The wide adoption of battery-powered devices and electric vehicles in recent years has evolved because of the high energy density and long service life of lithium-ion (Li-ion) batteries. However, energy storage systems are also integrated for the effective consumption and generation of energy to meet the sustainable development goals (SDGs) in terms of minimizing carbon emissions and meeting clean energy requirements by 2030. The health of Li-ion batteries in energy storage systems is monitored through a battery management system (BMS) by evaluating parameters such as the State of Health (SoH) and charge/discharge cycles. There are limited studies that create a real-time dataset of evaluation parameters for enhancing the BMS. This study addresses the limitations of previous studies on BMS by proposing an Internet of Things (IoT) approach with long-range (LoRa) communication to enhance performance and reliability in remote areas, an advanced Long Short-Term Memory (LSTM) model for real-time dataset generation, and a predictive model for analyzing battery aging in BMS. The model is trained using verified National Aeronautics and Space Administration (NASA) Li battery datasets and applied to real-time data for Remaining Useful Life (RUL) prediction. The experimental results show that this model has higher predictive and exponential accuracy than previous models, where it increases the Root Mean Square Error (RMSE) from 0.0949 to 0.00665. Although these results are promising, the performance of the model must be further validated with additional datasets and under different operational conditions. In future work, the authors plan to integrate their predictive model into working and physical BMS, and to conduct field tests to determine if the model applies to the actual management of Li-ion batteries.

Federated learning is a machine learning approach that allows many parties to collaborate on training a model without disclosing their raw data. Federated learning is critical in the context of the Internet of Vehicles (IoVs) because it allows cars to exchange sensitive data while maintaining privacy and security. This chapter of the book delves into federated learning-based frameworks for trustworthy and secure communication in IoVs. The chapter investigates the difficulties associated with training machine learning models in IoVs and evaluates the various federated learning frameworks offered for this context. The chapter examines the significance of secure communication and privacy protection in federated learning and the many strategies and procedures utilized to achieve these objectives. It investigates federated learning's possible applications in IoVs, such as traffic prediction and management, intelligent routing optimization, and vehicle safety and security enhancement. Finally, the chapter discusses future research areas for federated learning in IoVs and their implications for the discipline. While numerous federated learning frameworks have been developed for IoVs, privacy and security issues must be solved before federated learning can realize its full potential in IoVs. The chapter suggests several potential future research areas, including developing new federated learning frameworks that better address the challenges of IoVs, exploring additional federated learning applications in this context, and evaluating the performance and efficiency of different federated learning approaches in IoVs.

In the current scenario, the world is focused on renewable energy generation to achieve sustainability by 2030 regarding clean and affordable energy. Lithium-ion (Li-ion)-based Battery Energy storage (BES) is a prominent approach that is widely adopted for managing large-scale renewable energy generation. Battery Management Systems (BMS) play a critical role in optimizing battery performance of BES by monitoring parameters such as overcharging, the state of health (SoH), cell protection, real-time data, and fault detection to ensure reliability. Previous studies have concluded that the implementation of Internet of Things (IoT) with LoRa ensures effective real-time monitoring of the BMS of Li-ion batteries. This study proposed and implemented a customized LoRa and IoT-based hardware system with a gateway to acquire parameters such as terminal voltage, current, charge voltage, charge current, cycle, temperature, state of charge (SoC), and SoH, and log them into the cloud server. An OMnet++-based Framework for LoRa (FLoRa) simulation was implemented to analyze the power consumption and residual energy of the customized LoRa nodes. The simulation was configured with a spread factor of 7, a carrier frequency of 433 MHz, a bandwidth of 125 KHz, and a transmission power of 2 dBm. The simulation results indicated that Node 3 had the highest mean power consumption (0.028233) and total energy consumption (0.146592), whereas Node 0 exhibited the lowest mean power consumption (0.023413) and total energy consumption (0.070204). Additionally, a comprehensive dataset encompassing voltage, current, and time was created and utilized for precise calculations of the battery's capacity and state of health, with potential use in future predictions.

Cloud-edge computing and artificial intelligence proliferation make it necessary to learn how to properly manage energy in the environment. This paper will aim to design and assess a power trading framework on the possibility of adequately optimizing power allocation for cloud-edge AI systems. The problem that the paper seeks to address is the critical problem of energy acumen in cloud-edge AI. With the growing number of edge devices and the unpredictability of AI workload, power allocation must be effectively done. Therefore, the paper’s scope will include designing a power trading system that will effectively and dynamically allocate power resources in the AI to optimize and secure efficiency, effectiveness, reduce costs, and completely eliminate or reduce latencies. The research approach is wide and thorough. I conducted a collected theoretical framework, developed algorithms, acquired actual and credible real-world data, and analyzed big data. During this process, I conducted real-world simulations and deployed the framework in different case studies involving various cloud-edge AI environments. The results showed that power trading framework sufficiently optimizes power allocation, which has increased pyre efficacy, relatively reduced costs, and low latency. Implementing the framework will also secure sustainability ambitions, especially in reducing carbon emissions and depending less on non-renewable energy sources. The output of the study is more recommendable compared to traditional power management systems. This study has a promising starting point for power trading. However, some limitations proved to be challenging, such as some technical difficulties and problems in collecting and submitting data. Some future possibilities include creating new devices through collaboration or new novel applications. The framework can be applied to cloud-edge AI, and business and industries can apply it to their operations on how to manage energy in a rapidly dynamic and energy-intensive environment.

With the increasing demand for modern technologies and automation, there is a need to maintain plants by adopting such modern automated technologies. Every plant has some parameters that must be considered for its survival. Thus, the given paper proposes a novel and optimized system that enables plants to communicate with the user through the use of the Internet of Things (IoT). The parameters associated with the plants should be monitored and classified to ensure that the plants are healthy. In the proposed system, plant requirements are monitored with the help of several sensors and the IoT. The data is collected via sensors related to environmental conditions and sent to an Android application on the user's smart phone. Following this, the collected data is used to classify whether the plant is healthy or unhealthy. The classification is done by using a machine learning classifier named Random Forest (RF), and the experimental results show that the proposed framework is able to achieve higher accuracy (89.85

This study highlights the increasing demand for battery-operated applications, particularly electric vehicles (EVs), necessitating the development of more efficient Battery Management Systems (BMS), particularly lithium-ion (Li-ion) batteries used in energy storage systems (ESS). This research addresses some of the key limitations of current BMS technologies, with a focus on accurately predicting the remaining useful life (RUL) of batteries, which is a critical factor for ensuring operational efficiency and sustainability. Real-time data are collected from sensors via an Internet of Things (IoT) device and processed using Arduino Nano, which extracts values for input into a Long Short-Term Memory (LSTM) model. This model employs the National Aeronautics and Space Administration (NASA) Li-battery dataset and current, voltage temperature, and cycle values to predict the battery RUL. The proposed model demonstrates significant forecasting precision, attaining a root mean square error (RMSE) of 0.01173, outperforming all comparative models. This improvement facilitates more effective decision-making in BMS, particularly in resource allocation and adaptability to transient conditions. However, the practical implementation of real-time data acquisition systems at a scale and across diverse environments remains challenging. Future research will focus on enhancing the generalizability of the model, expanding its applicability to broader datasets, and automating data ingestion to minimize integration challenges. These advancements are aimed at improving energy efficiency in both industrial and residential applications in accordance with the Sustainable Development Goals (SDGs) of the UN.

Cooperative decision-making has always benefited from achieving objectives subjected to system constraints in a microgrid. This paper considers the power balance between generation and load as a significant power quality issue. Hence, frequency regulation is regarded as a primary challenge in the system. When the system operator works towards profit for the power producer, an integrated operation provides a solution. The sources considered in this system are Solar PhotoVoltaics (SPV), FuelCells (FC), Diesel Generators (DG) and Battery driven Electric Vehicles (BEV), where the BEVs operate in Vehicle microgrid mode. A central controller and local controllers are present to operate the generators at desired levels. A cascaded fuzzy controller is designed that chooses the best suitable BEV to be connected to the microgrid. The system is implemented in a MATLAB Simulation environment, and various scenarios and cases have been considered for evaluating the system response and its sustainability.

The threat of cyber-attacks is a major concern in today's world, and accurate and efficient detection of these attacks is crucial for ensuring the security of critical systems. Traditional approaches to cyber attack detection have limitations, and therefore, novel methods are necessary to overcome these challenges. However, predicting cyber attacks presents unique difficulties due to the long-range dependency and nonlinearity of attack data. In this study, we propose a deep learning model, called BRNN-LSTM, that uses bi-directional RNNs with LSTM to overcome these challenges and improve the accuracy of cyber attack detection. Our investigation shows that BRNN-LSTM achieves significantly higher prediction accuracy in detecting cyber-attack rates compared to statistical techniques commonly used in the field. The proposed model uses a bi-directional architecture that enables it to capture the dependencies in the input data from both past and future time steps. LSTM cells are employed to capture the long-term dependencies and nonlinearity of attack data. The results show that BRNN-LSTM is an effective model for detecting cyber attacks. The proposed model outperforms traditional statistical methods, and it can capture the complex dependencies in the data that are crucial for the accurate detection of cyber attacks. Therefore, we believe that BRNN-LSTM can be a valuable addition to the existing methods for cyber attack detection and can significantly improve the security of critical systems.

The increasing use of renewable energy and electric vehicles has led to the widespread adoption of battery management systems (BMS) in energy storage. As BMS becomes more advanced and also becomes more vulnerable to cyber threats. This research paper presents an analysis of the challenges and solutions for enhancing the cybersecurity of BMS. This study examines the current state of BMS cybersecurity, identifies the major threats, and discusses various strategies to enhance the cybersecurity of BMS. This research paper begins by providing an overview of BMS, followed by an analysis of the cybersecurity risks associated with these systems. The study discusses BMS's different types of threats, including malware and cyber-attacks, and also examines the challenges in enhancing the cybersecurity of BMS, such as the lack of awareness and the complexity of BMS. This study proposes solutions to enhance the cybersecurity of BMS, including the use of encryption, access controls, and security protocols, and also discusses best practices for cybersecurity in BMS, including the need for regular updates and patching of software and firmware. This research paper concludes by presenting case studies of cybersecurity threats and solutions in BMS. These case studies demonstrate the effectiveness of the proposed solutions and best practices in mitigating cyber threats to BMS. This research paper is a valuable resource for energy storage system designers, operators, and users, as it provides insights into the challenges and solutions for enhancing the cybersecurity of BMS.

Chronic Metabolic Syndrome Diabetes is often called a “silent killer” due to how little symptoms appear early on. High blood sugar occurs in people with diabetes because their bodies have a hard time maintaining normal glucose levels. Care for a recurrent sickness would be permanent. The two most common forms of diabetes are type 1 and type 2. A better prognosis can help reduce the high risk of developing diabetes. In order to better predict the likelihood that a PIMA Indian may develop diabetes, this study will use a machine learning-based algorithm. The demographic and health records of 768 PIMA Indians were used in the analysis. Standardisation, feature selection, missing value filling, and outlier rejection were all parts of the data preparation process. Machine learning techniques such as logistic regression, decision trees, random forests, the KNN model, the AdaBoost classifier, the Naive Bayes model, and the XGBoost model were used in the study. Accuracy, precision, recall, and F1 score were the only metrics utilised to assess the models' efficacy. The results demonstrate that. The results of this study reveal that diabetes risk may be reliably predicted using machine learning-based models, which has important implications for the early detection and prevention of this illness among PIMA Indians.

In recent years, in consumer electronics, the Health care system is the most important one to evaluate humans' diverse pathological activities. Collaborating with the biomedical field, electronics can find remedial designs for an artificial implant that functionalizes as a human organ to deal with different prostheses, therapy dialysis, etc., conditions and analyze the neural system responses. Using the advanced technological approach, the desired sub-blocks are to be designed to improve the Implant’s performance, especially in cost & market- production. This paper briefly explains the Bionic hearing-aid Implant recognized as a Cochlear implant and its architecture in.

The “metaverse,” the newest buzzword, has drawn a lot of interest from both industry and academia. Through the seamless blending of the actual and virtual worlds known as the metaverse, avatars are now able to take part in a variety of activities, such as commerce, social networking, entertainment, and production. Thus, it is possible to develop an interesting digital setting and change the real world for the good by investigating the metaverse. This research study explores the metaverse by discussing about the possible ways in which blockchain technology and machine learning interact with it by considering the cutting-edge research on its various elements, digital currencies, ML applications in the cybernetic world, and blockchain-enabled devices. Both academics and business will need to work together for additional manipulation and interdisciplinary research on the convergence of ML and Blockchain on the way to metaverse.

Cyber-attacks have become a growing concern for governments, organizations, and individuals worldwide. In this paper, we explore the use of blockchain technology to secure international law against cyber-attacks. We discuss the advantages of blockchain technology in providing secure and transparent data storage and transmission, and how it can enhance the security of international law. We also review the current state of international law regarding cyber-attacks and the need for a robust and effective legal framework to address cyber threats. The study proposes a blockchain-based approach to secure international law against cyber-attacks. We examine the potential of blockchain technology in providing a decentralized and tamper-proof database that can record and track the implementation of international laws related to cyber-attacks. We also discuss how smart contracts can be utilized to automate compliance with international laws and regulations related to cybersecurity. The study also discusses the challenges and limitations of using blockchain technology to secure international law against cyber-attacks. These include the need for interoperability between different blockchain networks, the high energy consumption of blockchain technology, and the need for international cooperation in implementing and enforcing international laws related to cybersecurity. Overall, this study provides a comprehensive overview of the potential of blockchain technology in securing international law against cyber-attacks. It highlights the need for a robust legal framework to address cyber threats and emphasizes the importance of international cooperation in implementing and enforcing international laws related to cybersecurity.

Numerous portable technologies, including electric vehicles, cell phones, and laptops, are powered by batteries. The use of batteries is increasing due to the widespread usage of battery energy storage in the generation of renewable energy. This has also resulted in an increase in the number of negative incidents related to batteries and had a significant negative economic impact on industries. The shortcomings in the traditional monitoring of lithium-ion batteries have been overcome through the implementation of advanced technologies. However, few studies have discussed the use of distinct datasets in the implementation of an intelligent battery management system (BMS). This paper presents a discussion on the choice of dataset variables and applied algorithms for the implementation of effective BMS with artificial intelligence (AI) and machine learning (ML). The study analyzed the use of different datasets, including the National Aeronautics and Space Administration (NASA) battery dataset, to improve BMS. It found that the dataset variables must include the terminal voltage, terminal current, charge current, charge voltage, internal resistance, temperature, and cycle to calculate the state of health (SoH). In future, BMS hardware will be developed to obtain more precise results using AI and ML-based prediction models, utilizing the selected dataset characteristics and variables to achieve longer battery life.

Energy storage systems (ESS) are among the fastest-growing electrical power system due to the changing worldwide geography for electrical distribution and use. Traditionally, methods that are implemented to monitor, detect and optimize battery modules have limitations such as difficulty in balancing charging speed and battery capacity usage. A battery-management system overcomes these traditional challenges and enhances the performance of managing battery modules. The integration of advancements and new technologies enables the provision of real-time monitoring with an inclination towards Industry 4.0. In the previous literature, it has been identified that limited studies have presented their reviews by combining the literature on different digital technologies for battery-management systems. With motivation from the above aspects, the study discussed here aims to provide a review of the significance of digital technologies like wireless sensor networks (WSN), the Internet of Things (IoT), artificial intelligence (AI), cloud computing, edge computing, blockchain, and digital twin and machine learning (ML) in the enhancement of battery-management systems. Finally, this article suggests significant recommendations such as edge computing with AI model-based devices, customized IoT-based devices, hybrid AI models and ML-based computing, digital twins for battery modeling, and blockchain for real-time data sharing.

The battery is the most crucial part of a car. Therefore, for best operation, each battery must be restored to its full potential. Lead Acid batteries are typically used in automobile batteries, and they need to undergo meticulous inspection to perform well under all circumstances. Consequently, a more organized battery control system is required to permit continuous monitoring of the battery's functioning. When it comes to batteries, the SoH (State of Health), SoC (State of Charging), and SoD (State of Discharging) are the most important features. Such parameters can be calculated using a number of cogent ways. However, as the battery's components, surroundings, and load will all have an impact on the parameters, such methods cannot produce precise results. A battery that has been overcharged releases gases like oxygen and hydrogen. In addition to attempting to detect the escape of various gases from the battery under overload situations, the Battery Management System (BMS) uses sensors and an STM controller to display the voltage, current, and temperature of the battery. Through the use of IOT and cloud technologies, this study focused on the detection of hydrogen gas released by batteries.