A State-Based Programming Model for Wireless Sensor Networks

7 State-based Programming in
Practice
The thesis formulated in the introduction of this dissertation was that the statebased
programming model incurs as little overhead as the event-based model,
yet allows to program more memory efficiently and allows to specify programs
more structured and more modular compared to event-driven programs. In the
following sections we will support this thesis.
Before turning to concrete examples, we present an intuitive approach to motivate
our claim in Sect. 7.1. We explain how event-driven programs can be
formulated as trivial OSM programs containing a single state only. Clearly, such
programs do not exhibit the benefits of state-based programming. However, the
initially trivial programs are easily refined, increasing their structure, modularity,
and memory efficiency. Such refined programs, on the other hand, cannot be
translated back in the event-driven model without loosing such benefits again.
In Sect. 7.2 we show how the structure and modularity of sensor-node programs
benefits from OSM using a concrete example from the literature. We will
compare the reimplementation of a large part of the EnviroTrack middleware in
OSM and compare it to the original NesC implementation. We will compare the
two implementations with respect to state management, accidental concurrency,
and state-bound resource initialization. We show that with OSM manual state
management can be removed entirely in favor of an explicit, high-level state notation.
As a result the programs are much more concise. In our example, we
show that the size of the OSM re-implementation is reduced by 31% (measured
in lines of code) with respect to the original nesC implementation.
In Sect. 7.3 we will illustrate how OSM can increase the memory efficiency of
programs compared to event-driven programs. Concretely, we will show how
state variables foster the reuse of memory for temporary data structures using
a small example. The memory savings achievable by using state variables very
much depend on the program structure. While very high memory savings can
be achieved in theory, we expect savings of 10 to 25% in real-world programs.
Sect. 7.4 summarizes this chapter.
7.1 An Intuitive Motivation
Traditional event-based programs can be formulated in OSM as depicted in
Fig. 7.1. There is a single state S0, which has attached all global variables vari
of the event-based program. For each possible event ej there is a self transition
with an associated action outj(), which has access to ej and to all state variables
of S0. Hence, OSM can be considered a natural extension of event-based programming.