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Figure 2.14 is a complete ATaG program based on neighbor-to-neighbor interaction, which is a common technique to implement collaborative computation where the processing at a given node is a function of its own state or the state of the immediate neighbors. The technique is common because such protocols require a fixed, typically low amount of resources, and they scale well with network size. The purpose of this program is to periodically compare its own temperature reading with that of its 1-hop neighbors. This comparison could be used for corroboration or calibration, or to detect unusual conditions such as a fire. Only a single abstract task and a single abstract data item is sufficient to capture this behavior, as shown in the figure. The output channel is annotated with a llocal because an output to the local data pool of the same type of data item that is also an input may cause an infinite loop and unpredictable system behavior, depending on the scheduling policies in the runtime system.
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In-network aggregation
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Fig. 2.16 is a complete ATaG program that sets up a data aggregation tree across the network. Such a mechanism is commonly used in the computation of system-wide properties such as the minimum or maximum reading in the entire system [64]. Note that although the program indicates a virtual topology (tree), it does not specify how the tree is to be constructed or maintained. The runtime system that supports the parent and children annotations is expected to manage the required protocols. Each node of the tree applies an aggregation function
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Figure 2.14 Neighbor-to-neighbor gradient monitoring.
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Mapping and communication: Neighbor-to-neighborprotocol.
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Figure 2.16 Tree-based aggregation.
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Figure 2.17 Mapping and communication: Tree-based data aggregation.
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to its own periodic reading (Sampler task) and the readings received from its child nodes. The result is then communicated up the tree to be incrementally aggregated. This is a continuous process, driven by the periodic sampling at each node. To reduce network traffic and save energy, the Aggregator could use static variables to maintain a count of incoming packets (local state) and communicate the reading up the tree only after a certain number of invocations.
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Table 2.5 Event-Reaction Pairs for Tree-Based Aggregation.
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Periodic timer expires Temperature reading available from own node or other nodes Predetermined number of applications of aggregation function completed
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Temperature sensor is sampled Apply aggregation function (say, MAX)
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Send aggregated reading to parent node
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Hierarchical data fusion
The data aggregation tree in the previous example is a useful but simple structure. More sophisticated applications can be efficiently programmed using hierarchical data fusion. In this pattern, the network is partitioned into domains, and each domain reports to its leader. The leaders in turn are successively organized into a hierarchy with a root node at the top. A quad tree is an example of such hierarchy, with applications in topographic querying of sensor fields [7]. Figure 2.18 is a complete ATaG program that sets up a two-level quad-tree. The network is divided into four domains, each managed by one instance of the LlFusion task. Leaf tasks report to the appropriate LlFusion task. The Root collects the data from LlFusion tasks. The data items are labeled LeafMap and LlMap motivated by the application discussed in [7]. The meaning of the domain annotation and the use of "/4" as a parameter for nodes-per-instance are explained in Tables 2.2 and 2.1 respectively.
Table 2.6 Event-Reaction Pairs for Hierarchical Data Fusion.
Periodic timer expires on leaf node Temperature reading available at leaf node Reading received at L1 clusterhead Predetemined number of readings received at clusterhead
Temperature reading sampled Reading sent to parent Apply aggregation function Send result of aggregation to root node
Figure 2.18 Hierarchical data fusion.
Figure 2.19 Mapping and communication: Hierarchical data fusion.
Event-triggered behavior instantiation
The set of collaborative behaviors used to compose distributed spatial computing applications is usually known at design time. However, it is not desirable from both a performance and functionality point of view to execute all behaviors at all times. Especially in systems that monitor and respond to events in the physical environment, there could be quiescent behaviors that are built into