Environmental Efficiency Research Articles

Durable and multifunctional coating design with superhydrophobicity, high transparency, radiative cooling for photovoltaic application

The market-dominant silicon-based solar cells are facing great challenges in further improving their benchmark efficiency. However, due to dust deposition and temperature rise, the actual operating efficiency is still far from the benchmark efficiency. The goal of this study is to develop a durable and multifunctional coating with superhydrophobicity, high light transmittance and strong infrared radiation, which is applied to the surface of photovoltaic glass to reduce dust deposition and lower the module temperature. Based on a silicon wafer template and die casting process, epoxy resin microcavities are prepared on the glass surface, and SiO2 nanoparticles are sprayed into the microcavities to complete the preparation of the multifunctional coating. The experimental test results show that the coating has a contact angle of about 160°, a visible transmittance over 91 %, and an infrared emissivity of 94.5 % among the atmospheric window, demonstrating the potential of self-cleaning and radiative cooling functions. The coating also shows good durability through sandpaper wear, scraper wear, tape peeling, and water jet tests. The multifunctional coating developed in this study is expected to be applied to different types of photovoltaic cells to improve their photoelectric conversion efficiency in outdoor environments.

Research on multi-UUV mission planning for mine countermeasures based on preferred multi-objective optimization

Facing rising maritime security threats, this study presents T-MOEA/D, a sophisticated evolutionary algorithm enhancing UUVs’ capabilities in mine detection and neutralization. The algorithm tackles the multi-objective challenge by balancing time and energy, integrating user preferences into its optimization process. It leverages genetic operators such as dual chromosome encoding and partially mapped crossover to evolve efficient solutions, outperforming T-NSGA-II and T-NSGA-III in hypervolume and operational time. The UUV, directed by T-MOEA/D, navigates to operational areas and employs StyleGAN and YOLOv9 for accurate mine perception, crucial for executing mine countermeasure tasks. The system’s effectiveness is confirmed through Unity3D simulations and real-world tests, demonstrating its practicality and reliability. The study’s findings offer strategic guidance for planning large-scale mine countermeasure missions with multiple UUVs, ensuring operational efficiency and safety in complex underwater environments. The Pareto optimal solutions align with user preferences, reflecting a tailored approach to mine countermeasure missions.

Matching vs. Mismatching Signals of External Human-Machine Interface and Vehicle Kinematics: An Examination of Pedestrian Crossing Behavior and Trust, Safety, and Affective Ratings in Interactions with Differently Sized Automated Vehicles

Highly automated vehicles (HAVs) will soon be introduced into mixed urban traffic. Pedestrians might have an idea of HAVs. Nevertheless, they probably have never interacted with them before. Moreover, pedestrians will not be able to communicate with HAVs like they are used to with manual vehicles. External human–machine interfaces (eHMIs) are possible design solutions for HAVs to ensure safe interaction with other road users. Light-based eHMIs positively affected pedestrians’ trust ratings, perceived safety, and willingness to cross. However, previous studies often neglected the effect of vehicle size, although larger-sized HAVs could be potentially perceived as the more significant threat. Additionally, the relationship between vehicle kinematics and eHMIs for differently sized HAVs remains an underexplored research topic. This study investigated the effects of vehicle size (small vs. large), eHMI state (dynamic eHMI vs. static eHMI vs. no eHMI), and vehicle kinematics (yielding vs. non-yielding) on pedestrian crossing behavior and their subjective assessment. In virtual reality, we created a shared space traffic scenario, in which the eHMI and vehicle kinematics matched or did not match. For yielding conditions, the results showed that participants felt more aroused with larger HAVs than with smaller HAVs. Moreover, pedestrians initiated their crossing significantly earlier when both vehicle sizes had a dynamic eHMI compared to a static eHMI vs. no eHMI. Additionally, pedestrians evaluated a dynamic eHMI with higher trust ratings, higher perceived safety, and more positive affective reactions. The results manifested that the use of dynamic eHMIs can effectively enhance pedestrian-vehicle communication with a large and a small HAV. For non-matching conditions, the participants tended to rely on the vehicle kinematics for both vehicle sizes. Overall, the study highlighted the potential of eHMIs for pedestrian interactions with HAVs of varying sizes when they are well-coordinated with the vehicle kinematics, aiming to enhance safety and efficiency in mixed-traffic environments.

Recent advances in microalgae-driven carbon capture, utilization, and storage: Strain engineering through adaptive laboratory evolution and microbiome optimization

The potential of microalgae as a biological resource for carbon capture, utilization, and storage (CCUS) has been extensively discussed. Although genetic engineering methods have been employed to improve microalgal phenotypes, they often face challenges related to public concerns regarding genetically modified organisms. By contrast, adaptive laboratory evolution (ALE) and microbiome optimization have emerged as promising non-genetic modification strategies, with notable success in bacterial models. In microalgae, ALE has been employed to improve resilience against varying environmental and stress factors and increase carbon capture efficiency and for the production of valuable bioproducts through gradual accumulation of beneficial mutations following manual or automated selection. Furthermore, advancements in the understanding of microbial symbiotic relationships in the phycosphere have facilitated microbiome optimization in microalgal cultivation systems, significantly improving their functionality and productivity. In this study, we provide a comprehensive overview of the latest advancements in ALE and microbiome optimization of microalgae for CCUS across different carbon emission scenarios, including flue gas, biogas, wastewater, and landfill leachate. We further discuss the current challenges and future directions for the integration of ALE with microbiome optimization, focusing on the potential synergies of these methodologies. Overall, ALE and microbiome optimization are promising approaches to direct microalgae for environmental and industrial CCUS applications, thereby reducing global carbon emissions and addressing climate change challenges.

Assessment of hydrogen peroxide, persulfate, and peroxymonosulfate as oxidizing agents in electrochemical oxidation of pyridine

The removal of pyridine, a toxic and refractory nitrogen heterocyclic compound from coal chemical wastewater, has been examined using electrochemical oxidation (EO) processes in the presence of hydrogen peroxide (H2O2), persulfate (PDS), and peroxymonosulfate (PMS). Degradation of pyridine was observed in EO/H2O2, EO/PDS, and EO/PMS systems with removal efficiencies of 41.3, 40.6, and 93.5 %, respectively. The energy consumption for the EO/H2O2, EO/PDS, and EO/PMS processes was calculated to be 1.4, 1.3, and 0.56 KWh g−1, respectively. The yield of ClO3- in the EO/PMS system was relatively higher than that in the EO/PDS system. Additionally, the overall concentration of total organic chlorine was lower in the EO/PDS (292.1 mg/L) and EO/PMS (120.1 mg/L) systems when SO4•– radicals were present. The contribution of reactive species were quantified. Specially, in the EO/H2O2 system, the ratio of OH• was 44 %. In the EO/PDS system, the ratios of OH•, SO4•–, and other reactive species were 31 %, 22 %, and 47 %, respectively. In the EO/PMS system, the ratios of OH•, SO4•–, and other reactive species were 18 %, 71 %, and 11 %, respectively. Specifically, in the EO/PMS system, while CCl3 was detected, no other chlorinated organic intermediates were observed. Compared to EO/H2O2 and EO/PDS systems, the EO/PMS system shows better suitability for high Cl– conditions. The pH had a negligible impact on the EO/H2O2 system. Under acid conditions, degradation rate constants for pyridine were highest in the EO/PMS and EO/PDS systems. Notably, the addition of PMS to coal chemical wastewater achieved a 30 % removal of ultraviolet light absorbance at 254 nm (UV254), indicating effective inhibition of trihalomethane formation. This study provides critical insights into optimizing the EO process for the effective removal of refractory pollutants like pyridine from coal chemical wastewater, enhancing environmental safety and treatment efficiency.

Optimizing Kubernetes Environments: Best Practices for Configuring and Managing Admission Webhooks

Abstract: Kubernetes has emerged as the leading platform for container orchestration, offering a robust framework for automating the deployment, scaling, and management of containerized applications. As its adoption increases, managing diverse workloads across various environments becomes increasingly complex, necessitating mechanisms for policy enforcement, compliance assurance, and dynamic configuration management. In Kubernetes, admission webhooks provide a means to intercept requests, enabling the mutation and validation of these requests to enforce custom policies. This paper presents comprehensive guidelines for configuring and operating admission webhooks to enhance security, consistency, and operational efficiency in Kubernetes environments. Through this paper, readers will gain hands-on experience in implementing best practices, thereby optimizing the use of admission webhooks to effectively maintain organizational policies and standards.

Physiological and enzyme dynamics of tomato under drought stress

Climate change leads to an increase in the frequency and severity of droughts, which have a negative impact on agriculture by altering plant growth and lowering water availability, placing food production systems at risk of sustainability. In order to improve drought resistance and ensure food security in the face of growing water shortages, this research was conducted to determine the physiological and enzymatic responses of tomato plants to drought stress. This study was conducted at Tamil Nadu Agricultural University, Coimbatore, involving 3 hybrids, viz., H1: EC169966 × LE118, H2: EC177824 × LE118 and H3: Arka Ashish × LE 27, along with their parents, P1: EC177824, P2: LE27, P3: EC169966, P4: LE118 and P5: Arka Ashish, under 50 % and 100 % field capacity conditions in factorial completely randomized design (FCRD) with 3 replications. Results indicated a significant change in physiological and enzymactic activities. Here, the parent P2 and hybrid H2 showed superior tolerance, with higher relative water content, proline, leaf water potential, membrane and chlorophyll stability index. Added to that, the response of enzyme activities including peroxidase, nitrate reductase, catalase, polyphenol oxidase and superoxide dismutase was found to be increase notably in drought-tolerant hybrids and parents, which correlated strongly with physiological markers of drought resistance. These modifications highlight the capacity of some genotypes to preserve photosynthetic efficiency and cellular integrity in water-limited environments. The results highlight the significance of choosing and developing drought-tolerant cultivars in order to maintain agricultural production in areas vulnerable to drought and address issues related to global food security.

Improving the CSIDH Protocol for Multi-party Cryptography: Rigorous Mathematical Analysis, Efficiency, and Security Comparison

This paper introduces a novel Distributed Key Generation (DKG) protocol based on the Commutative Supersingular Isogeny Diffie-Hellman (CSIDH) framework for secure multi-party cryptography. Our proposed protocol is designed to address scalability and security concerns, particularly in post-quantum cryptographic systems. The main contributions include the introduction of Piecewise Verifiable Proofs (PVPs) for non-interactive zero-knowledge verification of secret shares, and the provision of rigorous security analysis, including resistance to quantum adversaries via Shor’s and Grover’s algorithms. We analyze the protocol’s efficiency, ensuring low computational overhead even in large-scale systems, and compare it with other distributed cryptographic protocols such as RSA-based and lattice-based schemes. Through mathematical proofs and complexity analysis, we demonstrate that our protocol offers enhanced security, efficiency, and scalability in a post-quantum environment. The results presented in this paper provide a strong foundation for implementing secure multi-party computations in quantum-resistant systems.

Balancing Cost and Environmental Impact: A Linear Programming Approach to Sustainable Shopping

A linear programming model is devised for consumer purchasing decisions minimizing weighted cost and environmental impact using real-world data. Minor shifts towards environmental preferences can greatly reduce impact with minimal cost increase. Spending slightly more on the bundle than the cheapest option can cut environmental impact by a third. Conversely, less than 10% compromise in impact yields over 15% cost savings. Consumers can find efficient midpoints—cost-effective and environmentally sustainable options—through strategic decisions. Additionally, a synthetic dataset, modeling different societal dispositions through Beta distributions of cost-environment orientation parameters, simulates societal attitudes, showing that a 10% reduction in environmental impact is possible with a 23% higher economic burden, while 60% of this reduction can be achieved with only 3.1% increase in cost when maintaining a balanced societal disposition. This demonstrates the potential of optimization-based strategic purchasing decisions to achieve significant economic and environmental efficiencies when accompanied by increased environmental awareness.

Cloud migration and microservices optimization framework for large-scale enterprises

As large-scale enterprises increasingly adopt cloud computing, the need for a structured framework to guide seamless cloud migration and optimize microservices architecture becomes critical. This review presents a comprehensive designed to address the challenges and opportunities associated with transitioning to cloud environments and optimizing microservices for performance, scalability, and security. The review begins with an assessment of the enterprise’s current infrastructure, emphasizing a phased approach to cloud migration. It outlines key strategies such as lift-and-shift, re-platforming, and re-architecting, each tailored to specific organizational needs. The selection of the appropriate cloud model (public, private, or hybrid) and the use of multi-cloud strategies are discussed to ensure flexibility and risk mitigation during migration. For microservices optimization, focuses on designing services that are loosely coupled and highly scalable. Key elements include the use of service discovery, load balancing, API gateways, and database management strategies to improve the efficiency and reliability of microservices. Additionally, this introduces security best practices, including identity and access management (IAM), encryption protocols, and microservices-specific security measures such as Zero Trust Architecture. The review also highlights optimization strategies involving containerization (Docker) and orchestration (Kubernetes), alongside the integration of Continuous Integration/Continuous Delivery (CI/CD) pipelines for streamlined development and deployment. Performance monitoring, real-time logging, and proactive maintenance are emphasized for sustained efficiency and cost control in cloud environments. Case studies demonstrate the successful implementation of cloud migration and microservices optimization in large enterprises, showcasing both the benefits and common challenges. The review concludes by exploring future trends, such as serverless computing and AI-driven optimization, that will continue to shape cloud migration and microservices architecture.

Privacy preserving verifiable federated learning scheme using blockchain and homomorphic encryption

This paper introduces a novel Privacy-Preserving Verifiable Federated Learning (PPVFL) scheme that integrates blockchain technology and homomorphic encryption to address critical challenges in decentralized machine learning. The proposed scheme ensures data privacy, integrity, verifiability, robust security, and efficiency in collaborative learning environments, particularly in sensitive domains such as healthcare. By leveraging blockchain’s decentralized, immutable ledger and homomorphic encryption’s capability to perform computations on encrypted data, the model maintains the confidentiality of sensitive information throughout the learning process. The inclusion of Byzantine fault tolerance and Elliptic Curve Digital Signature Algorithm (ECDSA) further enhances the system’s security against malicious attacks and data tampering, while the optimization of computational processes ensures efficient model training and communication. The novelty of this work lies in the seamless integration of blockchain and homomorphic encryption within a federated learning framework, specifically tailored for post-quantum cryptography, a combination that has not been extensively explored in prior research. This research represents a significant advancement in secure and efficient federated learning, offering a promising solution for industries that prioritize data privacy, security, and trust in collaborative machine learning. The effectiveness, security, and efficiency of the PPVFL scheme were validated using the Glaucoma dataset. The proposed method outperformed baseline federated learning algorithms, achieving a Dice coefficient of 0.918 and a Hausdorff distance of 4.05 on Severe Glaucoma (SG) cases, compared to 0.905 and 5.27, respectively, with traditional FedAvg. Moreover, the integration of blockchain and homomorphic encryption ensured that data privacy was upheld without compromising model performance, while efficient computation and communication processes minimized latency and resource consumption. This study contributes a robust, privacy-preserving, secure, efficient, and verifiable federated learning framework that addresses the pressing need for secure and scalable data management in distributed machine learning environments.

Assessment of the efficiency of functioning of the environmental management system of enterprises

Purpose. A multifactor model is developed to assess the efficiency level of an enterprise’s environmental management system, depending on the effectiveness of organizational environmental measures. Methodology. The authors’ method for determining a comprehensive criterion for monitoring the effectiveness of the environmental management system, which characterizes the efficiency of operation and determines the level of environmental safety of enterprises, is proposed and used. To solve the tasks, a complex research method was also used, which included the analysis and generalization of literary and patent sources, and analytical, experimental research using computer and mathematical modeling methods. Findings. According to the studies and calculations of the generalized environmental quality indicator – 0.64, 0.66 and 0.66, the largest negative impact on the environment is caused by the activities of enterprises in the Pecherskyi, Podilskyi, and Solomianskyi districts of Kyiv, respectively. The obtained data testify to the relationship between the effectiveness of the implemented environmental measures (saving and rational use of resources, application of environmental technologies, advanced training, and environmental competence of employees) and the level of efficiency of the environmental management system of enterprises. Originality. As a result of the studies conducted using actual data, a system of indicators of the generalized environmental quality indicator has been proposed for the first time, which allows determining the environmental efficiency and effectiveness of the implemented environmental measures to assess the effectiveness of the functioning of the implemented environmental management system of the enterprise. Practical value. Based on the research results, a technique is proposed to assess the effectiveness of the environmental management system of enterprises by determining a generalized environmental quality indicator in terms of reducing the negative impact of the enterprise’s activities on the environment. Such an assessment system will help the management of the enterprise to promptly introduce corrective actions to improve the efficiency of the environmental management system and increase the level of environmental safety.

Trade-offs in AI assistant choice: Do consumers prioritize transparency and sustainability over AI assistant performance?

As artificial intelligence (AI) becomes more integrated into society, concerns have arisen about unintended biases in AI-driven decision-making and the environmental impact of AI technology development. AI assistants such as Siri and Alexa, while helpful, can obscure decision-making and contribute to increased energy use and CO2 emissions. The present study explores whether consumers prioritize transparency and environmental sustainability over performance when choosing AI assistants with conjoint designs. Japanese participants were presented with different AI assistant profiles, varying in performance quality, transparency, cost, and environmental efficiency. The results revealed that Japanese participants prioritized transparency over performance when choosing AI assistants, but they prioritized performance over environmental sustainability. Moreover, future-oriented participants placed more importance on sustainability than those with a present orientation, while participants with an internal locus of control valued transparency more than those with an external locus of control. The findings of this study enhance our understanding of how consumers choose AI options and offer valuable guidance for creating AI systems and communication strategies that work effectively.

Advancing urban mobility in developing countries: A mobile RSU approach for sustainable transportation

AbstractThe rapid and uncontrolled urbanization of cities in developing countries has engendered a plethora of urban mobility issues, including traffic congestion, accidents, and air pollution. To address these challenges, contemporary urban mobility trends are incorporating innovative technologies and sustainable governance practices. This article investigates how urban managers can leverage the opportunities presented by cost‐effective technologies and the management of urban data to enhance urban mobility in developing countries. Within this discourse, an RSU‐based approach is proposed that employs motorbike taxis for inter‐vehicular communication, given their status as the most widely used form of public transportation. This approach substantially diminishes investment costs and reinforces the sustainability of urban mobility. Through the implementation of this solution, a noteworthy reduction is anticipated in the emission of gases such as CO2, and NOx, known contributors to climate change and various respiratory diseases. To validate the efficacy of the proposed solution, four distinct scenarios are scrutinized in a case study centered on the city of Douala in Cameroon, utilizing tools such as OMNET++, SUMO, Veins, and INET. The proposed framework offers significant benefits in terms of environmental sustainability and operational efficiency. It enables a 10% reduction in CO2 emissions, a 15% reduction in NOx emissions, an 11% drop in fuel consumption, and a 15% reduction in waiting time in traffic jams. The envisaged solution aims to aid urban managers in their decision‐making processes, specifically in advancing sustainable urban mobility. Through the adoption of this approach, cities in developing countries can mitigate challenges associated with urban mobility and enhance the overall well‐being of their residents.

Spatial impact of green finance reform pilot zones on environmental efficiency: A pathway to mitigating China's energy trilemma

The energy trilemma is a critical global challenge requiring urgent resolution; however, existing solutions remain inefficient. This study utilizes a detailed environmental input-output indicator system, applying the SBM-DEA model for measurement and incorporating the Spatial Difference-in-Differences (SDID) model to assess the impact of Green Finance Policy (GFP) on environmental efficiency in Chinese cities from 2006 to 2021. The findings demonstrate that GFP significantly enhances environmental efficiency within a 500-km radius, as confirmed by various robustness tests. Mechanism analyses indicate that optimizing industrial structure, reducing energy consumption, advancing green innovation, and strengthening environmental regulations are critical pathways to improving environmental efficiency. Heterogeneity tests further examine the spatial policy effects under different factor endowments. Ultimately, the study concludes that improving environmental efficiency through GFP can alleviate the energy trilemma. This paper highlights the importance of implementing city-specific policies, offering new insights into energy economics, and providing fresh perspectives for policymakers, financial institutions, and enterprises. Despite the study's contributions, future research should further explore the comprehensive effects of GFP on energy, the environment, and the economy to provide more precise and holistic solutions for addressing the energy trilemma.

Non-thermal plasma decontamination of microbes: a state of the art.

Microbial decontamination is a critical concern in various sectors, from healthcare to food processing. Traditional decontamination methods, while effective to a degree, present limitations in terms of environmental impact, efficiency, and potential harm to the target material. This review investigates the emerging realm of non-thermal plasma (NTP) as a promising alternative for microbial decontamination, emphasizing its mechanisms, reactor designs and applications. The mechanism decomposed into physical, chemical and biological effects of plasma, are elaborated upon to provide a foundational understanding of the intrinsic principles of plasma decontamination. Except for the generation type of NTP, reactors and other parameters by which NTP achieves microbial decontamination, emphasizing the design considerations and parameters that influence its efficacy. Moreover, the latest applications of NTP in decontaminating air, water, and surfaces, supported by the latest research findings in each domain are explored. Additionally, the perspectives on the future research tendencies of NTP decontamination and disinfection are highlighted with potential avenues for exploration and innovation. Through this comprehensive review, the aim is to underscore the potential of NTP, particularly DBD plasma, as a versatile, efficient, and environmentally friendly method for microbial decontamination.

Assessing the Damage to Environmental Pollution: Discerning the Impact of Environmental Technology, Energy Efficiency, Green Energy and Natural Resources

The existing literature covers the topic of environmental pollution, but there is a scarcity of research that specifically examines the factors contributing to financial losses caused by carbon emissions. In this perspective, this ongoing analysis provides an understanding of the impact of environmental technology, energy efficiency, renewable energy consumption, natural resources, and economic growth on carbon dioxide damage in Organization for Economic Cooperation and Development (OECD) countries from 2000 to 2021 using the “Method of Moments Quantile Regression (MMQR)”, and “Dumitrescu–Hurlin (D-H)” causality test. The findings from the MMQR revealed that environmental control technology, renewable energy consumption, and energy efficiency contribute to reducing carbon dioxide damage at different quantiles. It was also found that economic growth and natural resources contribute to the increase in carbon dioxide damage in various quantities. Additionally, a one-way causality result was obtained from environmental technology, energy efficiency, renewable energy consumption, natural resources, and economic growth towards carbon dioxide damage. These results indicate that policymakers in OECD nations should provide suggestions on the efficient utilization of renewable energy sources and environmentally friendly technologies to minimize carbon dioxide damage.

Investigating the potential of waste oyster shell as a sustainable bio-mordant in natural dyeing

The use of natural bioresources in textile dyeing has attracted significant research interest due to their environmentally friendly and low-toxic nature. This study investigated the utilization of waste oyster shell (WOS) as a bio-mordant combined with sappan wood extract to dye silk fabrics using pre-, meta-, and post-mordanting method. The crystal structure of CaCO3 derived from WOS and their impact on color properties, color strength and color fastness were analyzed. The dyed fabrics displayed a range of shades from light pink to deep red and brown. FTIR analysis confirmed chemical interactions among dye, silk, and WOS mordants. Notably, silk treated with WOS at 800 °C as a pre-mordant showed color strength approximately 2.6 times higher than that of un-mordanted samples. Additionally, bio-mordanted samples exhibited improved color fastness compared to untreated ones (rating 2), with pre-mordanting offering the highest resistance to washing (rating 4–5). These findings shed light on the potential of waste oyster shell as an effective, sustainable alternative to conventional metal mordants in textile dyeing. This study not only support the utilization of waste but also enhances environmental and resource efficiency of the dyeing process.

Dissecting environmental efficiency: The role of technology adoption and usage

How could firms best reduce their environmental impact? Should they change technology? Or could they do better with what they already have? This paper shows that one size does not fit all. We analyse a sample of polluting production plants (i.e. installations) regulated under the EU Emission Trading System. We employ a mixture model estimation to dissect environmental efficiency into a technology adoption component (what type of technology is used) and a technology usage component (how a technology is used). Our installation-level analysis shows that the share of installations adopting frontier technologies is about 21%. We also find that the average environmental efficiency gains that installations could reach by improving technology adoption and technology usage are 75% and 80% respectively. The analysis of balance-sheet data on parent companies reveals that better environmental technologies are adopted by larger, listed, multi-installation and international companies, while older firms and firms with higher intangible assets intensity more commonly show improved technology usage.

Decentralized Machine Learning Framework for the Internet of Things: Enhancing Security, Privacy, and Efficiency in Cloud-Integrated Environments

The Internet of things (IoT) presents unique challenges for the deployment of machine learning (ML) models, particularly due to constraints on computational resources, the necessity for decentralized processing, and concerns regarding security and privacy in interconnected environments such as the Internet of cloud. In this paper, a novel decentralized ML framework is proposed for IoT environments characterized by wireless communication, dynamic data streams, and integration with cloud services. The framework integrates incremental learning algorithms with a robust decentralized model exchange protocol, ensuring that data privacy is preserved, while enabling IoT devices to participate in collaborative learning from distributed data across cloud networks. By incorporating a gossip-based communication protocol, the framework ensures energy-efficient, scalable, and secure model exchange, fostering effective knowledge sharing among devices, while addressing the potential security threats inherent in cloud-based IoT ecosystems. The framework’s performance was evaluated through simulations, demonstrating its ability to handle the complexities of real-time data processing in resource-constrained IoT environments, while also mitigating security and privacy risks within the Internet of cloud.