  Wireless sensor networks (WSN) acts as the backbone of Internet of Things
(IoT) technology. In WSN, field sensing and fusion are the most commonly seen
problems, which involve collecting and processing of a huge volume of spatial
samples in an unknown field to reconstruct the field or extract its features.
One of the major concerns is how to reduce the communication overhead and data
redundancy with prescribed fusion accuracy. In this paper, an integrated
communication and computation framework based on meta-learning is proposed to
enable adaptive field sensing and reconstruction. It consists of a
stochastic-gradient-descent (SGD) based base-learner used for the field model
prediction aiming to minimize the average prediction error, and a reinforcement
meta-learner aiming to optimize the sensing decision by simultaneously
rewarding the error reduction with samples obtained so far and penalizing the
corresponding communication cost. An adaptive sensing algorithm based on the
above two-layer meta-learning framework is presented. It actively determines
the next most informative sensing location, and thus considerably reduces the
spatial samples and yields superior performance and robustness compared with
conventional schemes. The convergence behavior of the proposed algorithm is
also comprehensively analyzed and simulated. The results reveal that the
proposed field sensing algorithm significantly improves the convergence rate.
