The Internet of Vehicles (IoV) lets cars talk to one another in smart ways by sharing data in real time between vehicles, infrastructure, and edge nodes. But as more and more parts become linked, the danger of advanced attacks rises. At the same time, traditional Intrusion Detection Systems (IDS) typically cannot keep up with IoV's changing, low-latency, and resource-limited needs. Deep learning has become a viable alternative, but it needs a lot of labeled data and architectures that are hard to compute, which makes it less useful in real-world vehicle situations. To solve this problem, we suggest Vehicular Intrusion Detection by Temporal-aware Attention (VITA), a lightweight, self-supervised, and label-free IDS framework made just for IoV. VITA uses an Adversarial Autoencoder (AAE) with Efficient Channel Attention (ECA) to pick out important spatial characteristics and residual Temporal Convolutional Networks (TCNs) to simulate long-range temporal relationships in a way that is efficient way. A latent-space smoothing technique is added to stabilize adversarial learning, and a log-cosh reconstruction loss is added to make the system more resistant to noisy vehicle telemetry. VITA works in real time and does not need a lot of processing power, so it can be used on edge devices in vehicles. Our proposed VITA framework shows superior performance across several critical criteria, including an average 30% reduction in latency compared to state-of-the-art models. VITA achieves an average 8% improvement in detection accuracy, effectively identifying a broad spectrum of vehicular attacks, such as replay, Global Positioning System (GPS) spoofing, and Denial-of-Service (DoS) attacks. Furthermore, VITA outperforms existing systems with an average 5x reduction in false positive rate, and shows robust performance under varying noise levels, with only a modest 4.7% drop in accuracy under high jitter conditions. Additionally, it excels in computational efficiency, requiring 50% less memory and processing power on average, making it highly suitable for real-time, edge-based IoV deployments.

This article introduces and evaluates adaptive polynomial Kolmogorov–Arnold network (AdaptPolyKAN) architectures for intelligent fault diagnosis, addressing limitations of classical KANs in dynamic and nonlinear industrial systems. While classical KANs offer interpretability, their fixed univariate mappings lack the flexibility needed for evolving operating conditions. The proposed AdaptPolyKAN uses adaptive polynomial expansions that adjust the degree of each basis according to local reconstruction errors, enabling real-time adaptation, improved accuracy, and efficient handling of complex nonstationary fault patterns. Three variants are examined-Standard PolyKAN, SplineKAN, and Online PolyKAN-alongside the proposed AdaptPolyKAN. Their performance is benchmarked against classical KAN, artificial neural networks, support vector machines, and random forest models in both real and simulated fault scenarios. The evaluation uses datasets from a cement rotary kiln at the Ain El Kebira plant, consisting of 768 normal samples, multiple simulated sensor faults, and one real fault. Monitored variables include temperatures, pressures, motor currents, and rotational speeds from 44 sensors recorded at 20-s intervals. The dataset contains noise, class imbalance, and limited duration, reflecting realistic industrial conditions. Comprehensive metrics—including accuracy, precision, recall, F1- score, false alarm rate (FAR), and missed detection rate (MDR)—demonstrate the superiority of the proposed approach. AdaptPolyKAN achieves 98.4% accuracy, balanced precision and recall (98.3% and 98.4%), and the lowest FAR (0.0060), while maintaining competitive MDR (0.178). Online PolyKAN adapts effectively to changing fault patterns, whereas classical KAN suffers from elevated false alarms. Overall, AdaptPolyKAN provides reliable detection in nonlinear and time-varying processes, offering a practical and interpretable solution for safety-critical industrial environments.

The accurate detection and diagnosis of faults are critical for maintaining optimal operation and ensuring the reliability of industrial processes. Notably, the topic of online fault detection and diagnosis has recently presented a significant challenge. This work mainly deploys a neural network technique for the comprehensive detection and diagnosis of faults within the Tennessee Eastman Process (TEP) on a low-computational power system, the Raspberry Pi board. The devolved methodology showcases a remarkable level of accuracy (94.50%) in diagnosing the various TEP faults, affirming its robustness and effectiveness. To elevate the practical applicability of the proposed approach, a meticulous investigation into the implementation of the suggested approach on a Raspberry Pi 4 card was undertaken. The successful realization of this implementation not only highlights the adaptability of the approach but also paves the way for its seamless integration into practical industrial applications.

The key idea behind this study is to integrate a moving window dynamic PCA (MW-DPCA) methodology for fault detection within the Tennessee Eastman process (TEP) into a low-computational power system, the Raspberry Pi 4 card, for real-time application. Indeed, the paramount importance of real-time fault detection (FD) in intricate industrial processes presents a critical challenge. Various data-driven techniques have been developed to ensure safety, maintain operational stability, and optimize productivity in such processes. Principal Component Analysis (PCA) is a fundamental data-driven technique that utilizes dimensionality reduction to extract the most informative features from high-dimensional data, simplifying analysis and potentially revealing underlying fault patterns. However, PCA primarily focuses on static relationships and may miss crucial temporal dynamics for fault identification. This is where dynamic PCA (DPCA) excels. By incorporating lagged values of variables, DPCA captures the temporal evolution of features, enabling a more comprehensive understanding of process behavior and improving the detection of faults involving dynamic changes. In order to address the stochastic measurements, a moving average filter tool is also employed. The results obtained and the successful realization of this implementation demonstrate the adaptability of the approach and pave the way for its seamless integration into practical industrial applications.

This study presents an embedded system (ES) designed for fault detection and diagnosis in grid-connected photovoltaic (GCPV) systems using transient regime analysis. The primary aim of transient regime analysis is to facilitate real-time decision-making, especially during critical faults. A neural network classifier, incorporating a Genetic Algorithm for automated hyperparameter optimization, is developed for GCPV fault classification. These classifiers are seamlessly integrated into a Raspberry Pi 4 platform for fault diagnosis in GCPV systems. Both simulation and experimental results substantiate the ES's viability for fault diagnosis in the examined GCPV system, achieving high accuracy and enabling prompt decision-making to enhance the reliability and safety of GCPV systems.

The safe and reliable operation of autonomous vehicles heavily relies on the precise identification and diagnosis of faults within their systems. Multiscale Kernel Principal Component Analysis (MSKPCA) model is well established for fault diagnosis. This study demonstrates the effectiveness of MSKPCA for detection in autonomous vehicles. It utilizes wavelet analysis and KPCA to extract pertinent feature from process data for fault detection. KPCA model transforms data into a feature space for extraction and selection pertinent data. However, the KPCA is often struggle with noisy data. To address this issue, we integrate a multiscale filtering technique with KPCA, to reduce the impact of the noise while preserving crucial features from the raw data. The proposed methodology uses raw data for training and testing across three sensor fault scenarios: sensor position faults, sensor velocity faults, and angle orientation faults. Square Predicted Error (SPE) and Hotelling T-squared (T2) statistics are computed in the feature space and compared to their respective thresholds to quantify the detection accuracies. The results show that the proposed approach MSKPCA performs well in fault detection in autonomous vehicles.

The interval-valued kernel PCA (UQ-KPCA) is a variation of the kernel PCA (KPCA) designed for interval-valued data, designed to handle data uncertainty by defining specific similarity measures and kernel functions for interval data. This paper introduces Uncertainty Quantification KPCA (UQ-KPCA) as a novel method to address uncertainties in data. UQ-KPCA converts the traditional KPCA model from single-valued to interval-valued representations, allowing for accurate error and uncertainty quantification. The process modeling using KPCA is then performed on data based on the interval model, followed by the computation of fault detection statistics such as T2, Q, and Φ. The method’s effectiveness is evaluated in the context of the cement rotary kiln process, and compared with the KPCA demonstrating superior performance in accurately identifying faults within a stochastic setting with unknown uncertainties.

This study introduces three novel dynamic interval-valued principal component analysis (DIPCA) methods: dynamic centers PCA (D-CPCA), dynamic vertices PCA (D-VPCA), and dynamic complete information PCA (D-CIPCA). These methods advance traditional interval-valued PCA (IPCA) by integrating dynamic aspects of industrial processes, thus addressing both data uncertainties and temporal correlations. The DIPCA methods were validated using real-world data from the Ain El Kebira cement plant. Results indicate significant improvements in fault detection accuracy, achieving lower false alarm rates and higher reliability compared to classical IPCA methods. Furthermore, an enhanced combined index for interval-valued data was developed, providing a single, comprehensive statistical measure for streamlined process monitoring.

The evolution of embedded systems has demonstrated their reliability as a solution for monitoring and controlling industrial systems, particularly in renewable energy conversion systems like photovoltaic (PV) energy. The increasing adoption of PV systems highlights the critical need for effective fault diagnosis to ensure their reliable operation. In this paper, we present a novel fault diagnosis approach utilizing Long Short-Term Memory (LSTM) networks optimized through Bayesian optimization techniques. Our methodology is implemented on a Raspberry Pi platform, demonstrating the feasibility of deploying sophisticated fault diagnosis algorithms in resource-constrained environments. Through extensive experiments, we demonstrate the effectiveness of our approach to accurately diagnose faults in grid-connected photovoltaic systems, thereby improving the reliability and efficiency of integrated environmental monitoring systems.The obtained results highlight the potential of combining advanced deep learning techniques with embedded systems to address complex diagnostic challenges, as demonstrated by achieving a 100% accuracy rate.

Grid Connected Photovoltaic (GCPV) sys-tems has been a rising research area in the industry fields. Therefore the high reliability, performance and safety operation of GCPV systems has become a high priority. Thus, it is important of developing an intel-ligent fault detection and diagnosis method that aims at increasing the efficiency of these systems. Therefore, the present study proposes an enhanced intelligent fault diagnosis approach. In the developed procedure, the measured normal and faulty data are applied together to extract the relevant feature, reduce the impact of noise, and then special features are fed to a neural network classifiers. To do that, a Multiscale Principal Component Analysis (MSPCA)-based Artificial Neural Network (ANN) method is proposed to provide the relia-bility and safety of the GCPV systems. From the GCPV measurements, features are appropriately extracted and scaled through multiscale principal component analysis. The ANN classifier is used in classifying twenty-one faults that can occur in GCPV systems operating under different working conditions. The diagnosis results show that the developed MSPCA-based ANN method not only able to detect faults, but also can effectively distinguish between different kinds of faults.

Safe production is of great significance in the process industry like the wind energy conversion (WEC) systems. An unexpected fault in part of the WEC system can damage the entire mechanical system, resulting in huge economic losses and even catastrophic failures. Therefore, this paper proposes an effective neural networks-based ensemble approach for fault de-tection and diagnosis (FDD) of WEC systems. The main contributions are twofold: first, an ensemble learning technique based on the combination of different neural network (ANN, CFNN, and GFNN) into one optimal model are developed in order to distinguish between the different WEC systems operating modes. Then, in order to enhance the results in terms of computation time and storage cost, a reduced version of the proposed neural network-based ensemble technique is presented. The main idea behind this proposal is to use the Hierarchical K-means (H-K-means) clustering to extract only the most significant samples from raw data. Then, the reduced data are introduced as inputs to the proposed neural network-based ensemble technique method to deal with the problem of fault classification. The experimental results demonstrated the feasibility and effectiveness of the proposed FDD techniques.

In this paper, new fault detection and di-agnosis (FDD) techniques dealing with uncertainties in wind energy conversion (WEC) systems are proposed. The uncertainty is addressed by using the interval-valued data representation. The main contributions are twofold: first, to simplify the Recurrent Neural Network (RNN) model in terms of training and computation time and storage cost as well, a reduced version of RNN is proposed. Reduced RNN is established on the H-K-means algorithms to treat the correlations between samples and extract a reduced number of observations from the training data matrix. The main idea behind using H-K-means algorithms for dataset size reduction is to simplify the RNN model in terms of training and computation time. Second, two reduced RNN-based interval-valued data techniques are proposed to distinguish between the different WEC system operating modes. Therefore, two reduced RNN-based interval centers and ranges and interval upper and lower bounds techniques are proposed to deal with the WEC system uncertainties. The presented results confirm the high feasibility and effectiveness of the proposed FDD techniques.

PV systems are prone to failure owing to aging and external/environmental factors. These failures can affect a range of system components, such as PV modules, connecting lines, and converters/inverters, re-sulting in decreased efficiency, performance, and even system failure. As a result, problem detection and diag-nosis (FDD) is an important issue in high-efficiency grid-connected PV systems. Deep learning techniques are the most well-known data-driven methodologies. The biggest advantage of deep learning algorithms, in diagnosis, are learning effectiveness, intelligent FDD becomes more effective. This paper therefore presents a comparative study of FDD based deep learning. The techniques include the Convolutional Neural Network (CNN) and Long Short Time Memory (LSTM). Finally, the FDD based frameworks are implemented using simulated PV data. The diagnosis results show that the CNN and LSTM-based fault diagnosis methods are able to detect and diagnose faults under different operating modes.

PV systems are subject to failures during their operation due to the aging effects and exter-nal/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the efficiency, performance, and fur-ther system collapse. Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). The most well-known data-driven methods are Deep Learning (DL) approaches. The biggest advantage of DL algorithms, in diagnosis, are that they try to learn high- level features from PV data in a high-order, non-linear and adaptive manners. Then, the fault is classified using soft-max activation function. This work therefore presents a comparative study of FDD based DL techniques. These techniques include Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM). The DL techniques-based fault diagnosis are implemented using an emulated Grid-Connected PV (GCPV) system. The classification results for the pretrained DL models is exhibited and performance of the models are evaluated.

This paper proposes an effective fault detection and diagnosis (FDD) paradigm in Wind Energy Converter (WEC) Systems. The developed FDD frame-work merges the benefits of kernel principal component analysis (KPCA) model and bidirectional long short-term memory (BiLSTM) feature classifier. KPCA is used to extract and select the most effective features. While, BiLSTM is used for classification purposes. The proposed KPCA-based BiLSTM approach involves two main steps; feature extraction and selection and fault classification. It is tackled in such a way that KPCA model is developed in order to select and extract the more efficient features where the final features are fed to BiLSTM to distinguish between different working modes. Different simulation scenarios are considered in this study in order to show the robustness and performances of the developed technique when compared to the conventional FDD methods.

The objective of this paper is to extend the Maximum Adaptive Exponential Weighted Moving Average (Max-AEWMA) chart to the Max Double AEWMA (Max-DAEWMA) chart. The Max-DAEWMA statistic is based on the Max of the absolute values of the two DAEWMA statistics, one for controlling the variance and the other for the mean. The combined novel technique, is called particle filter (PF)-based Max-DAEWMA for detecting faults of wastewater treatment plants (WWTP). The statistical chart, Max-DAEWMA is applied to detect the fault in mean and/or drifts in WWTP systems where the state variables are estimated using PF technique. The results show the effectiveness of the Max-DAEWMA method over Max-DEWMA and EWMA charts.

In this paper, we develop an improved fault detection (FD) technique in order to enhance monitoring abilities of nonlinear chemical processes. Kernel principal component analysis (KPCA) is an effective data driven technique for monitoring nonlinear processes. However, it is well known that data collected from complex and multivariate processes are multiscale due to the variety of changes that could occur in process with different localization in time and frequency. Thus, to enhance process monitoring abilities, we propose to combine advantages of KPCA and multiscale representation using wavelets by constructing a multiscale KPCA model and a new detection chart named multiscale kernel generalized likelihood ratio test (MS-KGLRT) is derived for fault detection. The detection performance of the new chart is studied using the Tennessee Eastman process (TEP).

It is well known that Exponentially Weighted Moving Average (EWMA) chart is designed to be optimal and efficient to quickly detect small faults. However, the classical EWMA can not perform well in the case of simultaneously large and small faults. To address this limitation, we propose to use an adaptive or a variable parameters control chart. Therefore, in this paper, we propose a novel approach, called particle filter (PF)-based adaptive EWMA (AEWMA) chart, with time-varying smoothing parameter lambda, to detect the fault in Wastewater Treatment Plant (WWTP) process. So that, the PF is applied to compute the residuals, and the AEWMA chart is used to detect the faults. The validation of the developed PF-based AEWMA technique is done using a simulated benchmark COST WWTP BSM1 model. The proposed PF-based AEWMA approach showed better detection abilities when compared to the classical EWMA and Shewhart charts.

Fault detection of nonlinear processes using Kernel Principal Component Analysis(KPCA) method has recently prompt a lot of interest due to its industrial practical importance. However, this method cannot be applied for data sets with a large amount of samples. To overcome this deficiency, this paper proposes a reduced KPCA method based on K-means clustering. This method aims to find a reduced data set among the training data in the input space and uses this reduced data set to built the reduced KPCA model in the feature space. The relevance of the proposed method is illustrated on an air quality monitoring network. The simulation results demonstrate the effectiveness of the new method when compared to the classical KPCA technique.

Statistical control charts are essential to ensure both safety and efficient operation of many industrial processes. Many dimensionality reduction techniques such as principal component analysis (PCA) and Partial Least Squares (PLS) regression exist, and are often employed for modeling purposes as they are relatively easy to compute. However, these techniques are only effective for modeling and monitoring linear processes. The Kernel Principal Component Analysis (KPCA) method is an extension of PCA that helps deal with any nonlinearities in the process data. However, KPCA-based fault detection methods may result in a higher false alarm rate than the conventional method. In this paper, an improved KPCA method is developed in order to tackle the issue of high false alarm rates, by utilizing a mean filter to smoothen the detection statistics that are obtained from the KPCA method. The advantages presented by the developed method are illustrated using a simulated nonlinear model. The results clearly show that the improved KPCA method provides improved fault detection results with low missed detection and false alarm rates, and smaller ARL1 values compared to the conventional methods.

Statistical process monitoring charts are critical in ensuring safety for many chemical processes. Principal Component Analysis (PCA) is often used, due to its computational simplicity. However, many chemical processes may be inherently nonlinear, and this degrades the performance of the linear PCA method. Kernel Principal Component Analysis (KPCA) is an extension of the conventional PCA chart, which can help deal with nonlinearity in a given process. Additionally, PCA assumes that process data are Gaussian and uncorrelated, and only contain a moderate level of noise. These assumptions do not usually hold in practice. Multiscale wavelet-based data representation produces wavelet coefficients that possess characteristics that are able to handle violations in these assumptions. A multiscale kernel principal component analysis (MSKPCA) method has already been developed to tackle all of these issues, but it usually provides a high false alarm rate. In this paper, an improved MKSPCA chart is developed in order to deal with the false alarm rate issue, by smoothening the detection statistic using a mean filter. The advantages brought forward by the improved method are demonstrated through a simulated example in which the developed fault detection method is used to monitor a continuous stirred tank reactor (CSTR). The results clearly show that the improved MSKPCA method provides lower missed detection and false alarm rates as well as ARL1 values compared to those provided by the conventional methods.

ABSTRACT This paper addresses target tracking in wireless sensor networks where the nonlinear observed system is assumed to progress according to a probabilistic state space model. Thus, we propose to improve the use of the quantized variational filtering by jointly selecting the optimal candidate sensor that participates in target localization and its best communication path to the cluster head. In the current work, firstly, we select the optimal sensor in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor networks. This selection is also based on the local cluster node density and their transmission power. Secondly, we select the best communication path that achieves the highest signal‐to‐noise ratio at the cluster head; then, we estimate the target position using quantized variational filtering algorithm. The best communication path is designed to reduce the communication cost, which leads to a significant reduction of energy consumption and an accurate target tracking. The optimal sensor selection is based on mutual information maximization under energy constraints, which is computed by using the target position predictive distribution provided by the quantized variational filtering algorithm. The simulation results show that the proposed method outperforms the quantized variational filtering under sensing range constraint, binary variational filtering, and the centralized quantized particle filtering. Copyright © 2012 John Wiley & Sons, Ltd.

We consider the problem of distributed and secure routing for target tracking in wireless sensor networks (WSN) based on quantized sensors measurements. We propose a new method for jointly selecting the optimal communication path between slave sensors and cluster head (CH), detecting the malicious sensors and estimating the target position. Firstly, we detect the malicious sensor nodes based on the information relevance of their measurements. Secondly, we select the optimal communication route in order to balance the energy dissipation and to provide the required data of the target in the WSN. This selection is also based on the transmission power between a sensor node and a cluster head. Then, we estimate the target position using Quantized Variational Filtering (QVF) algorithm. The computation of these criteria is based on the target position predictive distribution provided by the QVF algorithm. The performance of the proposed method is validated by simulation results in target tracking for WSN.

This work presents a multi-objective algorithm for jointly selecting the appropriate group of candidate sensors and optimizing the quantization for target tracking inWireless Sensor Networks (WSN). We focus on a more challenging problem of how to effectively utilize quantized sensor measurement for target tracking in sensor networks by considering sensors selection problem. Firstly, we jointly optimize the quantization level and the group of candidate sensors selection in order to provide the required data of the target and to balance the energy dissipation in the WSN. Then, we estimate the target position using quantized variational filtering (QVF) algorithm. The quantization optimization and the sensors selection are based on multi-objective (MO) that define the main parameters that may influence the relevance of the participation in cooperation for target tracking. This optimization is also based on the transmitting power between one sensor and the CH. The best sensors selection and quantization optimization are designed to reduce the communication cost and the estimation error, which leads to a significant reduction of energy consumption and an accurate target tracking. The simulation results show that the proposed method, outperforms the quantized variational filtering algorithm under sensing range constraint and the centralized quantized particle filter.

Wireless mesh networks (WMNs) are expected to be a next step toward future generation of wireless networks due to their rapidly deployable nature and to the wide variety of their potential use. On the other hand, the daily increase of multimedia applications over wireless networks has generated a vital need to provide Quality of Service (QoS) support in WMNs, and works in this area are not sufficient for the moment. In this paper, we propose a QoS model, named WiRS, to support real time traffic over WMNs. WiRS consists of an admission control and two traffic regulation schemes. The admission control is based on a temporary reservation process allowing multiple flows to opportunistically benefit from reserved resources when they are not used by their correspondent flow. The traffic regulation schemes aim to dynamically adjust the injected traffic into the mesh backbone in order to avoid the congestion and to maintain the QoS requirements. A service differentiation mechanism is provided also through one of the regulation schemes in order to control the best effort traffic. Extensive simulations show that our proposal is able to provide stable end-to-end delay, high throughput and improved packet delivery ratio.

The problem of secure quantized target tracking in wireless sensor networks (WSN) is investigated. Due to the limited energy supplies of nodes in WSN, optimizing their design under energy constraints, reducing their communication costs, securing their data aggregation are of paramount importance. To this goal and in order to efficiently solve the problem of target tracking in WSN with quantized measurements, we propose a new method for jointly selecting the appropriate group of candidate sensors that participate in data collection, detecting the malicious sensors and estimating the target position based on quantized proximity sensors. Firstly, we select the best group in order to provide the required data of the target and to balance the energy dissipation in the WSN. This selection is also based on the transmission power between one sensor and the cluster head. Secondly, we detect the malicious sensor nodes from learned data based on the information relevance of their measurements. Then, we estimate the target position using Quantized Variational Filtering (QVF) algorithm. The performance of the proposed method is validated by simulation results in target tracking for WSN.

This work presents the problem of target tracking and relay localization in wireless sensor networks (WSN) based on quantized proximity sensors. Thus, we use the quantized variational filtering (QVF) in order to estimate jointly the target position and the relay location. Recently, variational filtering has been proved to be suitable to the communication constraints of WSN. However, this problem has been proposed only for binary sensor networks neglecting the information relevance of sensor measurements and the transmission energy consumption. At each sampling instant, the adaptive scheme provides the estimates of the target position and the relay location by using the QVF algorithm. The efficiency of the proposed method is validated by simulation results in target tracking for wireless sensor networks.

Due to the limited energy supplies of nodes in wireless sensor networks (WSN), optimizing their design under energy constraints, reducing their communication costs are of paramount importance. To this goal and in order to efficiently solve the problem of target tracking in WSN with quantized measurements, we propose to jointly estimate the target position and select the optimal communication path between the cluster head (CH) and the slave sensors. Firstly, we select the optimal communication path between the candidate sensor and the CH. Then, we estimate the target position using Quantized Variational Filtering (QVF) algorithm. The optimal communication path is selected as well as the highest signal-to-noise ratio (SNR) at the CH. The efficiency of the proposed method is validated by extensive simulations in target tracking for wireless sensor networks.

This paper addresses multiple target tracking (MTT) in wireless sensor networks (WSN) where the nonlinear observed system is assumed to progress according to a probabilistic state space model. In this paper, we propose to improve the use of the quantized variational filtering (QVF) by optimally quantizing the data collected by the sensors and estimating the channel attenuation between sensors. Our proposed technique is intended to jointly estimate the multiple target positions by using the Hybrid QVF and Sequential Monte Carlo-based approach to data association (SMCDA) algorithm, optimize the number of quantization bits per observation and estimate the fading channel coefficient. The adaptive quantization is achieved by maximizing the predicted Fisher information and the fading channel coefficient is estimated by maximizing the a posteriori distribution. The simulation results show that the adaptive quantization algorithm, outperforms both the centralized quantized particle filter (QPF) and the VF algorithm based on binary sensors (BVF).

During a crisis situation, the incident commanders have to tackle several problems simultaneously, e.g., (1) the monitoring of a crisis evolution, (2) guiding and tracking of rescue persons and intervention robots, (3) monitoring rescue persons' health, etc. Thus, several solutions based on wireless sensor networks (WSNs) and cellular networks have been proposed. However, the real life experience has proved that the cellular networks' base stations may be collapsed or unreachable during a crisis situation. In addition, the WSNs have been generally deployed for one application, e.g., monitoring of an occurring event. However, today technologies offer sensor nodes (SN) with higher processing and communication capacity. Based on these capacities, this paper proposes a WSN solution, for crisis management, which is not based on any base station infrastructure. This solution proposes a tracking and data aggregation methods that could be run simultaneously to treat the three above mentioned problems. It is also empowered by a multi-agent system (MAS), which allows the SNs to cooperate and to better manage their batteries. Finally, the proposed solution has proved, through successive simulations, its efficiency in terms of tracking precision, end-to-end communication delay and power optimization.

We consider the problem of quantized target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose to improve the use of the quantized variational filtering (QVF) by jointly estimating the target position and selecting the best sensors that participate in data association. In fact, the QVF has been shown to be adapted to the communication constraints of sensor networks. Its efficiency relies on the fact that the online update of the filtering distribution and its compression are executed simultaneously. Firstly, we select the best sensor that provides satisfied data of the target and balances the energy level among all sensors and minimum node density in a local cluster. Then, we estimate the target position using the QVF algorithm. The best candidate sensors are obtained by maximizing the mutual information function under energy constraints. The efficiency of the proposed method is validated by simulation results in target tracking for wireless sensor networks.