Energy is a primary constraint in the design and deployment of wireless sensor networks (WSNs), since sensor nodes are typically powered by batteries with a limited capacity. Energy efficiency is generally achieved by reducing radio communication, for instance, limiting transmission/reception of data. Data compression and distributed aggregation can be a valuable tool in this direction. The limited resources available in a sensor node demand, however, the development of specifically designed algorithms.

In this talk we try to tackle the issue of reducing power consumption in these tiny devices firstly using a novel aggregation technique exploiting some basic fuzzy logic principles.

Then, taking lessons from JPEG, we analyse a simple lossless entropy compression algorithm by highlighting how its low complexity and the small amount of memory required for its execution make this algorithm particularly suited to be used on available commercial sensor nodes.

Finally, in the last part of the talk we outline a multiobjective evolutionary optimization framework able to transform the previous lossless compression algorithm into a lossy one, without introducing complexity overhead and sensible distortion in the reconstructed signal.