Optimized Data Fusion Research Articles

Energy optimized data fusion approach for scalable wireless sensor network using deep learning-based scheme

Energy efficiency and security are critical components of Quality of Service (QoS) and remain a challenge in WSN-assisted IoT owing to its open and resource-limited nature. Despite intensive research on WSN-IoT, only a few have achieved significant levels of energy efficiency and load balancing on clustering nodes. This study proposes a novel approach for dynamic cluster-based WSN-IoT networks to enhance the network’s resilience using data fusion techniques and eliminate illogical clustering. The Mean Value and Minimum Distance Method identifies the optimal cluster heads within the network by reducing data redundancy, resulting in improved quality of service, energy optimization, and enhanced lifetime. The proposed fused deep learning-based data mining method (RNN-LSTM) mitigates the data fitting and enhances the dynamic routing and balancing load at the WSN fusion center. The novel approach splits the network into layers, assigning sensor nodes to each layer, drastically reducing latency, data transfers, and the fusion center’s overhead. Distinct experiments evaluated the suggested approach’s efficacy by varying the hidden layer nodes and signaling intervals. The empirical verdicts exhibit that the presented routing algorithms surpass state-of-the-art conventional routing systems in energy depletion, average latency, signaling overhead, cumulative throughput, and route heterogeneity.

Optimized Data Fusion With Scheduled Rest Periods for Enhanced Smart Agriculture via Blockchain Integration

Optimized Data Fusion With Scheduled Rest Periods for Enhanced Smart Agriculture via Blockchain Integration

An optimized data fusion strategy for structural damage assessment using electromechanical impedance

This paper proposes a new sensor network optimized data fusion approach for structural health monitoring of metallic structures using electromechanical impedance (EMI) signals. The integrated approach used to fuse common healthy state baseline model based damage detection, quantification and classification in EMI technique. Towards this, the principal component analysis (PCA) is carried out and corresponding the root mean square deviation (RMSD) index is calculated to study the information of piezoelectric transducer’s impedance (|Z|), admittance (|Y|), resistance (R), and conductance (G) in the frequency domain. A new optimized data fusion approach is proposed which was realized at the sensor level using the PCA as well as at the variable level using self-organizing maps (SOMs). The SOM comparative studies are performed using the Q-statistics (Q index) and the Hotelling’s T2 statistic (T index). The proposed methodology is tested and validated for an aluminum plate with multiple drilled holes with variable size and locations. In the process, a centralized data-fused baseline eigenvector is prepared from a healthy structure and the damage responses are projected on this baseline model. The statistical, data-driven damage matrices are calculated and compared with the RMSD index and used in a fusion based data classification using SOM. The proposed method shows robust damage sensitivity for hole locations and hole enlargement irrespective of the wide frequency range selection, and the selected frequency range contains the resonant frequency range.

An Optimized Data Fusion Paradigm for WSN Based on Neural Networks

Wireless sensor networks (WSNs) have gotten a lot of attention as useful tools for gathering data. The energy problem has been a fundamental constraint and challenge faced by many WSN applications due to the size and cost constraints of the sensor nodes. This paper proposed a data fusion model based on the back propagation neural network (BPNN) model to address the problem of a large number of invalid or redundant data. Using three layered-based BPNNs and a TEEN threshold, the proposed model describes the cluster structure and filters out unnecessary details. During the information transmission process, the neural network’s output function is used to deal with a large amount of sensing data, where the feature value of sensing data is extracted and transmitted to the sink node. In terms of life cycle, data traffic, and network use, simulation results show that the proposed data fusion model outperforms the traditional TEEN protocol. As a result, the proposed scheme increases the life cycle of the network thereby lowering energy usage and traffic.

Robust Doppler-Based Gesture Recognition With Incoherent Automotive Radar Sensor Networks

In this letter, the capabilities of an incoherent radar sensor network for robust Doppler-based gesture recognition are investigated, and a significant performance boost is demonstrated. A comprehensive dataset is recorded with an incoherent sensor network consisting of three time-synchronized 77GHz frequency-modulated continuous wave radars. Based on this dataset, we show that differential Doppler features obtained from the varying viewing angles result in a significant multistatic gain for classification, particularly for high intraclass variations and low Doppler frequencies. For the most complex dataset, cross-user validation accuracy of a convolutional neural network with optimized data fusion is improved by 7.4% to an overall value of 87.1%, which we regard to be high as gestures are not designed for distinguishability but reflect everyday control and communication signals.

Optimal cluster head selection using modified rider assisted clustering for IoT

The vital concern of this research work is to propose a new clustering algorithm based on IoT devices. This process is made up with four stages namely, Cluster Formation, Splitting and merging, CH selection and Data transformation. The first and foremost step is the cluster formation; in this k-mean clustering is exploited to cluster the network, where the count of needed clusters is optimally selected using a new algorithm. Further, this selection is to either split or merge the cluster structure. Subsequently, the proposed clustering model defines the optimal cluster head selection of each cluster by the proposed algorithm. Finally, in the data transmission phase, the delay optimized data fusion tree is subjected for reducing the manipulation of data fusion on transmission delay. More importantly, to solve all the optimization problems (optimal count of clusters and cluster heads), this paper introduces a new Cyclic Rider Optimization Algorithm (C-ROA), which is the modification of Rider Optimization Algorithm (ROA). To the end, the performance of the adopted method is compared over the other classical models like FF, GWO, WOA and conventional ROA in terms of delay, normalized energy, alive nodes and cost function measures.

An optimized data fusion method and its application to improve lateral boundary conditions in winter for Pearl River Delta regional PM2.5 modeling, China

Lateral boundary conditions (LBCs) are essential for chemical transport models to simulate regional transport; however they often contain large uncertainties. This study proposes an optimized data fusion approach to reduce the bias of LBCs by fusing gridded model outputs, from which the daughter domain's LBCs are derived, with ground-level measurements. The optimized data fusion approach follows the framework of a previous interpolation-based fusion method but improves it by using a bias kriging method to correct the spatial bias in gridded model outputs. Cross-validation shows that the optimized approach better estimates fused fields in areas with a large number of observations compared to the previous interpolation-based method. The optimized approach was applied to correct LBCs of PM2.5 concentrations for simulations in the Pearl River Delta (PRD) region as a case study. Evaluations show that the LBCs corrected by data fusion improve in-domain PM2.5 simulations in terms of the magnitude and temporal variance. Correlation increases by 0.13–0.18 and fractional bias (FB) decreases by approximately 3%–15%. This study demonstrates the feasibility of applying data fusion to improve regional air quality modeling.

Mapping natural and urban environments using airborne multi-sensor ADS40–MIVIS–LiDAR synergies

The recent and forthcoming availability of high spatial resolution imagery from satellite and airborne sensors offers the possibility to generate an increasing number of remote sensing products and opens new promising opportunities for multi-sensor classification. Data fusion strategies, applied to modern airborne Earth observation systems, including hyperspectral MIVIS, color-infrared ADS40, and LiDAR sensors, are explored in this paper for fine-scale mapping of heterogeneous urban/rural landscapes. An over 1000-element array of supervised classification results is generated by varying the underlying classification algorithm (Maximum Likelihood/Spectral Angle Mapper/Spectral Information Divergence), the remote sensing data stack (different multi-sensor data combination), and the set of hyperspectral channels used for classification (feature selection). The analysis focuses on the identification of the best performing data fusion configuration and investigates sensor-derived marginal improvements. Numerical experiments, performed on a 20-km stretch of the Marecchia River (Italy), allow for a quantification of the synergies of multi-sensor airborne data. The use of Maximum Likelihood and of the feature space including ADS40, LiDAR derived normalized digital surface, texture layers, and 24 MIVIS bands represents the scheme that maximizes the classification accuracy on the test set. The best classification provides high accuracy (92.57% overall accuracy) and demonstrates the potential of the proposed approach to define the optimized data fusion and to capture the high spatial variability of natural and human-dominated environments. Significant inter-class differences in the identification schemes are also found by indicating possible sub-optimal solutions for landscape-driven mapping, such as mixed forest, floodplain, urban, and agricultural zones.

Optimized data fusion for K-means Laplacian clustering

Motivation: We propose a novel algorithm to combine multiple kernels and Laplacians for clustering analysis. The new algorithm is formulated on a Rayleigh quotient objective function and is solved as a bi-level alternating minimization procedure. Using the proposed algorithm, the coefficients of kernels and Laplacians can be optimized automatically.Results: Three variants of the algorithm are proposed. The performance is systematically validated on two real-life data fusion applications. The proposed Optimized Kernel Laplacian Clustering (OKLC) algorithms perform significantly better than other methods. Moreover, the coefficients of kernels and Laplacians optimized by OKLC show some correlation with the rank of performance of individual data source. Though in our evaluation the K values are predefined, in practical studies, the optimal cluster number can be consistently estimated from the eigenspectrum of the combined kernel Laplacian matrix.Availability: The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/oklc.html.Contact: shiyu@uchicago.eduSupplementary information: Supplementary data are available at Bioinformatics online.

An Energy-Efficient Routing Protocol for Event-Driven Dense Wireless Sensor Networks

The routing energy efficiency of a wireless sensor network is a crucial issue for the network lifetime. In this article, we propose MICRO (MInimum Cost Routing with Optimized data fusion), an energy-efficient routing protocol for event-driven dense wireless sensor networks. The proposed routing protocol is an improvement over the formerly proposed LEACH and PEGASIS protocol, which is designed to be implemented mainly with node computations rather than mainly with node communications. Moreover, in the routing computation the proposed scheme exploits a new cost function for energy balancing among sensor nodes, and uses an iterative scheme with optimized data fusions to compute the minimum-cost route for each event-detecting sensor node. Compared to the PEGASIS routing protocol, MICRO substantially improves the energy-efficiency of each route, by optimizing the trade-off between minimization of the total energy consumption of each route and the balancing of the energy state of each sensor node. It is demonstrated that the proposed protocol is able to outperform the LEACH and the PEGASIS protocols with respect to network lifetime by 100–300% and 10–100%, respectively.

Optimized Data Fusion in an Intelligent Integrated GPS/INS System Using Genetic Algorithm

Optimized Data Fusion in an Intelligent Integrated GPS/INS System Using Genetic Algorithm