PROGRAMMING OF DISTRIBUTED SENSOR NETWORKS in .NET

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PROGRAMMING OF DISTRIBUTED SENSOR NETWORKS
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@{ employee , 1234, John }
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0Producer addsin tuple toofthe tuple space. Note thatand producer a producer task is not defined terms input and output tuples
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task can choose to add any tuple it desires at any time
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@ Tuple exists independently in the tuple space. Note that creation
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of a tuple does not trigger the execution of consumer task and tuple exists till it is read and destroyed - depending on semantics of the language. Consumer invokes the in() method and provides a template that matches the tuple in the tuple space. Note that consumer task is not automatically triggered and the consumer can provide any template it desires at any time.
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Figure 1.7 Programming with tuple spaces. The producer and consumer tasks communicate via in and out primitives. The tuple persists in the tuple spaces until it is actively retrieved by the consumer.
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and means that both the sender and the receiver of a message are unaware of each other. ATaG also has this property because the tasks that produce and consume a particular data item in ATaG are not aware of each other. The data pool in ATaG is superficially similar to the notion of a tuple space. However, our active data pool moves the data items from producer to consumer(s) as soon as they are produced, and it schedules the consumer tasks based on their input interface and firing rules. This is different from the passive tuple space that merely buffers the produced data items and whose modifications are really a side effect of control-driven task execution.
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INTRODUCTION
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In fact, the concept of tuple spaces has its roots in Blackboard architectures [43] of A1 research. ATaG s active data pool is similar to the demoned data servers of DOSBART [34] that enabled distributed data-driven computation in a blackboard architecture. The notions of activity class and trigger activities of DOSBART are similar to the abstract tasks and their firing rules in the ATaG model, respectively.
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1.3 MACROPROGRAMMING: WHAT AND WHY
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The primary focus of this dissertation is on the programming of large-scale networked sensor systems. The purpose of the typical sensor network deployment is to gather and process data from the environment for a single end-to-end objective. The program that executes on each node is part of a larger distributed application that delivers the results of an implicit or explicit domain specific query. Each node is required to be aware of its role in accomplishing the overall objective; that is, it is required to implement a predefined protocol for information exchange within the network. Consider a sensor network deployed for object tracking. The desired result of the implicit and perennial domain specific query in this case is the current location of target(s) (if any) in the network. A node-centric approach to programming the network requires each node to be programmed with the following behavior. The acoustic sensor is sampled periodically with a fixed or varying frequency, a Fourier transform is applied to the time-domain samples, and the result is compared with a set of acoustic patterns of interest to the end user. If a match is found, the time- and location-stamped result is communicated to a designated clusterhead node which performs further processing such as line of bearing estimation in an attempt to predict the location of the target. This programming methodology where the desired global application behavior is manually decomposed by the programmer and subsequently coded into individual node-level programs is termed node-centric programming and is representative of state of the art. Node-centric programming has several limitations. Manual translation of global behavior into local actions is likely to be time-consuming and error prone for complex applications. If a new global behavior is to be added to an existing program, the modifications to the existing code are essentially ad hoc. The strong coupling of applicationlevel logic and system-level services such as resource management, routing, localization, etc., also results in high coding complexity. Macroprogramming broadly refers to programming methodologies for sensor networks that allow the direct specification of aggregate behaviors. The
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