Paper Info
Title
Learn to Sense: A Meta-Learning-Based Sensing and Fusion Framework for Wireless Sensor Networks
Abstract
Wireless sensor networks (WSNs) act 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.
Year
DOI
Venue
2019
10.1109/JIOT.2019.2919225
IEEE Internet of Things Journal
Keywords
DocType
Volume
Robot sensing systems,Wireless sensor networks,Internet of Things,Convergence,Adaptation models,Feature extraction
Journal
6
Issue
ISSN
Citations 
5
2327-4662
1
PageRank 
References 
Authors
0.35
0
5
Name
Order
Citations
PageRank
Hui Wu183.52
Z. Zhang22308198.54
Chunxu Jiao363.48
Chunguang Li422816.58
Tony Q. S. Quek53621276.75