Data Gateway Research Articles

Personal safety monitoring system of electric power construction site based on AIoT Technology

For the purpose of real-time monitoring the hazard information on the electric power construction site, a personal safety monitoring system based on Artificial intelligence internet of things (AIoT) technology is designed. After the system sensing layer collects the gas information of the construction site through the gas sensor, limit current oxygen sensor and DS1820B temperature sensor, the edge computing device of the edge layer directly stores its calculation in the database of the platform layer through the data gateway. The Artificial Intelligence (AI) analysis module of this layer invokes the monitoring data of the power construction site of the database, and uses the personal safety identification method of the power construction site based on artificial intelligence technology, to complete the abnormal identification of monitoring data and realize personal safety monitoring. In addition, the system is also equipped with a power-fail detection module, which can collect the working voltage through the voltage transformer and compare it with the mains power standard to judge whether there is a power-fail risk, so as to prevent the problem of threatening personal safety due to the power-fail of the energized equipment. After testing, the system can monitor the operation status of the construction site in real time to protect personal safety.

A satellite bioclimatology of Baluchistan in Southwestern Asia

.The present study aimed at investigating the relationship between two variables of temperature and precipitation with vegetation dynamics in one of the arid and semi-arid regions of the world, i.e. Baluchistan in Southwestern Asia, which is shared by the three countries of Iran, Pakistan and Afghanistan. In order to achieve the objectives, two different databases were used: 1. MODIS NDVI 16-day composite products (MOD13A3) of Terra satellite, with 1*1 km spatial resolution, which was obtained for a 17-year period (2000-2016) from the Earth Observing System (EOS) Data Gateway of the National Aeronautics and Space Administration (NASA); 2. Gridded monthly temperature and precipitation data was obtained for the same 17-year period from the Climate Research Unit (CRU) of the University of East Anglia. The Pearson product-moment correlation coefficient was also used to examine the relationship between vegetation dynamics and two climate variables of temperature and precipitation simultaneously as well as in three time lags i.e.; one month, two months and three months. The results of the analysis of a correlation between the mean temperature and monthly NDVI in different time lags indicated that in the humid and semi-humid regions in the northern half of Baluchistan, NDVI simultaneously reacted to temperature variations, while in the arid and semi-arid regions in the southern half of Baluchistan, NDVI had a one-month time lag with temperature. However, the results of the analysis of a correlation between precipitation and monthly NDVI in different time lags indicated that NDVI simultaneously reacted to precipitation variations, that is precipitation of each month had the greatest effect on the NDVI of the same month.

Earth Observation Capacity of the National Geoinformation Center of Bulgaria as Part of the Tools for Geo- and Anthropogenic-Hazard Management in EPOS

Exploring the nature of the Earth system and its multi-scale architecture requires an integrated symbiotic approach not only to understand our planet but also to reveal the hazards and footprints of their events on human life. Following the conceptual model of the European Plate Observing System (EPOS) we integrate a variety of research to obtain a multilateral use of geoinformation for scientific and practical purposes. In 2018, we started building a Bulgarian distributed scientific infrastructure called the National Geoinformation Center, aiming to provide data, products, and expertise to predict and prevent natural and anthropogenic risks and disasters, as well as environmental changes. The present paper demonstrates the capabilities of seismological, Global Navigation Satellite System (GNSS), geomagnetic and multi-scale laboratories (MSL) in Bulgaria that participate to the respective thematic core services of EPOS. More than 40 seismological stations register and transmit data in real time through the operational center of the Bulgarian Seismological Network. Data from 18 GNSS stations, part of the National GNSS network, are officially registered in the EPOS GNSS Data Gateway. Data and products are available via the EPOS GNSS Product Portal for interested users. Panagjurishte geomagnetic observatory (PAG) provides real time variations and long-period data series for the Earth’s magnetic field. The Palaeomagnetic Laboratory at the National Institute of Geophysics, Geodesy and Geography (NIGGG) participates in MSL with its unique facilities for paleo-, archeo- and environmental magnetism studies. Four case studies are presented to illustrate best practices for utilizing the data collected. The analysis of seismicity in a region of a long-time exploited salt deposit is upgraded with GNSS data to reveal the reason for crustal movements in the region. The results show that the majority of the seismic events took place in the upper part of the Earth’s crust at a depth of up to 5 km, where the salt body is buried. The observed significant local deformations of the Earth’s surface are very likely related to the technological process of deposit exploitation. A recent model of the geomagnetic declination over the Bulgarian territory is also presented. It tracks the variation of the regional magnetic field from 2015 to 2020 and adds short-wavelength signals from the secular network observations. Last but not least, the application of a mineral magnetic laboratory analysis along the depth of a Holocene soil profile is demonstrated. Results reveal the soil evolution in response to environmental changes adding a new magnetic proxy parameter for more precise identification and characterization of the soil formation processes.

What can we learn from administrative benefits data?

We present the opportunities and limitations of administrative benefits data held by local authorities for data linkage projects. Whilst the richness of this data has been exploited by practitioners for administration, its potential remains little explored by researchers. We discuss data quality, sample selection and legal gateways for data sharing.
 Drawing on our experience working with over 40 local authorities, we present the structure of three datasets: the Council Tax Reduction Scheme, the Single Housing Benefits Extract and the Universal Credit Data Share. We show what variables are usually included, under which legal gateways this data can be shared and how the cohorts represented within the data compare with the low-income population. We discuss how these datasets can be linked at the household level with a number of other data held by local authorities such as social rent and Council Tax arrears, Housing Benefit overpayments and Discretionary Housing Payments (DHPs).
 Administrative benefits data provides a comprehensive snapshot of a household’s financial situation. Local authorities can proactively use and share this data with external data processors to fulfil their statutory duties if a legal gateway allows. By identifying households at risk of cash shortfalls before they reach a crisis point, councils can target support when administering local welfare schemes and preventing homelessness. By assessing eligibility for benefits, they can run data-driven uptake campaigns. This data captures a proportion of the population on national and local benefits within a local authority at several points in time. Attrition is of concern since households may leave datasets over time. Some will see their income rise and no longer qualify for benefits. Others will move out of the constituency.
 Local authorities routinely process longitudinal data on households receiving means-tested benefits by administering housing benefits, council tax support, and discretionary support funds. This data provides a unique real-time insight into the socioeconomic situation of low-income households. Yet, we show that its promising potential for policy research remains largely untapped.

Development of IoT portable device for saline water monitoring in Bang Kachao Area of Thailand

This research developed a portable water salinity measurement device using internet of things technology to support farmers in Bang Kachao area in Samut Prakan province, Thailand, to select quality water for cultivation and consumption. The system hardware consists of a microcontroller ESP32 with a GPS module and an electrical conductivity sensor with an analog-to-digital conversion module for saline water measurement. This device can connect to an internet system via a Wi-Fi network and MQTT protocol to transmit the data of saline water value and measuring location to the cloud system. In the cloud system, the node-red system software is installed as a data gateway for device interfacing. In addition, a node-red dashboard is used to collect data from a portable device and previously installed water quality monitoring station for visualizing a portable device location and saline water data. This dashboard is a web page that allows people to monitor the water situation in the area that is suitable to use.

Analisa dan Perancangan Jaringan WAN dengan Sisco Gateway Load Balancing Protocol (GLBP) Menggunakan Simulator GNS3

Lines of communication cannot be separated from the various problems that can interfere with data traffic
 in it. One problem that arises is the rupture that caused the destruction of communication lines or fiber
 optic cable intermediary.With a high level of reliability demanded data on the company's lines of
 communication so that the need for technology that can provide solutions to problems in the network data
 communication lines. It is very necessary especially companies manage databases and the data is very
 important to maintain.The use of gateway load balancing technology (GLBP) of cisco is expected to provide
 data communications reliability in need in the company.Gateway Load Balancing Protocol is the
 technology division of the traffic load of data to each gateway router or a group who are members of the
 GLBP. This technology can also divert traffic data gateway router it is damaged or dies communication
 path to the gateway are functioning properly. GLBP is an improvement on previous technology hot standby
 routing protocol (HSRP) to provide load balancing functionality.

Internet of Things Aware Secure Dew Computing Architecture for Distributed Hotspot Network: A Conceptual Study

Building a widely distributed hotspot network is a very tedious task due to its complexity. Providing security, fully distributed network services, and a cost-conscious impact are the major challenges behind this goal. To overcome these issues, we have presented a novel distributed hotspot network architecture with five layers that can provide large-scale hotspot coverage as an assimilated result. Our contributions to this new architecture highlight important aspects. First, scalability can be increased by including many Internet of Things (IoT) devices with sensors and Wi-Fi and/or LoraWAN connectivity modules. Second, hotspot owners can rent out their hotspots to create a distributed hotspot network in which the hotspots can act as an ordinary data gateway, a full-fledged hotspot miner, and a light-weight hotspot miner to earn crypto tokens as rewards for certain activities. Third, the advantages of Wi-Fi and LoraWAN can be seamlessly leveraged to achieve optimal coverage, higher network security, and suitable data transmission rate for transferring sensor data from IoT devices to remote application servers and users. Fourth, blockchain is used to enhance the decentralized behavior of the architecture that is presented here by providing immutability and independence from a centralized regulator and making the network architecture more reliable and transparent. The main feature of our paper is the use of the dew-computing paradigm along with hotspots to improve availability, Internet backhaul-agnostic network coverage, and synchronous update capability, and dew-aware leasing to strengthen and improve coverage. We also discuss the key challenges and future roadmap that require further investment and deployment.

Real-Time Data Analytics for Monitoring Electricity Consumption Using IoT Technology

Rising electricity bills as a result of climate variability and new home electrical and electronic appliances are becoming a major source of concern for most end users. Consumers are typically unaware of their household electricity consumption patterns and the costs associated with them, making proper planning and budgeting difficult. Monitoring and controlling energy consumption on appliances can reduce energy costs for end-users. The Internet of Things (IoT) has the potential to provide remote monitoring and control of devices via automated monitoring and control. In this study, we propose an IoT-enabled system for monitoring and controlling energy consumption in homes to save money and make electricity more affordable to low-income people. Autonomous sensor nodes attached to power outlets collect and route power consumption data to the GSM-enabled gateway. Data aggregation is performed by the gateway for data received from all end-nodes within its coverage area. The gateway sends the data to the cloud, where real-time data analytics is performed. We also developed an algorithm that enables users to not only understand their usage patterns but also remotely control their appliances at any time. Mobile phones are used to remotely turn on and off household appliances via a machine-to-machine interface, as well as to provide real-time visualization of household energy consumption patterns.

The Near Fault Observatory community in Europe: a new resource for faulting and hazard studies

The Near Fault Observatories (NFOs) community is one of the European Plate Observing System (EPOS, http://www.epos-eu.org) Thematic Communities, today consisting of six research infrastructures that operate in regions characterised by high seismic hazard originating from different tectonic regimes. Earthquakes respond to complex natural systems whose mechanical properties evolve over time. Thus, in order to understand the multi-scale, physical/chemical processes responsible for the faulting that earthquakes occur on, it is required to consider phenomena that intersect different research fields, i.e., to put in place multidisciplinary monitoring. Hence, NFOs are grounded on modern and multidisciplinary infrastructures, collecting near fault high resolution raw data that allows generation of innovative scientific products. The NFOs usually complement regional backbone networks with a higher density distribution of seismic, geodetic, geochemical and other geophysical sensors, at surface and sometimes below grade. These dense and modern networks of multi-parametric sensors are sited at and around active faults, where moderate to large earthquakes have occurred in the past and are expected in the future. They continuously monitor the underlying Earth instability processes over a broad time interval. Data collected at each NFO results in an exceptionally high degree of knowledge of the geometry and parameters characterizing the local geological faults and their deformation pattern. The novel data produced by the NFO community is aggregated in EPOS and is made available to a diverse set of stakeholders through the NFO Federated Specific Data Gateway (FRIDGE). In the broader domain of the Solid Earth sciences, NFOs meet the growing expectations of the learning and communication sectors by hosting a large variety of scientific information about earthquakes as a natural phenomenon and a societal issue. It represents the EPOS concept and objective of aggregating and harmonising the European research infrastructures capabilities to facilitate broader scientific opportunity. The NFOs are at the cutting edge of network monitoring. They conduct multidisciplinary experiments for testing multi-sensor stations, as well as realise robust and ultra-low latency, transmission systems that can routinely accommodate temporary monitoring densification. The effort to continuously upgrade the technological efficiency of monitoring systems positions the NFO at the centre of marketing opportunities for the European enterprises devoted to new sensor technology. The NFOs constitute ideal test beds for generating expertise on data integration, creating tools for the next generation of multidisciplinary research, routine data analysis and data visualization. In particular focus is often on near-real time tools and triggering alarms at different levels are tested and implemented, strengthening the cooperation with the Agencies for risk management. NFOs have developed innovative operational actions such as the Testing Centre for Earthquake Early Warning and Source Characterisation (CREW) and detailed fast ground shaking and damage characterization. Complementing the recent growth of modern laboratory and computational models, the NFOs can provide interdisciplinary observations of comparable high resolution to describe the behaviour of fault slip over a vast range of spatial and temporal scales and aiding to provide more accurate earthquake hazard characterizations.

Availability of open data for spatial public health research.

Background: Preventive and health-promoting policies can guide (place- and space-specific) factors influencing human health, such as the physical and social environment. Required is data that can lead to a more nuanced decision-making process and identify both existing and future challenges. Along with the rise of new technologies, and thus the multiple opportunities to use and process data, new options have emerged to measure and monitor factors that affect health. Thus, in recent years, several gateways for open data (including governmental and geospatial data) have become available. At present, an increasing number of research institutions as well as (state and private) companies and citizens’ initiatives are providing data. However, there is a lack of overviews covering the range of such offerings regarding health. In particular, for geographically differentiated analyses, there are challenges related to data availability at different spatial levels and the growing number of data providers.Objectives: This paper aims to provide an overview of open data resources available in the context of space and health to date. It also describes the technical and legal conditions for using open data.Results: An up-to-date summary of results including information on relevant data access and terms of use is provided along with a web visualization. All data is available for further use under an open license.

Artificial Intelligence-Powered Blockchains for Cardiovascular Medicine

Clinical databases, particularly those composed of big data, face growing security challenges. Blockchain, the open, decentralized, distributed public ledger technology powering cryptocurrency, records transactions securely without the need for third-party verification. In the health care setting, decentralized blockchain networks offer a secure interoperable gateway for clinical research and practice data. Here, we discuss recent advances and potential future directions for the application of blockchain and its integration with artificial intelligence (AI) in cardiovascular medicine. We first review the basic underlying concepts of this technology and contextualise it within the spectrum of current, well known applications. We then consider specific applications for cardiovascular medicine and research in areas such as high-throughput gene sequencing, wearable technologies, and clinical trials. We then evaluate current challenges to effective implementation and future directions. We also summarise the health care applications that can be realised by combining decentralized blockchain computing platforms (for data security) and AI computing (for data analytics). By leveraging high-performance computing and AI capable of securely managing large and rapidly expanding medical databases, blockchain incorporation can provide clinically meaningful predictions, help advance research methodology (eg, via robust AI-blockchain decentralized clinical trials), and provide virtual tools in clinical practice (eg, telehealth, sensory-based technologies, wearable medical devices). Integrating AI and blockchain approaches synergistically amplifies the strengths of both technologies to create novel solutions to serve the objective of providing precision cardiovascular medicine.

Smart IOT based Short Term Forecasting of Power Generation Systems and Quality Improvement Using Resilient Back Propagation Neural Network

In modern times, the instant improvement of changeable energy generations especially as of wind and solar energy resources in the power grid led with these generations developing a significant resource of ambiguity with behavior of load however living being the major variable source. It is crucial for the economic scheduling in the generation and load balance of the generating units and in trades of electricity market. The forecasting of energy managed to alleviate several challenges to encounter which rises the resource uncertainty. Solar and wind power prediction is observing a intensifying interest from the community research and numerous investigations working on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. Therefore, this work presents a Resilient Back Propagation Neural Network (RBPN) model to produce solar and wind power Short Term Forecasting (STF) and monitoring using Internet of Things (IoT).However, STF is very complex to handle due to the random and nonlinear characteristics of solar irradiance and wind speed under changeable weather conditions. But the proposed Resilient Back Propagation Neural Network (RBPN) is suitable for STF modeling and also the proposed forecasting system is directly connected to IEEE-9 bus to reduce Total Harmonics Distortion (THD) and also reduce the power quality issues in various conditions, such as voltage unbalance control, active and reactive power control. The performance of proposed forecasting system is validated through both hardware and simulation, the simulation is developed by using Matlab Simulink software. In the proposed system, the sensitivity analysis for varied variables and the model comparison with the aspect of proper selection incorporating the persistence and multiple linear regression models. Therefore, execution of the Internet of Things (IoT) in the supervising of solar PV and wind forecasting system was recommended and its implementation was analyzed. Suggested system comprised of data acquisition, data gateway, and smartphone application demonstration.

End-User Needs of Fragmented Databases in Higher Education Data Analysis and Decision Making

In higher education, a wealth of data is available to advisors, recruiters, marketers, and program directors. These large datasets can be accessed using an array of data analysis tools that may lead users to assume that data sources conflict with one another. As users identify new ways of accessing and analyzing these data, they deviate from existing work practices and sometimes create their own databases. This study investigated the needs of end users who are accessing these seemingly fragmented databases. Analysis of a survey completed by eighteen users and ten semi-structured interviews from five colleges and universities highlighted three recurring themes that affect work practices (access, understandability, and use), as well as a series of challenges and opportunities for the design of data gateways for higher education. We discuss a set of broadly applicable design recommendations and five design functionalities that the data gateways should support: training, collaboration, tracking, definitions and roadblocks, and time.

Research on Data Secure Transmission and Processing Method of a LoRa IoT System

The smart grid covers the traditional power grid and the Internet of Things network, including smart meters, which helps manage and monitor various customer consumption patterns in near real-time. To achieve data reading and effective management of terminal, smart meters connected to the power metering system, the article proposes using low-power wireless network (LPWAN) technology to connect terminal, smart meters to data gateways and private power clouds to achieve smart power metering Technical solutions. The solution uses the LoRa wireless module to realize the two-way interaction of power metering data and has the advantages of reliable operation, low cost, low power consumption, and remote access. This article also focuses on using emerging LPWAN technology to help build smart meter wireless access solutions and demonstrates the system implementation for smart meter end-to-end LoRa connection.

Method of Analyzing and Managing Volleyball Action by Using Action Sensor of Mobile Device

With the development of electronic technology and sensor technology, more and more intelligent electronic devices integrate micro inertial sensors, which makes the research of human action recognition based on action sensing data have great application value. Data‐based action recognition is a new research direction in the field of pattern recognition, which is essentially a process of action data acquisition, feature extraction, feature extraction, and recognition, the process of classification and recognition. Inertial motion information includes acceleration and angular velocity information, which is ubiquitous in daily life. Compared with motion recognition based on visual information, it can more directly reflect the meaning of action. This study mainly discusses the method of analyzing and managing volleyball action by using the action sensor of mobile device. Based on the motion recognition algorithm of support vector machine, the motion recognition process of support vector machine is constructed. When the data terminal and gateway of volleyball players are not in the same LAN, the classification algorithm classifies the samples to be tested through the characteristic data, which directly affects the recognition results. In this paper, the support vector machine algorithm is selected as the data classification algorithm, and the calculation of the classification process is reduced by designing an appropriate kernel function. For multiclass problems, the hierarchical structure of directed acyclic graph is optimized to improve the recognition rate. We need to bind motion sensors to human joints. In order to realize real‐time recognition of human motion, mobile devices need to add windows to the motion capture data, that is, divide the data into a small sequence of specified length, and provide more application scenarios for the device. This method of embedding motion sensors into devices to read motion information is widely used, which provides a convenient data acquisition method for human motion pattern recognition based on motion information. The multiclassification support vector machine algorithm is used to train the classification algorithm model with action data. When the signal strength of the sensor is 90 t and the speed is 2.0 m/s and 0.5 m/s, the detection accuracy of the adaptive threshold is 93% and 95%, respectively. The results show that the SVM method based on hybrid kernel function can greatly improve the recognition accuracy of volleyball stroke, and the recognition time is short.

PV Monitoring System for a Water Pumping Scheme with a Lithium-Ion Battery Using Free Open-Source Software and IoT Technologies

The development of photovoltaic (PV) technology is now a reality. The inclusion of lithium-ion batteries in grid-connected PV systems is growing, and the sharp drop in prices for these batteries will enable their use in applications such as PV water pumping schemes (PVWPS). A technical solution for the monitoring and tracking of PV systems is shown in this work, and a novel quasi-real-time monitoring system for a PVWPS with a Li-ion battery is proposed in which open-source Internet of Things (IoT) tools are used. The purpose of the monitoring system is to provide a useful tool for the operation, management, and development of these facilities. The experimental facility used to test the monitoring system includes a 2.4 kWpk photovoltaic field, a 3.6 kVA hybrid inverter, a 3.3 kWh/3 kW lithium-ion battery, a 2.2 kVA variable speed driver, and a 1.5 kW submersible pump. To address this study, data acquisition is performed using commercial hardware solutions that communicate using a Modbus-RTU protocol over an RS485 bus and open software. A Raspberry Pi is used in the data gateway stage, including a PM2 free open-source process manager to increase the robustness and reliability of the monitoring system. Data storage is performed in a server using InfluxDB for open-source database storage and Grafana as open-source data visualization software. Data processing is complemented with a configurable data exporter program that enables users to select and copy the data stored in InfluxDB. Excel or .csv files can be created that include the desired variables with a defined time interval and with the desired data granularity. Finally, the initial results of the monitoring system are presented, and the possible uses of the acquired data and potential users of the system are identified and described.

Lightweight Coordination Patterns for Applications of the Internet of Things

Abstract Applications of the Internet of Things (IoT) are famously known for connecting devices via the internet. The main purpose of IoT systems (wireless or wired) is to connect devices together for data collection, buffering and data gateway. The collected large size of data is often captured from remote sources for automatic data analytics or for direct decision making by its users. This paper applies the programming pattern for Big Data in IoT systems that makes use of lightweight Java methods, introduced in the recently published work on ClientNet Distributed Cluster. Considering Big Data in IoT systems means the sensing of data from different resources, the network of IoT devices collaborating in data collection and processing; and the gateways servers where the resulting big data is supposed to be directed or further processed. This mainly involves resolving the issues of Big Data, i.e., the size and the network transfer speed along with many other issues of coordination and concurrency. The computer network that connects IoT may further include techniques such as Fog and Edge computing that resolve much of the network issues. This paper provides solutions to these problems that occur in wireless and wired systems. The talk is about the ClientNet programming model and its application in IoT systems for orchestration, such as coordination, data communication, device identification and synchronization between the gateway servers and devices. These devices include sensors attached with appliances (e.g., home automations, supply chain systems, light and heavy machines, vehicles, power grids etc.) or buildings, bridges and computers running data processing applications. As described in earlier papers, the introduced ClientNet techniques prevent from big data transfers and streaming that occupy more resources (hardware and bandwidth) and time. The idea is motivated by Big Data problems that make it difficult to collect it from different resources through small devices and then redirecting it. The proposed programming model of ClientNet Distributed Cluster stores Big Data on the nearest server coordinated by the nearest coordinator. The gateways and the systems that run analytics programs communicate by running programs from other computers when it is essentially required. This makes it possible to let Big Data rarely move across a communication network and allow only the source code to move around the network. The given programming model greatly simplifies data communication overheads, communication patterns among devices, networks and servers.

US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC): 1-m Pixel Land Cover Class Definitions and Guidance.

This article defines the land cover classes used in Meter-scale Urban Land Cover (MULC), a unique, high resolution (one meter2 per pixel) land cover dataset developed for 30 US communities for the United States Environmental Protection Agency (US EPA) EnviroAtlas. MULC data categorize the landscape into these land cover classes: impervious surface, tree, grass-herbaceous, shrub, soil-barren, water, wetland and agriculture. MULC data are used to calculate approximately 100 EnviroAtlas metrics that serve as indicators of nature's benefits (ecosystem goods and services). MULC, a dataset for which development is ongoing, is produced by multiple classification methods using aerial photo and LiDAR datasets. The mean overall fuzzy accuracy across the EnviroAtlas communities is 88% and mean Kappa coefficient is 0.84. MULC is available in EnviroAtlas via web browser, web map service (WMS) in the user's geographic information system (GIS), and as downloadable data at EPA Environmental Data Gateway. Fact Sheets and metadata for each MULC Community are available through EnviroAtlas. Some MULC applications include mapping green and grey infrastructure, connecting land cover with socioeconomic/demographic variables, street tree planting, urban heat island analysis, mosquito habitat risk mapping and bikeway planning. This article provides practical guidance for using MULC effectively and developing similar high resolution (HR) land cover data.

Integrating consortium blockchain into edge server to defense against ransomware attack

An edge server acts as a data gateway in an IoT network between IoT devices and backend servers. If the edge server is under the ransomware attack, the server’s operation would be interfered by locking its critical files, leading to a single point of failure in the IoT network. This paper proposes a blockchain solution, called Blockchain-enabled Recovery Service (BRS), to tackle the malware injection attack in edge servers. In BRS, edge servers are implemented by nodes in a blockchain network which allows IoT data distributed in edge servers over multiple data connections. It ensures data availability and consistency while improving the network throughput. A simulation test has been conducted to evaluate the performance of edge servers in the blockchain network. The result showed that the size of data record in a ledger is crucial when maximizing the efficiency of a ledger and minimizing data loss in ransomware attack are considered. Moreover, even though the blockchain network is under the attack, data availability and data recovery can be achieved.

AGRO-GAIN - AN ABSOLUTE AGRICULTURE BY SENSING AND DATA-DRIVEN THROUGH IOT PLATFORM

Abstract The Agro-gain provides an end-to-end lot platform for data-driven agriculture, that gives an seamless data collection from various sensor type like drones, cameras and sensors. Agro-gain can ensure the availability of system even when the power and internet outages caused by bad weather. First, to enable connectivity within the farm, Agro-gain leverages recent work in unlicensed TV White Spaces (TVWS) to setup a high band width link from the farmer’s home Internet connection to an IoT base station on the farm. Sensors, cameras and drones can connect to this base station over a Wi-Fi frontend. This ensures high bandwidth connectivity within the farm. However, due to the lack of power on the farm, the base station is powered by battery- backed solar power which suffers from power unreliability depending on weather conditions. As shown in past work, cloudy weather can reduce solar power output significantly and drain the batteries of the base station to shut it down. To solve this problem, Agro-gain uses a novel weather-aware IoT base station design. Specifically, it uses weather forecasts to appropriately duty cycle different components of the base station. To the best of our knowledge, this is the first weather-aware IoT base station design. Second, internet connection to the farm is typically weak and it is not sufficient to ship high bandwidth drone videos to the cloud. The Agro-gain uses a Gateway based design, where in a PC at the farmer’s home serves as a gateway for the farm data.