its point of origin to the root node, the details of its implementation should not be the application developers concern. The ATaG programming paradigm is based on the observation that specification of functional aspects of the networked sensing application in an imperative style and the nonfunctional aspects in a declarative style affords a tradeoff between the need for control over application execution and the need to reduce the complexity of communication and coordination. The latter is a substantial fraction of a networked sensing application and can really be considered as a service offered by the system instead of an integral and integrated part of the application code. More importantly, ATaG enables architecture independence by clearly separating the when and where of processing from the what. The former constitutes the declarative part and is specified through parameterized spatial and temporal attributes for a generic network architecture. The latter constitutes the imperative part and is the actual task code supplied by the user. The same program can be compiled for a different network size and topology by interpreting the declarative part in the context of that network architecture while the imperative part remains unchanged.
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