As the world fights climate change and depletes fossil fuel reserves, EVs, RES, and IoT offer sustainable transportation and energy management. This research paper examines the technological advances, regulatory initiatives, and market trends that have brought these three sectors together. EVs can reduce carbon footprints, energy efficiency, and urban pollution, but the article discusses their pros and cons in transportation. Batteries, charging infrastructure, and vehicle-to-grid (V2G) capabilities are essential to integrating EVs into the power grid and realizing their full potential as distributed energy resources. The article also discusses how electric vehicles and renewable energy are growing in the electricity industry. Clean and sustainable energy mixes include solar, wind, hydro, and biomass. Demand response and energy storage can help integrate RES smoothly into the grid, according to the article. The study explores how IoT can change electric vehicles, renewable energy, and the power grid. Smart charging stations, V2I connectivity, and intelligent energy management systems could change energy consumption and distribution. IoT-enabled real-time data analytics and automation for EV charging, dynamic load balancing, and grid stability improve energy management and carbon footprint. This study examines the prospects and challenges of sustainable transportation and energy management with EVs, RES, and the Internet of Things. This includes uniform communication protocols, intermittent renewable energy, and strict cybersecurity. The report concludes with future research. The paper encourages policymakers, industry stakeholders, and academics to collaborate on new business models, policy frameworks, and technological advances to accelerate this integrated approach's adoption.
Lead (Pb) is well known for the containment of soil surfaces. In the last few decades, phytoremediation has been the most ideal technology to extract Pb from soil, involving numerous chemical reactions and cost analysis. The aim of this study is to model and to optimize Pb extraction from the contaminated soil via Pelargonium hortorum by comparing two modeling approaches: response surface methodology (RSM) and artificial neural networks (ANNs) with the genetic algorithm (GA). To determine the significance of the proposed solution, in vitro essays were performed to check the Pb tolerance of bacterial strains (NCCP 1844, 1848, 1857, and 1862), followed by the co-application of bacteria and citric acid on a Pb hyperaccumulator (Pelargonium hortorum L.) on Murashige and Skoog (MS) agar medium. Afterwards, a pot culture experiment was performed to optimize Pb extraction competency from Pb-spiked (0 mg kg−1, 500 mg kg−1, 1000 mg kg−1, and 1500 mg kg−1) soil by Pelargonium hortorum L., to which citric acid (5 and 10 mmol L−1) and Microbacterium paraoxydance (1 and 1.5 OD) were applied. Plants were harvested at 30, 60, and 90 day intervals, and they were analyzed for dry biomass and Pb uptake characteristics. The maximum Pb extraction efficiency of 86.0% was achieved with 500 mg kg−1 soil Pb for 60 days. Furthermore, RSM, based on the Box–Behnken design (BBD) and the ANN-based Levenberg–Marquardt Algorithm (LMA), were applied to model Pb extraction from the soil. The significance of the predicted values from RSM and LMA were close to 36.0% and 86.05%, respectively, compared to the laboratory values. The comprehensive evaluation of these findings encouraged the accuracy, reliability, and efficiency of the ANN for the optimization process. Therefore, experimental results showed that ANN is an accurate technique to optimize an integrated phytoremediation system for sustainable Pb removal, besides being environmentally friendly and potentially cost-effective.
Over the last decade, much has been done to improve healthcare services and technology. A recent study found that IoT can connect sensors, medical devices and professionals to deliver high-quality remote medical care. It has improved operational efficiency, reduced healthcare costs, and increased patient safety in the healthcare industry. Using the internet of things, this study offers a distributed architecture based on the monitoring of human biomedical signals during physical activity. The proposed system novelties are healthcare applications that are flexible and can be computed using resources from the user's body area network. This proposed framework can be used in a variety of mobile scenarios, especially if data collecting and processing is a major concern. To support our idea of monitoring a human heart rate during some activities has been considered. A major social benefit of the real-time data collected by these gadgets is the ability to anticipate not only fatalities but also injuries.This paper gives a complete review of IoT-based healthcare applications by enabling technology, healthcare services, and particular applications, the HIoT is tackling a number of healthcare challenges. Other potential issues with the HIoT technology are discussed. The current study provides future researchers with extensive knowledge of the different applications of HIoT.
Every type of system may replace or enhance the functionality currently delivered by legacy systems to new system, regardless of the type of project/application; some data conversion may take place. Difficulties arise when we take the information currently maintained by the legacy system and transform it to fit into the new system. We refer to this process as data migration. Data migration is a common element among most system implementations. It can be performed once, as with a legacy system redesign, or may be an ongoing process as in storage of historical data in the form of a data warehouse. Some legacy system migrations require ongoing data conversion if the incoming data requires continuous cleansing. It should be that any two systems that maintain the same sort of data must be doing very similar things and, therefore, should map from one to another with ease. Legacy systems have historically proven to be far too lenient with respect to enforcing integrity at the atomic level of data. Another common problem has to do with the theoretical design differences between hierarchical and relational systems. In data migration one method apply in twice (i.e. automated and manual). This paper explores the steps to migrate date in form of manual, i.e. process of data migration without the help of any special tool those made for data migration. Manual data cleaning is commonly performed in migration to improve data quality, eliminate redundant or obsolete information, and match the requirements of the new system in correct and efficient form.
Smart lights, ovens, and industrial data-gathering equipment are all instances of internet-connected gadgets. With 41.6 billion IoT devices being used, 79.4 ZB of data will be generated by 2025, according to research firm IDC. The earliest IoT gadgets sent information to the cloud for analysis. When sending many billions of gigabytes to the cloud, the data pipeline clogs up. With computing at the edge, IoT gadgets can analyze data without transferring it to a remote server. Data is handled locally, at the "edge" of your network, rather than being transmitted elsewhere. Technology's malleability makes it a valuable tool in the field of education. The specifications for edge computing in IoT networks vary from those of other networks. At the edge, well-suited setting to handle the massive volumes of devices and data generated by the Internet of Things. Several tasks can be moved to the device's edge, which can help keep costs down. The importance of IoT edge computing architecture in education has grown as a result of its deployment in real-time applications. The research simplifies the process of analyzing the structural layout of educational systems by proposing a framework for Internet of Things (IoT) Edge computing in the field of education. There will be a focus on concerns and issues with the IoT that will be brought out by the framework. In this research, potential and pitfalls of using IoT edge computing in education institutions is explored. In addition to this, the research article analyzes the effectiveness of edge computing for Internet of Things applications in an educational environment.
As the importance of making the switch to renewable energy sources becomes more widely acknowledged, conventional energy sources have fallen out of favor. However, maximizing the benefits of renewable energy sources calls for solutions to complex problems like intermittency, grid integration, and resource optimization. Data science has become increasingly important in recent years as a method for analyzing large datasets and deriving actionable insights from them. This paper investigates the relationship between data science and renewable energy, specifically how big data analytics can cause a paradigm shift in the renewable energy industry, improving efficiency, reliability, and sustainability. Beginning with an examination of the background and current status of renewable energy technologies, the paper goes on to highlight the inherent variability and uncertainty of renewable resources. Data science's potential to process, manage, and analyze diverse datasets generated by renewable sources, weather patterns, energy consumption, and grid operations are then discussed in depth. The importance of key data science techniques in solving pressing problems is discussed. These techniques include machine learning, time-series analysis, and optimization algorithms. The research paper provides a number of case studies and examples of real-world applications of data-driven approaches in the field of renewable energy. Some examples are demand-side management, smart grid optimization, real-time forecasting of renewable energy generation, and predictive maintenance for renewable energy infrastructure. Increased use of renewable energy sources, decreased carbon emissions, and lessened climate change impacts are all areas where data-driven strategies shine. The paper also discusses potential hurdles that must be carefully managed to ensure the widespread adoption of data science applications in renewable energy, such as data privacy concerns, data quality issues, and scalability challenges. By showcasing how smart analysis of big data can revolutionize energy systems, this study concludes that data science has the potential to significantly impact the renewable energy sector. The use of data science methods in the renewable energy sector will allow for greater efficiency and sustainability, ultimately leading to a more environmentally friendly future. As data science develops, it will be crucial for researchers, energy experts, and policymakers to work together to shape the future of renewable energy.
Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL IoT Edge Computing and Blockchain for High-Performance and Decentralized Health Monitoring System 9 Pages Posted: 12 Jul 2022 See all articles by Md. Imran AlamMd. Imran Alam Department of Computer and Network Engineering, College of Computer Science and Information Technology, Jazan UniversityAgha Salman HaiderJazan University - Department of Information Technology and SecurityAlighazi SiddiquiJazan University - Department of Computer ScienceMd. Rafeek KhanImam Abdulrahman Bin Faisal University (IAU) - College of Computer Science and Information TechnologyShams Tabrez SiddiquiJazan UniversityHaneef KhanJazan University - Department of Computer Science Date Written: July 14, 2022 Abstract Since so many people have turned to medical science as a lifeline in recent years and the field of big data in medicine has grown rapidly over the last decade. Doctors and other healthcare professionals are using wearable technology based on the Internet of Things to speed up diagnosis and treatment (IoT). As technology gets better, an increasing number of internet-connected sensors and gadgets are easily available in markets. The Internet of Medical Things (IoMT) is used to perform a variety of tasks and applications in providing healthcare services.Doctors and nurses may be able to provide patients with more accurate diagnoses and treatments in less time by using remote-monitoring sensors and medical devices. The use of Internet of Things (IoT)-enabled devices to automate the delivery of medical services is the primary focus of this article, which covers a wide range of topics. According to the institute of medicine, doctors may be able to provide better therapy to patients while also keeping them healthy and safe if they adopt IoT-driven healthcare models. It has also been brought to light that healthcare frameworks can reduce costs while improving performance in the delivery of care. It is possible to store and distribute sensitive information using a blockchain system without the risk of it being hacked. The "edge processing" of the Internet of Things (IoT) is a sort of supercomputing that connects high-performance computing machines to healthcare service devices. Edge computing can reduce latency, energy consumption, scalability, and bandwidth. Patient data can be transmitted safely at high rates and with rapid response times thanks to the implementation of blockchain technology in conjunction with 5G technology, which is currently being introduced. It is crucial to encourage the growth of the community health service system, the hospital information system, and other similar systems, as well as their utilization. However, because interoperability alone is insufficient to handle health information in depth, it is difficult to establish various information systems' compatibility with one another. An investigation into the medical-specific architecture of 5G edge computing in order to solve the challenges. Keywords: Blockchain, IoT, 5G, Internet of Medical Thing, Hospital Information System, Edge computing Suggested Citation: Suggested Citation Alam, Md. Imran and Haider, Agha Salman and Siddiqui, Alighazi and Khan, Md. Rafeek and Siddiqui, Shams Tabrez and Khan, Haneef, IoT Edge Computing and Blockchain for High-Performance and Decentralized Health Monitoring System (July 14, 2022). Available at SSRN: https://ssrn.com/abstract=4157232 Md. Imran Alam (Contact Author) Department of Computer and Network Engineering, College of Computer Science and Information Technology, Jazan University ( email ) Saudi Arabia Agha Salman Haider Jazan University - Department of Information Technology and Security ( email ) Saudi Arabia Alighazi Siddiqui Jazan University - Department of Computer Science ( email ) Saudi Arabia Md. Rafeek Khan Imam Abdulrahman Bin Faisal University (IAU) - College of Computer Science and Information Technology ( email ) Saudi Arabia Shams Tabrez Siddiqui Jazan University College Of CS and ITJazan UniversityJazan, GA Jazan 114Saudi Arabia Haneef Khan Jazan University - Department of Computer Science ( email ) Saudi Arabia Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? 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