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arise from the requirements of distributed networked sensing. The differentiating factors include the notion of abstract tasks and data items, the use of data-driven program flow semantics of the graph, the elevation of data items as a first class entity in the graph representation along with the computational tasks, the concept of spatial scope of a directed edge, etc. There is a growing interest in defining macroprogramming languages [23, 421 and application development environments [ 12, 531 for sensor networks. ATaG enables a methodology for architecture-independent development of networked sensing applications. The same ATaG program may be automatically synthesized for different network deployments, or adapted as nodes fail or are added to the system. Furthermore, it allows application development to proceed prior to decisions being made about the final configuration of the nodes and the network, and in future implementations it will permit dynamic reconfiguration of the application as the underlying network changes. ATaG provides application-neutral support for macroprogramming. Using a small set of basic abstractions, ATaG allows programmers to define their own semantics for tasks and data items. The modularity and composability of ATaG programs means that a library of common behaviors in a particular domain can be defined by the programmer and can later be plugged into other applications that need not know the implementation details of the library component. This approach provides the benefits of using predefined domain-specific features while avoiding the restrictiveness of a domain-specific, custom built runtime system.
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Data-Driven ATaG Runtime (DART)
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ATaG is supported by a runtime system called DART whose structure and function is not visible to the programmer. DART has a component-based software architecture for modularity and flexibility. Each component of DART provides one or more well-defined services to other components. The implementation of a service is hidden from the users of the service. The current DART design can be easily implemented on operating systems that support preemptive priority-based scheduling, multi-threaded execution, mutual exclusion semaphores, message queues, and other mechanisms to handle concurrent access to critical sections and coordinate interactions between threads. Most traditional operating system kernels provide these facilities. A prototype version of DART has been implemented in Java, and is designed to run on relatively heavy duty sensor nodes, although Java Virtual Machines for resource-constrained architectures are also available [48]. DART is also being implemented on the pC/OS-I1 real-time 0s kernel [39], which has been ported to a vast number of devices.
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The performance of DART is unlikely to compare favorably with handoptimized runtime systems where different functionalities are tightly integrated into an inflexible, monolithic structure, and many cross-layer optimizations are incorporated into the design. However, the tradeoff between usability and flexibility, on one hand, and hand-optimized performance, on the other, is common in all methodologies that seek to automate the design of complex systems. A greater level of experience with implementing different applications on a real DART-based system will guide future design choices for the ATaG runtime.
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Software synthesis
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In the context of the ATaG-based programming framework, software synthesis is the process of generating code for each node of the target sensor network deployment for the selected ATaG program. The code that is associated with each application-level functionality (abstract task) is to be provided by the programmer. The task of the software synthesis process is to generate the remainder of the software that is responsible for coordination and communication between the abstract tasks. To ease the task of software synthesis, we designed DART such that a majority of the code base either is agnostic to the application level functionality or can be customized by means of a configuration file that is generated by the software synthesizer. As an example, approximately 3000 lines of Java code runs on each sensor node in the ATaG program for object tracking (Section 2.5.1), of which only 100 lines are actually provided by the application developer and the rest is comprised of DART code that is used essentially unchanged and some glue code that is generated by the software synthesizer. The newly generated glue code is only 15 lines of Java that basically embeds the declarative part of the ATaG program into the runtime system, along with a one-line configuration file for each node in the target network that provides some state information to govern the node s behavior during the simulation.
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