A State-Based Programming Model for Wireless Sensor Networks

9 Conclusions and Future Work
In this final chapter, we conclude by summarizing the contributions of our work
and by discussing its limitations. We also propose future work addressing concrete
limitations of our approach.
9.1 Conclusions
Event-driven programming is a popular paradigm in the domain of sensor networks
in general. For sensor networks that are operating at the very low end
of the resource spectrum it is in fact the predominant programming model. Unlike
the multi-threaded programming model, system support for event-driven
programming requires very little of a system’s resources. The event-driven programming
model has been adopted by a large number of programming frameworks
for sensor networks, among them TinyOS / NesC, which currently may
be the most popular of all.
Despite its popularity, the event-driven programming model has significant
shortcomings. Particularly in large and complex programs these shortcomings
lead to issues with the readability and structure of the program code, its modularity
and correctness, and, ironically, also the memory efficiency of the developed
programs. Concretely, an event-driven program typically uses more RAM
as a functionally equivalent sequential program because in the event-driven
model a lot of temporary data has to be stored in global variables. A sequential
program would use local variables instead, which are automatically memory
managed and thus their memory is reused.
With respect to these problems, the main contribution of this dissertation is to
show how the event-based model can be extended so that it allows to specify
well-structured, modular, and memory-efficient programs, yet requires as little
runtime support as the original model. We have significantly improved sensornode
programming by extending the event-driven model to a state-based model
with a explicit notion of hierarchical and concurrent program states. We have
also introduced a novel technique to use states as a scoping and lifetime qualifier
for variables, so they can be automatically memory managed. This can lead to
significant memory savings in temporary data structures. In the following we
will list our contributions towards our solution in more detail.
9.2 Contributions
Below we will summarize our contributions towards the problem analysis, the
solution approach and its implementation, as well as the evaluation. Some contributions
have also been published in [21, 72, 74].