Data fault detection for wireless sensor networks using multi-scale PCA method

Wireless Sensor Networks (WSNs) are essential for real-time monitoring and control, but sensor faults can compromise data integrity and reduce the reliability of decisions. Supervised fault detection techniques typically require large amounts of labeled data, which are often unavailable in real-world scenarios. This paper proposes a data-efficient, unsupervised fault detection method that combines an LSTM-Attention Autoencoder with a dynamic thresholding mechanism. The model captures temporal patterns in normal sensor behavior and detects anomalies through context-aware reconstruction error analysis. Unlike conventional static thresholds, the dynamic approach adapts to changing sensor conditions, enhancing robustness in noisy or drifting environments. Experimental results on a synthetic dataset generated from real temperature sensor readings show that the proposed method achieves an F1-score of 98.90% and a recall of 99.24%, outperforming baseline methods including plain autoencoders, LSTM autoencoders, isolation forest, and one-class SVM. These findings demonstrate the model's effectiveness for real-time, label-free fault detection in resource-constrained WSNs.

Outlier detection is an important task for intrusion detection and fault diagnosis in wireless sensor networks (WSNs). Outliers in sensed data may be caused due to compromised or malfunctioning sensor nodes. In this paper, we propose a centralized and a distributed approach based on the principal component analysis (PCA) for outlier detection in WSNs. In the distributed approach, we partition the network into multiple groups of sensor nodes. Each group has a group head and several member nodes. Every member node uses a fixed- width clustering algorithm and sends a description of its local sensed data to the group head. The group head then applies a distributed PCA to establish a global normal pattern and detect outliers. This pattern is periodi- cally updated using weighted coefficients. We compare the performance of the centralized and distributed approaches based on the real sensed data collected by 54 Mica2Dot sensors deployed in Intel Berkeley Re- search Lab. The experimental results show that the distributed approach reduces both communication over- head and energy consumption, while achieving comparable accuracy.

A wireless sensor network (WSN) is a large collection of sensor nodes with limited power supply and constrained computational capability. Clustering routing method in wireless sensor networks has been considered as an important field of research recently to prolong the network lifetime of WSNs. We present a novel clustering method that can balance the energy consumption and extend the lifetime of WSN. Network nodes can be divided into densely connected subgroups through the algorithm of detecting community structure in complex networks. Moreover, the role of cluster-head is scheduled among the cluster members according to the residual energy of nodes, and then the cluster heads send the data to the sink directly. Based on the community clustering strategy, a novel routing protocol, called community structure clustering routing protocol (CSCR), has been raised for WSN. Performance evaluation has shown that the proposed method can achieve improvement compared with LEACH and SEP.

An energy-efficient distributed detection method for wireless sensor networks is considered in this paper. It is assumed that a group of sensors observe the same quantity in independent additive observation noises with possibly different variances. We assume that the links between the nodes and the fusion center are subject to Rayleigh fading wireless channels. At the fusion center, incorporating the effect of fading in the detection process, a decision fusion rule based on the linear minimum mean square error(LMMSE) is investigated. In this paper, two types of constraints are considered: 1) transmission power constraints at the nodes, and 2) the communication channel between the nodes and the decision center. we propose a optimal distributed estimation algorithm in energy-constrained wireless sensor networks. By numerical examples, we illustrate the performance of the proposed serial detection scheme.

Due to the major operating restrictions, ensuring security is the fundamental problem of Wireless Sensor Networks (WSNs). Because of their inadequate security mechanisms, WSNs are indeed a simple point for malware (worms, viruses, malicious code, etc.). According to the epidemic nature of worm propagation, it is critical to develop a worm defense mechanism in the network. This concept aims to establish novel malware detection in WSN that consists of several phases: “(i) Preprocessing, (ii) feature extraction, as well as (iii) detection”. At first, the input data is subjected for preprocessing phase. Then, the feature extraction takes place, in which principal component analysis (PCA), improved linear discriminant analysis (LDA), and autoencoder-based characteristics are retrieved. Moreover, the retrieved characteristics are subjected to the detection phase. The detection is performed employing combined shallow learning and DL. Further, the shallow learning includes decision tree (DT), logistic regression (LR), and Naive Bayes (NB); the deep learning (DL) includes deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Here, the DT output is given to the DNN, LR output is subjected to CNN, and the NB output is given to the RNN, respectively. Eventually, the DNN, CNN, and RNN outputs are averaged to generate a successful outcome. The combination can be thought of as an Ensemble classifier. The weight of the RNN is optimally tuned through the Self Improved Shark Smell Optimization with Opposition Learning (SISSOOL) model to improve detection precision and accuracy. Lastly, the outcomes of the suggested approach are computed in terms of different measures.

Sensor nodes fault detection for agricultural wireless sensor networks based on NMF

How to sense and monitor the environment with high quality is an important research subject in the Internet of Things (IOT). This paper deals with the important issue of the balance between the quality of target detection and lifetime in wireless sensor networks. Two target-monitoring schemes are proposed. One scheme is Target Detection with Sensing Frequency K (TDSFK), which distributes the sensing time that currently is only on a portion of the sensing period into the entire sensing period. That is, the sensing frequency increases from 1 to K. The other scheme is Target Detection with Adjustable Sensing Frequency (TDASF), which adjusts the sensing frequency on those nodes that have residual energy. The simulation results show that the TDASF scheme can improve the network lifetime by more than 17.4% and can reduce the weighted detection delay by more than 101.6%.

Wireless Sensor Network (WSN) technology has recently moved out of controlled laboratory settings to real-world deployments. Many of these deployments experience high rates of failure. Common types of failure include node failure, link failure, and node reboot. Due to the resource constraints of sensor nodes, existing techniques for fault detection in enterprise networks are not applicable. Previously proposed WSN fault detection algorithms either rely on periodic transmission of node status data or inferring node status based on passive information collection. The former approach significantly reduces network lifetime, while the latter achieves poor accuracy in dynamic or large networks. Herein, we propose Sequence-Based Fault Detection (SBFD), a novel framework for network fault detection in WSNs. The framework exploits in-network packet tagging using the Fletcher checksum and server-side network path analysis to efficiently deduce the path of all packets sent to the sink. The sink monitors the extracted packet paths to detect persistent path changes which are indicative of network failures. When a failure is suspected, the sink uses control messages to check the status of the affected nodes. SBFD was implemented in TinyOS on TelosB motes and its performance was assessed in a testbed network and in TOSSIM simulation. The method was found to achieve a fault detection accuracy of 90.7% to 95.0% for networks of 25 to 400 nodes at the cost of 0.164% to 0.239% additional control packets and a 0.5% reduction in node lifetime due to in-network packet tagging. Finally, a comparative study was conducted with existing solutions.

Wireless sensor networks (WSNs) consist of many sensor nodes that are densely deployed throughout a randomized geographical area to monitor, detect, and analyze various physical phenomena. The primary obstacle encountered in WSNs pertains to the significant reliance of sensor nodes on finite battery power for wireless data transfer. Sensors as a crucial element inside Cyber-Physical Systems (CPS) renders them vulnerable to failures arising from intricate surroundings, substandard manufacturing, and the passage of time. Various anomalies can appear within WSNs, mostly attributed to defects such as hardware and software malfunctions and anomalies and assaults initiated by unauthorized individuals. These anomalies significantly impact the overall integrity and completeness of the data gathered by the networks. Therefore, it is imperative to provide a critical mechanism for the early detection of faults, even in the presence of constraints imposed by the sensor nodes. Machine Learning (ML) techniques encompass a range of approaches that may be employed to identify and diagnose sensor node faults inside a network. This paper presents a novel Energy-aware and Context-aware fault detection framework (ECFDF) that utilizes the Extra-Trees algorithm (ETA) for fault detection in WSNs. To assess the effectiveness of the suggested methodology for identifying context-aware faults (CAF), a simulated WSN scenario is created. This scenario consists of data from humidity and temperature sensors and is designed to emulate severe low-intensity problems. This study examines six often-seen categories of sensor fault, including drift, hard-over/bias, spike, erratic/precision, stuck, and data loss. The ECFDF approach utilizes an Energy-Efficient Fuzzy Logic Adaptive Clustering Hierarchy (EE-FLACH) algorithm to select a Super Cluster Head (SCH) within WSNs. The SCH is responsible for achieving optimal energy consumption within the network, and this selection process facilitates the early detection of faults. The results of the simulation indicate that the ECFDF technique has superior performance in terms of Fault Detection Accuracy (FDA), False-Positive Rate (FPR), and Mean Residual Energy (MRE) when compared to other detection and classification methods.

This paper characterizes an approach to data compression and defect isolation for the multi-variable process by introducing the wavelet principal component analysis (PCA) and the genetic algorithms. In the defect analysis process, data compression is a critical phase. In our case of study, this can be achieved through the wavelet PCA. We can, through the use of such a technique, filter the data noise and the measurement errors by combining the PCA and the wavelet analysis. The genetic algorithm optimization feature is then employed in structuring the residues of the defect isolation. We present the principle of the PCA as well as the wavelet compression property. Then, we describe both the defect isolation classical method by structuring the residues and the principle of optimizing a problem by the genetic algorithms. The proposed approach is implemented on a statistic system and the Tennessee Eastman process. In comparison with other previous methods, the obtained results mirror the performance of the approach to defect isolation.

In paper mill plants, the competition for increasing efficiency and reducing costs is a primary purpose. Fault detection and diagnosis can help by minimize the loss of production. In particular for the stock preparation sub-process a signal based fault detection and isolation procedure is developed. Multi-Scale Principal Component Analysis (MSPCA) is used to monitor some critical variables of the stock preparation of a paper mill plant in order to diagnose faults and malfunctions. MSPCA simultaneously extracts both, cross correlation across the sensors (PCA approach) and auto-correlation within a sensor (Wavelet approach). The advantage of MSPCA is validated on considered paper mill plant where several sensors are installed to control and monitor the automation system.

Event detection in wireless sensor networks is a sophisticated method for processing sampled data directly on the sensor nodes, thereby reducing the need for multihop communication with the base station of the network. In contrast to application-agnostic compression or aggregation techniques, event detection pushes application-level knowledge into the network. In-network event detection - especially the distributed form involving multiple sensor nodes - has thus an exceptional potential to increase energy efficiency, thus prolonging the lifetime of the network. In this article, we summarize recently proposed system architectures and algorithms employed for event detection in wireless sensor networks. On the example of the AVS-Extrem platform, we illustrate how energy-efficient event detection can be implemented through a combination of custom hardware design and distributed event detection algorithms. We then continue to present a brief evaluation of the detection accuracy and the energy consumption that is achievable by current systems.

Wireless Sensor Networks (WSNs) are increasingly vulnerable to sophisticated cyber threats, necessitating advanced intrusion detection systems (IDS) that balance high accuracy with interpretability. This paper presents a Knowledge-Improved Hybrid Deep Neural Network-Kolmogorov Arnold Network (DNN-KAN) Framework for intrusion detection in WSNs, integrating data-driven learning with domain-specific knowledge to enhance detection performance. The proposed framework preprocesses and merges multiple datasets (WSN, NSL-KDD, and CICIDS2017), extracts features using Principal Component Analysis (PCA), and constructs a knowledge graph to embed expert-defined rules via Graph Convolutional Networks (GCNs). The hybrid architecture employs a DNN for hierarchical feature extraction and replaces the fully connected layer with a KAN layer, leveraging the Kolmogorov-Arnold Theorem to decompose complex relationships into interpretable univariate functions. Evaluated on key metrics, the model achieves 99.87% accuracy, 99.85% precision, and a 0.9985 ROC-AUC score, significantly outperforming traditional DNNs, standalone KANs, and state-of-the-art models like CNN-LSTM and Transformer-based IDS. By incorporating domain knowledge into the loss function, the framework reduces false positives and improves generalization, even with limited training data. This work demonstrates that combining knowledge-driven constraints with hybrid neural architectures can yield highly accurate, interpretable, and robust intrusion detection for WSN security.

A wide range of applications have started to use Wireless Sensor Network (WSN) as an information collection and monitoring tool. Reliable and accurate performance of sensor nodes is necessary in critical applications. For characteristics of the WSN data stream environment, the limitations of conventional principal component analysis (PCA) method which depend on the fixed model in practical application are analyzed, an online fault detection framework in WSN nodes based on recursive PCA (RPCA) model is proposed. The algorithm applies RPCA algorithm to sequentially update the model representing normal behavior of the sensed data adaptively and realize the online fault detection. Experiments with real data show that our online fault detection algorithm not only tracks the normal changes well, but also achieves good detection performance for typical node faults.

A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.