Contextual outlier detection for wireless sensor networks

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.

Sensor nodes in Wireless Sensor Networks (WSNs) operate with limited power resources such as small batterieswhich are difficult to be either recharged or replaced in some environments when depleted. Power consumptionrepresents one of the most constraints impact the design of WSNs, leading to various protocols and algorithmsaimed at minimizing the power consumption and extending batteries' lifetime. Sensor nodes in WSNs transmittheir periodic packets continuously to central nodes (receivers) which are responsible for decoding packets andtransmitting them to other communication networks. In addition, sensors usually follow various MAC strategieswhich allow accessing to wireless communication channels. However, sensors may attempt to access thewireless channels at the same time, potentially, leading to collisions among multiple nodes. In fact, central nodesin WSNs most often consume a large amount of power due to the necessity to decode every received packetregardless of the fact that the transmission may suffer from packets collision which impede the networkperformance. Therefore, in the receiver side of WSNs current collision detection mechanisms have largely beenrevolving around direct demodulation and decoding of received packets and deciding on a collision based onsome form of parity bits in each packet for error control. From information theoretic prospective full decoding ofreceived packets with error control bits at central nodes can achieve an efficient usage of network capacity,however, such an approach represents a major burden on power-constrained sensors. This drawback comes fromthe need to expend a significant amount of energy and processing complexity at sink nodes in order tofully-decode a packet, only to discover the packet is illegible due to a collision. In this paper, we propose a morepractical power saving approaches which achieve a significant power saving with low-complexity at the expenseof low throughput losses. Based on studying the statistics of received packets, central nodes can make a fastdecision to detect a collision without the need for full-decoding of the whole received packets. Our novelapproaches not only reduces processing complexity and hence power consumption, but it also reduces the delayincurred to detect a collision since it operates on only a small number of IQ samples in the beginning of areceived packet. In such a paradigm, our approaches operate directly at the output of the receiver’sAnalog-to-Digital-Converter (ADC) and eliminate the need to pass the corrupted packets through the entiredemodulator/decoder line-up. The performance gain of our proposed approach is illustrated through thecomparison between the computational complexity of our Statistical Discrimination (SD) approaches and someexisting Full Decoding (FD) algorithms(note 1). Our results show that the SD approaches has significant powersavings and low computational complexities over existing FD algorithms with low False-Alarm and Missprobabilities, which qualify our SD approaches to be considered as reliable collision detection mechanisms inWSNs. We also show how to tune various design parameters in order to allow a system designer multipledegrees of freedom for design trade-offs and optimization.

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.

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.

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%.

Data reliability in Wireless Sensor Networks (WSNs) has a substantial influence on their smooth functioning and resource limitations. In a WSN, the data aggregated from clustered sensor nodes are forwarded to the base station for analysis. Anomaly Detection (AD) focuses on detecting outlier data to ensure consistency during data aggregation. As WSNs have critical resource limitations concerning energy consumption and sensor node lifetime, AD is supposed to provide data integrity with minimum energy consumption, which has been an active research problem. Hence, researchers are striving for methods to improve the accuracy of data handled with a concern on the constraints of WSNs. This paper introduces a Feed-forward Autoencoder Neural Network (FANN) model to detect abnormal instances with improved accuracy and reduced energy consumption. The proposed model also acts as a False Positive Reducer intending to reduce false alarms. It has been compared with the other dominant unsupervised algorithms over robustness and other significant metrics with real-time datasets. Relatively, our proposed model yields an improved accuracy with fewer false alarms thereby supporting a sustainable WSN.

A statistical approach to distributed edge detection in wireless sensor networks is proposed in this research. Rather than trying to estimate the real edge of a phenomenon, the technique of edge sensor detection is adopted to determine whether a target sensor is located within the edge area or not based on information collected from its neighboring sensors. Due to the nature of wireless sensor networks, it is desirable to have a distributed algorithm that has a low computational complexity and a low data communication cost among sensors. With some reasonable assumptions and the aid of composite hypothesis testing, we propose data fusion as well as decision fusion methods for edge sensor detection to fulfill the aforementioned constraints. Numerical experiments are used to demonstrate the efficiency of the proposed algorithm.

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an algorithm that (1) is flexible with respect to the outlier definition, (2) works in-network with a communication load proportional to the outcome, and (3) reveals its outcome to all sensors. We examine the algorithm’s performance using simulation with real sensor data streams. Our results demonstrate that the algorithm is accurate and imposes a reasonable communication load and level of power consumption.

Since the nonstationary distribution of the detected objects is general in the real world, the accurate and efficient outlier detection for data analysis within wireless sensor network (WSN) is a challenge. Recently, with high classification precision and affordable complexity, one-class quarter-sphere support vector machine (QSSVM) has been introduced to deal with the online and adaptive outlier detection in WSN. Regarding the one-sided consideration of optimization or iterative updating algorithm for QSSVM model within current techniques, we have proposed comprehensive outlier detection methods in WSN based on the QSSVM algorithm. To reduce the complexity of optimization algorithm for QSSVM model in existing techniques, a fast optimization algorithm based on average Euclidean distance has been developed and employed to the comprehensive outlier detection method. Evaluated by real and synthetic WSN data sets, our methods have shown an excellent outlier detection performance, and they have been proved to meet the requirements of online adaptive outlier detection in the case of nonstationary detection tasks of WSN.

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.

Outlier detection is a key component for the proper functioning of a wireless sensor network. It helps in identifying malicious events taking place in the field and faulty sensors. This paper presents a distributed outlier detection technique that separates outlying cases from normal data locally at sensors and sends only the summarized information of data to sink nodes to minimize the communication overhead in the resource-constraint wireless sensor network. The proposed solution employs variance of normal data as a measure to identify outliers. The performance of the proposed method has been evaluated on real-world datasets collected by WSN deployment projects. Experimental results prove the effectiveness of the proposed method in terms of higher outlier detection rate, lower false alarm rate and lower communication overhead as compared to the simulated centralized approach.KeywordsData varianceOutlier detectionWireless sensor networks

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.

Widely used computer systems have always become a target for different type of attacks. With the specifications of easiness in deploying and setting up low cost and low power consumption systems, Wireless Sensor Networks (WSN) have also become a specific target for attackers. The WSNs, which consist of sensor nodes that sends information they perceive and forward to a base station/sink, cannot be directly protected by the traditional security precautions because of low resources of nodes, such as memory and computing capability. Hence, it is tried to find out different methods for intrusion detection in WSNs. This paper surveys frameworks for intrusion detection in WSNs by using a swarm optimization algorithm as ant colony approach.

Rare events detection is one of the main applications in Wireless Sensor Networks (WSN) and is currently a central concern of a vast literature. Compressed Sensing (CS) theory has been proved to be quite adapted to this objective. Although this is not the first work on applying CS to sparse events detection in WSN, it is the first to highly justify the validity of the targets detection and counting problem formulation. In order to enhance the CS recovery capacity in WSN, this work considers an approach based on a coherence reduction of the sensing matrix premised on the transmitted power control (PC). Simulation results prove that, under the constraint of equal power consumption, the detection and counting performance is improved when the proposed power control scheme is employed compared to the case without PC.

Wireless sensor networks are gaining more and more attention these days. They gave us the chance of collecting data from noisy environment. So it becomes possible to obtain precise and continuous monitoring of different phenomenons. However wireless Sensor Network (WSN) is affected by many anomalies that occur due to software or hardware problems. So various protocols are developed in order to detect and localize faults then distinguish the faulty node from the right one. In this paper we are concentrated on a specific type of faults in WSN which is the outlier. We are focus on the classification of data (outlier and normal) using three different methods of machine learning then we compare between them. These methods are validated using real data obtained from motes deployed in an actual living lab.