  With the advent of huges volumes of data produced in the form of fast
streams, real-time machine learning has become a challenge of relevance
emerging in a plethora of real-world applications. Processing such fast streams
often demands high memory and processing resources. In addition, they can be
affected by non-stationary phenomena (concept drift), by which learning methods
have to detect changes in the distribution of streaming data, and adapt to
these evolving conditions. A lack of efficient and scalable solutions is
particularly noted in real-time scenarios where computing resources are
severely constrained, as it occurs in networks of small, numerous,
interconnected processing units (such as the so-called Smart Dust, Utility Fog,
or Swarm Robotics paradigms). In this work we propose LUNAR, a streamified
version of cellular automata devised to successfully meet the aforementioned
requirements. It is able to act as a real incremental learner while adapting to
drifting conditions. Extensive simulations with synthetic and real data will
provide evidence of its competitive behavior in terms of classification
performance when compared to long-established and successful online learning
methods.
