Model-driven development with optimization of non-functional constraints in sensor network

Current methods for sensor network programming lead developers to cope with not only high-level concerns such as application logic, quality of service, adaptability, reliability, but also low-level mechanism like ad-hoc routing and communication, resource management, data filtering, and aggregation. This makes the development of software expensive and error-prone, even for expert programmers. Model-driven development (MDD) allows designers to model their systems at different abstraction levels and thus reduces the complexity of the development task. However MDD does not help system architects to solve the problem of optimizing the trade-off between different constraints such as memory usage, latency, and power consumption in the application design process. This paper proposes an approach to address this issue by combining MDD with Evolutionary Algorithms (EA). In our approach various metamodels of the system to be developed are generated and evolved, the optimal model is selected in terms of evaluating the trade-off between different constraint criteria and performance value.