ReviewSensors (Basel, Switzerland)2022
Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- An energy-trust balanced and attack-resilient cluster head election scheme for resource-constrained wireless sensor networks.Scientific reports · 2026Article
- Quantum-inspired hybrid optimization framework for energy-efficient clustering and routing in wireless sensor networks.Scientific reports · 2026Article
- A Lightweight IDS Based on Blockchain and Machine Learning for Detecting Physical Attacks in Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2026Article
- A Secure and Lightweight ECC-Based Authentication Protocol for Wireless Medical Sensors Networks.Sensors (Basel, Switzerland) · 2025Article
- Machine learning based dynamic trust estimation framework for Securing wireless sensor networks.Scientific reports · 2025Article
- An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial-Temporal Features.Sensors (Basel, Switzerland) · 2025Article
- Reinforcement Q-Learning-Based Adaptive Encryption Model for Cyberthreat Mitigation in Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2025Article
- A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes.Sensors (Basel, Switzerland) · 2025Article
- A Heterogeneity-Aware Semi-Decentralized Model for a Lightweight Intrusion Detection System for IoT Networks Based on Federated Learning and BiLSTM.Sensors (Basel, Switzerland) · 2025Article
- The Intersection of Machine Learning and Wireless Sensor Network Security for Cyber-Attack Detection: A Detailed Analysis.Sensors (Basel, Switzerland) · 2024Review
- Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2024Article
- SentinelFusion based machine learning comprehensive approach for enhanced computer forensics.PeerJ. Computer science · 2024Article
- RETRACTED: Optimising barrier placement for intrusion detection and prevention in WSNs.PloS one · 2024Article
- Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks.Sensors (Basel, Switzerland) · 2023Article
- An Intelligent Healthcare System Using IoT in Wireless Sensor Network.Sensors (Basel, Switzerland) · 2023Article
- Secure Edge-Based Energy Management Protocol in Smart Grid Environments with Correlation Analysis.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.