Resource Management Infrastructure Research Articles

The ESG Evolution: Bridging the Gap Between Global Standards and Local Realities in Emerging Markets

This systematic literature review explores the challenges faced by companies operating in emerging markets concerning Environmental, Social, and Governance (ESG). The review uses both institutional and stakeholder theoretical frameworks to analyse the complex landscape of ESG implementation in the context of emerging markets.The findings of these study majorly identify three critical dimensions of challenges. First, environmental challenges are primarily the result of poor regulatory enforcement, limited monitoring capacities, and poor resource management infrastructure. Second, social challenges appear in the form of labour right issues, tough community relations and enduring social equity barriers. Third, governance problems are generated out of concentrated ownership structures, information asymmetries and poor risk management framework. Moreover, this study also contributes to the literature by gaining a systematic view into ESG challenges in emerging markets along with effective response strategies. These findings have important implications for corporate managers, policymakers, and researchers of sustainability initiatives in emerging economies. Research directions for the future are suggested in terms of implementation effectiveness and the impact of institutional development on the implementation of ESG practises.

Optimizing Infrastructure Resources with Artificial Intelligence: A Technical Analysis

The revolutionary role of artificial intelligence in improving infrastructure resource management in business settings is examined in this technical article. The article explores how AI-driven solutions transform data center operations through advanced predictive analytics, intelligent workload distribution, energy efficiency optimization, proactive defect detection, and dynamic resource allocation. Organizations can improve system reliability, save maintenance costs, and increase operational efficiency by utilizing automated systems and machine learning algorithms. While offering thorough best practices and suggestions for the successful adoption of AI, it explores implementation factors, such as technological needs and integration challenges. The article also looks at how AI infrastructure optimization will develop in the future, highlighting new technologies, automation potential, and sustainability factors that will influence how businesses operate in the coming years

Sustainable Urban Tourism Development in Doha: Balancing Economic Growth and Environmental Stewardship

This study investigates the development of sustainable urban tourism in Doha, Qatar, with a focus on balancing economic growth and environmental stewardship. The research explores the integration of sustainable practices within Doha’s tourism sector, the implementation of key policies such as the Qatar National Vision 2030, and the impact of the FIFA World Cup 2022 on tourism and sustainability efforts. Using a mixed-methods approach, including qualitative interviews with 30 key stakeholders and quantitative data analysis, the findings reveal that while Doha has made substantial progress in areas such as resource management and green infrastructure development, challenges remain—particularly in retrofitting older buildings and enforcing sustainability standards. The FIFA World Cup 2022 acted as a catalyst for sustainable practices, but the long-term success of these efforts depends on continued investment and policy enforcement. This study provides valuable insights for policymakers, urban planners, and tourism officials working to promote sustainable urban tourism in rapidly developing cities.

Recursos genéticos y recursos biológicos: una aproximación jurídica desde su rol en la biodiversidad como un interés tutelado

The paper analyzes the conceptual and regulatory frameworks for genetic and biological resources, as well as the State's responsibility for protecting biodiversity. Through a combined conceptual and legal approach, the author examines both types of resources and presents a comprehensive study on the appropriation of associated knowledge and derived products. The analysis focuses on the contract for access to genetic resources as an environmental management tool. Employing a systematic documentary analysis methodology, the paper interprets specific regulations (internal, supranational, and international) and relevant technical information. This results in a comprehensive legal context for the appropriation of biological resources, associated knowledge, and products derived from genetic resources. The author concludes that challenges remain, including the ratification of the 2010 Nagoya Protocol and the need for improved capabilities, personnel, and infrastructure for resource management. Nevertheless, the contract for access to genetic resources offers a promising mechanism for contributing to effective global biodiversity conservation.

Unveiling the Dynamics and Sustainability of the Nubian Aquifer System in El-Marashda, Egypt Through Geophysical, and Hydrogeochemical Investigations

AbstractThis research investigates the geophysical, hydrogeochemical, and geological characteristics of the Nubian Aquifer System in the El-Marashda area, located beneath the Eastern Sahara Desert in northeastern Africa. The primary objective is identifying the aquifer geometry, assessing groundwater quality, and evaluating environmental pollution levels. A Magnetotelluric survey was employed to examine the subsurface geological composition and electrical resistivity characteristics of the aquifer system. The study revealed a freshwater-saturated Nubian Sandstone layer at depths between 986 and 1009 m with resistivity values of 101 to 182 Ohm.m, indicating a substantial and potentially sustainable groundwater resource. Overlying limestone and Marly Limestone layers influence the aquifer’s permeability and storage capacity. The chemical analysis of water from three wells showed suitability for drinking and irrigation, with no sodium and magnesium levels hazards. The Heavy Metal Pollution Index (HPI) and Heavy Metal Evaluation Index (HEI) indicated low to medium contamination, posing potential environmental health risks. Human health risk assessments using USEPA methodology found acceptable non-carcinogenic risks, with Hazard Quotients (HQs) for heavy metals below 1 and a negligible Hazard Index (HI). The comprehensive investigation underscores the potential for sustainable groundwater utilization in El-Marashda. It highlights the aquifer’s suitability for irrigation, with low contamination levels and minimal health risks. These findings provide a foundation for informed and sustainable water resource management, environmental protection, and resilient infrastructure development in the region. The methodologies and results align with global standards, demonstrating their applicability in globally diverse geological contexts. This research contributes valuable baseline data for future studies and monitoring efforts, whether at local or global sites, emphasizing the importance of continued investigation to ensure sustainable groundwater management.

The effectiveness of applying the economic and technological dimensions of the kingdom of Saudi Arabia’s vision 2030 to achieve the competitive advantage of the Hail Region

This research focuses on examining the impact of Saudi Arabia’s Vision 2030 on the energy industry, specifically in the Hail region, by exploring the role of technological and economic strategies in fostering sustainable energy development. The study aims to identify key factors that facilitate the implementation of Vision 2030 to assess the current energy landscape in Saudi Arabia. By delving into the economic and technological dimensions of Vision 2030, the research seeks to determine the essential components that the Kingdom of Saudi Arabia needs to establish systems and initiatives that align with global scientific and technological advances, ensuring competitiveness and progress in the global arena. By surveying 60 faculty members from Hail University, the study employs a descriptive-analytical approach to unravel how economic activities and technological advancements collaborate to shape a sustainable and competitive energy sector. The findings highlight the crucial role of technological innovations, including advances in administrative processes, human resource management, regulatory frameworks, and technical infrastructure related to energy efficiency and renewable energy technologies. Furthermore, the study evaluates economic aspects such as labor market reforms, skill development programs, and support for emerging energy enterprises. The results underscore the positive impact of technological and economic interventions on the energy sector in the Hail region, with significant improvements in energy project management efficiency and the creation of a conducive environment for sustainable energy practices. The study reveals that the integration of technological and economic initiatives has led to notable improvements in the energy sector, reflected in average values ranging from 68% to 84%. These results underscore the fundamental role of Vision 2030 in advancing sustainable energy development and underscore the importance of leveraging economic and technological strategies to achieve energy sustainability and competitive advantage. This research contributes to understanding how Saudi Arabia’s Vision 2030 is driving progress in the energy industry and emphasizes the importance of integrating economic and technological approaches in shaping a sustainable energy landscape. The findings provide valuable information for policymakers and stakeholders in the energy sector, highlighting the need for concerted efforts to harness economic and technological advancements to promote sustainable energy development. By showcasing the transformative impact of Vision 2030 on the energy sector in the Hail region, this study underscores the originality and importance of research in advancing the goals of sustainable energy development in Saudi Arabia.

Improving Autonomous Underwater Vehicle Navigation: Hybrid Swarm Intelligence for Dynamic Marine Environment Path-finding

Underwater research and monitoring operations rely significantly on Autonomous Underwater Vehicles (AUVs) for scientific investigations, resource management, and monitoring, and underwater infrastructure is provided maintenance levels amid other applications. Efficient navigation and preventative methods are only a couple of the numerous challenges that Path-Finding (PF) in rapidly changing and sophisticated Underwater Environments (UE) requires overcoming. Dynamic environments and real-time improvements are problems for traditional models. In order to provide superior solutions for navigating uncertain UE, this work suggests a hybrid optimization technique that combines Ant Colony Optimization (ACO) for local path selection with Particle Swarm Optimization (PSO) for global path scheduling. Runtime efficiency, accuracy, and distance focused on decrease are three metrics that demonstrate how the PSO-ACO hybrid method outperforms conventional algorithms, proving its significance for improving AUV navigation. The improvement of AUV functions in fields such as underwater research, along with others, is supported by the current research, which further assists with the invention of Autonomous Underwater Navigation Systems (AUNS). The PSO+ACO hybrid method is superior to the PSO, ACO, and GA algorithms in pathfinding with a 6.43-second execution time and 93.5% accuracy—the ACO model completed in 12.53 seconds, superior to the proposed system.

Международный опыт применения управленческих технологий в системе государственного управления

This article examines the impact of innovation on public administration and planning in the context of comparative analysis. The author emphasizes key aspects such as the development of digital technologies, ensuring the availability of information, increasing citizen participation and protecting data privacy. The main focus is on analytics to ensure public safety, counter insider threats, human resource management and urban infrastructure development. Methods for utilizing Big Data and artificial intelligence in public administration are presented, along with outcomes from governmental projects based on Big Data. It is noted that the collection and analysis of vast amounts of statistical information allows authorities to develop targeted support programs for specific social groups, optimize urban infrastructure and transport, and improve public safety by identifying financial crimes and illegal activities. Furthermore, the introduction of big data technologies can bring the quality of public services to a new level and make the political decision-making process more transparent and citizen-centric.

Digitalization of Educational Quality Management in Higher Education

Purpose: This research aims to determine the digitalization-based education quality management system at Prof. K. H. Saifuddin Zuhri Purwokerto and Nahdlatul Ulama Al Ghazali University Cilacap, so various challenges can be found in implementing the digitalization system for education quality management at these two universities. Methods: This research uses descriptive qualitative research methods with data collection techniques, namely interviews, observation and documentation. This research involves data analysis which includes compiling, categorizing and combining data to form a pattern of education quality management in digital-based higher education at Islamic State University (UIN) Prof. K. H. Saifuddin Zuhri Purwokerto and Nahdlatul Ulama Al Ghazali University Cilacap. Result and Conclusion: The result is the digitization of education quality management at the Prof. State Islamic University. K. H. Saifuddin Zuhri Purwokerto and Nahdlatul Ulama Al Ghazali University Cilacap have proven positive steps towards efficiency and effectiveness in education management, even though they still face obstacles such as weak human resource management and limited infrastructure. The success of digitalization is very dependent on the ability and capacity of human resources, so continuous efforts are needed such as increasing competence through training and development. Research Implications: The urgency of digitizing management in higher education is absolutely necessary, especially UIN Prof. K. H. Saifuddin Zuhri Purwokerto needs to continue to be promoted, especially information technology, human resources who are experts in the field of information technology, so that good digital literacy from all levels in students, educational staff and lecturers. There is a need to digitize through publications such as social media in the form of Instagram, TikTok, Flayer and everything that is digital based. However, for Nahdlatul Ulama Al Ghazali University, Cilacap, maintenance or maintenance processes are still needed so that it can be ascertained whether an asset can function properly or recover, the distribution of levels is not yet perfect, there are still some lecturers who may not be familiar with accessing the system used, and sources. limited power. For this reason, academic leaders must try to motivate, mobilize, remind lecturers, always improve the system so that their desires can be achieved, increase the use of more contemporary or modern systems and increase human resources by sending teaching staff, especially for academic digitalization services. Originality/ Value: This study offers original insights into digitalization of higher education quality management is very necessary in improving all services at higher education, both state and private universities in Indonesia.

The impact of human resource management, learning, and academic infrastructure in pre-service teachers on pedagogical competence: A multifaceted analysis

This study investigated the influence of the learning process, human resources, and infrastructure on the pre-service teachers' competencies, particularly their pedagogical competence as future professional teachers. This study employed survey research, a type of quantitative investigation. The study's population consisted of all 182 Pre-Service Teachers from the Indonesian Language and Literature Education Department, Mathematics, and English Education Department of the Teacher Professional Education Program, Faculty of Teacher Training and Education, Universitas Islam Malang. Primary and secondary data were the two data types used in this investigation: questionnaires and documentation. The main source of information was the assessment of the lecture process in Batches 1 and 2 of the Pre-Service Teacher of Teacher Professional Education Program. The course grades from LMS courses taken by pre-service PPG students made up the secondary data. Based on the results and discussion, it can be concluded that the learning process and human resources positively influenced the pre-service teacher's competence, especially in the pre-service teacher professional education program. Meanwhile, the data showed that academic infrastructure did not impact pre-service teacher competence.

Trends of Sediment Resuspension and Budget in Southern Lake Michigan Under Changing Wave Climate and Hydrodynamic Environment

AbstractSediment suspension and transport driven by waves and currents play a significant role in both the ecological and physical environments of large lakes. Lake Michigan has faced a rapidly increasing water level associated with intensified wind waves in the past decade. To investigate the spatiotemporal characteristics of suspended sediment concentration (SSC) and associated coastal sediment budgets in southern Lake Michigan, a 30‐year (1991–2020) hindcast was performed using a coupled wave‐current‐sediment model (SWAN‐FVCOM‐CSTMS). We found that in southern Lake Michigan, the basin‐wide mean SSC increased, and the coastal sediment loss accelerated dramatically, corresponding with intensified waves, currents and lake water level rises over the past decade. The basin‐wide mean SSC, coastal sediment loss, wave height, wind speed, current speed, and water level in southern Lake Michigan are highly correlated. Spatially, the results reveal decreases in coastal SSC and sediment loss in the western portion of the southern basin, while the eastern sectors show an increase in both metrics. This reflects a clear shift in the wave climate and hydrodynamic environment. The alterations in long‐term coastal sediment budgets imply that considerable shoreline transformations are being influenced by modifications in the wave climate. Understanding the spatiotemporal characteristics of SSC and coastal sediment budgets is crucial for strategic water resource management and coastal infrastructure planning.

Synergizing Environmental Sustainability: Unveiling the Nexus between Life Cycle Analysis and Circular Economy in the Water Sector

Over-extraction and pollution surge have created a serious management challenge for the world's water resources. Rising demand, inadequate water resource management infrastructure and unequal distribution intensify the scarcity crisis. Sustainable solutions are necessary to ensure availability, environmental conservation and resilience in the face of escalating water challenges worldwide. In today's world, the primary goal is to mitigate impact on resources. Thus, life cycle analysis (LCA), a widely embraced methodology for impact assessment can be applied for sustainable management of resources. Circular economy (CE) is the new sustainable approach by which we can reduce the waste generation by reusing and recycling the products to form a loop. Utilising LCA, environmental consequences of human activities and developing solutions for reducing waste, can put CE into practice effectively. Transforming waste into a valuable resource, whether through natural or scientific means, plays a pivotal role in advancing the principles of circular economy. The detailed application of LCA and the efficient incorporation of CE principles contribute significantly to environmental sustainability. This study articulates the interplay between CE and LCA, outlining key steps in water management. It provides a comprehensive overview of literature on life cycle analysis and circular economy, specifically focusing on their major applications in the water and wastewater treatment domain. To successfully apply circular economy, future research should concentrate on improving the processes for incorporating circular economy ideas into life cycle assessments (LCAs) and encouraging cooperation between academicians, decision-makers and stake holders.

Analysis Of DevOps Infrastructure Methodology and Functionality of Build Pipelines

The DevOps pipeline for infrastructure is a critical component in modern software development and operations practices. It involves automating the provisioning, configuration, and management of infrastructure resources, enabling organizations to achieve agility, scalability, and reliability. This paper presents a plagiarism-free analysis of the DevOps pipeline for infrastructure, conducted through comprehensive research, evaluation of industry best practices, and examination of case studies. The DevOps methodology would collapse without the use of a DevOps pipeline. The phrase is often used to discussions of the methods, procedures, and automation frameworks that go into the creation of software objects. Jenkins, an open-source Java program, is the most well-known DevOps pipeline and is often credited as the catalyst for the whole DevOps movement. Today, we have access to a plethora of DevOps pipeline technologies, such as Travis CI, GitHub Actions, and Argo. To keep up with the need for new and improved software systems, today's development organizations must overcome a number of obstacles. The research highlights key findings, including the importance of automation, infrastructure as code, continuous integration and delivery, security, and monitoring/logging capabilities. These practices have been shown to enhance efficiency, reduce errors, and accelerate deployment cycles. By evaluating tools and technologies, gathering user feedback, and analyzing performance metrics, organizations can identify gaps and develop a roadmap for pipeline improvement. To maintain academic integrity, this analysis adheres to proper citation and referencing practices. Paraphrasing and summarizing research findings and adding personal analysis and interpretations ensure the originality and authenticity of the analysis. Plagiarism detection tools are used to confirm the absence of unintentional similarities with existing content.

Selective electrodeposition of Co-Ni alloys from synthetic quasi LiB NMC 532 cathode sulphate solutions using rotating plate potentiostatic electrowinning

The interest in recycling spent lithium-ion batteries (Li-ionB) has surged due to the rising demand for valuable metals (e.g., Co, Ni, Li and Mn) and concerns about environmental repercussions emanating from conventional battery waste disposal. This research is centered on the recovery of the valuable Ni-Co alloys from synthetic Ni, Co, Mn and Li sulphate solutions mimicking the NMC 532 ratio of elements using a hydro-electrometallurgy process route that integrates hydrometallurgy and potentiostatic electrometallurgy techniques. This quasi-model is done to elucidate the effect of multiple influencing parameters, through isolation and varying, on the selective electrodeposition of Co-Ni from multi-ion (Li, Ni, Mn and Co) complex solutions before applying it using real cathode leachates. The selective electrowinning metal recovery process route is a cost-effective alternative to the energy, cost and material-intensive hydrometallurgy intermediate purification processes such as solvent extraction, selective precipitation, and ion-exchange. The study delves into the effects of various electrowinning parameters, including applied potential, temperature, pH, Co, Ni, Na2SO4, NaH2PO4 buffer concentration, and cathode rotational speed. These parameters were thoroughly investigated and effectively optimised to achieve the recovery of 98.2% pure Ni0.65Co0.35 at a rate of 0.060 g/cm2.h with an impressive 89.25 % current efficiency. The composition of the electrowon deposit was meticulously quantified using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) and subjected to analysis through a Scanning Electron Microscope (SEM-EDS). Additionally, the phase composition was evaluated using X-Ray Diffraction analysis (XRD). The results successfully demonstrate the technical feasibility of recovering Ni-Co alloys, yielding high quantities of industrial-grade pure Ni-Co alloys. This comprehensive electro-hydrometallurgical process, designed for both closed and loop recycling purposes, promotes a more environmentally preservative approach to recycling spent lithium-ion battery cathode material. The approach contributes significantly to the development of sustainable resource management infrastructure.

FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms.

The emerging serverless computing has become a captivating paradigm for deploying cloud applications, alleviating developers' concerns about infrastructure resource management by configuring necessary parameters such as latency and memory constraints. Existing resource configuration solutions for cloud-based serverless applications can be broadly classified into modeling based on historical data or a combination of sparse measurements and interpolation/modeling. In pursuit of service response and conserving network bandwidth, platforms have progressively expanded from the traditional cloud to the edge. Compared to cloud platforms, serverless edge platforms often lead to more running overhead due to their limited resources, resulting in undesirable financial costs for developers when using the existing solutions. Meanwhile, it is extremely challenging to handle the heterogeneity of edge platforms, characterized by distinct pricing owing to their varying resource preferences. To tackle these challenges, we propose an adaptive and efficient approach called FireFace, consisting of prediction and decision modules. The prediction module extracts the internal features of all functions within the serverless application and uses this information to predict the execution time of the functions under specific configuration schemes. Based on the prediction module, the decision module analyzes the environment information and uses the Adaptive Particle Swarm Optimization algorithm and Genetic Algorithm Operator (APSO-GA) algorithm to select the most suitable configuration plan for each function, including CPU, memory, and edge platforms. In this way, it is possible to effectively minimize the financial overhead while fulfilling the Service Level Objectives (SLOs). Extensive experimental results show that our prediction model obtains optimal results under all three metrics, and the prediction error rate for real-world serverless applications is in the range of 4.25∼9.51%. Our approach can find the optimal resource configuration scheme for each application, which saves 7.2∼44.8% on average compared to other classic algorithms. Moreover, FireFace exhibits rapid adaptability, efficiently adjusting resource allocation schemes in response to dynamic environments.

Solar irrigation in sub-Saharan Africa: economic feasibility and development potential

Irrespective of water resource abundance, agriculture in sub-Saharan Africa (SSA) is predominantly rainfed. Along with fertilization, irrigation could support smallholder farmers with stabilizing crop yields, increasing incomes, and achieving food security. A key barrier to irrigation uptake is inadequate rural electricity supply for pumping and distributing water, besides other infrastructure deficits. Here we devise a spatially explicit integrated modelling framework to show that over one third of unmet crop water requirements of 19 major crops in smallholder cropland of SSA could be supplied with standalone solar photovoltaic (PV) irrigation systems that can be paid back by farmers within 20 years. This accounts for 60 km3 yr−1 of blue irrigation water requirements distributed over 55 million ha of currently rainfed harvested area (about 40% of the total). Crucially, we identify 10 million ha with a profit potential >$100 ha−1 yr−1. To finance such distributed small-scale infrastructure deployment and operation, we estimate an average discounted investment requirement of $3 billion yr−1, generating potential profits of over $5 billion yr−1 from increased yields to the smallholder farmers, as well as significant food security and energy access co-benefits. We demonstrate the critical importance of business models and investment incentives, crop prices, and PV & battery costs in shaping the economic feasibility and profitability of solar irrigation. Yet, we find that without strong land and water resources management infrastructure and governance, a widespread deployment of solar pumps may drive an unsustainable exploitation of water sources and reduce environmental flows. Our analysis supports public and private stakeholders seeking to target investments along the water–energy–food–economy–sustainable development nexus.

Optimized variable selection of Bayesian network for dam risk analysis: A case study of earth dams in the United States

Dams are vital infrastructure for water resource management, and their safety is threatened by the complex interplay of multiple risk factors. Consequently, for dam safety maintenance, it is significant to conduct dam risk analysis by detecting the relationship between dam failure and risk factors and identifying the critical risk factors. Bayesian network (BN) is an effective tool for this issue and has been widely used in previous studies. However, there is still a lack of variable selection methods in BN that can automatically select predictors from numerous candidates by considering both predictability and parsimony. Herein, an optimized variable selection is proposed for BN and used in the risk analysis of widespread earth dams in the United States. The results showed that the proposed method can automatically determine the optimal predictor set. The BN with selected predictors not only exhibits good performance but also has a significantly more concise network than the BN without variable selection and significantly weakens overfitting. Using the established BN, the clear causal network of earth dam failure and 12 risk factors were quantified through influence strength analysis. The three foremost risk factors based on their sensitivity analysis were identified to be insufficient spillway capacity, extreme flood, and slope instability, which can serve as a reference for the safety maintenance of earth dams in the United States. Our study highlights that the proposed variable selection is a powerful step of BN for dam risk analysis and contributes to the development of an intelligent BN modeling framework.

Vulnerability Evaluation of Port Logistics System Under Public Health Emergencies

It is the goal that evaluating scientifically port logistics system vulnerability level under public health emergencies, exploring the influencing factors of brittleness and improving the production operation and management efficiency of port logistics system. According to the production practice of port logistics system and vulnerability theory, a vulnerability evaluation index system of port logistics system under public health emergencies is established in five aspects, including external risk disturbance, human resource management level, infrastructure level, operation and management level, and intelligence level. The combinatorial weights are obtained by combinatorial empowerment method of game theory, and it is used to process data that evidence theory improved by conflict correction process and mass function with normal cloud model. The vulnerability of Dalian port logistics system is analyzed empirically, and the result is better effectiveness.

Material Stock and Embodied Greenhouse Gas Emissions of Global and Urban Road Pavement.

Roads play a key role in movements of goods and people but require large amounts of materials emitting greenhouse gases to be produced. This study assesses the global road material stock and the emissions associated with materials' production. Our bottom-up approach combines georeferenced paved road segments with road length statistics and archetypical geometric characteristics of roads. We estimate road material stock to be of 254 Gt. If we were to build these roads anew, raw material production would emit 8.4 GtCO2-eq. Per capita stocks range from 0.2 t/cap in Chad to 283 t/cap in Iceland, with a median of 20.6 t/cap. If the average per capita stock in Africa was to reach the current European level, 166 Gt of road materials, equivalent to the road material stock in North America and in East and South Asia, would be consumed. At the urban scale, road material stock increases with the urban area, population density, and GDP per capita, emphasizing the need for containing urban expansion. Our study highlights the challenges in estimating road material stock and serves as a basis for further research into infrastructure resource management.

A Secure Convergence of Data Transmission and Intelligent Authentication Using a Sensing Approach for the Internet of Thing

The IoT’s millions of sensor-equipped gadgets upload data to computers for management and use in smart grids, intelligent transportation networks, healthcare networks, and smart cities. Sensor data needs a safe server-sensor connection. Studying altered data could have catastrophic consequences. Internet-of-Things sensors must authenticate with the reader and base station before sending data. IoT sensors can securely and effectively communicate data. Correction evidence in the suggested technique reveals that the required data is created at the receiver using the normal Euclidean parameters of IoT sensors. The proposed approach is compatible with most assaults, making it a good security option. A framework for edge IoT devices can optimize latency, security, and other performance characteristics. Constraints Application Protocol (COAP) and The Stream Control Transmission Protocol (AESCQTTP) combine cooperative resource management, machine learning, context-aware computing, and flexible infrastructure to handle communication and computing difficulties. To reduce end-to-end latency by (7.13–7.35)%, raise security by 98.99%, and increase efficient pocket distribution to 98%. This study examined existing research issues and edge-computing technologies and proposed a novel strategy for optimizing edge-IoT system performance metrics. This arrangement could let future networks communicate securely.