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

## TL;DR

This paper introduces a meta-learning based framework for adaptive sensing and data fusion in wireless sensor networks, significantly reducing data redundancy and communication costs while improving field reconstruction accuracy.

## Contribution

It proposes a novel two-layer meta-learning framework combining SGD and reinforcement learning for adaptive sensing in WSNs, enhancing efficiency and robustness.

## Key findings

- Reduces spatial samples needed for accurate field reconstruction
- Improves convergence rate of sensing algorithms
- Outperforms conventional sensing schemes in robustness

## Abstract

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.