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Citation - Scopus: 5
Emotion Detection Using Multivariate Synchrosqueezing Transform via 2D Circumplex Model
(Institute of Electrical and Electronics Engineers Inc., 2018) Ozel, Pinar; Akan, Aydin; Yilmaz, Bulent; Özel, Pınar; Akan, Aydin I.; Yilmaz, Bulent
Emotion detection by utilizing signal processing methods is a challenging area. An open issue in emotional modeling is to obtain an optimum feature set to use for the classification process. This study proposes an approach for emotional state classification by the investigation of EEG signals via multivariate synchrosqueezing transform (MSST). MSST is a post-processing technique to compose a localized time-frequency representation yielding multivariate syncyrosqueezing coefficients. After obtaining these coefficients from EEG signals for 18 subjects from DEAP dataset, coefficients and self-assessment-mannequins (SAM) labels of those subjects are used for emotional state classification by using support vector machines (SVM) nearest neighbor, decision tree, and ensemble methods. The accuracy rate is 70.6% for high valence high arousal (HVHA), 75.4% for low valence high arousal (LVHA), 77.8% for high valence low arousal (HVLA), and 77.2% for low valence low arousal (LVLA) cases using SVM. © 2019 Elsevier B.V., All rights reserved.
Sustainable Water Management and Rehabilitation in the Mining Lakes, Ilgin-Konya, Turkey
(Agro Arge Danismanlik San ve Tic As, 2016) Delibalta, M. S.; Uzal, N.; Lermi, A.
The processes during the search, production and enrichment of mining operations naturally affects the air, soil, water resources in turn the natural environment and living organisms. In general, the environmental impact of coal opencast mining operations is much more significant than that of underground mining and mineral processing. After stripping of the material filling the holes in coal opencast production, with the rise of surface water and ground water level is composed of large or small ponds. Low pH (acidic characteristic) and high metal concentrations (Al, Ca, Mn, Fe, Cu, Zn, Pb) of these ponds, containing sulfide minerals and the waste materials, for the sustainability of natural resources is one of the biggest environmental problems. This paper is to investigate geochemical characteristics of the pond waters in the Ilgm Coal deposit area. Geochemical analyses were made by ICP-MS in waters taken from ponds in each three-month periods. Highest heavy metal contents 1839 ppb Mn and 9777 ppb Fe, the average pH values 6.49-7.81, turbidity (NTU) 0.1263.6, sulphate content 0.05-2.67 mg SO4/L, chemical oxygen demand 4-136 mg O-2/L, and electrical conductivity 285 mu S/cm4.68 mS/cm have been measured during the monitoring study of five different lignite opencast mine post-production lakes of the TKI GLI Ilgm. Analyses were performed in three-month periods. The results were evaluated within the framework of relevant laws and regulations.
Citation - WoS: 23
Citation - Scopus: 23
Pressure-Induced Amorphization of MOF-5: A First Principles Study
(Wiley-VCH Verlag GmbH, 2018) Erkartal, Mustafa; Durandurdu, Murat; Erkartal, Mustafa; Durandurdu, Murat
Amorphous metal-organic frameworks (MOFs) and the amorphization of crystalline MOFs under mechanical stimuli are attracting considerable interest in last few years. However, we still have limited knowledge on their atomic arrangement and the physical origin of crystalline-to-amorphous phase transitions under mechanical stimuli. In this study, ab initio simulations within a generalized gradient approximation are carried out to investigate the high-pressure behavior of MOF-5. Similar to the previous experimental findings, a pressure-induced amorphization is observed at 2 GPa through the simulations. The phase transformation is an irreversible first order transition and accompanied by around 68% volume collapse. Remarkably, the transition arises from local distortions and, contrary to previous suggestions, does not involve any bond breaking and formation. Additionally, a drastic band gap closure is perceived for the amorphous state. This study has gone some way towards enhancing our understanding of pressure-induced amorphization in MOFs.
Citation - Scopus: 1
Data-Driven Discovery and DFT Modeling of Fe4H on the Atomistic Level
(Elsevier B.V., 2024) Zagorac, Dejan; Zagorac, Jelena; Djukic, Milos B.; Bal, Burak; Schön, Johann Christian
Since their discovery, iron and hydrogen have been two of the most interesting elements in scientific research, with a variety of known and postulated compounds and applications. Of special interest in materials engineering is the stability of such materials, where hydrogen embrittlement has gained particular importance in recent years. Here, we present the results for the Fe-H system. In the past, most of the work on iron hydrides has been focused on hydrogen-rich compounds since they have a variety of interesting properties at extreme conditions (e.g. superconductivity). However, we present the first atomistic study of an iron-rich Fe4H compound which has been predicted using a combination of data mining and quantum mechanical calculations. Novel structures have been discovered in the Fe4H chemical system for possible experimental synthesis at the atomistic level. © 2024 Elsevier B.V., All rights reserved.
Citation - WoS: 5
Near- and Far-Field Characterization of Planar mm-Wave Antenna Arrays With Waveguide-to Transition
(Springer, 2016) Salhi, Mohammed Adnan; Kazemipour, Alireza; Gentille, Gennaro; Spirito, Marco; Kleine-Ostmann, Thomas; Schrader, Thorsten
We present the design and characterization of planar mm-wave patch antenna arrays with waveguide-to-microstrip transition using both near- and far-field methods. The arrays were designed for metrological assessment of error sources in antenna measurement. One antenna was designed for the automotive radar frequency range at 77 GHz, while another was designed for the frequency of 94 GHz, which is used, e.g., for imaging radar applications. In addition to the antennas, a simple transition from rectangular waveguide WR-10 to planar microstrip line on Rogers 3003 (TM) substrate has been designed based on probe coupling. For determination of the far-field radiation pattern of the antennas, we compare results from two different measurement methods to simulations. Both a far-field antenna measurement system and a planar near-field scanner with near-to-far-field transformation were used to determine the antenna diagrams. The fabricated antennas achieve a good matching and a good agreement between measured and simulated antenna diagrams. The results also show that the far-field scanner achieves more accurate measurement results with regard to simulations than the near-field scanner. The far-field antenna scanning system is built for metrological assessment and antenna calibration. The antennas are the first which were designed to be tested with the measurement system.
Citation - Scopus: 2
Street Vendor Detection: Helping Municipalities Make Decisions With Actionable Insights
(IEEE, 2021) Agba, Hatice Nur; Tahir, Abdullah
Street vendors are quite common in countries across the world. By the prevalence of mobile surveillance systems, increasing demand for automatic detection of street vendors for further decisions and planning by the city administrators emerged. In this paper, an object detector is developed using a MobileNet SSD object detection algorithm to detect vendors on the street. For this study images were used, however, in the future this technique could be used for real time video footage from street cameras. Since this is the first study tackling this issue, a data set was created from scratch. The accuracy achieved by the algorithm is promising considering the size of the data set and the minimal computational power available. The goal of this research is to pave the way for more work to be done in this area and help municipalities improve their decision making process regarding street vendor activities in countries like Mexico, Pakistan, China, Turkey, etc.
Citation - WoS: 6
Citation - Scopus: 6
Experimental Measurements of Some Thermophysical Properties of Solid CdSb Intermetallic in the Sn-Cd Ternary Alloy
(Springer, 2016) Ozturk, Esra; Aksoz, Sezen; Altintas, Yemliha; Keslioglu, Kazum; Marasli, Necmettin
The equilibrated grain boundary groove shapes of solid CdSb in equilibrium with Sn-Cd-Sb eutectic liquid were observed from a quenched sample by using a radial heat flow apparatus. The Gibbs-Thomson coefficient, solid-liquid interfacial energy and grain boundary energy of the solid CdSb intermetallic were determined from the observed grain boundary groove shapes. The thermal conductivity of the eutectic solid and the thermal conductivity ratio of eutectic liquid to the eutectic solid in the Sn-35.8 at.%Cd-6.71 at.%Sb eutectic alloy at its eutectic melting temperature were also measured with a radial heat flow apparatus and a Bridgman-type growth apparatus, respectively.
Evaluation of Hybrid Classification Approaches: Case Studies on Credit Datasets
(Springer Verlag service@springer.de, 2018) Cetiner, Erkan; Güngör, Vehbi Çağrı; Kocak, Taskin
Hybrid classification approaches on credit domain are widely used to obtain valuable information about customer behaviours. Single classification algorithms such as neural networks, support vector machines and regression analysis have been used since years on related area. In this paper, we propose hybrid classification approaches, which try to combine several classifiers and ensemble learners to boost accuracy on classification results. We worked with two credit datasets, German dataset which is a public dataset and a Turkish Corporate Bank dataset. The goal of using such diverse datasets is to search for generalization ability of proposed model. Results show that feature selection plays a vital role on classification accuracy, hybrid approaches which shaped with ensemble learners outperform single classification techniques and hybrid approaches which consists SVM has better accuracy performance than other hybrid approaches. © 2018 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
Man-Hour Prediction for Complex Industrial Products
(Institute of Electrical and Electronics Engineers Inc., 2023) Unal, Ahmet Emin; Boyar, Halit; Kuleli Pak, Burcu Kuleli; Cem Yildiz, Mehmet; Erten, Ali Erman; Güngör, Vehbi Çağrı
Accurately predicting the cost is crucial for the success of complex industrial projects. There can be several sources contributing to the cost. Traditional methods for cost estimation may not provide the required accuracy and speed to ensure the success of the project. Recently, machine learning techniques have shown promising results in improving cost estimation in various industrial products. This study investigates the performance of gradient-boosting machine learning models and feature engineering techniques on a private dataset of metal sheet project man-hour costs. A comparison of distinct models is conducted, key aspects influencing cost are identified, and the implications of incorporating domain-specific knowledge, including its advantages and disadvantages, are assessed based on performance outcomes. Experimental results demonstrate that LightGBM and XGBoost outperform other models, and feature selection and synthetic data generation techniques improve the performance. Overall, this study highlights the potential of machine learning in metal sheet sampling projects and emphasizes the importance of feature engineering and domain expertise for better model performance. © 2024 Elsevier B.V., All rights reserved.
Citation - WoS: 251
Citation - Scopus: 263
Surface-Enhanced Raman Spectroscopy (SERS): An Adventure from Plasmonic Metals to Organic Semiconductors as SERS Platforms
(Royal Soc Chemistry, 2018) Demirel, Gokhan; Usta, Hakan; Yilmaz, Mehmet; Celik, Merve; Alidagi, Husniye Ardic; Buyukserin, Fatih; Demirel, Gokhan; Usta, Hakan; Yilmaz, Mehmet; Celik, Merve; Alidagi, Husniye Ardic; Buyukserin, Fatih
The quantitative determination and identification of bio-/chemical molecules at ultra-low concentrations is a hot topic in several fields including medical diagnostics, environmental science, and homeland security. Molecular detection techniques are conventionally based on optical, electrochemical, electronic, or gravimetric methodologies. Among these methods, surface-enhanced Raman spectroscopy (SERS) is considered as one of the most reliable, sensitive and selective techniques for non-destructive molecular analysis through the amplification of electromagnetic fields and/or creation of charge-transfer states between the chemisorbed analyte molecule and SERS active platform. Unfortunately, the applicability of SERS is rather limited, which is mainly due to the lack of highly sensitive SERS platforms with good stability and reproducibility. In line with this, metal nanoparticles (e.g., Au, Ag, and Cu) have been extensively exploited as SERS active platforms. Although the utilization of metallic nanoparticles in SERS is simple and cost-effective, the poor controllability of the structures and limited formation of hot spots in the detection zone leads to discrepancy in the resulting SERS signals. For these reasons, in the past few years, researchers have focused on fabricating 3-dimensional (3D) SERS platforms, which increase the adsorption of analyte molecules and facilitate hot spot formation in all three dimensions. However, the fabrication of 3D SERS platforms is mostly expensive and technologically demanding. Therefore, the discovery of non-metal alternative approaches is of great interest not only to widen SERS applications but to further elucidate fundamental questions. Considering recent developments on the fabrication and application of SERS active platforms, this review is structured in 3 main directions; (1) implementation of the plasmonic nanoparticles having different shapes into SERS-active platforms, (2) highlighting recent developments in the fabrication and application of 3D SERS-active platforms, and (3) examination of recent novel inorganic and organic semiconductor based platforms for SERS applications. At the end, we conclude with the promises and challenges for the future evolution of SERS.
Citation - WoS: 8
Citation - Scopus: 8
Low Velocity Oblique Impact Behavior of Adhesively Bonded Single Lap Joints
(Taylor & Francis Ltd, 2019) Atahan, M. Gokhan; Apalak, M. Kemal; Atahan, M. Gokhan; Apalak, M. Kemal
This article addresses the low velocity oblique impact behavior of adhesively bonded single lap joints, and the effects of adherend strength and plastic ductility, impact energy, overlap length and oblique impact angle on the damage initiation and propagation in the adhesive layer. The experimental contact force-time, contact force-central displacement variations, axial separation lengths through the adhesive layer and permanent central deflections of overlap region, adhesive fracture surfaces were evaluated in detail. In the explicit finite element analyses, the adhesive layer was divided into three zones: upper and lower adhesive interfaces and the adhesive layer between these interfaces. The adhesive interfaces were modeled with cohesive zone approach to predict the failure initiation and propagation along both upper and lower adhesive-adherend interfaces, whereas the elastic-plastic material model was implemented for the middle adhesive region between the upper and lower adhesive interfaces. The proposed finite element model predicted reasonably the damage initiation and propagation through the adhesive layer, and the contact force-time/central displacement variations. Especially, the test and analysis results were compared with those of the adhesively bonded single lap joints under a normal transverse impact load. Increasing oblique impact angle resulted in lower peak contact forces, shorter contact durations and earlier damage initiation and propagation through the adhesive layer. The peak contact forces increased, the contact duration decreased with increasing impact energy. The strength and plastic deformation capability of adherend materials also affected the damage initiation and propagation through the adhesive layer as well as the after-impact joint geometry.
Citation - WoS: 6
Citation - Scopus: 7
Synergistic Effect of Organic Acid on the Dissolution of Mixed Nickel-Cobalt Hydroxide Precipitate in Sulphuric Acid Solution
(Edp Sciences S A, 2019) Kursunoglu, Sait; Kursunoglu, Sait
The synergistic effect of an organic acid on the dissolution of nickel and cobalt from a mixed nickel-cobalt hydroxide precipitate (MHP) in sulphuric acid solution was studied. The effects of sulphuric acid concentration, the type of organic acid, leaching time, leaching temperature and stirring speed on the dissolution of the metals were experimentally investigated. It was observed that there is no beneficial effect of leaching temperature and stirring speed on the dissolution of the metals from the used MHP product which contains 37.7% Ni, 2.1% Co and 5.6% Mn. It was found that citric acid was more effective than oxalic acid for the dissolution of nickel and manganese, whereas oxalic acid was more effective than citric acid for the dissolution of cobalt. The addition of oxalic acid into the leaching system, however, affected the dissolution of nickel negatively because nickel precipitate as nickel oxalate. Therefore, the use of citric acid as synergist for sulphuric acid leaching of MHP product is more promising. After 60 min of leaching, 90.9% Ni, 84.2% Co and 98.1% Mn were dissolved under the following conditions: 0.75 M sulphuric acid, 2 g citric acid, 1/10 solid-to-liquid ratio, 400 rpm stirring speed and 30 degrees C temperature. The experimental results demonstrate that the addition of citric acid as a synergist for sulphuric acid leaching of a MHP product provides beneficial effect for the dissolution of nickel, cobalt and manganese.
Citation - WoS: 5
Citation - Scopus: 5
Formation of a Very High-Density Amorphous Phase of Carbon and Its Crystallization into a Simple Cubic Structure at High Pressure
(Elsevier B.V., 2021) Durandurdu, M.
We report a direct computational evidence of a two-step transformation sequence for tetrahedral amorphous carbon (ta-C) with increasing pressure. First, ta-C gradually transforms into a very high-density amorphous phase (VHDA) phase. Second, the VDHA phase converts into a simple cubic (SC) crystal. The structural defects formed during the high-pressure treatment play important roles for the formation and stabilization of the SC structure, rather than favorable the SC4 crystal. These phase transformations are reversible. © 2021 Elsevier B.V., All rights reserved.
Citation - WoS: 10
Self-Healing Performance of Biogranule Containing Microbial Self-Healing Concrete Under Intermittent Wet/Dry Cycles
(Gazi Univ, 2021) Ersan, Yusuf Cagatay
Development of self-sensing and self-healing concrete is essential to minimize the labour-intensive monitoring and repair activities conducted for the maintenance of concrete structures. A type of self-healing concrete can be achieved by using microbial agents that induce calcium carbonate precipitation inside a concrete crack. Recently, biogranules consist of nitrate reducing microorganisms were presented as a new generation microbial healing agent and biogranule containing specimens revealed decent healing performance under completely submerged conditions. However, their performance under intermittent wetting conditions, a common case for various concrete structures, remains unknown. This study presents the self-healing performance of biogranule containing biomortar specimens under intermittent wet/dry conditions. In-house produced biogranules were incorporated into mortar specimens at a dose of 1.45% w/w cement (1.00% of bacteria w/w cement) and self-healing performance of cracked specimens were investigated under alternating wet/dry conditions for a crack width range of 50 to 600 um. Upon alternating wet/dry treatment for 4 weeks, cracks up to a 400 um crack width were effectively healed in biomortar specimens. Their water tightness regain was 44% better than control specimens due to their enhanced healing performance. Overall, non-axenic biogranules appear to be useful in development of self-healing bioconcrete for applications under spraying or intermittent wetting conditions.
Citation - WoS: 15
Citation - Scopus: 16
Stacking Ensemble Learning-Based Wireless Sensor Network Deployment Parameter Estimation
(Springer Heidelberg, 2023) Akbas, Ayhan; Buyrukoglu, Selim
In wireless sensor network projects, it is generally desired to cover the area to be monitored at a given cost and to achieve the maximum useful network lifetime. In the deployment of the wireless sensors, it is necessary to know in advance how many sensor nodes will be required, how much the distance between the nodes should be, etc., or what the transmit power level should be, etc. depending on the channel parameters of the area. This necessitates accurate calculation of variables such as maximum network lifetime, communication channel parameters, number of nodes to be used, and distance between nodes. As numbers reach to the order of hundreds, calculation tends to a NP hard problem to solve. At this point, we employed both single-based and stacked ensemble-based machine learning models to speed up the parameter estimations with highly accurate outcomes. Adaboost was superior over other models (Elastic Net, SVR) in single-based models. Stacked ensemble models achieved best results for the WSN parameter prediction compared to single-based models.
Citation - WoS: 11
Citation - Scopus: 11
Biogas Intake Pressure and Port Air Swirl Optimization to Enhance the Diesel RCCI Engine Characteristics for Low Environmental Emissions
(Elsevier, 2024) Dalha, Ibrahim B.; Koca, Kemal; Said, Mior A.; Rafindadi, Aminu D.
Exhaust emission and combustion control in RCCI (reactivity-controlled compression ignition) focused mainly on the direct-injected fuel parameters, urging to investigate the advantages of port-fuel intake parameters. The engine was modified for port injection of Biogas at the valve and RCCI mode. The influence of port swirl ratio (PSR, 0 - 80%) and biogas injection pressure (BIP, 1 - 4 bar) on the diesel RCCI combustion and emissions was tested and optimized at varied loads and 1600 rpm in a port injection at the valve (PIVE) approach. Established kinetic mechanisms were combined with multi-objective optimization to further investigate, predict, and analyze emissions occurrence and trade-offs for reduced environmental impacts. The results show that the radiation absorption triggered by increased CO2 lowers combustion temperature, resulting in prolonged ignition. Setting the airflow to swirl lowers the in-cylinder pressure at elevated BIP while raising the heat generated across the BIPs. Increasing the PSR slows the combustion while BIP speeds up the process. BIP and PSR show great trade-off reduction ability among all emission parameters. The optimum unburned hydrocarbon, nitrogen oxide, particulate, and carbon monoxide emissions for the injection at the valve were found to be 109.58, 0.577, and 2.336 ppm, and 0.103%, respectively, at low-load, low-BIP, and high-PSR. The emissions were lowered by 6.58, 91.26, 80.65, and 13.45% compared to the premixed RCCI mode, respectively. Therefore, introducing lowpressure biogas amid high swirling air at the valve elevates the in-cylinder condition while lowering the emissions, mitigating their environmental implications.
Design and Development of Functional Organic Small Molecules and Polymers for Optoelectronics
(Amer Chemical Soc, 2016) Usta, Hakan; Demirel, Gokhan; Facchetti, Antonio; Muccini, Michele
Citation - WoS: 5
Node-Level Error Control Strategies for Prolonging the Lifetime of Wireless Sensor Networks
(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Tekin, Nazli; Yildiz, Huseyin Ugur; Gungor, Vehbi Cagri
In Wireless Sensor Networks (WSNs), energy-efficiency and reliability are two critical requirements for attaining a long-term stable communication performance. Using error control (EC) methods is a promising technique to improve the reliability of WSNs. EC methods are typically utilized at the network-level, where all sensor nodes use the same EC method. However, improper selection of EC methods on some nodes in the network-level strategy can reduce the energy-efficiency, thus the lifetime of WSNs. In this study, a node-level EC strategy is proposed via mixed-integer programming (MIP) formulations. The MIP model determines the optimum EC method (i.e., automatic repeat request (ARQ), forward error correction (FEC), or hybrid ARQ (HARQ)) for each sensor node to maximize the network lifetime while guaranteeing a pre-determined reliability requirement. Five meta-heuristic approaches are developed to overcome the computational complexity of the MIP model. The performances of the MIP model and meta-heuristic approaches are evaluated for a wide range of parameters such as the number of nodes, network area, packet size, minimum desired reliability criterion, transmission power, and data rate. The results show that the node-level EC strategy provides at least 4.4% prolonged lifetimes and 4.0% better energy-efficiency than the network-level EC strategies. Furthermore, one of the developed meta-heuristic approaches (i.e., extended golden section search) provides lifetimes within a 3.9% neighborhood of the optimal solutions, reducing the solution time of the MIP model by 89.6%.
Citation - WoS: 18
Citation - Scopus: 20
A Simulation-Based Approximate Dynamic Programming Approach to Dynamic and Stochastic Resource-Constrained Multi-Project Scheduling Problem
(Elsevier, 2024) Satic, U.; Jacko, P.; Kirkbride, C.
We consider the dynamic and stochastic resource -constrained multi -project scheduling problem which allows for the random arrival of projects and stochastic task durations. Completing projects generates rewards, which are reduced by a tardiness cost in the case of late completion. Multiple types of resource are available, and projects consume different amounts of these resources when under processing. The problem is modelled as an infinite -horizon discrete -time Markov decision process and seeks to maximise the expected discounted long -run profit. We use an approximate dynamic programming algorithm (ADP) with a linear approximation model which can be used for online decision making. Our approximation model uses project elements that are easily accessible by a decision -maker, with the model coefficients obtained offline via a combination of Monte Carlo simulation and least squares estimation. Our numerical study shows that ADP often statistically significantly outperforms the optimal reactive baseline algorithm (ORBA). In experiments on smaller problems however, both typically perform suboptimally compared to the optimal scheduler obtained by stochastic dynamic programming. ADP has an advantage over ORBA and dynamic programming in that ADP can be applied to larger problems. We also show that ADP generally produces statistically significantly higher profits than common algorithms used in practice, such as a rule -based algorithm and a reactive genetic algorithm.
Citation - WoS: 44
Citation - Scopus: 52
CBI4.0: A Cross-Layer Approach for Big Data Gathering for Active Monitoring and Maintenance in the Manufacturing Industry 4.0
(Elsevier, 2021) Faheem, Muhammad; Butt, Rizwan Aslam; Ali, Rashid; Raza, Basit; Ngadi, Md Asri; Gungor, Vehbi Cagri
Industry 4.0 (I4.0) defines a new paradigm to produce high-quality products at the low cost by reacting quickly and effectively to changing demands in the highly volatile global markets. In Industry 4.0, the adoption of Internet of Things (IoT)-enabled Wireless Sensors (WSs) in the manufacturing processes, such as equipment, machining, assembly, material handling, inspection, etc., generates a huge volume of data known as Industrial Big Data (IBD). However, the reliable and efficient gathering and transmission of this big data from the source sensors to the floor inspection system for the real-time monitoring of unexpected changes in the production and quality control processes is the biggest challenge for Industrial Wireless Sensor Networks (IWSNs). This is because of the harsh nature of the indoor industrial environment that causes high noise, signal fading, multipath effects, heat and electromagnetic interference, which reduces the transmission quality and trigger errors in the IWSNs. Therefore, this paper proposes a novel cross-layer data gathering approach called CBI4.0 for active monitoring and control of manufacturing processes in the Industry 4.0. The key aim of the proposed CBI4.0 scheme is to exploit the multi-channel and multi-radio architecture of the sensor network to guarantee quality of service (QoS) requirements, such as higher data rates, throughput, and low packet loss, corrupted packets, and latency by dynamically switching between different frequency bands in the Multichannel Wireless Sensor Networks (MWSNs). By performing several simulation experiments through EstiNet 9.0 simulator, the performance of the proposed CBI4.0 scheme is compared against existing studies in the automobile Industry 4.0. The experimental outcomes show that the proposed scheme outperforms existing schemes and is suitable for effective control and monitoring of various events in the automobile Industry 4.0.

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