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Wireless sensor networks (WSN) acts as the backbone of Internet of Things(IoT) technology. In WSN, field sensing and fusion are the most commonly seenproblems, which involve collecting and processing of a huge volume of spatialsamples 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 dataredundancy with prescribed fusion accuracy. In this paper, an integratedcommunication and computation framework based on meta-learning is proposed toenable adaptive field sensing and reconstruction. It consists of astochastic-gradient-descent (SGD) based base-learner used for the field modelprediction aiming to minimize the average prediction error, and a reinforcementmeta-learner aiming to optimize the sensing decision by simultaneouslyrewarding the error reduction with samples obtained so far and penalizing thecorresponding communication cost. An adaptive sensing algorithm based on theabove two-layer meta-learning framework is presented. It actively determinesthe next most informative sensing location, and thus considerably reduces thespatial samples and yields superior performance and robustness compared withconventional schemes. The convergence behavior of the proposed algorithm isalso comprehensively analyzed and simulated. The results reveal that theproposed field sensing algorithm significantly improves the convergence rate.

Learn to Sense: a Meta-learning Based Sensing and Fusion Framework for Wireless Sensor Networks