Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Evolutionary Based Approaches in Wireless Sensor Networks: A Survey
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 05
Multi-objective Cross-Layer Algorithm for Routing over Wireless Sensor Networks
SENSORCOMM '09 Proceedings of the 2009 Third International Conference on Sensor Technologies and Applications
A compilation framework for macroprogramming networked sensors
DCOSS'07 Proceedings of the 3rd IEEE international conference on Distributed computing in sensor systems
SEUS'07 Proceedings of the 5th IFIP WG 10.2 international conference on Software technologies for embedded and ubiquitous systems
Macro-programming wireless sensor networks using Kairos
DCOSS'05 Proceedings of the First IEEE international conference on Distributed Computing in Sensor Systems
Wireless sensor network application development: an architecture-centric MDE approach
ECSA'07 Proceedings of the First European conference on Software Architecture
Modeling and analyzing performance of software for wireless sensor networks
Proceedings of the 2nd Workshop on Software Engineering for Sensor Network Applications
Proceedings of the 2nd Workshop on Software Engineering for Sensor Network Applications
Proceedings of the 3rd workshop on Biologically inspired algorithms for distributed systems
Model driven development for data-centric sensor network applications
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
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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.