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• A new method for diagnosis of melanoma from dermoscopic images. • The method is an automatic pipeline technique. • Feature selection and final classification are optimized by a new developed metaheuristic. • The used metaheuristic is a developed version of Archimedes optimization algorithm. In recent years, skin cancer has been recognized as the most dangerous and common type of cancer in humans. Melanoma is a common skin cancer, and the diagnosis of melanoma in the early stages of the disease can significantly prevent death from this deadly skin cancer. Providing a method that facilitates the diagnosis of melanoma in the early stages is very useful and valuable. The current paper proposes a new methodology for the best diagnosis of melanoma cancer using dermoscopic images. The proposed method begins with a normalization of scaling the data to a standard range and a histogram equalization to enhance the quality of the input images. Then, some different features based on the Gray-Level Co-occurrence Matrix (GLCM) are extracted from the image. GLCM features capture the spatial distribution and correlation of the pixel intensities, which reflect the texture information of the images. For reducing the complexity of the method, minimum features have been selected using a newly Developed version of Archimedes Optimization Algorithm (DAOA). Then, the selected features are classified by a Support Vector Machine (SVM) to distinguish between benign and malignant lesions. The proposed method is applied to the American Cancer Society (ACS) dataset, which consists of 68 pairs of TLM and XLM images with a size of 180 × 180 pixels. The results have been compared with five different methods based on five performance indicators: precision, sensitivity, accuracy, specificity, and F-measure. The results indicate that the presented approach gives a proper efficiency for the diagnosis of the melanoma. The proposed method achieves the highest values for all the performance indicators among the compared methods. The proposed method achieves an accuracy of 88 %, a sensitivity of 96 %, a specificity of 81 %, a precision of 97 %, and an F-measure of 97 % for the diagnosis of melanoma. [ABSTRACT FROM AUTHOR]

Water quality is affected by increased urbanization as pollutants produced in the urban environment settle and contaminate water, and there is an increase in competition of water among cities, industries, agriculture, etc. The quality and quantity of water are affected by alterations in the microclimate, water dynamics, geomorphology, ecology, and biogeochemistry. As more pavements get created, it becomes increasingly difficult for water to soak into the ground and this causes a decrease in the water table. Impervious structures like streets and roofs when washed with rain deposit excessive pollutants in water bodies. The overall increase in water pollution is a potential health hazard for humans and aquatic life. Hence it is necessary to take adequate measures for addressing the water pollution issue that may potentially arise due to increased urbanization. In this study, we tackle the issue using two approaches. The first approach deals with analyzing the water quality to determine its potability using fifteen different types of machine learning techniques like random forests, decision trees, support vector machines, artificial neural networks, etc. The model has been evaluated using metrics such as precision, recall, accuracy, and F-1 score. The second approach deals with identifying marine litter from beaches in many parts of the world using machine learning algorithms. We also explore the different types of beach environments and the type of litter that is found in different locations using extensive exploratory analysis. Both approaches can be used for ensuring sustainable urban development by reducing water pollution. • Analyzing the quality of water using machine learning techniques • Identifying marine litter from the beach and coastal areas from multiple locations over the world • Explore the different types of beach environments and the type of litter [ABSTRACT FROM AUTHOR]

• To establish trust in the smart home IoT environment using machine learning methods. • We rely on the Change Finder algorithm to observe change points in our time series plots. The algorithm deploys a log-likelihood function on Sequentially Discounting Autoregressive (SDAR) algorithm for evaluating scores. • The study incorporates several machine learning algorithms such as ARIMA, SARIMA, LSTM, Prophet, Light GBM, and VAR for the analysis. • Performed and the analysis has been supported and validated using data visualization techniques as well as evaluation parameters like MSE, RMSE, and MAE. The IoT network is expected to harbor several zettabytes of information in the future. Since trust and integrity are critical to IoT, it is essential to imbibe trust into the IoT environment for ensuring dependability and reliability. We propose a machine learning-based trustworthy system for the IoT-based smart home environment. Multiple appliances connected through the internet are susceptible to privacy issues, hence utmost care must be taken to ensure trust in the network. We consider the energy data and weather information with respect to smart homes, for comprehending the relationship between energy consumption by appliances and time period for detecting anomalous usage of appliances using the SDAR-based Change Finder algorithm. Time series analysis is performed using ARIMA, SARIMA, LSTM, Prophet, Light GBM, and VAR. The evaluation has been performed using RMSE, MSE, and MAE, and the study establishes that the ARIMA model outperforms the other models. [Display omitted] [ABSTRACT FROM AUTHOR]

• A Hybrid Deep Learning with Improved Salp Swarm Optimization-Based Multi-Class Grape Disease Classification (HDLISSA-MGDC) model is proposed. • The Median Filtering (MF) technique is applied for image pre-processing of the HDLISSA-MGDC. • grape disease classification, the ISSA with Convolutional Neural Network-Gated Recurrent Unit (CNN-GRUs) model is employed. • The proposed HDLISSA-MGDC model was experimentally validated using the plant leaf disease dataset. The recent revolutions in Computer Vision (CV) and Artificial Intelligence (AI) techniques have found many applications in grapevine and smart agriculture processes. Recently, Deep Learning (DL) techniques like Convolutional Neural Networks (CNN) have been broadly applied in smart agriculture, leaf disease recognition, and scene perception. In this background, the current study develops a Hybrid Deep Learning with Improved Salp Swarm Optimization-based Multi-class Grape Disease Classification (HDLISSA-MGDC) model. The proposed HDLISSA-MGDC model focuses on the classification of grape leaf images into four distinct classes such as black measles, black rot, Isariopsis leaf spot and healthy. Initially, the Median Filtering (MF) technique is applied for image pre-processing, which eliminates the noise present in the images. In addition, the HDLISSA-MGDC model designs a feature extractor with the help of Dilated Residual Network (DRN) and Adam optimizer. For grape disease classification, the Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model is employed in this study. Finally, the ISSA is exploited to adjust the hyperparameter values of the CNN-GRU method. The proposed HDLISSA-MGDC method was simulated using the plant leaf disease dataset. The simulation results show the significant performance of the proposed HDLISSA-MGDC model. [Display omitted] [ABSTRACT FROM AUTHOR]

• A new A new Internet of Nano Health Things (IoNHT) was proposed and designed for Nano-body Sensor Network (NBSN) considering signal journey from body to cloud. • A Best Path Selection in IoNHT (BPS-IoNHT) is proposed using single or two nano-master nodes. • A new relay selection criteria and path selection are proposed based on number of nano-master node and nano-sensors node and the it is mathematically formulated. • Outage probability has been analysed and formulated of the A Best Path Selection in IoNHT (BPS-IoNHT). • A new metric has been analysed and formulated, bandwidth efficiency of the proposed A Best Path Selection in IoNHT (BPS-IoNHT) • The outage probability has been reduced by 29%, and bandwidth efficiency improved by 35% compared to recent work. Terahertz communication inside the human body uses minimal power; therefore, the destination may not receive the signal properly due to losses caused by molecular absorption and path loss. In this paper, we developed a new cooperative communication for the Internet of Nano Health Things (IoNHT), named the Best Path Selection in IoNHT (BPS-IoNHT). In addition, an innovative communication scenario for Nano Body Sensors Networks (NBSN) has been proposed. The proposed protocol has been thoroughly analysed and mathematically formulated regarding path loss, outage probability and bandwidth efficiency. The objective of the work is to reduce the outage probability and increase bandwidth efficiency. The results show that the proposed protocol performs better than recently published work. The outage probability has been reduced by 29%, and bandwidth efficiency improved by 35% compared to recent work. [Display omitted] [ABSTRACT FROM AUTHOR]

Smart grid integrates the physical power system infrastructure with internet-of-things-based digital communication networks that work together for grid stability, sustainability, and reliability. A significant number of smart devices converge in cyber-physical systems to make the smart grid more competitive and efficient in addressing the energy challenges and vulnerabilities in power system confidentiality, integrity, and availability in smart grid cyber-physical security systems. False data injection attacks are the most malicious threats in the smart grid paradigm and have been widely applied recently. Last few years, several detection algorithms for identifying the false data injection attack have been developed. Addressing these issues, this paper reports a false data injection attack and threat mathematical model, impacting the on-grid system, economy, and society. The classification of false data injection attack detection algorithms and mathematical models are mainly presented. Finally, issues and challenges are identified from existing research and recommended for future research direction. [ABSTRACT FROM AUTHOR]

• Device identification using several machine learning algorithms such as LR, KNN, SVC, DT, RF, GB, AdaBoost, LGBM, XGB, CNN, and LSTM. • Performance evaluation of models using multiple statistical parameters such as accuracy, precision, recall, F1 scores, etc. • Blockchain-based architecture for the smart home environment incorporating blockchain-based structure and smart contracts. • Blockchain-based architecture for two case studies, i.e., data marketplace and access control mechanism. Due to data collection, there is a potential risk concerning security and privacy, so IoT reliability and survivability are of utmost concern. In this paper, we address the concern using two methods. The first method is device identification, which uses an extensive set of machine learning algorithms for identifying IoT devices. The algorithms include Logistic Regression, K- Nearest Neighbour, Support Vector Classifier, Random Forest, Gradient Boosting, AdaBoost, Light Gradient Boosting Machine, Extreme Gradient Boosting Convolution Neural Networks, and Long Short Term Memory are used for device identification. The performance of these models has been evaluated using multiple statistical measures, and weobserve that LSTM outperforms all other baseline models. The second method proposed for ensuring the survivability of theIoT environment is a blockchain-based architecture for smart homes to ensuretransparency and data protection. [Display omitted] [ABSTRACT FROM AUTHOR]

The research paper mainly deals with waste heat recovery from internal combustion engines (ICE) using the organic Rankine cycle (ORC) and Thermoelectric generator (TEG). Simultaneously recovering the wasted heat of both exhaust gases and coolant, a novel configuration named two-stage is proposed. Then a comprehensive thermo-economic analysis and optimization are conducted. Produced power and total cost rate are selected as the objective function of the optimization. Also, the first and second stage pressures of the ORC system are considered as decision variables. Finally, a sensitivity analysis is performed to study the effect of expander inlet temperature, pumps isentropic efficiency, and expander isentropic efficiency on the objective function. [Display omitted] • Hybrid power generation system is proposed based on internal combustion engine, organic Rankine cycle, and thermoelectric generator. • Thermoelectric generator is used instead the condenser. • Energy, exergy, and economic analysis are performed. • Multiobjective optimization is conducted and the optimum operating condition is specified. [ABSTRACT FROM AUTHOR]

Given that it provides nourishment for more than half of humanity, rice is regarded as one of the most significant plants in the world in agriculture. The quantity and quality of the product may be impacted by diseases that can damage rice plants which can occasionally cause crop losses ranging from 30 to 60%. This manuscript proposed a Convolutional Neural Network (CNN) and Visual Geometry Group (VGG)19 i.e. CNN-VGG19 model with a transfer learning-based method for the precise identification and classification of rice leaf diseases. This scheme employs a transfer learning technique based on the VGG19 which can identify the brown spot class. The accuracy is 93.0% in the deployment of the dataset of rice leaf disease. The other parameters are sensitivity, specificity, precision and F1-score with 89.9%, 94.7%, 92.4% and 90.5% respectively. The developed technique obtained better results as compared to the existing baseline models. Proposed CNN-VGG19 model for detection of rice leaf disease. [Display omitted] • The CNN-VGG19 model is developed for the recognition of brown spot rice leaf diseases. • The CNN-VGG19 is based on transfer learning for the precise identification of the brown spot leaf disease. • The highest accuracy, precision, and sensitivity in deploying the developed model are 93.0%, 92.4%, 89.9% respectively. [ABSTRACT FROM AUTHOR]

Cloud service providers are aggressively expanding their data centers to meet end-user demand, resulting in increased power and energy consumption. As a result, it becomes extremely important to implement server consolidation solutions in the context of cloud computing. One of the difficult problems in cloud data centers is efficient server consolidation, which is related to both environmental and financial aspects. Using resources efficiently and reducing energy consumption are the main goals of consolidating servers for cloud computing. Regrettably, the majority of methods now in use have been geared towards either lowering system performance or reducing energy consumption. Using the Markov chain principle for each server transition, we suggested a strategy in this study to optimize the server consolidation process. An early review of the study's findings is encouraging and comes to the conclusion that a successful server consolidation strategy may be implemented with the least amount of resources and the best possible energy use. The suggested strategy takes into account the following three elements: consolidating the resources, cutting down energy consumption, and trading off of both (composite case). [Display omitted] • Presented an optimized server consolidation approach in Cloud environment. • Shows how resource usage and energy consumption can be optimised together. • Employs Markov chain-based scenarios for cloud server consolidation. • Lowers the cost of VM migration by reducing the number of migration counts. • Shows how QoS has improved while categorising servers based on resource demand. [ABSTRACT FROM AUTHOR]

The Internet of Drones (IoD) allows for coordinated control of airspace for Unmanned Aerial Vehicles (UAVs), also known as drones. The decreasing costs of processors, sensors, and wireless connectivity have made it possible to use UAVs in many variety of military to civilian applications. While most applications utilizing the drones in the IoD have been real-time related, users are now interested in obtaining real-time services from drones that are tailored to a specific fly zone. This study develops a Sea Turtle Foraging Algorithm with Hybrid Deep Learning-based Intrusion Detection (STFA-HDLID) as a algorithm that recognizes and categorizes intrusions in the IoD environment. For this purpose, it is necessary to implement data pre-processing to standardize the input data via min-max normalization. Additionally, the feature selection process is also based on the STFA. Finally, a Deep Belief Network (DBN) with a Sparrow Search Optimization (SSO) algorithm is used for classification. A comprehensive experimental analysis is performed on a benchmark dataset to demonstrate the performance of the STFA-HDLID, which achieves maximum accuracy of 99.51% and 98.85% on the TON_IoT and UNSW-NB15 datasets, respectively, outperforming other techniques. [ABSTRACT FROM AUTHOR]

The massive population growth and rising environmental issues raise several challenges in the agriculture sector, such as agricultural land scarcity, overuse of pesticides, and global food demand. To meet global food demand, farmer uses large quantities of pesticides to enhance crop quality and quantity. However, consuming food from pesticide crops reduces human life expectancy. To overcome the aforementioned issue and improve human life expectancy, we proposed a blockchain and artificial intelligence (AI)-empowered smart agriculture framework to predict pesticide crop's beyond the threshold. The blockchain is integrated to confront the data manipulation attack, where the crop that uses minimum pesticides is securely stored inside the blockchain's immutable ledger. Finally, the proposed framework is evaluated with performance metrics, such as accuracy, blockchain scalability, and latency. The result shows that the proposed framework outperforms in terms of accuracy, scalability, and latency compared to the baseline approaches. [Display omitted] • This article proposed a reliable framework that predicts the human life expectancy. • Public blockchain alleviates data manipulation attacks to enhance security. • Proposed framework enhances the latency and packet drop ratio using a 6G network. [ABSTRACT FROM AUTHOR]

Aggressive, unexpected, and catastrophic changes in the environment-induced or impacted by the cultivation of land, crops, and cattle are known as agricultural disasters. In agriculture, the volume of data unpredictability, processing, and data management standards for interoperability are significant concerns. While natural catastrophes are still a considerable problem, the enormous amount of data available has opened up new avenues for coping. Accordingly, big data analytics has profoundly changed the way people respond to disasters in the agriculture sector. In this paper, the Data handling model using big data analytics (DHM-BDA) explores the role of big data in managing agricultural disasters and highlights the technical status of delivering practical and efficient disaster management solutions. DHM-BDA is used to address the essential sources of big data that include climatic causes and associated successes and developing technological problems in different disaster management phases. In addition, it aids in the monitoring, mitigation, alleviation, and acceptance of agricultural catastrophes and the process of recovery and rebuilding. The simulation findings have been executed, and the suggested model enhances the prediction ratio of 98.9%, decision-making level of 97.8%, data management of 96.5%, production ratio of 95.6%, and risk reduction ratio of 97.1% compared to other existing approaches. • Aggressive, unexpected, and catastrophic changes in the environment-induced. • Data unpredictability, processing, and data management standards for interoperability. • Data handling model using big data analytics (DHM-BDA)explores. • The simulation findings have been executed, and the suggested model enhances. [ABSTRACT FROM AUTHOR]

Coal is a key fuel source and an important resource for a wide range of businesses. The hazardous and potentially poisonous nature of the work also has to be taken into consideration. High temperatures, humidity, and the discharge of hazardous gases are just a few of the challenges coal miners confront on a daily basis. This produces a risky work environment that places employees at risk of serious injury or death. In this paper, IoT based Dynamic Sensor Information Control System (IoT-DSICS) has been proposed to solve warm humidity, precipitation, and unhealthy carbon emissions of the coal mine. Using sensor networks and control systems that have been deployed in many sectors, the Industrial Internet of Things (IIoT) is combined in this article. The present security examination of information management has been evaluated since the national coal mining output remains serious and significant accidents are successfully limited. Wi-Fi microcontroller system IIoT is used to monitor and operate the prototypes, activate fans in the Pittsburgh Investigation of Mine, and trigger a surface alert to track the low cost of opening alternative coal. The findings of this feasibility study existing communication and tracking infrastructure are leveraged to examine the potential of IIoT in underground coal mines. • This produces a risky work environment that places employees at risk. • IoT based Dynamic Sensor Information Control System (IoT-DSICS). • The present security examination of information management. • Wi-Fi microcontroller system IIoT is used to monitor. [ABSTRACT FROM AUTHOR]

This paper proffers a secure and anonymous authenticated key exchange (AKE) scheme for SGs, called SAAS-SG, for establishing a secure communication channel between smart meter (SM) and service provider (SPR). SAAS-SG utilizes the hash algorithm, Esch256, and authenticated encryption algorithm AEGIS, to perform the AKE process. Besides, SAAS-SG ensures the integrity and confidentiality of AKE messages while preserving the anonymity of SMs and SPR. Besides, for encrypted transmission in the future, SAAS-SG empowers SMs and SPR to set up an analogous secret session key (SK) after performing the mutual authentication. SK encrypts the sensitive information exchanged between SMs and service providers (SPR) over the public Internet. Moreover, we illustrate that SAAS-SG is capable of resisting different security vulnerabilities by conducting informal and Scyther-based security analyses. In addition, the random oracle model is operated to validate the security of the established secret SK. Furthermore, comparing SAAS-SG with other related AKE schemes explicates that SAAS-SG requires low communication, storage, and computational overheads, respectively, while accomplishing the AKE phase and renders enhanced security features. [ABSTRACT FROM AUTHOR]

• Develop an IoT assisted optimal deep learning-driven weed detection model. • Employ hybrid leader optimizer with YOLO-v5 model for detection process. • Present KELM model for classification of weeds and crops. • Validate the performance on benchmark dataset and achieves 98.87% accuracy. Recent technological advancements of Cloud Computing (CC), Internet of Things (IoT), Artificial Intelligence (AI), computer vision, etc. enable the transformation of traditional agricultural practices into smart agricultural practices. In this background, the current article introduces a novel Hybrid Leader-based Optimization with DL-driven Weed Detection in IoT-enabled Smart Agriculture (HLBODL-WDSA) model. The prime aim of the proposed HLBODL-WDSA model is to collect the images using IoT devices and recognize the weeds automatically. Initially, the HLBODL-WDSA model enables the IoT devices to capture the farm images and transmits the images to the cloud server for examination. Next, the HLBODL-WDSA model applies YOLO-v5-based weed detection process in which HLBO algorithm is exploited as a hyperparameter optimizer. Finally, the Kernel Extreme Learning Machine (KELM) model is applied for effective classification of the weeds. The proposed HLBODL-WDSA model was experimentally validated and the outcomes established the better performance of the proposed HLBODL-WDSA model over recent approaches. [Display omitted] [ABSTRACT FROM AUTHOR]

• Develop an AI enabled Coconut Tree Disease Detection model. • Present AIE-CTDDC model with Bayesian fuzzy clustering-based segmentation. • Apply Harris Hawks Optimizer with gated recurrent unit for classification. • Achieves higher accuracy of 97.75% on coconut tree disease classification. Real-time and accurate plant disease recognition systems help in the development of disease mitigation strategies and ensure food security on a large scale compounded with small-scale economic crop protection. The current research article presents an Artificial Intelligence Enabled Coconut Tree Disease Detection and Classification (AIE-CTDDC) model for smart agriculture. The aim of the presented AIE-CTDDC technique is to classify the coconut tree diseases in a smart farming environment so as to enhance the crop productivity. Firstly, the AIE-CTDDC model applies median filtering-based noise removal technique. Then, the Bayesian fuzzy clustering-based segmentation method is employed for the detection of the affected leaf regions. Besides, the capsule network (CapsNet) method is exploited as a feature extractor. In this study, the Harris Hawks Optimization (HHO) with Gated Recurrent Unit (GRU) model is exploited for the detection of diseases in coconut trees. The experimental analysis was conducted upon AIE-CTDDC model and the outcomes confirmed the better performance of the proposed AIE-CTDDC model over recent state-of-the-art techniques. [Display omitted] [ABSTRACT FROM AUTHOR]

• Develop a secure communication system for internet of drones environment. • Derive OPRFS-ODFNN model for intrusion detection in IoD environment. • Present an oppositional poor and rich optimization based feature selection. • Apply optimal deep feed-forward neural network for intrusion detection. As a result of technological advancements and the need for continuous reduction in manufacturing costs, the concept of Internet of Things (IoT), consisting of Unmanned Aerial Vehicles (UAVs), has entered the industrial production units. IoT devices have not only penetrated the day-to-day activities of human beings, but also in defence. In recent years, there is a widespread application of Internet of Drones (IoD) in areas such as television and film shooting, meteorological monitoring, forest fire detection, agricultural monitoring, emergency rescue, etc. In this background, Intrusion Detection System (IDS) plays an important role to effectually secure the IoD network. The current research work develops an Opposition Poor and Rich Optimization-based Feature Selection with Optimal Deep Feed-forward Neural Network (OPRFS-ODFNN) model for intrusion detection in IoD communication system. The aim of the presented OPRFS-ODFNN technique is to accomplish enhanced security in IoD communication system. In order to achieve the objective, OPRFS-ODFNN model initially executes feature scaling as a pre-processing step. Then, OPRFS technique is applied for effective selection of the features. Moreover, Improved Mayfly Optimization (IMFO) is applied with ODFNN model for intrusion detection and classification processes. In order to validate the enhanced performance of the proposed OPRFS-ODFNN method, extensive simulations were conducted. [Display omitted] [ABSTRACT FROM AUTHOR]

The utilization of 3D display systems offers significant advantages to end-users. Sequencing technologies are essential for accessing an autonomous, auto-balanced, non-glass 3D world with greatly improved visual acuity. To have both social and emotional depth cues on a 3D display is difficult. Because of the theoretically constant feature vectors provided, certain 3D overall volume display approaches cannot produce shadows or structures. Hence, in this paper, a Wireless Sensor Assisted Image Correlation framework (WSAICF) has been proposed to enhance the current competitiveness, performance experience, and better visual understanding of the 3d display systems. In 3D presentations, the Steep Learning algorithm is used to enhance social depth estimations rapidly. The WSAICF method is integrated with the Deep Learning algorithm to enhance the emotional depth indicators of 3D display systems. The simulation result shows that an image correlation system achieves 97 % reliability, 99 % performance, 96 % viability, 11 % resilience, and 95 % efficiency. [ABSTRACT FROM AUTHOR]

• Present an optimal SSDAE to classify cervical cancer on pap smear images. • Employ Kapur's entropy segmentation and efficientnet feature extraction. • Propose modified firefly optimization algorithm for hyperparameter tuning. • Validate the performance of proposed model on Herlev database. Cervical cancer is the most commonly diagnosed cancer among women globally, with high mortality rate. For early diagnosis, automated and accurate cervical cancer classification approaches can be developed through effective classification of Pap smear cell images. The current study introduces a novel Modified Firefly Optimization Algorithm with Deep Learning-enabled cervical cancer classification (MFFOA-DL3) model for the classification of Pap Smear Images (PSI). The proposed MFFOA-DL3 model examines the PSI for the existence of cervical cancer cells. To accomplish this, the proposed MFFOA-DL3 model primarily applies Bilateral Filtering (BF)-based noise removal approach to get rid of the noise. Then, Kapur's entropy-based image segmentation technique is applied to determine the affected regions. Moreover, EfficientNet technique is also applied to generate the feature vectors. Finally, MFFOA with Stacked Sparse Denoising Autoencoder (SSDA) model is exploited to classify the PSI. In current study, MFFOA is utilized to appropriately modify the parameters related to SSDA model. The proposed MFFOA-DL3 model was experimentally validated using benchmark dataset. The results attained from extensive comparative analysis highlighted the better performance of MFFOA-DL3 model over other recent approaches. [Display omitted] [ABSTRACT FROM AUTHOR]

The adaptive Joint Photographic Experts Group 2000 (JPEG2000) image compression approach employing the wavelet image transform had proposed with the rise of the optimized Video Internet of Things (VIoT) using image transmission security using Elliptic Curve Cryptography (ECC) techniques. Compressive Sensing (CS) for periodic data transfers has shown to be an excellent option for Wireless Sensor Networks (WSNs) since CS-based sensor communications drastically reduce data transmissions and enhance energy efficiency. However, another issue that arises when utilizing the optimized VIoT with image transmission compression is data loss as a result of various security risks during transmission. The numerous cooperative communication strategies proved their feasibility in different ways. However, additional issues must be handled while handling image transmission in VIoT. For example, when preparing to transmit an image, you may expend a lot of energy. The main objective of this paper is to increase image quality while minimizing processing time and error rates. However, force aptitude is vital to the research problem for the Wireless Multimedia Sensor Network (WMSN), as high-dimensional digital images use greater processing capabilities of sensor nodes. The image in WMSN is transmitted through a large number of relays. The experimental findings demonstrate the effectiveness of the suggested paradigm. [ABSTRACT FROM AUTHOR]