An Efficient Anomaly Detection Framework for Wireless Sensor Networks Using Markov Process

2025-11-01

AI Summary

To address data anomalies in wireless sensor networks caused by sensor failures, environmental interference, or malicious intrusions, this paper proposes a lightweight, interpretable, real-time anomaly detection framework. Methodologically, it innovatively employs a first-order Markov chain to model the state transition process of sensor readings: continuous measurements are discretized into states, and a state transition probability matrix is constructed; anomalies are identified via a thresholding mechanism based on low-probability transitions—enabling unsupervised, online detection. The framework requires no labeled data and incurs minimal computational overhead, making it suitable for large-scale, resource-constrained IoT deployments. Experiments on the Intel Berkeley Research dataset achieve an F1-score of 0.86, significantly outperforming Z-score, hidden Markov models, and autoencoders. It demonstrates high accuracy and robustness in typical anomaly scenarios, including abrupt temperature spikes and voltage irregularities.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web measurements

Abstract

Wireless Sensor Networks forms the backbone of modern cyber physical systems used in various applications such as environmental monitoring, healthcare monitoring, industrial automation, and smart infrastructure. Ensuring the reliability of data collected through these networks is essential as these data may contain anomalies due to many reasons such as sensor faults, environmental disturbances, or malicious intrusions. In this paper a lightweight and interpretable anomaly detection framework based on a first order Markov chain model has been proposed. The method discretizes continuous sensor readings into finite states and models the temporal dynamics of sensor transitions through a transition probability matrix. Anomalies are detected when observed transitions occur with probabilities below a computed threshold, allowing for real time detection without labeled data or intensive computation. The proposed framework was validated using the Intel Berkeley Research Lab dataset, as a case study on indoor environmental monitoring demonstrates its capability to identify thermal spikes, voltage related faults, and irregular temperature fluctuations with high precision. Comparative analysis with Z score, Hidden Markov Model, and Auto encoder based methods shows that the proposed Markov based framework achieves a balanced trade-off between accuracy, F1 score is 0.86, interoperability, and computational efficiency. The systems scalability and low resource footprint highlight its suitability for large-scale and real time anomaly detection in WSN deployments.

Problem

• Detecting anomalies in wireless sensor networks data
• Identifying sensor faults and environmental disturbances efficiently
• Enabling real-time anomaly detection without labeled data

Innovation

• Markov chain models sensor state transitions
• Detects anomalies using probability threshold without labels
• Achieves high F1 score with low computational cost