  Energy Efficiency of a wireless sensor network (WSN) relies on its main
characteristics, including hop-number, user's location, allocated power, and
relay. Identifying nodes, which have more impact on these characteristics, is,
however, subject to a substantial computational overhead and energy
consumption. In this paper, we proposed an active learning approach to address
the computational overhead of identifying critical nodes in a WSN. The proposed
approach can overcome biasing in identifying non-critical nodes and needs much
less effort in fine-tuning to adapt to the dynamic nature of WSN. This method
benefits from the cooperation of clustering and classification modules to
iteratively decrease the required number of data in a typical supervised
learning scenario and to increase the accuracy in the presence of uninformative
examples, i.e., non-critical nodes. Experiments show that the proposed method
has more flexibility, compared to the state-of-the-art, to be employed in large
scale WSN environments, the fifth-generation mobile networks (5G), and
massively distributed IoT (i.e., sensor networks), where it can prolong the
network lifetime.
