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for many environment monitoring applications, where sampler tasks on each sensor node perform periodic sampling and filtering of sensor values before further computation on them can take place. Figure 2.4 shows the result of instantiating a task with a density of one per three nodes. Figure 2.5 shows a similar density-based instantiation that is defined in terms of area and not in terms of the sensor nodes. Each cell of the grid in the figure denotes one square meter. Although we do not show an example application that uses this type of density-based instantiation in this book, this annotation can be used in conjunction with the k-nearest-pc: 1channel annotation to create a dynamic, hierarchical data collection pattern in the application. The use of nodes-per-instance : /k and area-per-instance :/k to partition the network into virtual domains in terms of nodes and area, respectively, is shown in Figure 2.6 and Figure 2.7, respectively. The dashed circles in the figures show the grouping of tasks into domains that are implicitly created by the use of these annotations. The exact partitioning of the area of deployment or the set of nodes into domains is up to the compiler, and various algorithms can be applied at this state to optimize performance metrics such as energy balance and network lifetime. In the current implementation, no optimization is performed. These annotations can be used to create tree structures with a fixed number of levels and a fixed number of nodes at each level. ATaG currently has no mechanism that will allow the creation of a flexible number of levels. For instance, consider an application with a hierarchical data collection pattern where the programmer wants four leaf (level 0) nodes to report to each level 1 node, four level 1 nodes to report to each level 2 node, and so on. Now, if the number of children of each internal node are fixed (in this case, four), then the number of levels in the tree will depend on the total number of nodes in the network. ATaG does not currently support the specification of such variable structures. The patterns shown in Figures 2.6 and 2.7 can be used to create tree structures with a fixed number of levels. Figure 2.8 and Figure 2.9 illustrate a combination of task placement annotations and channel annotations to achieve different patterns of data dissemination. Although these examples are very simple, they form powerful building blocks in combination with other annotations listed in Tables 2.1 and 2.2. The case study in 5 will demonstrate the use of such building blocks to develop and deploy example "real-world" applications for sensor networks. Figure 2.8 shows the result of compiling the depicted ATaG program for the 9node sensor network Task T1 is mapped onto node 4 and transmits data items
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Figure 2.4 Instantiating one instance of task T1 per three sensor nodes.
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Figure 2.6 Partitioning the sensor nodes into three equal sets and assigning one instance of task T1 per set.
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Figure 2.7 Partitioning the area of deployment into two equal regions and assigning one instance of task TI per region.
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to its 1-hop neighbors. The neighborhood maintenance and the mechanism for disseminating the instance of data item D to the five neighboring nodes is implemented in the runtime system. If the placement annotation for T I is changed to nodes-per-instance : 1, it results in the neighbor-to-neighbor interaction pattern that is used in applications such as contour detection. Mapping an abstract task to a specific node (in this case, node 4) and transmitting a data item to its Ic nearest nodes shown in Figure 2.9. Consider an application where node 4 represents a handheld device that is used by a supervisor to move around the area of deployment, use a sensor interface to take readings at various points in the network, and send the information back to the supervisor station for logging. Specifying this behavior is extremely simple in the ATaG model using a pattern similar to the one shown in the figure. Suppose the data item D indicates the reading that is to be sent to the supervisor node, task T1 is the task hosted on the handheld device that performs the sampling when desired by the user, and task T2 is mapped onto the supervisor node (say node 8) and logs the readings when received. To accomplish the desired functionality, the following steps are required:
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