Detection In Wireless Sensor Networks Research Articles

Joint Collaboration and Compression Design for Random Signal Detection in Wireless Sensor Networks

In this work, we propose a joint collaboration-compression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a collaboration matrix) with each other. Then a subset of sensors selected to communicate with the FC linearly compress their observations before transmission. We design near-optimal collaboration and linear compression strategies under power constraints via alternating minimization of the sequential minimum mean square error. We show that the objective function for collaboration design can be non-convex depending on the network topology. We reformulate and solve the collaboration design problem using quadratically constrained quadratic program (QCQP). Moreover, the compression design problem is also formulated as a QCQP. We propose two versions of compression design, one centralized where the compression strategies are derived at the FC and the other decentralized, where the local sensors compute their individual compression matrices independently. It is noted that the design of decentralized compression strategy is a non-convex problem. We obtain a near-optimal solution by using the bisection method. In contrast to the one-shot estimator, our proposed algorithm is capable of handling dynamic system parameters such as channel gains and energy constraints. Importantly, we show that the proposed methods can also be used for estimating time-varying random vector parameters. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.

Recurrent neural network NARX for distributed fault detection in wireless sensor networks

Recurrent neural network NARX for distributed fault detection in wireless sensor networks

Recurrent neural network NARX for distributed fault detection in wireless sensor networks

Wireless sensor networks (WSNs) comprise a collection of sensors used to collect data, which allow knowing the state of a zone or a monitored system. Sensor are usually deployed in harsh environmen...

A New Regularized Recursive Dynamic Factor Analysis With Variable Forgetting Factor and Subspace Dimension for Wireless Sensor Networks With Missing Data

The dynamic factor analysis (DFA) is an effective method for reducing the dimension of multivariate time series measurements in wireless sensor networks (WSNs) for prediction, monitoring, and anomaly detection. In large-scale systems, it is crucial to be able to track the time-varying loadings (or subspace) and the underlying factor signals, identify their dimensions, and impute missing or outlier samples, which can arise from transmission loss, hardware failure, and other factors. This article proposes a new regularized recursive DFA algorithm with a variable forgetting factor (FF) and subspace dimension, which is computationally efficient and capable of missing data imputation. A novel multiple deflation (MD) and rank-one-modification-based approach is proposed to recursively estimate the eigenvalues and associated eigenvectors from the progressively extracted subspace via MD. This allows us to update efficiently the subspace dimension and loading (eigen) vectors. By modeling the factor signals as independent univariate autoregressive (AR) processes, one can utilize the forecast value for outlier detection and missing samples' imputation. Furthermore, variable FF and regularization schemes are proposed to improve the tracking capability and reduce the variance of the proposed recursive DFA algorithm. Experimental results using real WSN datasets show that the proposed algorithm achieves better tracking performance and accuracy than other conventional approaches, which may serve as a useful tool for prediction, monitoring, and anomaly detection in WSNs.

Design and analysis of efficient neural intrusion detection for wireless sensor networks

SummaryWireless sensor networks (WSNs) are important building blocks of the communication infrastructure in smart cities, intelligent transportation systems, Industry, Energy, and Agriculture 4.0, the Internet of Things, and other areas quickly adopting the concepts of fog and edge computing. Their cybernetic security is a major issue and efficient methods to improve their safety and reliability are required. Intrusion detection systems (IDSs) are complex systems that discover cybernetic attacks, detect malicious network traffic, and, in general, protect computer systems. Artificial neural networks are used by a variety of advanced intrusion detection systems with outstanding results. Their successful use in the specific conditions of WSNs requires efficient learning, adaptation, and inference. In this work, the acceleration of a neural intrusion detection model, developed specifically for wireless sensor networks, is proposed and studied, especially from the learning and classification accuracy and energy consumption points of view.

Data-Driven Anomaly Detection Approach for Time-Series Streaming Data.

Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series streaming data of sensor nodes is a challenging but critical fault diagnosis task, particularly in large-scale WSNs. The data-driven approach is becoming essential for the goal of improving the reliability and stability of WSNs. We propose a data-driven anomaly detection approach in this paper, named median filter (MF)-stacked long short-term memory-exponentially weighted moving average (LSTM-EWMA), for time-series status data, including the operating voltage and panel temperature recorded by a sensor node deployed in the field. These status data can be used to diagnose device anomalies. First, a median filter (MF) is introduced as a preprocessor to preprocess obvious anomalies in input data. Then, stacked long short-term memory (LSTM) is employed for prediction. Finally, the exponentially weighted moving average (EWMA) control chart is employed as a detector for recognizing anomalies. We evaluate the proposed approach for the panel temperature and operating voltage of time-series streaming data recorded by wireless node devices deployed in harsh field conditions for environmental monitoring. Extensive experiments were conducted on real time-series status data. The results demonstrate that compared to other approaches, the MF-stacked LSTM-EWMA approach can significantly improve the detection rate (DR) and false rate (FR). The average DR and FR values with the proposed approach are 95.46% and 4.42%, respectively. MF-stacked LSTM-EWMA anomaly detection also achieves a better F2 score than that achieved by other methods. The proposed approach provides valuable insights for anomaly detection in WSNs by detecting anomalies in the time-series status data recorded by wireless sensor nodes.

Fast outlier detection for high-dimensional data of wireless sensor networks

This article addresses the problem of outlier detection for wireless sensor networks. As increasing amounts of observational data are tending to be high-dimensional and large scale, it is becoming increasingly difficult for existing techniques to perform outlier detection accurately and efficiently. Although dimensionality reduction tools (such as deep belief network) have been utilized to compress the high-dimensional data to support outlier detection, these methods may not achieve the desired performance due to the special distribution of the compressed data. Furthermore, because most existed classification methods must solve a quadratic optimization problem in their training stage, they cannot perform well in large-scale datasets. In this article, we developed a new form of classification model called “deep belief network online quarter-sphere support vector machine,” which combines deep belief network with online quarter-sphere one-class support vector machine. Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. Then, an online testing method is proposed to perform online outlier detection without supervision. Finally, we compare the proposed method with the state of the arts using extensive experiments. The experimental results show that our method not only reduces the computational cost by three orders of magnitude but also improves the detection accuracy by 3%–5%.

Reduce Effect of Dependent Malicious Sensor Nodes in WSNs using Pairs Counting and Fake Packets

In this paper, we propose a new technique for the enhancement of target detection in Wireless Sensor Networks (WSNs) in which sensor nodes are responsible for taking binary decisions about the presence or absence of a given target and reporting the output to the fusion center. We introduce the algorithm; Fail Silent Pair (FSP) to calculate global decision in the fusion center. The FSP algorithm randomly distributes all sensor nodes into pairs then considers pairs of the same local decision. Also, we present new detection and prevention methods to reduce the effect of dependent malicious sensor nodes. The detection method is based on the deception of suspicious sensor nodes with fake packets to detect a subset of the malicious sensor nodes, as these nodes eavesdrop on other sensor nodes packets to use their local decisions as a reference to build an intelligent decision. While the prevention method allows the fusion center to correct local decisions of some malicious sensor nodes with identified strategies, assisting in the increase of the accuracy of global decisions. We introduce a mathematical analysis to verify our methods, in addition to simulation experiments to validate our technique.

Role of Machine Learning Algorithms Intrusion Detection in WSNs: A Survey

Machine Learning is capable of providing real-time solutions that maximize the utilization of resources in the network thereby increasing the lifetime of the network. It is able to process automatically without being externally programmed thus making the process more easy, efficient, cost-effective, and reliable. ML algorithms can handle complex data more quickly and accurately. Machine Learning is used to enhance the ability of the Wireless Sensor Network environment. Wireless Sensor Networks (WSN) is a combination of several networks and it is decentralized and distributed in nature. WSN consists of sensor nodes and sinks nodes which have a property of self-organizing and self-healing. WSN is used in other applications, such as biodiversity and ecosystem protection, surveillance, climate change tracking, and other military applications.Now-a-days, a huge development is seen in WSNs due to the advancement of electronics and wireless communication technologies, several drawbacks like low computational capacity, small memory, and limited energy resources infrastructure needs physical vulnerability to require source measures where privacy plays a key role.WSN is used to monitor the dynamic environments and to adapt to such situation sensor networks need Machine Learning techniques to avoid unnecessary redesign. Machine learning techniques survey for WSNs provide a wide range of applications in which security is given top priority. To secure data from attackers the WSNs system should be able to delete the instruction if any hackers/attackers are trying to steal data.

Outlier Detection in Wireless Sensor Networks Based on Neighbourhood

Wireless sensor networks contain millions of nodes deployed in a spatially dispersed manner. These sensors are low battery powered devices having limited storage and computation power. The data collected by these sensors may be subjected to error due to environmental fluctuations, interference in wireless communication or wearing of sensors with time. These erroneous data deviate significantly from the rest of the data. To solve this issue, we present a new technique named Outlierness Factor based on Neighbourhood to detect and analyse the outliers in sensor network. Proposed detection approach is time efficient and scalable. Further, outlier data are classified as errors due to sensor malfunctioning or actual detected events such as fire detection, weather changes, earthquakes, landslide etc. The capabilities of the proposed approach have been evaluated on real dataset obtained from Intel Berkeley research lab and synthetic datasets. The results show the effectiveness of the proposed approach in contrast to the previously dealt approaches.

Black Hole Attack Detection in Healthcare Wireless Sensor Networks Using Independent Component Analysis Machine Learning Technique

Background: Wireless Sensor Networks (WSNs) are self-configured infrastructure-less networks are comprising of a number of sensing devices used to monitor physical or environmental quantities such as temperature, sound, vibration, pressure, motion etc. They collectively transmit data through the network to a sink where it is observed and analyzed. Materials and Methods: The major issues in WSN are interference, delay and attacks that degrade their performance due to their distributed nature and operation. Timely detection of attacks is imperative for various real time applications like healthcare, military etc. To improve the Black hole attack detection in WSN, Projected Independent Component Analysis (PICA) technique is proposed herewith, which detects black hole attack by analyzing collected physiological data from biomedical sensors. Results: The PICA technique performs attack detection through Mutual information to measure the dependence in the joint distribution. The dependence among the nodes is identified based on the independent probability distribution functions and mutual probability function. Conclusion: The black hole attack isolation is then performed through the distribution of the attack separation message. This supports to improve Packet Delivery Ratio (PDR) with minimum delay. The simulation is carried out based on parameters such as black hole attack detection rate (BHADR), Black Hole Attack Detection Time (BHADT), False Positive Rate (FPR), PDR and delay.

Supervised Machine Learning Classification Algorithmic Approach for Finding Anomaly Type of Intrusion Detection in Wireless Sensor Network

From the last decade, the use of internet and its growth is continuously increasing. Similarly, numbers of services are coming out along with the internet and it is being used for providing facilities to human beings. Wireless sensor have been used for various application such as fire safety, military application, petroleum industry, security system, monitoring and environmental condition and many more. WSN node exposes itself to various security related attacks due to low battery power supply, low bandwidth support, data transmission over multi hop node, dependency on intermediate or other nodes, distributed in nature and self-organization. The WSN attacks observe in all layers of OSI model. Wireless sensor nodes has various issues because of that, it experiences number problem related to its functionalities and some malfunction due to attacks. It is require to build defence and network monitoring system for identifying attacks and prevent them. Intrusion detection system (IDS) plays an important role to detect threads inside the system and generate the alert related to the attack. In this work, supervised classification models for intrusion detection are built using such as Random Forest classifier, Support Vector Machine, Decision Tree Classifier, LGBM Classifier, Extra Tree Classifier, Gradient Boosting Classifier, Ada Boost Classifier, K Nearest Neighbour Classifier, MLP Classifier, Gaussian Naive Bayes Classifier and Logistic Regression Classifier. The NSLKDD, i.e. Modified version of the KDD99 Data Set on which we checks these algorithms. Experimental results how the highest accuracy relative to other classification systems in the support vector machine.

Anomaly Detection in Wireless Sensor Networks: A Proposed Framework

With the rise of IOT devices and the systems connected to the internet, there was, accordingly, an ever-increasing number of network attacks (e.g. in DOS, DDOS attacks). A very significant research problem related to identifying Wireless Sensor Networks (WSN) attacks and the analysis of the sensor data is the detection of the relevant anomalies. In this paper, we propose a framework for intrusion detection system in WSN. The first two levels are located inside the WSN, one of them is between sensor nodes and the second is between the cluster heads. While the third level located on the cloud, and represented by the base stations. In the first level, which we called light mode, we simulated an intrusion traffic by generating data packets based on TCPDUMP data, which contain intrusion packets, our work, is done by using WSN technology. We used OPNET simulation for generating the traffic because it allows us to collect intrusion detection data in order to measure the network performance and efficiency of the simulated network scenarios. Finally, we report the experimental results by mimicking a Denial-of-Service (DOS) attack. <em> </em>

AI-Based Techniques for DDoS Attack Detection in WSN: A Systematic Literature Review

Wireless Sensor Networks (WSNs) are currently being used in various industries such as healthcare, engineering, the environment and so on. Security is a significant issue for WSN due to its infrastructure and limited physical security. Distributed Denial of Service (DDoS) is one of the most vulnerable attacks that can be defined as attacks launched from multiple ends into a set of legitimate sensor nodes in the WSN to drain their inadequate energy resources. Nowadays, Artificial intelligence techniques are performing better accuracy than the traditional methods to detect intrusion for the various attack. This Systematic Literature Review (SLR) attempts to investigate the current status of DDoS detection techniques and to identify the most capable and effective detection system using artificial intelligence to detect distributed DoS attack. Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) statement is used to conduct this review. Based on 15 out of 983 that met inclusion criteria, Support Vector Machine (SVM) and Artificial Neural Network (ANN) is the most used AI-based techniques to detect distributed denial of service attack in the wireless sensor network. The performance of AI techniques-based detection system for DDoS attack in WSN is remarkable.

Differential Evolution with Artificial Bee Colony Optimization Algorithm based Sink Hole Detection in Wireless Sensor Networks

Differential Evolution with Artificial Bee Colony Optimization Algorithm based Sink Hole Detection in Wireless Sensor Networks

Quality of service enabled intelligent water drop algorithm based routing protocol for dynamic link failure detection in wireless sensor network

Quality of service enabled intelligent water drop algorithm based routing protocol for dynamic link failure detection in wireless sensor network

A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks

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.

Efficient Coverage Hole Detection Algorithm Based on the Simplified Rips Complex in Wireless Sensor Networks

The appearance of coverage holes in the network leads to transmission links being disconnected, thereby resulting in decreasing the accuracy of data. Timely detection of the coverage holes can effectively improve the quality of network service. Compared with other coverage hole detection algorithms, the algorithms based on the Rips complex have advantages of high detection accuracy without node location information, but with high complexity. This paper proposes an efficient coverage hole detection algorithm based on the simplified Rips complex to solve the problem of high complexity. First, Turan’s theorem is combined with the concept of the degree and clustering coefficient in a complex network to classify the nodes; furthermore, redundant node determination rules are designed to sleep redundant nodes. Second, according to the concept of the complete graph, redundant edge deletion rules are designed to delete redundant edges. On the basis of the above two steps, the Rips complex is simplified efficiently. Finally, from the perspective of the loop, boundary loop filtering and reduction rules are designed to achieve coverage hole detection in wireless sensor networks. Compared with the HBA and tree-based coverage hole detection algorithm, simulation results show that the proposed hole detection algorithm has lower complexity and higher accuracy and the detection accuracy of the hole area is up to 99.03%.

Anomaly detection in wireless sensor network using machine learning algorithm

Security in the Wireless Sensor Network(WSNs) is an essential and a challenging task. Anomaly detection is a key challenge to ensure the security in WSN. WSNs are vulnerable to various threats which may cause the node to get damaged and produce faulty measurements. The detection of such anomalous data is required to reduce false alarms. Machine learning algorithm based detection of anomalous data becomes popular now. Most of the current machine anomaly detection algorithms run in a stationary environment and require the entire training data to be kept in the node. In this paper, we formulate an Online Locally Weighted Projection Regression (OLWPR) for anomaly detection in Wireless Sensor Network. Linear Weighted Projection Regression methods are non parametric and the current predictions are performed by local functions that use only the subset of data. So, the computation complexity is low which is one of the requirements in Wireless Sensor Network. The dimensionality reduction in LWPR is done online by Principal Component Analysis (PCA) to handle the irrelevant and redundant data in the input data. After the prediction process, the dynamic threshold value is determined by a dynamic thresholding method to find the deviations of predicted value from the actual sensed value. OLWPR attains the detection rate of 86 percentage and very low error rate of only 16%.

Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach

SummaryWireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this research is to design a local time‐series‐based data noise and anomaly detection approach for WSN. The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data. An adaptive Bayesian network is used as the classification algorithm for prediction and identification of outliers in each sensor node locally. Results of our approach are compared with four well‐known algorithms using benchmark real‐life datasets, which demonstrate that LODA can achieve higher (up to 89%) accuracy in the prediction of outliers in real sensory data.