ML-Based Wireless Sensor Network Projects
Wireless networks like MANETs, WSNs, and IoT are widely used in critical applications but face security threats. Machine Learning (ML) can enhance WSN performance by detecting anomalies, optimizing routing, and improving energy efficiency.
Background of Wireless Sensor Networks
Wireless Sensor Networks are deployed for monitoring, data collection, and communication in various applications. Challenges like network congestion, energy consumption, and security threats require intelligent solutions. ML-based approaches provide effective solutions to optimize network performance and security.
Machine Learning for WSN
Machine Learning algorithms such as SVM, Random Forest, and Deep Learning help identify anomalies, optimize routing, and predict network failures in WSNs. This ensures secure, energy-efficient, and reliable wireless communication.
FAQs on ML-Based WSN Projects
Q1: Why use ML in WSN? ML improves efficiency, detects anomalies, and enhances network security.
Q2: Which ML models are used? Models like Random Forest, SVM, and Deep Learning are widely applied.
Q3: Which simulation tools are used? Tools like ns-3, OMNeT++, and Cooja Contiki help simulate WSN scenarios.
Q4: What metrics are evaluated? Metrics include accuracy, energy efficiency, throughput, and delay.
Q5: Can ML be applied in IoT WSN? Yes, lightweight ML models can optimize IoT-enabled sensor networks.
ML-Based WSN Detection System
An Intrusion Detection System identifies anomalies and malicious behavior in wireless sensor networks. Nodes that appear out of range are flagged for potential attacks.
Two modules are used: centralized and distributed. The centralized module validates node distances and discards duplicate packets. The distributed module shares neighbor information and forwards packets efficiently. ML algorithms ensure accurate detection and mitigation.
Experiments
Experiments measure detection rates, energy consumption, and packet overhead. Centralized modules often deliver better results, making them suitable for large-scale ML-based WSN projects.
