A State-Based Programming Model for Wireless Sensor Networks

152 Chapter 9. Conclusions and Future Work
9.2.1 Problem Analysis: Shortcomings of Event-driven
Programming
In Chapter 4 we have contributed a thorough analysis of the original eventdriven
model in the context of sensor networks. Firstly, we have analyzed
the two main problems of the existing event-driven programming paradigm.
While the basic problem leading to unstructured and un-modular program code,
namely manual state management, has already been identified and documented
in previous work, the memory inefficiency of event-driven sensor-node programs
incurred by manual state management has not been analyzed before.
Secondly we have contributed by analyzing the anatomy of sensor node applications.
We have found that, though easy and intuitive for small and simple
programs, event-driven programs do not describe typical sensor-node applications
very well. That is, the conceptual models that programmers create in their
minds before implementing a particular program or algorithm does not map
easily to the event-driven programming model. In particular, we have identified
the lack of an abstraction that allows programmers to structure their programs
along the time domain into discrete program phases. We have shown that several
algorithms from the literature are indeed described as systems partitioned
into discrete phases, each having distinctive data state and behavior. The findings
of our problem analysis now allows to better evaluate current and future
programming models for sensor networks.
9.2.2 Solution Approach: State-based Programming with
OSM
To alleviate the problems described above, we have proposed a sensor-node programming
model that is extending the event-driven model with well-known
state-machine abstractions. In Chapter 5 we contribute by presenting such a
model, which we call the OBJECT STATE MODEL (OSM). OSM is based on abstractions
of hierarchically and concurrent state machines. Though they have
been used successfully in embedded-systems programming for several years,
they have not yet been applied to the field of sensor networks.
Besides relying on proven programing abstractions, our OSM model also introduces
the concept of state variables. State variables supports the memoryefficient
storage and use of temporary data. State variables resemble local variables
of sequential programs; both are automatically memory managed and thus
their memory is automatically reused when no longer needed. State variables
can help overcome the inherent memory inefficiency of event-driven programming
for storing temporary data. Besides that, the maximum memory requirements
of all parts of even highly-concurrent programs can be calculated at compile
time. This allows to check whether a particular program can run on a given
sensor node and to easily identify program parts susceptible for optimizations.
Finally, OSM introduces the concept of entry and exit actions (also referred to
as incoming and outgoing actions) to traditional state-machine programming
models. They allow to elegantly and efficiently specify the initialization and
release of state-specific resources, which are common tasks in real-world sensornode
programs.