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.
Aerosols are fine solid particles (particulate matter: PM) or liquid droplets in gas (usually air). Its origin can be natural or anthropogenic. Air PM pollution exposure is linked to diverse human health problems and to many environmental effects. Air samplers are used to study particles in air. Systematic periodic air sampling is needed to have confident air quality assessment. In this work we present a device (named RDMA) and a software application (named Enviro-Air Sampling) we have developed to enable access to environmental data, flow data, geolocation, and meteorological conditions from high volume air samplers (HVAS) with no data acquisition capabilities. One of the objectives of the RDMA (designed ab-initio to be an easy add-on to Tisch HVAS) is to enable a more precise determination (compared to Tisch Dickinson chart recorder) of the mass concentration of particles (MC) and of the standard mass concentration (SMC). In this paper we present some aspects of the work done that involved the use of IoT, Cloud, and DLT (Distributed Ledger Technology) technologies, that are enabling and driving Digital Transformation.
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.
Utilities have traditionally employed or contracted meter readers to collect natural gas usage data, which is expensive and time consuming, and thus necessitates the need of smart natural gas metering. Existing gas metering systems mainly focus on measuring the amount of gas flowing through an microelectro mechanical system (MEMS) thermal gas flow sensor and simply ignore the detailed gas composition. From computational fluid mechanics simulations, however, we discover that gases with different compositions will cause different effects on the reading of an MEMS sensor. Based on a thorough analysis of the working principle of MEMS thermal gas flow sensor, we propose an innovative mechanism to compensate the errors caused by different types of natural gases on the sensor's reading. The proposed solution first measures the physical property of metered gas to derive the composition correction coefficient that will then be used to correct the meter's reading errors, considering the relation between the calorific value and physical property of natural gases. In this way, the proposed solution realizes a real-time multicomposition gas metering via thermal gas flow sensors. We implement and evaluate the proposed gas metering technique in various Internet of Things systems, including industrial flow metering, gas metering in smart home, and gas metering in low-power wide-area networks. Experiment results verify the innovative design and confirm that the proposed solution features high sensitivity, high precision, and high range ratio.