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Sensors
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Reinforcement Learning and Intelligent Systems in Sensor Networks and Applications
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Reinforcement Learning and Intelligent Systems in Sensor Networks and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 763

Special Issue Editor

Dr. Hyunsu Lee

E-Mail Website
Guest Editor
Department of Physiology, School of Medicine, Pusan National University, Yangsan, Republic of Korea
Interests: reinforcement learning; spiking neural networks; information processing; large-language model
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the integration of advanced intelligent systems, specifically reinforcement learning (RL), into the realm of sensor technology. RL has demonstrated powerful potential for optimizing sensor behavior, enhancing adaptability, and enabling decision-making capabilities in dynamic environments.

The proposed Special Issue will cover the following topics:

  • Application of RL in sensor networks for improved data collection and fusion.
  • Intelligent optimization and control of multi-sensor systems.
  • Use of RL to enhance sensing capabilities in IoT environments.
  • AI-enabled sensors with a focus on learning-based adaptation.
  • Reinforcement learning for efficient energy management in sensor systems.
  • Human–computer Interaction in RL-based smart sensor systems.
  • Intelligent navigation and positioning through sensor data fusion.
  • Neuromorphic chips for advanced sensing and decision-making.
  • Energy-efficient systems for autonomous sensing applications.
  • Spiking neural networks in intelligent sensor networks.
  • Machine learning approaches for adaptive sensor networks.
  • Embedded AI in real-time sensor applications.
  • Cognitive sensors for enhanced environment interaction. 
  • Bio-inspired sensing for autonomous systems.
  • Neural processing units for efficient data analysis in sensor systems.

This Special Issue aims to provide a platform for researchers to share innovative methodologies and practical applications that demonstrate how intelligent systems, and specifically reinforcement learning, can address challenges in sensor networks, sensor fusion, navigation, and data processing.

Dr. Hyunsu Lee
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensor networks
  • reinforcement learning
  • intelligent systems

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Published Papers (1 paper)

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Research

26 pages, 7374 KiB  
Article
Noise Resilience of Successor and Predecessor Feature Algorithms in One- and Two-Dimensional Environments
by Hyunsu Lee
Sensors 2025, 25(3), 979; https://doi.org/10.3390/s25030979 - 6 Feb 2025
Viewed by 558
Abstract
Noisy inputs pose significant challenges for reinforcement learning (RL) agents navigating real-world environments. While animals demonstrate robust spatial learning under dynamic conditions, the mechanisms underlying this resilience remain understudied in RL frameworks. This paper introduces a novel comparative analysis of predecessor feature (PF) [...] Read more.
Noisy inputs pose significant challenges for reinforcement learning (RL) agents navigating real-world environments. While animals demonstrate robust spatial learning under dynamic conditions, the mechanisms underlying this resilience remain understudied in RL frameworks. This paper introduces a novel comparative analysis of predecessor feature (PF) and successor feature (SF) algorithms under controlled noise conditions, revealing several insights. Our key innovation lies in demonstrating that SF algorithms achieve superior noise resilience compared to traditional approaches, with cumulative rewards of 2216.88±3.83 (mean ± SEM), even under high noise conditions (σ=0.5) in one-dimensional environments, while Q learning achieves only 19.22±0.57. In two-dimensional environments, we discover an unprecedented nonlinear relationship between noise level and algorithm performance, with SF showing optimal performance at moderate noise levels (σ=0.25), achieving cumulative rewards of 2886.03±1.63 compared to 2798.16±3.54 for Q learning. The λ parameter in PF learning is a significant factor, with λ=0.7 consistently achieving higher λ values under most noise conditions. These findings bridge computational neuroscience and RL, offering practical insights for developing noise-resistant learning systems. Our results have direct applications in robotics, autonomous navigation, and sensor-based AI systems, particularly in environments with inherent observational uncertainty. Full article
(This article belongs to the Special Issue Reinforcement Learning and Intelligent Systems in Sensor Networks and Applications)
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