Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization

Abstract

Location is key to spatialize internet-of-things (IoT) data. However, it is challenging to use low-cost IoT devices for robust unsupervised localization (i.e., localization without training data that have known location labels). Thus, this paper proposes a deep reinforcement learning (DRL) based unsupervised wireless-localization method. The main contributions are as follows. (1) This paper proposes an approach to model a continuous wireless-localization process as a Markov decision process (MDP) and process it within a DRL framework. (2) To alleviate the challenge of obtaining rewards when using unlabeled data (e.g., daily-life crowdsourced data), this paper presents a reward-setting mechanism, which extracts robust landmark data from unlabeled wireless received signal strengths (RSS). (3) To ease requirements for model re-training when using DRL for localization, this paper uses RSS measurements together with agent location to construct DRL inputs. The proposed method was tested by using field testing data from multiple Bluetooth 5 smart ear tags in a pasture. Meanwhile, the experimental verification process reflected the advantages and challenges for using DRL in wireless localization.

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• Distributed Reconfigurable Intelligent Surfaces for Energy Efficient Indoor Terahertz Wireless Communications
• DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection
• A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals
• On the Ground and in the Sky: A Tutorial on Radio Localization in Ground-Air-Space Networks
• Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge?
• Soft Constrained Autonomous Vehicle Navigation using Gaussian Processes and Instance Segmentation
• Deep Transfer Learning for WiFi Localization