Nadia Dahmani, Imen Ben Salem, Syed Muhammad Salman Bukhari
Abstract Air quality (AQ) related health risks are increasing globally, creating urgent demand for artificial intelligence (AI) systems that are privacy-preserving, transparent, and auditable. Although Federated Learning (FL), Distributed Ledger Technologies (DLT), and Explainable AI (XAI) are widely recognized as core components of trustworthy AI, existing research remains divided across technical and application domains. This study conducts a computational, multistage systematic review (SR) of scholarly literature records published between 2011 and 2025 using the Semantic Scholar Graph API with intersection and union search strategies. A total of 13,458 records were retrieved and refined into a research-grade corpus of 7,215 publications through DOI-based deduplication, abstract quality diagnostics, venue and publication type screening, and exclusion of non-research artifacts. We used synergy detection, bibliometric analysis, and semantic clustering to study how these technologies are combined and distributed across research domains. The semantic clustering was performed using Sentence-BERT embeddings and UMAP. The results reveal a major structural gap. Among the 6,942 technology-tagged publications in the final 7,215-paper corpus, only 25 studies were identified as triple-synergy candidates that jointly mention or report the combined use of FL, DLT, and XAI, accounting for approximately 0.4%. Among these 25 triple-synergy publications, only two studies (8%) addressed climate or environmental applications, whereas 23 studies (92%) focused on healthcare or general AI. Semantic analysis further categorizes the studies into four areas: environmental sensing, provenance, governance, and clinical risk modeling. This division creates a critical challenge for scalable deployment, cross-domain interoperability, and policy-ready AI systems for AQ-health decision-making. To address this gap, we propose the Green-AI-Trust Stack, a layered architectural framework that integrates three complementary pillars (FL for privacy-preserving distributed learning, DLT for verifiable data and model provenance, and XAI for interpretable inference and decision support within a unified AQI-health pipeline). The findings provide practical direction for researchers, policymakers, and practitioners by identifying key research gaps, methodological needs, and future opportunities for reliable, auditable, and explainable AQI-health AI systems.
Industrial carbon emissions play a major role in environmental pollution and climate change. Because of this, industries are required to continuously monitor their emissions and ensure they follow environmental regulations. Traditional emission monitoring systems generally rely on centralized databases, which can sometimes lead to problems such as delayed reporting, lack of transparency, and the possibility of data being altered. To overcome these issues, this paper introduces CarbonChain, a decentralized carbon emission monitoring system that combines Internet of Things (IoT) sensing technologies with blockchain verification. Environmental parameters such as gas concentration and particulate matter are collected in real time using sensors connected to microcontroller units. The sensor readings are then transmitted to a backend server where the data is validated and categorized. After validation, the emission records are stored on the blockchain through smart contracts, generating secure transaction hashes that ensure the integrity of the data. A web-based dashboard allows regulators and industry stakeholders to monitor emission levels, check compliance status, and verify blockchain records in real time. By combining IoT-based sensing with blockchain technology, CarbonChain creates a transparent and tamper-resistant monitoring platform that can support environmental auditing and carbon credit verification.
The interest in blockchain for Industry 4.0 applications and the integration of the Industrial Internet of Things (IIoT) has grown considerably. This is due to the unique characteristics of blockchain, including the immutability of the distributed ledger, transparency, traceability of transactions, and security based on cryptographic techniques that certify the integrity of the data. So, the integration of blockchain can enhance data integrity, transparency, and security while eliminating trusted third parties. However, these characteristics make blockchains difficult to scale and typically involve high costs for a large number of transactions. It makes it difficult to manage large amounts of data which are generally produced in IIoT environments. Some recent results have demonstrated how the use of private blockchains can improve the issues of scalability and transaction costs. This paper addresses the following research question: Can blockchain technology, in its current state, be suitable for implementing industrial monitoring applications that use IIoT devices? Our objective is to propose a blockchain-based application integrated with IIoT devices in order to control the air and water pollution produced by industrial activities. The application we propose complies with current European regulations. Finally, we perform an empirical evaluation of the solution's performance to understand its applicability in real-world scenarios.
Montaser N.A. Ramadan, Mohammed A. H. Ali, Hadi Jaber, Mohammad Alkhedher
Air pollution in industrial zones significantly impacts environmental safety and worker health. This paper presents a novel decentralized IoT-federated learning (FL) framework, uniquely integrated with blockchain security, designed to provide a mechanistic understanding and accurate predictive modeling of air pollutant exposure in industrial environments. The novelty lies in the integration of a hybrid EMD-Transformer-BiLSTM prediction model with a blockchain-backed federated learning mechanism, providing secure, tamper-proof decentralized model updates. Three IoT-based sensing units, deployed across an industrial facility for five months, continuously monitored pollutants (PM2.5, PM10, COâ, VOCs, CHâO, CO, and Oâ) and environmental factors (temperature, humidity). The innovative model improved prediction accuracy from 83.12 % to 92.5 % for short-term (5-minute) forecasts, stabilizing at 84.7 % for 60-minute predictions after 15 FL rounds. Model validation indicated strong predictive reliability (R² = 0.89), significantly reducing prediction errors (Mean Absolute Error and Root Mean Square Error). Blockchain integration successfully ensured data integrity, identifying and rejecting over 98.7 % of unauthorized updates. Additionally, a swarm intelligence approach optimized decentralized model aggregation, minimizing communication overhead despite increased security latency (FL rounds increased from 7.5 s to 13.5 s for 500 clients). Real-time RGB-based air quality index visualization and cloud-based spatio-temporal mapping provided actionable insights into pollutant dynamics. This study demonstrates a distinct advancement in air pollution monitoring by combining federated learning, blockchain technology, and real-time adaptive visualization for enhanced environmental safety in industrial settings.
A pressing challenge in verifiable monitoring of anthropogenic impacts on the environment under conditions of intensive industrialization lies in the development of conceptual approaches to designing architectures of secure sensor systems that integrate cryptographic authentication, distributed verification, and blockchain technologies. The SmartDust concept, which envisages the deployment of distributed sensor swarms composed of micro-electromechanical systems, demonstrates considerable potential for building scalable ecological and analytical infrastructures. However, unresolved issues remain with respect to ensuring the cryptographic authenticity of sensor nodes, protecting telemetry data from tampering, and maintaining resilience against cyber-physical attacks and electromagnetic interference. This article proposes a conceptual architecture grounded in a multi-layered paradigm of trusted interaction. The architectural model incorporates cryptographic identification and lightweight digital signing of telemetry packets at the sensor level, swarm-based cross-verification of data using decentralized consensus algorithms, and the integration of gateways with a distributed ledger to guarantee data immutability and retrospective verifiability. Simulation results indicate that the proposed architecture substantially enhances the trustworthiness of environmental monitoring data, minimizes the risks of data falsification, and ensures the resilience of the sensor network to disruptive influences.
The increasing complexity of modern aircraft systems necessitates advanced monitoring solutions to ensure operational safety and efficiency. Traditional aircraft health monitoring systems (AHMS) often rely on reactive maintenance strategies, detecting only visible faults while leaving underlying issues unaddressed. This gap can lead to critical failures and unplanned downtime, resulting in significant operational costs. To address this issue, this paper proposes the integration of artificial intelligence (AI) and blockchain technologies within an enhanced AHMS, utilizing the iceberg model as a conceptual framework to illustrate both visible and hidden defects. The model highlights the importance of detecting and addressing issues at the earliest possible stages, ensuring that hidden defects are identified and mitigated before they evolve into significant failures. The rationale behind this approach lies in the need for a predictive maintenance system capable of identifying and mitigating hidden risks before they escalate. Key tasks completed in this study include: a comparative analysis of the proposed system with existing monitoring solutions, the selection of AI algorithms for fault prediction, and the development of a blockchain-based infrastructure for secure, transparent data sharing. The evolution of AHMS is discussed, emphasizing the shift from traditional monitoring to advanced, predictive, and prescriptive maintenance approaches. This integrated approach demonstrates the potential to significantly improve fault detection, optimize maintenance schedules, and enhance data security across the aviation industry.
Ahmed Kamel Abdelghany Hassan, Mohamed S. Saraya, Amr M. T. Ali-Eldin, Mohamed M. Abdelsalam
Air pollution is a growing concern due to severe threats to public health and the environment. The need for reliable air quality monitoring solutions has never been more critical. This research paper introduces an innovative approach to addressing this challenge by deploying a low-cost Internet of Things (IoT) air monitoring station and providing a blockchain technology solution to enhance environmental data transparency, reliability, and accessibility. Our paper adopts a concept of merging IoT and blockchain technologies and collecting some parameters that help to assess air quality by using three sensors, DHT11, MQ7, and MQ135, to collect temperature, humidity, carbon monoxide, and carbon dioxide parameters, respectively, to measure the gases and thus indicate the air quality within the surrounding area. Collecting and sharing these types of valuable data will be very important for various stakeholders, such as governmental bodies, researchers, and the public. This approach is consistent with the principles of sustainable development, facilitating informed decision-making and promoting eco-friendly policies. This research explores the technical architecture of the IoT air monitoring stations, offering a promising solution for addressing air pollution concerns while promoting sustainable development goals. The proposed system is a model for leveraging emerging technologies to advance environmental monitoring and create smarter, livable cities. This approach aligns with the principles of sustainable development and eco-friendly initiatives. This research offers a promising model for enhancing environmental monitoring efforts and advancing the creation of smarter, more sustainable urban environments. The proposed IoT, cloud platform and blockchain-based system not only addresses pressing air pollution challenges but also sets a benchmark for leveraging emerging technologies in environmental science.
<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.
Ethereum is a major public blockchain. Besides being the second-largest digital currency by market capitalization for its cryptocurrency, the Ether (Î), it is also the foundation of Web3 and decentralized applications, or DApps, that are fuelled by Smart Contracts. At the time of this writing, Ethereum still uses Proof of Work (PoW) consensus algorithm to ensure the integrity of the blockchain and to prevent double spend. PoW requires the participation of miners, who are incentivized to assemble blocks of transactions by being rewarded with cryptocurrency paid by transaction originators and by the blockchain network itself via newly minted Î. Network fees for transaction submissions are called gas, by analogy to the fuel used by cars, and are negotiable. They are also highly volatile and hence it is critical to predict the direction they are heading into, so that one can time transaction submissions, when feasible. There have been several efforts to predict gas prices, including usage of large Mempools, analysis of committed blocks, and more recent ones using Facebook's Prophet model [Taylor, S. J., & Letham, B. (2017). Forecasting at scale. PeerJ Preprints, 5, e3190v2. https://doi.org/10.7287/peerj.preprints.3190v2]. In this study, we introduce an innovative approach that employs the DeepAR [Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181â1191. https://doi.org/10.1016/j.ijforecast.2019.07.001] model, known for its superior forecasting accuracy over conventional methods by virtue of its ability to learn from multiple related time series. This methodology not only offers immediate advantages but also holds promise for ongoing enhancements. We substantiate our claims through empirical testing, utilizing data extracts from the Ethereum blockchain and cryptocurrency price feeds. This document is an extended version of our ICCS 2022 paper on the same topic. In this paper, we dive deeper into the internals of DeepAR forecasting algorithm [Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181â1191. https://doi.org/10.1016/j.ijforecast.2019.07.001], analyse the correlation between the on-chain/off-chain sample data, and describe additional experiments that empirically prove our findings and, finally, perform a comparison of our outputs with those from the Prophet [Taylor, S. J., & Letham, B. (2017). Forecasting at scale. PeerJ Preprints, 5, e3190v2. https://doi.org/10.7287/peerj.preprints.3190v2] model.
Nan Ma, Alex Waegel, Max Hakkarainen, William W. Braham ¡ 6 authors
Electric demand flexibility in buildings is highly dependent on occupant behavior. Evaluating and incentivizing these behaviors can provide grid-responsive support and encourage demand response (DR) participation. To achieve these goals, we developed an infrastructure for connecting Internet of Things (IoT) sensors to a distributed ledger (blockchain network) for long-term monitoring of energy and environmental performance. This study presents a novel Blockchain + IoT paradigm for the building science research community, applied in a real-world application. This Blockchain + IoT Network (BIN) uses Raspberry Pi minicomputers as platforms for connecting sensors to a blockchain network, to provide and analyze real-time indoor environmental quality (IEQ), energy, and carbon intensity data. As part of the study, we propose various metrics to evaluate the environmental footprints of building users. Novel algorithms for normalizing energy usage and carbon intensity, with consideration of a variety of related environmental factors, are executed as smart contracts on the blockchain network. All measurements and the smart contract transactions are reported and visualized on live dashboards. The use of smart contract allocates tokens based on the reward algorithms to incentivize individualsâ energy conservation, and similarly to DR pricing, can help influence occupant consumption patterns towards carbon reduction goals. We further test the smart contractâs algorithm in relation to real sensor data we have collected in two case studies: single-unit households and carbon intensity in the energy market. The combination of proposed metrics translates measured sensor data into token awards, demonstrates upper and lower limits dictated by the grid generation mix profile, and indicates that there is the potential for load shifting to minimize carbon emissions without considering the scale of consumption.
Cryptocurrency is a new sort of asset that has emerged as a result of the advancement of financial technology and it has created a big opportunity for researches. Cryptocurrency price forecasting is difficult due to price volatility and dynamism. Around the world, there are hundreds of cryptocurrencies that are used. This paper proposes three types of recurrent neural network (RNN) algorithms used to predict the prices of three types of cryptocurrencies, namely Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models show excellent predictions depending on the mean absolute percentage error (MAPE). Results obtained from these models show that the gated recurrent unit (GRU) performed better in prediction for all types of cryptocurrency than the long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models. Therefore, it can be considered the best algorithm. GRU presents the most accurate prediction for LTC with MAPE percentages of 0.2454%, 0.8267%, and 0.2116% for BTC, ETH, and LTC, respectively. The bi-LSTM algorithm presents the lowest prediction result compared with the other two algorithms as the MAPE percentages are: 5.990%, 6.85%, and 2.332% for BTC, ETH, and LTC, respectively. Overall, the prediction models in this paper represent accurate results close to the actual prices of cryptocurrencies. The importance of having these models is that they can have significant economic ramifications by helping investors and traders to pinpoint cryptocurrency sales and purchasing. As a plan for future work, a recommendation is made to investigate other factors that might affect the prices of cryptocurrency market such as social media, tweets, and trading volume.
Nowadays, electricity consumption has increased worldwide due to the activity of cryptocurrency miners. Much of Iranâs electricity is generated by fossil fuel power plants. So, generating more electricity means producing more air pollutants in Iran. There is not sufficient information about the effects of cryptocurrency mining on Iranâs air pollution. This study aims to estimate the amount of carbon monoxide (CO), sulfur oxides (SOx), nitrogen oxides (NOx), volatile organic compounds (VOCs), and particulate matter (PM) emitted by Iranâs power plants when they generate extra electricity for cryptocurrency miners. In this study, we firstly estimated the amount of fuel used for the electricity needed for cryptocurrency miners. Then, the amounts of emitted NOx, CO, VOCs, SOx, and total PM for generation of such electricity were estimated via the guidelines of the European Environment Agency for emission inventory estimation. The results showed that an on average of 3530, 1547, 103, 11, and 35 tons of NOx, CO, VOCs, SOx, and total PM, respectively, have been emitted into the atmosphere in Iran annually.
Seungwon Cho, Muhammad Khan, Jae-Ho Pyeon, Chansik Park
In total, 44.3% of particle matter 10 (PM10) is fugitive dust, and one of the main sources of fugitive dust generation in Korea is construction work (22%). Construction sites account for 84% of the total business places that have reported fugitive dust generation. Currently, the concentration of fine dust at construction sites is being remotely monitored by government inspection agencies through IoT sensors, but it is difficult to trust that appropriate fine dust reduction measures are being taken, because contractors can avoid taking these measures by submitting false reports or photos. In addition, since the fine dust monitoring system under government management is not an open platform and centralized system, residents near construction sites encounter difficulties in accessing information about fine dust. Therefore, in this study, we designed and constructed a blockchain network model to transparently and reliably provide network participants with the information associated with IoT data and fine dust reduction measures. To operate the blockchain network, we designed the chaincode, DApp, and network architecture. In addition, information on fine dust concentration and reduction measure photos were shared with the participants via the blockchain search tool (Hyperledger Explorer). The proposed blockchain network is expected to form a trust protocol among contractors, government inspection agencies, and citizens.
The need to protect sensitive data is growing, and environmental data are now considered sensitive. The application of last-generation procedures such as blockchains coupled with the implementation of new air quality monitoring technology allows the data protection and validation. In this work, the use of a blockchain applied to air pollution data is proposed. A blockchain procedure has been designed and tested. An Internet of Things (IoT)-based sensor network provides air quality data in terms of particulate matter of two different diameters, particulate matter (PM)10 and PM2.5, volatile organic compounds (VOC), and nitrogen dioxide (NO2) concentrations. The dataset also includes meteorological parameters and vehicular traffic information. This work foresees that the data, recovered from traditional Not Structured Query Language (NoSQL) database, and organized according to some specifications, are sent to the Ethereum blockchain daily automatically and with the possibility to choose the period of interest manually. There was also the development of a transaction management and recovery system aimed at retrieving data, formatting it according to the specifications and organizing it into files of various formats. The blockchain procedure has therefore been used to track data provided by air quality monitoring networks unequivocally.
YuâPin 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 âbackward pollution source tracingâ (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.
European buildings are producing a massive amount of data from a wide spectrum of energy-related sources, such as smart metersâ data, sensors and other Internet of things devices, creating new research challenges. In this context, the aim of this paper is to present a high-level data-driven architecture for buildings data exchange, management and real-time processing. This multi-disciplinary big data environment enables the integration of cross-domain data, combined with emerging artificial intelligence algorithms and distributed ledgers technology. Semantically enhanced, interlinked and multilingual repositories of heterogeneous types of data are coupled with a set of visualization, querying and exploration tools, suitable application programming interfaces (APIs) for data exchange, as well as a suite of configurable and ready-to-use analytical components that implement a series of advanced machine learning and deep learning algorithms. The results from the pilot application of the proposed framework are presented and discussed. The data-driven architecture enables reliable and effective policymaking, as well as supports the creation and exploitation of innovative energy efficiency services through the utilization of a wide variety of data, for the effective operation of buildings.
Markus Lßcking, Niclas Kannengieber, Maurice Kilgus, Till Riedel ¡ 7 authors
Pollution-monitoring systems (PMSs) are used worldwide to sense environmental changes, such as air quality conditions or temperature increases, and to monitor compliance with regulations. However, organizations manage the environmental data collected by such PMSs in a centralized manner, which is why recorded environmental data are vulnerable to manipulation. Moreover, the analysis of pollution data often lacks transparency to outsiders, which may lead to wrong decisions regarding environmental regulations. To address these challenges, we propose a software design for PMSs based on distributed ledger technology (DLT) and the long-range (LoRa) protocol for flexible, transparent, and energy-efficient environment monitoring and data management. To design the PMS, we conducted a comprehensive requirements analysis for PMSs. We benchmarked different consensus mechanisms (e.g., BFT-SMaRt and Raft) and digital signature schemes (e.g., ECDSA and EdDSA) to adequately design the PMS and fulfill the identified requirements. On this basis, we designed and implemented a prototype PMS and evaluated it in the field. The evaluation shows the effectiveness of DLT-based PMSs that include portable low-energy sensor nodes and demonstrates the applicability of the proposed software design for PMSs in contexts other than air pollution.
The purpose of this descriptive research paper is to initiate discussions on the use of innovative technologies and their potential to support the research and development of pan-Canadian monitoring and surveillance activities associated with environmental impacts on health and within the health system. Its primary aim is to provide a review of disruptive technologies and their current uses in the environment and in healthcare. Drawing on extensive experience in population-level surveillance through the use of technology, knowledge from prior projects in the field, and conducting a review of the technologies, this paper is meant to serve as the initial steps toward a better understanding of the research area. In doing so, we hope to be able to better assess which technologies might best be leveraged to advance this unique intersection of health and environment. This paper first outlines the current use of technologies at the intersection of public health and the environment, in particular, Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT). The paper provides a description for each of these technologies, along with a summary of their current applications, and a description of the challenges one might face with adopting them. Thereafter, a high-level reference architecture, that addresses the challenges of the described technologies and could potentially be incorporated into the pan-Canadian surveillance system, is conceived and presented.
Shengjing Sun, Xiaochen Zheng, Javier Villalba-DĂez, JoaquĂn OrdieresâMerĂŠ
Indoor air pollution has been ranked among the top five environmental risks to public health. Indoor Air Quality (IAQ) is proven to have significant impacts on people's comfort, health, and performance. Through a systematic literature review in the area of IAQ, two gaps have been identified by this study: short-term monitoring bias and IAQ data-monitoring solution challenges. The study addresses those gaps by proposing an Internet of Things (IoT) and Distributed Ledger Technologies (DLT)-based IAQ data-monitoring system. The developed data-monitoring solution allows for the possibility of low-cost, long-term, real-time, and summarized IAQ information benefiting all stakeholders contributing to define a rich context for Industry 4.0. The solution helps the penetration of Industrial Internet of Things (IIoT)-based monitoring strategies in the specific case of Occupational Safety Health (OSH). The study discussed the corresponding benefits OSH regulation, IAQ managerial, and transparency perspectives based on two case studies conducted in Spain.
Global warming caused by air pollution and ecosystem changes are very important. Pollutants such as harmful gases and fine dust generated in industrial sites are the main causes of air pollution. Strict control of emissions of air pollutants from industrial sites is required. In this paper, we propose a real-time air pollution index measurement platform using 5G wireless network and blockchain. The proposed platform breaks the existing passive method and collects data in real time through IoT sensor based on 5G wireless network. In order to prevent forgery and tampering of collected data, we use blockchain technology to encrypt and transmit to cloud and provide real-time air pollution index measurement platform. You can extract technology-intensive data through edge computing and cloud computing. This paper describes the architecture of the proposed platform and introduces use cases.
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.