Large-scale, distributed sensor networks are the projected weapon of choice for future pervasive computing applications such as, for example, envi- ronment monitoring, surveillance, (big) data mining and patient monitoring. However, state-of-the-art approaches face major challenges: specialized sen- sors are expensive and require careful calibration. Hardware sensors operating in uncertain, harsh environments eventually suffer from stress, ageing and phys- ical damage, which leads to unforeseen effects that can render the device and the data recorded useless. Highly-tuned data processing algorithms are often not scalable and are not robust against faulty sensors delivering wrong data. Gener- ally, systems can only adapt, if at all, in some predefined limited ways and are not capable of autonomously “inventing” new ways of adapting to unexpected changes in their internal and external environment
