Data-Driven Fuzzy Modeling for Wireless Ad-hoc Networks

The present book considers Wireless Ad-hoc Networks (WANETs) in the context of 4G network technologies and Ambient Intelligence. The book proposes how to reduce the power dissipation on mobile devices due to signal transmittion and increased computational requirements due to more sophisticated services. The book lustrates how to manage uncertainties and introduce some flexibility in determine service protocol algorithms by unsupervised learning from dynamic network topology context. Being context aware, services in WANETs enhance their underlying protocols to evolve in the future exploiting data-driven evolving fuzzy modeling.