  Wireless Sensor Networks (WSNs) have become increasingly valuable in various
civil/military applications like industrial process control, civil engineering
applications such as buildings structural strength monitoring, environmental
monitoring, border intrusion, IoT (Internet of Things), and healthcare.
However, the sensed data generated by WSNs is often noisy and unreliable,
making it a challenge to detect and diagnose anomalies. Machine learning (ML)
techniques have been widely used to address this problem by detecting and
identifying unusual patterns in the sensed data. This survey paper provides an
overview of the state of the art applications of ML techniques for data anomaly
detection in WSN domains. We first introduce the characteristics of WSNs and
the challenges of anomaly detection in WSNs. Then, we review various ML
techniques such as supervised, unsupervised, and semi-supervised learning that
have been applied to WSN data anomaly detection. We also compare different
ML-based approaches and their performance evaluation metrics. Finally, we
discuss open research challenges and future directions for applying ML
techniques in WSNs sensed data anomaly detection.
