# Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

arXiv: https://arxiv.org/abs/1405.4463 · HTML: https://mlsanity.com/paper/1405.4463/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2014.

## Built on by (most cited first, 9 of 9)

- [UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach](https://mlsanity.com/paper/2106.15734/) (2021)
- [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2102.07572/) (2021)
- [Machine Learning in NextG Networks via Generative Adversarial Networks](https://mlsanity.com/paper/2203.04453/) (2022)
- [The Impact of Feature Scaling In Machine Learning: Effects on Regression and Classification Tasks](https://mlsanity.com/paper/2506.08274/) (2025)
- [Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2210.13547/) (2022)
- [Continuous Deep Q-Learning with Simulator for Stabilization of Uncertain Discrete-Time Systems](https://mlsanity.com/paper/2101.05640/) (2021)
- [Performance Evaluation of Machine Learning Techniques for DoS Detection in Wireless Sensor Network](https://mlsanity.com/paper/2104.01963/) (2021)
- [Transformer-Based Cognitive Radio: Adaptive Modulation Strategies Using Transformer Models](https://mlsanity.com/paper/2601.10519/) (2026)
- [An AI-Driven Framework for Energy-Efficient Environmental Monitoring in Smart Cities Using Edge Intelligence](https://mlsanity.com/paper/2605.22824/) (2026)

## Abstract (arXiv)

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.