CASE STUDY: APPLICATION DEVELOPMENT WITH ATAG in .NET

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CASE STUDY: APPLICATION DEVELOPMENT WITH ATAG
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Figure 5.4 election.
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of the TargetAlert and the node with the maximum reading (i.e., which is closest to the target in this sense) should elect itself the leader. If the target can be detected at a maximum distance of d from the acoustic sensor on a particular sensor node, the maximum distance between nodes that can detect the same target is 2d. Hence, the output channel annotation for the sampling and thresholding task should require the target alert to be sent to all nodes within distance 2d of the node where the alert is produced. In this example, the 10m label implies that the sensing radius of the acoustic sensor is 5 meters. Now that the TargetAlert is disseminated from each node that has detected the object to every other node that has detected the object, it is necessary to define the mechanism of leader election in the ATaG program. In this case, we define a Leader task that is instantiated on each node of the network. This task consumes all TargetAlerts that are sent to the node from its neighboring nodes and also from the sampling task on the same node. The task maintains local state that consists of the readings received from the neighborhood. Using this information, the task can compare its own reading to the neighbor s readings; and if its own reading is the maximum, it declares itself the leader and also generates a TargetInfo notification to the Supervisor task on the root node. Now, communicationin the sensor network is inherently asynchronous because the sampling of the sensor on the nodes within range of the target is unlikely to be precisely synchronized. Hence, each node will generate its TargetAlert at different points in time. Also, multi-hop packet transmissions could introduce other delays in transmitting the TargetAlert.
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DESIGNING THE MACROPROGRAMS
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Figure 5.5 Abstract syntax: ATaG program for object tracking by local leader election.
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It is desirable to avoid situations where two tasks that have detected the same target elect themselves the leader based on incomplete and different pictures of the same reality. In the sample program of Figure 5.4, the Leader task generates a LeaderID data item when it declares itself a leader-that is, at the same time that it generates the TargetInf o item. This item is sent to all the nodes that may have detected the target, and it is meant to act as a preemptive notification to other nodes that might be about to elect themselves leaders. Naturally, this scheme could suffer from the same shortcomings as the problem it is designed to solve - i.e., the LeaderID notification could be delivered at different times to different nodes, etc. The intent here is not to propose this as a foolproof solution to the distributed leader election problem, but to illustrate how the ATaG channel annotations and other features can be used to create sophisticateddistributedbehaviors using a concise notation. The program shown in the figure has all the advantages of the ATaG programming model: It is architecture-independent,the tasks are decoupled from each other by defining them in terms of input and output data items, etc. The ATaG program that we actually implement in this case study is shown in Figure 5.5 and is a slightly modified and simplified version of the earlier program. In this program, the LeaderID data item is not generated and the Leader task makes the self-election decision and generates the TargetInf o node without attemptingto notify its neighborhood of the decision. The details of the imperative part of this program are discussed in Section 5.5.
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CASE STUDY: APPLICATION DEVELOPMENT WITH ATAG
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Figure 5.6 Concrete syntax: GME model of ATaG program for gradient monitoring.
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SPECIFYING THE DECLARATIVE PORTION
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The concrete syntax of this program as modeled in GME is shown in Figure 5.6. Note that the the concrete syntax of the declarative part of the ATaG program is identical to the abstract syntax of the task graph. The programmer directly translates the task graph into the GME model by dragging, naming, and annotating the desired number of abstract tasks, data items, and channels into the modeling window. The ease of use this engenders is perhaps the most significant advantage of visual ATaG programming through GME. The GME model of the ATaG program for object tracking is shown in Figure 5.7. Again, the concrete syntax is identical to the abstract syntax.
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