Joseph malisaba, Barah Obinna Onyebuchi, Samuel George Onep, Emmanuel Ninsiima
<ns5:p> Background Access to safe drinking water remains a persistent challenge in low-resource settings such as Ishaka Municipality, Uganda, where surface and groundwater sources are frequently contaminated and access to reliable electricity is limited. This study presents the design, modeling, and performance evaluation of a solar-powered hybrid water treatment system integrated with a biosensor-based microbial detection unit, enabling autonomous operation and real-time water quality monitoring for decentralized applications. Methods A total of 384 water samples were collected from springs, wetlands, wells, and tap sources and analyzed for key physicochemical and microbial parameters, including turbidity, pH, and indicator organisms. The proposed system integrates sedimentation, activated carbon filtration, reverse osmosis, and solar thermal disinfection to achieve multi-barrier treatment. Hydraulic and filtration performance were modeled using fluid flow and porous media principles, while microbial inactivation was described using first-order kinetic models. The photovoltaic subsystem was evaluated through detailed loss modeling, incorporating temperature effects, partial shading, and inverter inefficiencies to assess overall system reliability. Results Baseline results indicated significant contamination, with <ns5:italic>Escherichia coli</ns5:italic> concentrations reaching 210 CFU/100 mL and turbidity values up to 146 NTU. The hybrid system achieved over 95% removal of contaminants, complete elimination of <ns5:italic>E. coli</ns5:italic> , and compliance with World Health Organization drinking water standards. Solar thermal disinfection provided a 4–6 log reduction in microbial indicators. The integrated biosensor demonstrated rapid response times (45–90 seconds) and strong correlation with laboratory biochemical oxygen demand measurements (R <ns5:sup>2</ns5:sup> = 0.89–0.94). The photovoltaic subsystem maintained a performance ratio of 0.84–0.88, consistently meeting 100% of operational energy demand under varying environmental conditions. Conclusion These results demonstrate that the proposed system provides an effective, energy-autonomous solution for decentralized water purification with real-time monitoring capability, offering significant potential for improving access to safe drinking water in rural and resource-limited environments. </ns5:p>
Illegal, Unreported, and Unregulated (IUU) fishing remains a major threat to marine ecosystems and coastal livelihoods, yet existing enforcement mechanisms rely on periodic inspections, manual reporting, or static financial incentives. We propose a novel closed-loop compliance-to-finance system in which multi-sensor vessel data are transformed into real-time financial signals that directly govern access to capital. In the proposed architecture, heterogeneous onboard and port-side sensors feed into an off-chain AI compliance model whose outputs are transmitted on-chain via decentralized oracle services. These compliance attestations programmatically adjust lending terms in Decentralized Finance (DeFi) protocols, dynamically reducing interest rates and increasing liquidity for compliant operators while restricting capital access for non-compliance. Loans are issued in USD-pegged stablecoins and overcollateralized using real-world fishing assets, including vessels, licenses, quotas, and contracts. Unlike prior approaches that treat sustainability incentives as external subsidies or reputational mechanisms, this system embeds regulatory compliance directly into the cost of capital, creating continuous, automated enforcement with minimum centralized intermediaries. We illustrate the feasibility of this architecture using existing low-cost sensing technologies, oracle infrastructure, and DeFi lending primitives, and discuss its potential to expand sustainable financing in small-scale and low-income fisheries where IUU fishing is most prevalent.
Ravi Khile, Shravani Karvande, Pradnya Katbane, Saniya Shaikh
Water quality degradation has become a pressing global challenge due to rapid industrialization, urbanization, and population growth. Conventional water quality monitoring systems rely on manual testing or cloud-based IoT frameworks, which often face vulnerabilities such as data tampering, network latency, and security breaches. To address these limitations, this research proposes a Blockchain-Based Secure Data Framework for IoT Water Monitoring using ESP32 and LoRa communication integrated with Firebase Cloud. The proposed system ensures tamper-proof, transparent, and decentralized data management for multi-parameter water quality monitoring. IoT sensor nodes equipped with pH, turbidity, TDS, and temperature sensors collect real-time data transmitted via LoRa gateways to a blockchain-enabled cloud interface. The blockchain layer secures sensor data through cryptographic hashing, consensus validation, and distributed ledger mechanisms. Experimental validation demonstrates that blockchain integration reduces unauthorized data manipulation by 98% and enhances system trust and traceability. The framework achieves an average latency of 1.2 seconds per transaction and consumes 27% less power compared to traditional cloud-only solutions. The results highlight blockchain’s potential to revolutionize secure environmental monitoring and ensure reliable, transparent water data management for sustainable smart cities.
Prince Jebedass Isaac Chandran, Hana Ahmed Khalil, PK Hashir, S Veerasingam
Aquaculture is vital for global food security, yet traditional methods often struggle with inefficiencies, disease outbreaks, and environmental concerns. This review explores how an integrated framework of Internet of Things (IoT), Artificial Intelligence (AI), and blockchain technology can transform aquaculture into a more efficient, sustainable, and intelligent industry. IoT enhances real-time monitoring and precision feeding, AI optimizes disease detection and resource management, and blockchain ensures transparency and traceability across the supply chain. This novel approach not only mitigates existing challenges but also fosters predictive analytics, automation, and data-driven decision-making. Although high costs and technical constraints pose challenges, adopting emerging technologies like 5 G, edge computing, and decentralized ledgers can accelerate industry-wide adoption and enhance resilience and scalability. Future innovations in AI-driven aquaculture must focus on adaptive machine learning models and cross-disciplinary collaborations to ensure resilience and scalability in the face of global demands.
Water quality degradation has turned out to be of crucial importance due to various factors over the past decade. Pollution, climate change, and population growth are the factors that affect water quality. Contaminations such as microorganisms, heavy metals, and excessive nitrogen and phosphorous disrupt water pH levels, posing significant health risks. Despite the innovation in the Internet of Things(IoT), allowing balancing the pH by adding chlorine and fluoride after the disinfection step, several security issues(e.g., distributed denial of service, data manipulation, and session hijacking) manoeuvre the operational performance of the water treatment plants. This causes people to consume polluted water, which has many adverse effects on human health and reduces life expectancy. To address this critical concern, we propose a novel approach integrating artificial intelligence(AI) and blockchain technology into water treatment plant management. Our methodology utilizes a standard water quality dataset, which has features such as pH and total hardness, which is used for binary classification, indicating water as potable or not potable. We employ various AI classifiers such as stochastic gradient descent classifier (SGDC), decision tree (DT), Naive Bayes (NB), K nearest neighbours (KNN), and logistic regression (LR). Furthermore, an InterPlanetary File System(IPFS)-based public blockchain is integrated to resist the data manipulation attack, where the potable water sample is securely stored in the blockchain’s immutable ledger. The proposed model is evaluated using various performance metrics such as confusion matrix analysis, learning curve assessment, training accuracy, and blockchain scalability. Notably, the DT model emerges as the best-performing classifier with an accuracy of 99.41% and scalability of 35 with 120 data transactions.
<title>Abstract</title> The delivery and management of clean water are crucial for the long-term growth of Smart Cities. However, controlling water quality and delivery in a smart city is a difficult and time-consuming process. In this work, we suggest a unique solution for smart water quality monitoring and distribution in Smart Cities that combines Internet of Things (IoT) with blockchain technology. We describe a system based on Hyperledger Fabric that provides safe and efficient data gathering, authentication, preservation, and smart contract execution. A continuous monitoring of water quality parameters, such as pH, temperature, turbidity, and dissolved oxygen, is achieved using the proposed IoT system. The collected data is stored on a secure blockchain ledger using Hyperledger Fabric, ensuring transparency, immutability, and security. Smart contracts are used to automate the water distribution process, enabling the system to efficiently allocate water resources based on demand and quality. Furthermore, the use of blockchain technology ensures that water quality data cannot be tampered with, providing a high degree of trust and accountability in the system. Overall, the proposed system represents a significant step towards a sustainable and secure future for water management in cities. This technology can revolutionize the way we manage and distribute water resources, ensuring safe and clean drinking water for future generations.
Mazin Abed Mohammed, Abdullah Lakhan, Karrar Hameed Abdulkareem, Mohd Khanapi Abd Ghani · 9 authors
INTRODUCTION: The Industrial Internet of Water Things (IIoWT) has recently emerged as a leading architecture for efficient water distribution in smart cities. Its primary purpose is to ensure high-quality drinking water for various institutions and households. However, existing IIoWT architecture has many challenges. One of the paramount challenges in achieving data standardization and data fusion across multiple monitoring institutions responsible for assessing water quality and quantity. OBJECTIVE: This paper introduces the Industrial Internet of Water Things System for Data Standardization based on Blockchain and Digital Twin Technology. The main objective of this study is to design a new IIoWT architecture where data standardization, interoperability, and data security among different water institutions must be met. METHODS: We devise the digital twin-enabled cross-platform environment using the Message Queuing Telemetry Transport (MQTT) protocol to achieve seamless interoperability in heterogeneous computing. In water management, we encounter different types of data from various sensors. Therefore, we propose a CNN-LSTM and blockchain data transactional (BCDT) scheme for processing valid data across different nodes. RESULTS: Through simulation results, we demonstrate that the proposed IIoWT architecture significantly reduces processing time while improving the accuracy of data standardization within the water distribution management system. CONCLUSION: Overall, this paper presents a comprehensive approach to tackle the challenges of data standardization and security in the IIoWT architecture.
Muhammad Tayyab Naqash, Toqeer Ali Syed, Saad S. Alqahtani, Muhammad Shoaib Siddiqui · 6 authors
Sustainable urban water management is essential to handle water scarcity, leakage, and inefficient distribution. This paper covers water management in urban areas, including an introduction, an overview of water management practices, the characteristics and functioning of water distribution systems, monitoring and control systems for efficient distribution, smart systems for optimization, strategies for water conservation and waste management, per capita water demand analysis, and desalination plant overviews. The article proposes a blockchain-based water management architecture with IoT sensors for accurate reporting. The framework uses blockchain technology to authenticate and share real-time data between sensors and the water distribution dashboard. It also has a modular API for water leakage detection and flow control to decrease water waste and enhance distribution. The suggested approach might enhance water management; however, its execution is complex. Maintaining the framework’s efficacy is advised. The research provides insights into water management and proposes a technology solution employing blockchain and IoT sensors for trustworthy data reporting and effective water distribution to promote sustainable urban water management.
Pavlos Papadopoulos, William J. Buchanan, Sarwar Sayeed, Nikolaos Pitropakis
Aim: A fish farm is an area where fish are raised and bred for food. Fish farm environments support the care and management of seafood within a controlled environment. Over the past few decades, there has been a remarkable increase in the calorie intake of protein attributed to seafood. Along with this, there are significant opportunities within the fish farming industry for economic development. Determining the fish diseases, monitoring the aquatic organisms, and examining the imbalance in the water element are some key factors that require precise observation to determine the accuracy of the acquired data. Similarly, due to the rapid expansion of aquaculture, new technologies are constantly being implemented in this sector to enhance efficiency. However, the existing approaches have often failed to provide an efficient method of farming fish. Methods: This work has kept aside the traditional approaches and opened up new dimensions to perform accurate analysis by adopting distributed ledger technology. Our work analyses the current state-of-the-art of fish farming and proposes a fish farm ecosystem that relies on a private-by-design architecture based on the Hyperledger Fabric private-permissioned distributed ledger technology. Results: The proposed method puts forward accurate and secure storage of the retrieved data from multiple sensors across the ecosystem so that the adhering entities can exercise their decision based on the acquired data. Conclusion: This study demonstrates a proof-of-concept to signify the efficiency and usability of the future fish farm.
In all the smart applications, evolution of the Internet of Things (IOT) is utilized as a complete matured technology and in the future internet generations, established itself. Blockchain is also the blooming technique like Internet of things in which the distributed ledger which enhances the security contained in the each node of the block-chain. In the block-chain network, any fault transaction is not done by the illegal users. The block-chain is combined with the Internet of Things for the improvement of real time application performance. IOT based smart water management system is designed in this paper for the agriculture which ensures the effectiveness of the agriculture water management. The remote monitoring with the IOT is used for this purpose. By linking with 2D modelling, the control and management of the agriculture water were performed. Finally, a system is implemented for the agriculture water management through the real time data collection. The obtained result shows the data that updates the water monitoring interface with the varying number of hours. The IoT technology and remote monitoring technology is utilized to the existing water management infrastructure. For water resources management and water supply, this is the very efficient technology.
Yu‐Pin Lin, Hussnain Mukhtar, Kuan-Ting Huang, Joy R. Petway · 7 authors
Real-time identification of irrigation water pollution sources and pathways (PSP) is crucial to ensure both environmental and food safety. This study uses an integrated framework based on the Internet of Things (IoT) and the blockchain technology that incorporates a directed acyclic graph (DAG)-configured wireless sensor network (WSN), and GIS tools for real-time water pollution source tracing. Water quality sensors were installed at monitoring stations in irrigation channel systems within the study area. Irrigation water quality data were delivered to databases via the WSN and IoT technologies. Blockchain and GIS tools were used to trace pollution at mapped irrigation units and to spatially identify upstream polluted units at irrigation intakes. A Water Quality Analysis Simulation Program (WASP) model was then used to simulate water quality by using backward propagation and identify potential pollution sources. We applied a “backward pollution source tracing” (BPST) process to successfully and rapidly identify electrical conductivity (EC) and copper (Cu2+) polluted sources and pathways in upstream irrigation water. With the BPST process, the WASP model effectively simulated EC and Cu2+ concentration data to identify likely EC and Cu2+ pollution sources. The study framework is the first application of blockchain technology for effective real-time water quality monitoring and rapid multiple PSPs identification. The pollution event data associated with the PSP are immutable.
Sina Rafati Niya, Sanjiv S. Jha, Thomas Bocek, Burkhard Stiller
This work proposes an IoT- and Blockchain-based, distributed system, for automated measuring, storing, and monitoring of water and air quality in environments such as lakes, mountains, urban areas, or factories. Comparable state-of-the-art solutions, require human interaction to access the data or require high power consumption or space requirements, or they are based on centralized architectures. The proposed pollution monitoring system here, on one hand, employs LoRa to address the high power consumption and long-range transmission challenges of IoT protocols. On the other hand, it is designed to be fully decentralized by using the Ethereum Blockchain to store and retrieve the data recorded by IoT sensors. Thus, data integrity is provided without the need for a Trusted Third Party (TTP) and data is collected and captured automatically without any manual operations needed. Observations on the four different types of sensors for measuring Potential Hydrogen (PH), Turbidity, Carbon monoxide (CO), and Carbon dioxide (CO2), revealed a high accuracy with the expected time-lines of measurements, non-falsified experimental values collected and can be used as reliable evidence of presence of pollution.