A State-Based Programming Model for Wireless Sensor Networks

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80 Chapter 4. Event-driven Programming in Practice 4.3.1 Automatic State Management First off all, to avoid manual state management, a sensor-node programming model requires an explicit abstraction of what we have presented as phases: a common context in which multiple actions can perform. Furthermore it requires mechanisms to compose phases sequentially, hierarchically, and concurrently. A natural abstraction of program phases are states of finite state machines (FSM). Finite state machines—in their various representations—have been extensively used for modeling and programming reactive embedded systems. Reactive embedded systems share many of the characteristics and requirements of sensor nodes, such as resource efficiency and complex program anatomies. Particularly the Statecharts model and its many descendants have seen widespread use. The Statecharts model combines elements form the eventdriven programming model (events and associated actions) as well as from finite state machines (states and transitions among states). To these elements it adds state hierarchy and concurrency. As such, the Statecharts model is well suited to describe sensor-node programs. Statecharts can help to solve the manual state-management issues. Particularly, it allows to specify the program’s control flow in terms of the model’s abstractions (namely transitions between possibly nested and concurrent states), rather then requiring the programmer to specify the control flow programmatically. Indeed, we have already used the graphical Statechart notation to illustrate the flow of control through phases in the examples throughout this chapter. The Statechart model builds the foundation for our sensor-node programming model, which we will present in the next chapter. 4.3.2 Automatic Stack Management However, neither the Statecharts model nor any of its descendants do solve the issue of manual stack management. As we have detailed previously, phases of sensor-node programs have associated data state. Statecharts and Statechartlike models typically do not provide an abstraction for such data state. The few models which do, however, lack a mechanism to manage such state automatically, that is, to initialize the data state upon state entry (e.g., by allocating and initializing a state specific variable) and to release it upon exit. The same holds for all (de)initializations related to phases, such as setting up or shutting down timers, switching on or off radios and sensors, etc. In general, Statechart and descendant programming models do not support the (de)initialization of states. To fix these issues, we propose to make two important additions to the basic Statechart model: Firstly, states have dedicated initialization and deinitialization functions. These functions are modeled as regular actions. They are associated with both a state and a transition, and are executed within the context of the associated state. Secondly, states can have associated data state. This state can be considered as the local variables of states. The lifetime and scope of state variables are bound to their state. State variables are an application of general (de)initialization functions: data state is allocated and initialized in the state’s initialization function and released upon the state’s exit in its deinitialization function. (In our proposed model however, initialization and release of state variables is transparent to the programmer.) However, state variables are more
4.3. Extending the Event Model: a State-Based Approach 81 than semantic sugar: modeling data state with the explicit state-variable abstraction allows compile-time analysis of the program’s memory requirements and only requires static memory management for their implementation in the system software—two invaluable features for sensor-node programing.
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158 Appendix A. OSM Code Generation
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182 Bibliography [11] Charles Andr
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184 Bibliography [36] Cormac Duffy,
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186 Bibliography [60] Jason Lester
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188 Bibliography [85] Alan Mainwari
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190 Bibliography [110] Karsten Stre
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A State-Based Programming Model for Wireless Sensor Networks

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