A State-Based Programming Model for Wireless Sensor Networks

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4 Chapter 1. Introduction for sensor nodes that is based on the OSM model, (3) a compiler for the proposed language that generates event-driven program code, and (4) an execution environment for the generated code on BTNODE sensor-node prototypes. The following paragraphs present the key concepts of our solution, namely using an explicit notion of state for structuring programs, automated state management, efficient memory management based on the use of state as a lifetime qualifier for variables, and the automatic transformation of OSM programs into conventional event-driven programs. 1.4.1 Modular and Well-Structured Design In the state-based model of OSM, the main structuring element of program code is an explicit notion of program state. For every explicit program state the programmer can specify any number of actions together with their trigger conditions (over events). At runtime, actions are only invoked when their associated program state is assumed and when their trigger condition evaluates to true. As a consequence, the association of events to actions in OSM depends on the current state (unlike in the event-driven model), that is, invocations of actions become a function of both the event and the current program machine state. This concept greatly enhances modularity, since every state and its associated actions can be specified separately, independent of other states and their actions. Thus, modifying the state space of a program requires only local changes to the code base, namely to the implementations of the affected states. In contrast, in the event-driven model, such modifications typically affect several actions throughout much of the entire code. 1.4.2 Automated State Management Generally, OSM moves program-state management to a higher abstraction layer and makes it an explicit part of the programming model. Introducing state into the programming model allows to largely automate the management of state and state transitions—tasks which had to be performed manually before. The elimination of manually written code for the management of state-keeping variables and state-based function demultiplexing allows programmers to focus on the logical structure of the application and removes a typical source of error. 1.4.3 Memory-Efficient State Variables Even in state-based models, variables can be a convenient means for keeping program state, for example, to model a very large state space that would be impractical to specify explicitly or to store state that does not directly affect the node’s behavior. Instead of having to revert to wasteful global variables (as in the event-driven model), OSM supports variables that can be attributed to an explicitly modeled state. This state can then be utilized to provide a novel scope and lifetime qualifier for these variables, which we call state variables. State variables exist and can share information—even over multiple action invocations— when their associated state is assumed at runtime. During compilation, state variables of mutually exclusive states are automatically mapped to overlapping
1.5. Thesis Organization 5 memory regions. The memory used to store state variables can be managed automatically by the compiler, very much like automatic variables in procedural languages. However, this mapping is purely static and thus requires neither a runtime stack nor dynamic memory management. State variables can reduce or even eliminate the need to use global variables or manually memory-managed variables for modeling temporary program state. Since state variables provide automatic memory management, they are not only more convenient to use but also more memory efficient and less error-prone. Because of state variables OSM programs are generally more memory efficient compared to event-driven programs. 1.4.4 Light-Weight Execution Environment The state-based control structure of OSM programs can be automatically compiled into conventional event-driven programs. Generally, the automatic transformation, which is implemented by the OSM compiler, works like manually coding state in event-driven systems. In the transformation, however, the program-state space is automatically reduced and then converted into a memory-efficient representation as variables of the event program. State-specific actions of OSM are transformed into functions of a host language. Then, code for dispatching the control flow to the generated functions is added to the actions of the event program. After the transformation into event-driven code, OSM programs can run in the same efficient execution environments as conventional event-driven systems. Thus, the state-based model does not incur more overhead than the (very efficient) event-driven model and requires only minimal operating system support. 1.4.5 Evaluation We use the state-based OSM language to show that a first class abstraction of state can indeed support well-structured and modular program specifications. We also show that an explicit abstraction of state can support memory-efficient programming. To do so, we present an implementation of state variables for the OSM compiler. More concretely, we present an algorithm deployed by our OSM compiler that automatically reclaims unused memory of state variables. Finally, we show that code generated from OSM specifications can run in the very light-weight event-driven execution environment on BTnodes. 1.5 Thesis Organization The remainder of this dissertation is structured as follows. In Chapter 2 we discuss general aspects of wireless sensor networks. First we characterize wireless sensor networks and present envisioned as well as existing applications. Then we discuss state-of-the-art sensor-node hardware, ranging from early research prototypes to commercialized devices. In Chapter 3 we present a general background on programming individual sensor nodes. We start by discussing some general requirements of sensor-node
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182 Bibliography [11] Charles Andr
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186 Bibliography [60] Jason Lester
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A State-Based Programming Model for Wireless Sensor Networks

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