Optimization techniques for smart integrated sensor networks in environmental monitoring

Sensor networks are an emerging field of research which presents significant system challenges involving the use of large numbers of resource-constrained nodes operating essentially unattended and exposed to potential local communication failures. Current sensor networks address problems of meeting standards for accuracy and also delivering data from remote locations with an appropriate level of spatial and temporal resolution. Today advances in sensor technology, wireless communications and digital electronics make it possible to produce large amount of small-size, low-cost sensors which integrate together sensing, processing, and communication capabilities. The advantages are evident not only in the reduction of size, but also in the increase of functional performance and reliability, and a unit-cost reduction in mass production lines. In this work hybrid evolutionary algorithms are applied to optimize the design of cluster formation in wireless sensor networks, guaranteeing at the same time a full network connectivity and a minimum energy consumption. The proposed techniques have been tested in respect of the most known test functions with good results obtained in all the considered cases, especially for optimization of large domain objective functions. This feature makes these algorithms suitable for a wide range of applications, capable of outperforming classical procedures.