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Sathishkumar, D., and C. Karthikeyan. "Adaptive power management strategy-based optimization and estimation of a renewable energy storage system in stand-alone microgrid with machine learning and data monitoring." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 01 (September 4, 2019): 1941023. http://dx.doi.org/10.1142/s0219691319410236.
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This work is carried out with the optimum design of a stand-alone hybrid energy storage system (HESS) based on solar, wind and super capacitor (SC) with battery storage system which is effectively optimized in the grid power system. The distribution of the power source is mainly considered on the Hybrid renewable energy power sources. This discourse presents an adaptive power optimizing the three-phase inverter and grid-connected hybrid renewable energy resources efficiently. In this analysis, the similar parameters are taken for the compensation such as voltage fluctuation, harmonics and Frequency imbalance by implementing Adaptive Power Management Strategy (APMS) and the obtained issues are synchronized by inverter control. All these comparative activities of the inverter are done either discretely or combined to stabilize the unbalanced impacts of a wide range of adjusted, uneven, power loss at the circulation level. A battery and SC energy management are essential for maintaining the energy sustainability in renewable energy system. Combination of solar and wind with the battery and SC is used to test the proposed stand-alone grid management. The proposed hybrid power system is designed to work under classical-based energy management and this performance is monitored with the help of the Internet of Things (IoT) and machine learning based on Polynomial Linear Regression Algorithm. The focus of the suggested HESS is reduced by the loss in stand-alone grid system with an economic performances.
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Bishnoi, Prevesh Kumar. "Cloud Based Electrical Device Control Middleware APIs for IoT." International Journal of Emerging Trends in Engineering Research 8, no. 2 (February 15, 2020): 464–70. http://dx.doi.org/10.30534/ijeter/2020/34822020.
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Oktian, Yustus Eko, Sang-Gon Lee, and Hoon Jae Lee. "Hierarchical Multi-Blockchain Architecture for Scalable Internet of Things Environment." Electronics 9, no. 6 (June 25, 2020): 1050. http://dx.doi.org/10.3390/electronics9061050.
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Many researchers challenge the possibility of using blockchain and smart contracts to disrupt the Internet of Things (IoT) architecture because of their security and decentralization guarantees. However, the state-of-the-art blockchain architecture is not scalable enough to satisfy the requirements of massive data traffics in the IoT environment. The main reason for this issue is one needs to choose the consensus trade-off between either coping with a high throughput or a high number of nodes. Consequently, this issue prevents the applicability of blockchain for IoT use cases. In this paper, we propose a scalable two-tiered hierarchical blockchain architecture for IoT. The first tier is a Core Engine, which is based on a Practical Byzantine Fault Tolerance (PBFT) consensus to cope with a high throughput, that supervises the underlying subordinate engines (sub-engines) as its second tier. This second tier comprises of the Payment, Compute, and Storage Engine, respectively. We can deploy multiple instances of these sub-engines as many as we need and as local as possible near to the IoT domains, where IoT devices reside, to cope with a high number of nodes. Furthermore, to further extend the scalability of the proposed architecture, we also provide additional scalability features on the Core Engine such as request aggregation, request prioritization, as well as sub-engine parallelism. We implement all of our engines and expose them to IoT applications through the Engine APIs. With these APIs, developers can build and run IoT applications in our architecture. Our evaluation results show that our proposed features on the Core Engine can indeed enhance the overall performance of our architecture. Moreover, based on our proof-of-concept IoT car rental application, we also show that the interoperability between sub-engines through the Core Engine is possible, even when the particular sub-engine is under sub-engine parallelism.
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Jin, Wenquan, Rongxu Xu, Sunhwan Lim, Dong-Hwan Park, Chanwon Park, and Dohyeun Kim. "Integrated Service Composition Approach Based on Transparent Access to Heterogeneous IoT Networks Using Multiple Service Providers." Mobile Information Systems 2021 (May 28, 2021): 1–19. http://dx.doi.org/10.1155/2021/5590605.
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The Internet of Things (IoT) enables the number of connected devices to be increased rapidly based on heterogeneous technologies such as platforms, frameworks, libraries, protocols, and standard specifications. Based on the connected devices, various applications can be developed by integrating domain-specific contents using the service composition for providing improved services. The management of the information including devices, contents, and composite objects is necessary to represent the physical objects on the Internet for accessing the IoT services transparently. In this paper, we propose an integrated service composition approach based on multiple service providers to provide improved IoT services by combining various service objects in heterogeneous IoT networks. In the proposed IoT architecture, each service provider provides web services based on Representational State Transfer (REST) Application Programming Interface (API) that delivers information to the clients as well as other providers for integrating the information to provide new services. Through the REST APIs, the integration management provider combines the service result of the IoT service provider to other contents to provide improved services. Moreover, the interworking proxy is proposed to bridge heterogeneous IoT networks for enabling transparent access in the integrated services through proving protocol translating on the entry of the device networks. Therefore, the interworking proxy is deployed between the IoT service provider and device networks to enable clients to access heterogeneous IoT devices through the composited services transparently.
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Alasbali, Nada, Saaidal Razalli Bin Azzuhri, Rosli Bin Salleh, Miss Laiha Mat Kiah, Ahmad Aliff A. S. Ahmad Shariffuddin, Nik Muhammad Izwan bin Nik Mohd Kamel, and Leila Ismail. "Rules of Smart IoT Networks within Smart Cities towards Blockchain Standardization." Mobile Information Systems 2022 (February 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/9109300.
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Motivation. Standardization in smart city applications is restricted by the competitive pressures of proprietary innovation and technological compartmentalization. Interoperability across networks, databases, and APIs is essential to achieving the smart objectives of technology-supported urban environments. Methodology. The issues that smart cities face, as well as the usage of blockchain in Internet of Things (IoT) applications, are discussed in this research paper. Problem Statement. The study shows the obstacles to the establishment of an IoT-driven smart city agenda, including system security, dispersed node interoperability, data resource management, and scalability of a diverse IoT network. Results. To resolve these challenges, this research proposes a working infinite loop model for establishing a standardized, intermediary cloud-based blockchain for IoT networking within smart cities. The blockchain intermediary function will resolve critical gaps in the existing, distributed IoT-based smart cities’ standards, drawing connections between nodes, users, and service providers that are enabled through autonomous, immutable, and nonrepudiated transactions.
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Surianarayanan, Chellammal, and Pethuru Raj Chelliah. "Integration of the Internet of Things and Cloud." International Journal of Cloud Applications and Computing 13, no. 1 (July 10, 2023): 1–30. http://dx.doi.org/10.4018/ijcac.325624.
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The integration of IoT and cloud poses increased security challenges. Implementing security mechanisms in IoT systems is challenging due to the availability of limited resources, large number of devices, heterogeneity of devices, generation of bulk data, etc. Likewise, cloud resources are also vulnerable to security issues due to virtualization, insider threats, data loss, data breaches, insecure APIs, etc. Security is of major concern with the integration of IoT and cloud. The primary objective of this review is to highlight the security issues associated with an IoT system and cloud system and with the integration of the two, as well as to highlight solutions in each case. The secondary objective is to describe popular IoT-cloud platforms and also to highlight how such platforms facilitate secure integration. Ultimately a highlight on a shared responsibility model of implementing security is emphasized as both IoT users and cloud service providers have to cooperatively share the responsibility to deploy secure cloud-based IoT applications.
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Alexakis, George, Spyros Panagiotakis, Alexander Fragkakis, Evangelos Markakis, and Kostas Vassilakis. "Control of Smart Home Operations Using Natural Language Processing, Voice Recognition and IoT Technologies in a Multi-Tier Architecture." Designs 3, no. 3 (July 1, 2019): 32. http://dx.doi.org/10.3390/designs3030032.
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The Internet of Things (IoT) is an emerging Internet-based architecture, enabling the exchange of data and services in a global network. With the advent of the Internet of Things, more and more devices are connecting to the Internet in order to help people get and share data or program actions. In this paper, we introduce an IoT Agent, a Web application for monitoring and controlling a smart home remotely. The IoT Agent integrates a chat bot that can understand text or voice commands using natural language processing (NLP). With the use of NLP, home devices are more user-friendly and controlling them is easier, since even when a command or question/command is different from the presets, the system understands the user’s wishes and responds accordingly. Our solution exploits several available Application Programming Interfaces (APIs), namely: the Dialogflow API for the efficient integration of NLP to our IoT system, the Web Speech API for enriching user experience with voice recognition and synthesis features, MQTT (Message Queuing Telemetry Transport) for the lightweight control of actuators and Firebase for dynamic data storage. This is the most significant innovation it brings: the integration of several third-party APIs and open source technologies into one mash-up, highlighting how a new IoT application can be built today using a multi-tier architecture. We believe that such a tiered architecture can be very useful for the rapid development of smart home applications.
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Koptyra, Katarzyna, and Marek R. Ogiela. "Steganography in IoT: Information Hiding with APDS-9960 Proximity and Gestures Sensor." Sensors 22, no. 7 (March 29, 2022): 2612. http://dx.doi.org/10.3390/s22072612.
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This article describes a steganographic system for IoT based on an APDS-9960 gesture sensor. The sensor is used in two modes: as a trigger or data input. In trigger mode, gestures control when to start and finish the embedding process; then, the data come from an external source or are pre-existing. In data input mode, the data to embed come directly from the sensor that may detect gestures or RGB color. The secrets are embedded in time-lapse photographs, which are later converted to videos. Selected hardware and steganographic methods allowed for smooth operation in the IoT environment. The system may cooperate with a digital camera and other sensors.
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Nock, Oliver, Jonathan Starkey, and Constantinos Marios Angelopoulos. "Addressing the Security Gap in IoT: Towards an IoT Cyber Range." Sensors 20, no. 18 (September 22, 2020): 5439. http://dx.doi.org/10.3390/s20185439.
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The paradigm of Internet of Things has now reached a maturity level where the pertinent research goal is the successful application of IoT technologies in systems of high technological readiness level. However, while basic aspects of IoT connectivity and networking have been well studied and adequately addressed, this has not been the case for cyber security aspects of IoT. This is nicely demonstrated by the number of IoT testbeds focusing on networking aspects and the lack of IoT testbeds focusing on security aspects. Towards addressing the existing and growing skills-shortage in IoT cyber security, we present an IoT Cyber Range (IoT-CR); an IoT testbed designed for research and training in IoT security. The IoT-CR allows the user to specify and work on customisable IoT networks, both virtual and physical, and supports the concurrent execution of multiple scenarios in a scalable way following a modular architecture. We first provide an overview of existing, state of the art IoT testbeds and cyber security related initiatives. We then present the design and architecture of the IoT Cyber Range, also detailing the corresponding RESTful APIs that help de-associate the IoT-CR tiers and obfuscate underlying complexities. The design is focused around the end-user and is based on the four design principles for Cyber Range development discussed in the introduction. Finally, we demonstrate the use of the facility via a red/blue team scenario involving a variant of man-in-the-middle attack using IoT devices. Future work includes the use of the IoT-CR by cohorts of trainees in order to evaluate the effectiveness of specific scenarios in acquiring IoT-related cyber-security knowledge and skills, as well as the IoT-CR integration with a pan-European cyber-security competence network.
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Klochko, Oksana V., Vasyl M. Fedorets, Maksym V. Mazur, and Yurii P. Liulko. "An IoT system based on open APIs and geolocation for human health data analysis." CTE Workshop Proceedings 10 (March 21, 2023): 399–413. http://dx.doi.org/10.55056/cte.567.
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Development of applications based on open API is becoming increasingly popular today. Innovative projects using these technologies provide new opportunities for real-time human health monitoring. Such opportunities are also implemented using Internet of Things (IoT), artificial intelligence (AI) and cloud computing technologies. In the study, we developed an application based on open APIs using smart gadgets and meteorological geographic information system in the process of generating a message about the dangers to human health associated with: the presence of pollen in the air (grass pollen, birch pollen and olive pollen) indicating the level of its concentration in the air; problems with air quality, if the air quality indicator exceeds the permissible standards. The addition of such functions expands the possibilities to provide timely information about potential risks and threats and, accordingly, is an "anthropo-geo-sensor-digital" prerequisite for effective decision-making, prevailing. The implementation of this IoT system has significant methodological and technological potential that can be used to improve the efficiency of Healthcare, both in extreme conditions and in conditions of sustainable existence. First of all, this is relevant during and after the COVID-19 pandemic. The system we have developed can also be seen as one of the ways to innovate in Healthcare, in the educational process in institutions of higher education and in further scientific research on this topic. Further research in this area may be related to data processing in Healthcare systems based on machine learning, deep learning.
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Liu, Lan, Jingjing Fan, Chengfan Li, and Xuefeng Liu. "A Method of the Active and Passive Event Service Based on the Sensor Web." Security and Communication Networks 2022 (January 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/2578744.
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The intelligent information system constructed by the sensor web can monitor all kinds of sudden abnormal events, improve the ability of event discovery and rapid disposal, and promote the development of smart city and the construction of the Internet of Things (IoT). In this paper, we consider the problem of complex integration and poor expansibility in the large-scale full-network operation and maintenance system construction and propose an active and passive event service (APES) method based on the sensor web. In the APES, a system framework with the perception layer, data service layer, event service layer, and user layer is firstly defined and constructed. Secondly, a middleware with the ability to active and passive event service (APES) is designed and implemented based on the system framework. Finally, taking abnormal weather and fire warning as examples, the performance of the proposed event service middleware is tested, respectively. Experimental results show that the proposed APES model in this paper has the advantages of high precision, stable operation, and strong practicability and solves “Information Island” and low reusability in the whole network operation and maintenance system. This is an attempt at the structural design of a similar intelligent information system.
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Aydin, Sahin, and Mehmet Nafiz Aydin. "Semantic and Syntactic Interoperability for Agricultural Open-Data Platforms in the Context of IoT Using Crop-Specific Trait Ontologies." Applied Sciences 10, no. 13 (June 28, 2020): 4460. http://dx.doi.org/10.3390/app10134460.
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In recent years, Internet-of-Things (IoT)-based applications have been used in various domains such as health, industry and agriculture. Considerable amounts of data in diverse formats are collected from wireless sensor networks (WSNs) integrated into IoT devices. Semantic interoperability of data gathered from IoT devices is generally being carried out using existing sensor ontologies. However, crop-specific trait ontologies—which include site-specific parameters concerning hazelnut as a particular agricultural product—can be used to make links between domain-specific variables and sensor measurement values as well. This research seeks to address how to use crop-specific trait ontologies for linking site-specific parameters to sensor measurement values. A data-integration approach for semantic and syntactic interoperability is proposed to achieve this objective. An open-data platform is developed and its usability is evaluated to justify the viability of the proposed approach. Furthermore, this research shows how to use web services and APIs to carry out the syntactic interoperability of sensor data in agriculture domain.
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Xu, Rongxu, Wenquan Jin, Yonggeun Hong, and Do-Hyeun Kim. "Intelligent Optimization Mechanism Based on an Objective Function for Efficient Home Appliances Control in an Embedded Edge Platform." Electronics 10, no. 12 (June 18, 2021): 1460. http://dx.doi.org/10.3390/electronics10121460.
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In recent years the ever-expanding internet of things (IoT) is becoming more empowered to revolutionize our world with the advent of cutting-edge features and intelligence in an IoT ecosystem. Thanks to the development of the IoT, researchers have devoted themselves to technologies that convert a conventional home into an intelligent occupants-aware place to manage electric resources with autonomous devices to deal with excess energy consumption and providing a comfortable living environment. There are studies to supplement the innate shortcomings of the IoT and improve intelligence by using cloud computing and machine learning. However, the machine learning-based autonomous control devices lack flexibility, and cloud computing is challenging with latency and security. In this paper, we propose a rule-based optimization mechanism on an embedded edge platform to provide dynamic home appliance control and advanced intelligence in a smart home. To provide actional control ability, we design and developed a rule-based objective function in the EdgeX edge computing platform to control the temperature states of the smart home. Compared to cloud computing, edge computing can provide faster response and higher quality of services. The edge computing paradigm provides better analysis, processing, and storage abilities to the data generated from the IoT sensors to enhance the capability of IoT devices concerning computing, storage, and network resources. In order to satisfy the paradigm of distributed edge computing, all the services are implemented as microservices. The microservices are connected to each other through REST APIs based on the constrained IoT devices to provide all the functionalities that accomplish a trade-off between energy consumption and occupant-desired environment setting for the smart home appliances. We simulated our proposed system to control the temperature of a smart home; through experimental findings, we investigated the application against the delay time and overall memory consumption by the embedded edge system of EdgeX. The result of this research work suggests that the implemented services operated efficiently in the raspberry pi 3 hardware of IoT devices.
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Jang, Joonhyouk. "Open API-based Conversational Voice Interaction Scheme for Intelligent IoT Applications for the Digital Underprivileged." Korean Institute of Smart Media 11, no. 10 (November 30, 2022): 22–29. http://dx.doi.org/10.30693/smj.2022.11.10.22.
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Voice interactions are particularly effective in applications targeting the digital underprivileged who are not proficient in the use of smart devices. However, applications based on open APIs are using voice signals only for short, fragmentary input and output due to the limitations of existing touchscreen-oriented UI and API provided. In this paper, we design a conversational voice interaction model for interactions between users and intelligent mobile/IoT applications and propose a keyword detection algorithm based on the edit distance. The proposed model and scheme were implemented in an Android environment, and the edit distance-based keyword detection algorithm showed a higher recognition rate than the existing algorithm for keywords that were incorrectly recognized through speech recognition.
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Amiroh, Khodijah, Bernadus Anggo Seno Aji, and Farah Zakiyah Rahmanti. "Real-Time Accident Detection Using KNN Algorithm to Support IoT-based Smart City." JURNAL NASIONAL TEKNIK ELEKTRO 11, no. 1 (March 29, 2022): 65–70. http://dx.doi.org/10.25077/jnte.v11n1.999.2022.
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Surabaya is a city with an area of 326.81 km2 and is the center of land transportation in the eastern part of Java Island. The construction of digital infrastructure in the Surabaya area will make it easier for the City Government to make efficient services. Traffic accidents that occurred in Surabaya until 2017 recorded 1,365 incidents. EVAN (Emergency Vehicle Automatic Notification) is a research topic that focuses on the field of transportation, especially in real-time traffic accidents which can be integrated with city information centers and hospitals for primary assistance in accidents. The purpose of this research is to make it easier for the Surabaya city government to provide first aid in the event of an accident. The design of the device on the user side is made using the Arduino, the accelerometer sensor and the gyroscope in the form of the MPU6050 sensor and the u-blox gps module. Crash detection on the system using the k-Nearest neighbors algorithm (KNN). On the operator side, the design is done on a web basis by utilizing the ReactJs framework which is integrated with the Google Maps APIs. The results of the accuracy of the accident detection system reached 97% and the detection of accident locations and the nearest hospital from the location reached 100%. Thus, real-time accident detection can be implemented in Surabaya city to support the smart city.
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Burgio, Alessandro, Domenico Cimmino, Andrea Nappo, Luigi Smarrazzo, and Giuseppe Donatiello. "An IoT-Based Solution for Monitoring and Controlling Battery Energy Storage Systems at Residential and Commercial Levels." Energies 16, no. 7 (March 30, 2023): 3140. http://dx.doi.org/10.3390/en16073140.
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Today, increasing numbers of batteries are installed in residential and commercial buildings; by coordinating their operation, it is possible to favor both the exploitation of renewable sources and the safe operation of electricity grids. However, how can this multitude of battery storage systems be coordinated? Using the Application Programming Interfaces of the storage systems’ manufacturers is a feasible solution, but it has a huge limitation: communication to and from storage systems must necessarily pass through the manufacturers’ cloud infrastructure. Therefore, this article presents an IoT-based solution which allows monitoring/controlling battery storage systems, independently from the manufacturers’ cloud infrastructure. More specifically, a home gateway locally controls the battery storage using local APIs via Wi-Fi on the condition that the manufacturer enables them. If not, an auxiliary device allows the home gateway to establish a wired communication with the battery storage via the SunSpec protocol. Validations tests demonstrate the effectiveness of the proposed IoT solution in monitoring and controlling ABB, Sonnen and SolarEdge storage systems.
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Xu, Jiayi, Wei Pan, Yue Teng, Yang Zhang, and Qiqi Zhang. "Internet of Things (IoT)-Integrated Embodied Carbon Assessment and Monitoring of Prefabricated Buildings." IOP Conference Series: Earth and Environmental Science 1101, no. 2 (November 1, 2022): 022031. http://dx.doi.org/10.1088/1755-1315/1101/2/022031.
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Abstract Buildings contribute significantly to carbon emissions over their life cycle. Recently, embodied carbon (EC) accounts for an increasing share of life cycle carbon emissions of new buildings as their energy efficiency is improving. However, traditional methods of data collection and communication from diverse carbon sources such as manual recording lead to low-efficient and prone-to-error estimation of embodied carbon emissions. Therefore, this paper aims to propose an Internet of Things (IoT)-integrated embodied carbon assessment and monitoring system (ECAMS) for prefabricated buildings. This system involves three layers, i.e., of data collection, data communication, and data analysis. To provide a theoretical foundation, the EC assessment model is firstly modified to distribute carbon emissions into twelve construction statuses and at five levels of analysis of prefabricated buildings. IoT sensors including radio frequency identification (RFID), acceleration transducer, and global positioning system (GPS) are employed for automated real-time data collection. IoT data will be communicated with building information modelling (BIM) and carbon assessment software through application programming interfaces (APIs). Laboratory tests were conducted to demonstrate the feasibility of sensor-based data collection and communication. The proposed system facilitates more efficient and accurate estimations of prefabricated buildings’ embodied carbon, which should help practitioners to explore effective carbon reduction strategies.
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Pustišek, Matevž, Dejan Dolenc, and Andrej Kos. "LDAF: Low-Bandwidth Distributed Applications Framework in a Use Case of Blockchain-Enabled IoT Devices." Sensors 19, no. 10 (May 21, 2019): 2337. http://dx.doi.org/10.3390/s19102337.
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In this paper, we present Low-Bandwidth Distributed Applications Framework (LDAF)—an application-aware gateway for communication-constrained Internet of things (IoT) devices. A modular approach facilitates connecting to existing cloud backend servers and managing message formats and APIs’ native application logic to meet the communication constraints of resource-limited end devices. We investigated options for positioning the LDAF server in fog computing architectures. We demonstrated the approach in three use cases: (i) a simple domain name system (DNS) query from the device to a DNS server, (ii) a complex interaction of a blockchain—based IoT device with a blockchain network, and (iii) difference based patching of binary (system) files at the IoT end devices. In a blockchain smart meter use case we effectively enabled decentralized applications (DApp) for devices that without our solution could not participate in a blockchain network. Employing the more efficient binary content encoding, we reduced the periodic traffic from 16 kB/s to ~1.1 kB/s, i.e., 7% of the initial traffic. With additional optimization of the application protocol in the gateway and message filtering, the periodic traffic was reduced to ~1% of the initial traffic, without any tradeoffs in the application’s functionality or security. Using a function of binary difference we managed to reduce the size of the communication traffic to the end device, at least when the binary patch was smaller than the patching file.
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Mtonga, Kambombo, Santhi Kumaran, Chomora Mikeka, Kayalvizhi Jayavel, and Jimmy Nsenga. "Machine Learning-Based Patient Load Prediction and IoT Integrated Intelligent Patient Transfer Systems." Future Internet 11, no. 11 (November 12, 2019): 236. http://dx.doi.org/10.3390/fi11110236.
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A mismatch between staffing ratios and service demand leads to overcrowding of patients in waiting rooms of health centers. Overcrowding consequently leads to excessive patient waiting times, incomplete preventive service delivery and disgruntled medical staff. Worse, due to the limited patient load that a health center can handle, patients may leave the clinic before the medical examination is complete. It is true that as one health center may be struggling with an excessive patient load, another facility in the vicinity may have a low patient turn out. A centralized hospital management system, where hospitals are able to timely exchange patient load information would allow excess patient load from an overcrowded health center to be re-assigned in a timely way to the nearest health centers. In this paper, a machine learning-based patient load prediction model for forecasting future patient loads is proposed. Given current and historical patient load data as inputs, the model outputs future predicted patient loads. Furthermore, we propose re-assigning excess patient loads to nearby facilities that have minimal load as a way to control overcrowding and reduce the number of patients that leave health facilities without receiving medical care as a result of overcrowding. The re-assigning of patients will imply a need for transportation for the patient to move from one facility to another. To avoid putting a further strain on the already fragmented ambulatory services, we assume the existence of a scheduled bus system and propose an Internet of Things (IoT) integrated smart bus system. The developed IoT system can be tagged on buses and can be queried by patients through representation state transfer application program interfaces (APIs) to provide them with the position of the buses through web app or SMS relative to their origin and destination stop. The back end of the proposed system is based on message queue telemetry transport, which is lightweight, data efficient and scalable, unlike the traditionally used hypertext transfer protocol.
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SMEU, George-Alexandru, Constantin GHIȚĂ, Aurel Ionuț CHIRILĂ, Dragoș Ioan DEACONU, and Valentin NĂVRĂPESCU. "COMPOSITION OF A DRINKING WATER PUMP GROUP PREPARED FOR IOT TECHNOLOGY." ACTUALITĂŢI ŞI PERSPECTIVE ÎN DOMENIUL MAŞINILOR ELECTRICE (ELECTRIC MACHINES, MATERIALS AND DRIVES - PRESENT AND TRENDS) 2021, no. 1 (November 19, 2021): 1–16. http://dx.doi.org/10.36801/apme.2021.1.14.
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Technological developments in the field of sensors, transducers and data transmission lead to the development of industrial automation. In the field of drinking water distribution networks, this evolution brings a significant value contribution, in the form of savings at the time of implementation, as well as savings in operation. The paper seeks to highlight a method of sizing a pump set prepared for IoT (Internet of Things) technology. In essence, this form of pump group eliminates the need to implement industrial automation for group management and operation. The whole working logic is based on artificial intelligence and the statistics of the data accumulated by the network interconnected by intelligent sensors.
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Kim, Soram, Myungseo Park, Sehoon Lee, and Jongsung Kim. "Smart Home Forensics—Data Analysis of IoT Devices." Electronics 9, no. 8 (July 28, 2020): 1215. http://dx.doi.org/10.3390/electronics9081215.
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A smart home is a residence that provides a variety of automation services based on Internet of Things (IoT) devices equipped with sensors, cameras, and lights. These devices can be remotely controlled through controllers such as smartphones and smart speakers. In a smart home, IoT devices collect and process data related to motion, temperature, lighting control, and other factors and store more diverse and complex user data. This data can be useful in forensic investigations but it is a challenge to extract meaningful data from various smart home devices because they have different data storage methods. Therefore, data collection from different smart home devices and identification and analysis of data that can be used in digital forensics is crucial. This study focuses on how to acquire, classify, and analyze smart home data from Google Nest Hub, Samsung SmartThings, and Kasa cam for forensic purposes. We thus analyzed the smart home data collected using companion apps, Web interfaces, and APIs to identify meaningful data available for the investigation. Moreover, the paper discusses various types of smart home data and their usage as core evidence in some forensic scenarios.
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Liao, Shih-wei, Cheng-Han Hsu, Jeng-Wei Lin, Yi-Ting Wu, and Fang-Yie Leu. "A Deep Learning-Based Chinese Semantic Parser for the Almond Virtual Assistant." Sensors 22, no. 5 (February 28, 2022): 1891. http://dx.doi.org/10.3390/s22051891.
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Almond is an extendible open-source virtual assistant designed to help people access Internet services and IoT (Internet of Things) devices. Both are referred to as skills here. Service providers can easily enable their devices for Almond by defining proper APIs (Application Programming Interfaces) for ThingTalk in Thingpedia. ThingTalk is a virtual assistant programming language, and Thingpedia is an application encyclopedia. Almond uses a large neural network to translate user commands in natural language into ThingTalk programs. To obtain enough data for the training of the neural network, Genie was developed to synthesize pairs of user commands and corresponding ThingTalk programs based on a natural language template approach. In this work, we extended Genie to support Chinese. For 107 devices and 261 functions registered in Thingpedia, 649 Chinese primitive templates and 292 Chinese construct templates were analyzed and developed. Two models, seq2seq (sequence-to-sequence) and MQAN (multiple question answer network), were trained to translate user commands in Chinese into ThingTalk programs. Both models were evaluated, and the experiment results showed that MQAN outperformed seq2seq. The exact match, BLEU, and F1 token accuracy of MQAN were 0.7, 0.82, and 0.88, respectively. As a result, users could use Chinese in Almond to access Internet services and IoT devices registered in Thingpedia.
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Jeong, Seungmyeong, Seongyun Kim, and Jaeho Kim. "City Data Hub: Implementation of Standard-Based Smart City Data Platform for Interoperability." Sensors 20, no. 23 (December 7, 2020): 7000. http://dx.doi.org/10.3390/s20237000.
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Like what happened to the Internet of Things (IoT), smart cities have become abundant in our lives as well. One of the smart city definitions commonly used is that smart cities solve city problems to enhance citizens’ life quality and make cities sustainable. From the perspective of information and communication technologies (ICT), we think this can be done by collecting and analyzing data to generate insights. The City Data Hub, which is a standard-based city data platform that has been developed, and a couple of problem-solving examples have been demonstrated. The key elements for smart city platforms have been chosen and they have been included in the core architecture principles and implemented as a platform. It has been proven that standard application programming interfaces (APIs) and common data models with data marketplaces, which are the keys, increase interoperability and guarantee ecosystem extensibility.
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Muhammad Raza Naqvi, Muhammad Waseem Iqbal, Syed Khuram Shahzad, Iqra Tariq, Marium Malik, Faseeha Ehsan, Natash Ali Mian, and Nadia Tabassum. "A Concurrence Study on Interoperability Issues in IoT and Decision Making Based Model on Data and Services being used during Inter-Operability." Lahore Garrison University Research Journal of Computer Science and Information Technology 4, no. 4 (December 28, 2020): 73–85. http://dx.doi.org/10.54692/lgurjcsit.2020.0404116.
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The Internet-of-Things (IoT) has become an important topic among researchers owing to its potential to change the way we live and use smart devices. In recent years, many research work found in the world are interrelated and convey via the existing web structure which makes a worldwide system called IoT. This study focused on the significant improvement of answers for a wider scope of gadgets and the Internet of Things IoT stages in recent years. In any case, each arrangement gives its very own IoT framework, gadgets, APIs, and information configurations promoting interoperability issues. These issues are the outcome of numerous basic issues, difficulty to create IoT application uncovering cross-stage, and additionally cross-space, trouble in connecting non-interoperable IoT gadgets to various IoT stages, what's more, eventually averts the development of IoT innovation at an enormous scale. To authorize consistent data sharing between various IoT vendors, endeavors by a few academia, industrial, and institutional groups have accelerated to support IoT interoperability. This paper plays out a far-reaching study on the cutting-edge answers for encouraging interoperability between various IoT stages. Likewise, the key difficulties in this theme are introduced.
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Chaturvedi, Kanishk, and Thomas Kolbe. "Towards Establishing Cross-Platform Interoperability for Sensors in Smart Cities." Sensors 19, no. 3 (January 29, 2019): 562. http://dx.doi.org/10.3390/s19030562.
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Typically, smart city projects involve complex distributed systems having multiple stakeholders and diverse applications. These applications involve a multitude of sensor and IoT platforms for managing different types of timeseries observations. In many scenarios, timeseries data is the result of specific simulations and is stored in databases and even simple files. To make well-informed decisions, it is essential to have a proper data integration strategy, which must allow working with heterogeneous data sources and platforms in interoperable ways. In this paper, we present a new lightweight web service called InterSensor Service allowing to simply connect to multiple IoT platforms, simulation specific data, databases, and simple files and retrieving their observations without worrying about data storage and the multitude of different APIs. The service encodes these observations “on-the-fly” according to the standardized external interfaces such as the OGC Sensor Observation Service and OGC SensorThings API. In this way, the heterogeneous observations can be analyzed and visualized in a unified way. The service can be deployed not only by the users to connect to different sources but also by providers and stakeholders to simply add further interfaces to their platforms realizing interoperability according to international standards. We have developed a Java-based implementation of the InterSensor Service, which is being offered free as open source software. The service is already being used in smart city projects and one application for the district Queen Elizabeth Olympic Park in London is shown in this paper.
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Kim, Heejung, Misun Ahn, Seunghyun Hong, SeungGwan Lee, and Sungwon Lee. "Wearable Device Control Platform Technology for Network Application Development." Mobile Information Systems 2016 (2016): 1–20. http://dx.doi.org/10.1155/2016/3038515.
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Application development platform is the most important environment in IT industry. There are a variety of platforms. Although the native development enables application to optimize, various languages and software development kits need to be acquired according to the device. The coexistence of smart devices and platforms has rendered the native development approach time and cost consuming. Cross-platform development emerged as a response to these issues. These platforms generate applications for multiple devices based on web languages. Nevertheless, development requires additional implementation based on a native language because of the coverage and functions of supported application programming interfaces (APIs). Wearable devices have recently attracted considerable attention. These devices only support Bluetooth-based interdevice communication, thereby making communication and device control impossible beyond a certain range. We propose Network Application Agent (NetApp-Agent) in order to overcome issues. NetApp-Agent based on the Cordova is a wearable device control platform for the development of network applications, controls input/output functions of smartphones and wearable/IoT through the Cordova and Native API, and enables device control and information exchange by external users by offering a self-defined API. We confirmed the efficiency of the proposed platform through experiments and a qualitative assessment of its implementation.
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Chamunorwa, Tinashe, Horia Alexandru Modran, Doru Ursuțiu, Cornel Samoilă, and Horia Hedeșiu. "Reconfigurable Wireless Sensor Node Remote Laboratory Platform with Cloud Connectivity." Sensors 21, no. 19 (September 25, 2021): 6405. http://dx.doi.org/10.3390/s21196405.
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Thanks to the recent rapid technological advancement in IoT usage, there is a need for students to learn IoT-based concepts using a dedicated experimental platform. Furthermore, being forced into remote learning due to the ongoing COVID-19 pandemic, there is an urgent need for innovative learning methods. From our perspective, a learning platform should be reconfigurable to accommodate multiple applications and remotely accessible at any time, from anywhere, and on any connected device. Considering that many of the university courses are now held online, the reliability and scalability of the system become critical. This paper presents the design and development of a wireless configurable myRIO-based sensor node that connects to SystemLink Cloud. The sensors that were used are for ambient light, temperature, and proximity. A graphical programming environment (G-LabVIEW) and related APIs were used for rapid concept-to-development process. Distinct applications have been developed for the instructor and students, respectively. The students can select which sensor and application to run on the system and observe the measurements on the local student’s application or the cloud platform at a specific moment. They can also read the data on the cloud platform and use them in their LabVIEW application. In the context of remote education, we strongly believe that this platform is and will be suitable for the COVID and Post-COVID eras as well because it creates a much better remote laboratory experience for students. In conclusion, the system that was developed is innovative because it is software reconfigurable from the device, from the instructor’s application and cloud via a web browser, it is intuitive, and it has a user-friendly interface. It meets most of the necessary requirements in the current era, being also highly available and scalable in the cloud.
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Khan, Asfandyar, Arif Iqbal Umar, Arslan Munir, Syed Hamad Shirazi, Muazzam A. Khan, and Muhammad Adnan. "A QoS-Aware Machine Learning-Based Framework for AMI Applications in Smart Grids." Energies 14, no. 23 (December 6, 2021): 8171. http://dx.doi.org/10.3390/en14238171.
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The Internet of things (IoT) enables a diverse set of applications such as distribution automation, smart cities, wireless sensor networks, and advanced metering infrastructure (AMI). In smart grids (SGs), quality of service (QoS) and AMI traffic management need to be considered in the design of efficient AMI architectures. In this article, we propose a QoS-aware machine-learning-based framework for AMI applications in smart grids. Our proposed framework comprises a three-tier hierarchical architecture for AMI applications, a machine-learning-based hierarchical clustering approach, and a priority-based scheduling technique to ensure QoS in AMI applications in smart grids. We introduce a three-tier hierarchical architecture for AMI applications in smart grids to take advantage of IoT communication technologies and the cloud infrastructure. In this architecture, smart meters are deployed over a georeferenced area where the control center has remote access over the Internet to these network devices. More specifically, these devices can be digitally controlled and monitored using simple web interfaces such as REST APIs. We modify the existing K-means algorithm to construct a hierarchical clustering topology that employs Wi-SUN technology for bi-directional communication between smart meters and data concentrators. Further, we develop a queuing model in which different priorities are assigned to each item of the critical and normal AMI traffic based on its latency and packet size. The critical AMI traffic is scheduled first using priority-based scheduling while the normal traffic is scheduled with a first-in–first-out scheduling scheme to ensure the QoS requirements of both traffic classes in the smart grid network. The numerical results demonstrate that the target coverage and connectivity requirements of all smart meters are fulfilled with the least number of data concentrators in the design. Additionally, the numerical results show that the architectural cost is reduced, and the bottleneck problem of the data concentrator is eliminated. Furthermore, the performance of the proposed framework is evaluated and validated on the CloudSim simulator. The simulation results of our proposed framework show efficient performance in terms of CPU utilization compared to a traditional framework that uses single-hop communication from smart meters to data concentrators with a first-in–first-out scheduling scheme.
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Ko, Eunbyeol, Jinsung Kim, Younghoon Ban, Haehyun Cho, and Jeong Hyun Yi. "ACAMA: Deep Learning-Based Detection and Classification of Android Malware Using API-Based Features." Security and Communication Networks 2021 (December 29, 2021): 1–12. http://dx.doi.org/10.1155/2021/6330828.
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As a great number of IoT and mobile devices are used in our daily lives, the security of mobile devices is being important than ever. If mobile devices which play a key role in connecting devices are exploited by malware to perform malicious behaviors, this can cause serious damage to other devices as well. Hence, a huge research effort has been put forward to prevent such situation. Among them, many studies attempted to detect malware based on APIs used in malware. In general, they showed the high accuracy in detecting malware, but they could not classify malware into detailed categories because their detection mechanisms do not consider the characteristics of each malware category. In this paper, we propose a malware detection and classification approach, named ACAMA, that can detect malware and categorize them with high accuracy. To show the effectiveness of ACAMA, we implement and evaluate it with previously proposed approaches. Our evaluation results demonstrate that ACAMA detects malware with 26% higher accuracy than a previous work. In addition, we show that ACAMA can successfully classify applications that another previous work, AVClass, cannot classify.
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Abougarair, Ahmed J., Mohamed KI Aburakhis, and Mohamed O. Zaroug. "Design and implementation of smart voice assistant and recognizing academic words." International Robotics & Automation Journal 8, no. 1 (February 24, 2022): 27–32. http://dx.doi.org/10.15406/iratj.2022.08.00240.
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This paper approaches the use of a Virtual Assistant using neural networks for recognition of commonly used words. The main purpose is to facilitate the users’ daily lives by sensing the voice and interpreting it into action. Alice, which is the name of the assistant, is implemented based on four main techniques: Hot word detection, Voice to Text conversion, Intent recognition, and Text to Voice conversion. Linux is the operating system of choice, for developing and running the assistant because it is in the public domain, also, Linux has been implemented on most Single-board computers. Python is chosen as a development language due to its capabilities and compatibility with various APIs and libraries, which are deemed necessary for the project. The virtual assistant will be required to communicate with IoT devices. In addition, a speech recognition system is created in order to recognize the significant technical words. An artificial neural network (ANN) with different structure networks and training algorithms is utilized in conjunction with the Mel Frequency Cepstral Coefficient (MFCC) feature extraction technique to increase the identification rate effectively and find the optimal performance. For training purposes, the Levenberg-Marquardt (LM) and BGFS Quasi-Newton Resilient Backpropagation are compared using 10 MFCC, utilizing from 10 to 50 neurons increasing in increments of 10 similarly for 13MFCC the training is done utilizing from between 10 to 50 neurons.
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Yurchenko, Irina. "Improvement of digital technologies for the formation of agroecosystem reclamation regime." Melioration and Water Management, no. 6 (January 22, 2021): 8–12. http://dx.doi.org/10.32962/0235-2524-2020-6-8-12.
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The relevance of research is due to the increasing role of digital technologies for managing agricultural production on reclaimed lands in ensuring sustainable growth of agricultural production efficiency. The object of research is precision (precise) management of agricultural production. The subject is advanced technologies for the rapid formation and regulation of the reclamation regime of agroecosystems that meet the requirements of sustainable development of the crop production system of the domestic agro-industrial complex. The purpose of this work is to improve methodological and technological approaches to digitalization of technologies for managing the reclamation regime of the agroecosystem. the Scientific novelty of the work is to substantiate the functional orientation and list of priority tasks solved by the created digital systems, which required studying the specifics of applied technologies for managing agricultural production; determining the effective direction of improving automated management. The research was based on the analysis of normative-methodological and legal documents, literary sources, as well as personal developments of the author on the subject under consideration. Methods of comparative and system analysis and expert assessments were used in the work. Factors of competitiveness of economic entities implementing support of management decisions by digital systems are identified. Uncovered causes that reduce the effectiveness of the implementation of the digital control systems of agricultural production on reclaimed lands in the Russian agricultural sector Shows the priority approaches to the structure of control system of technological processes of agricultural production, emerging based on the integration of access to information for business processes of enterprise management. A list of priority tasks of automation of technological processes of reclamation agriculture has been developed. Priority requirements for improving digital technologies for managing the reclamation regime of agroecosystems in terms of information, technology and software are formulated. The proposed transformation of approaches to the creation and use of automated production process control systems (APCS) and General approaches to enterprise management (APCS) meets the requirements of the «Internet of things (IOT)», one of the newest trends in the evolution of modern Internet technologies. The technological process of precision management of agricultural production based on digital systems Maxim built into the management procedures of the hydroreclamation system, rationalized, with significantly increased efficiency transforms the interaction of producers and consumers, bringing them flexibility, organizing the exchange of information via the Internet.
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Zhou, Zi-Tong, Shao-Hua Yan, Wei-Sheng Zhao, and Qun-Wen Leng. "Research progress of tunneling magnetoresistance sensor." Acta Physica Sinica 71, no. 5 (2022): 058504. http://dx.doi.org/10.7498/aps.71.20211883.
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Sensors play an important role in Internet of Things (IoT) industry and account for a rapidly growing market share. Among them, the magnetic sensor based on tunneling magnetoresistance (TMR) effect possesses great potential applications in the fields of biomedical, navigation, positioning, current detection, and non-destructive testing due to its extremely high sensitivity, small device size and low power consumption. In this paper, we focus on the development of TMR sensor technology routes, covering a series of research advances from a sensor transducer to three-dimensional magnetic field detection, and then to the applications. Firstly, we recall the development history of TMR sensors, explain its working principle, and discuss the method to improve the output linearity of single magnetic tunnel junction. Next, we state the Wheatstone-bridge structure, which can inhibit temperature drift in detail and review several methods of fabricating the full bridge of TMR sensors. Furthermore, for the market demand of three-dimensional magnetic field detection, we summarize the methods of designing and fabricating three-dimensional sensing structure of the TMR sensor. At the same time, we list several optimization schemes of TMR sensor performance in terms of sensitivity and noise level. Finally, we discuss two types of emerging applications of TMR sensors in recent years. The TMR sensors can also be used in intelligence healthcare due to their ultra-high sensitivity. In addition, devices from the combination of spin materials and MEMS structure have attracted wide attention, especially, because of the large commercial market of microphones, spin-MEMS microphones utilized TMR techniques will be the next research hotspot in this interdisciplinary field.
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S. Rubí, Jesús N., and Paulo R. L. Gondim. "IoMT Platform for Pervasive Healthcare Data Aggregation, Processing, and Sharing Based on OneM2M and OpenEHR." Sensors 19, no. 19 (October 3, 2019): 4283. http://dx.doi.org/10.3390/s19194283.
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Pervasive healthcare services have undergone a great evolution in recent years. The technological development of communication networks, including the Internet, sensor networks, and M2M (Machine-to-Machine) have given rise to new architectures, applications, and standards related to addressing almost all current e-health challenges. Among the standards, the importance of OpenEHR has been recognized, since it enables the separation of medical semantics from data representation of electronic health records. However, it does not meet the requirements related to interoperability of e-health devices in M2M networks, or in the Internet of Things (IoT) scenarios. Moreover, the lack of interoperability hampers the application of new data-processing techniques, such as data mining and online analytical processing, due to the heterogeneity of the data and the sources. This article proposes an Internet of Medical Things (IoMT) platform for pervasive healthcare that ensures interoperability, quality of the detection process, and scalability in an M2M-based architecture, and provides functionalities for the processing of high volumes of data, knowledge extraction, and common healthcare services. The platform uses the semantics described in OpenEHR for both data quality evaluation and standardization of healthcare data stored by the association of IoMT devices and observations defined in OpenEHR. Moreover, it enables the application of big data techniques and online analytic processing (OLAP) through Hadoop Map/Reduce and content-sharing through fast healthcare interoperability resource (FHIR) application programming interfaces (APIs).
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Ray, Partha Pratim, Dinesh Dash, and Debashis De. "Analysis and monitoring of IoT-assisted human physiological galvanic skin responsefactor for smart e-healthcare." Sensor Review 39, no. 4 (July 15, 2019): 525–41. http://dx.doi.org/10.1108/sr-07-2018-0181.
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Purpose Background: Every so often, one experiences different physically unstable situations which may lead to possibilities of suffering through vicious physiological risks and extents. Dynamic physiological activities are such a key metric that they are perceived by means of measuring galvanic skin response (GSR). GSR represents impedance of human skin that frequently changes based on different human respiratory and physical instability. Existing solutions, paved in literature and market, focus on the direct measurement of GSR by two sensor-attached leads, which are then parameterized against the standard printed circuit board mechanism. This process is sometimes cumbersome to use, resulting in lower user experience provisioning and adaptability in livelihood activities. The purpose of this study is to validate the novel development of the cost-effective GSR sensing system for affective usage for smart e-healthcare. Design/methodology/approach This paper proposes to design and develop a flexible circuit strip, populated with essential circuitry assemblies, to assess and monitor the level of GSR. Ordinarily, this flexible system would be worn on the back palm of the hand where two leads would contact two sensor strips worn on the first finger. Findings The system was developed on top of Pyralux. Initial goals of this work are to design and validate a flexible film-based GSR system to detect an individual’s level of human physiological activities by acquiring, amplifying and processing GSR data. The measured GSR value is visualized “24 × 7” on a Bluetooth-enabled smartphone via a pre-incorporated application. Conclusion: The proposed sensor-system is capable of raising the qualities such as adaptability, user experience, portability and ubiquity for possible application of monitoring of human psychodynamics in a more cost-effective way, i.e. less than US$50. Practical implications Several novel attributes are envisaged in the development process of the GSR system that made it different from and unique as compared to the existing alternatives. The attributes are as follows: (i) use of reproductive sensor-system fabrication process, (ii) use of flexible-substrate for hosting the system as proof of concept, (iii) use of miniaturized microcontroller, i.e. ATTiny85, (iv) deployment of energy-efficient passive electrical circuitry for noise filtering, (v) possible use case scenario of using CR2032 coin battery for provisioning powering up the system, (vi) provision of incorporation of internet of things (IoT)-cloud integration in existing version while fixing related APIs and (vii) incorporation of heterogeneous software-based solutions to validate and monitor the GSR output such as MakerPlot, Arduino IDE, Fritzing and MIT App Inventor 2. Originality/value This paper is a revised version R1 of the earlier reviewed paper. The proposed paper provides novel knowledge about the flexible sensor system development for GSR monitoring under IoT-based environment for smart e-healthcare.
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Giallonardo, Ester, Francesco Poggi, Davide Rossi, and Eugenio Zimeo. "Semantics-Driven Programming of Self-Adaptive Reactive Systems." International Journal of Software Engineering and Knowledge Engineering 30, no. 06 (June 2020): 805–34. http://dx.doi.org/10.1142/s0218194020400082.
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In recent years, new classes of highly dynamic, complex systems are gaining momentum. These classes include, but are not limited to IoT, smart cities, cyber-physical systems and sensor networks. These systems are characterized by the need to express behaviors driven by external and/or internal changes, i.e. they are reactive and context-aware. A desirable design feature of these systems is the ability of adapting their behavior to environment changes. In this paper, we propose an approach to support adaptive, reactive systems based on semantic runtime representations of their context, enabling the selection of equivalent behaviors, i.e. behaviors that have the same effect on the environment. The context representation and the related knowledge are managed by an engine designed according to a reference architecture and programmable through a declarative definition of sensors and actuators. The knowledge base of sensors and actuators (hosted by an RDF triplestore) is bound to the real world by grounding semantic elements to physical devices via REST APIs. The proposed architecture along with the defined ontology tries to address the main problems of dynamically re-configurable systems by exploiting a declarative, queryable approach to enable runtime reconfiguration with the help of (a) semantics to support discovery in heterogeneous environment, (b) composition logic to define alternative behaviors for variation points, (c) bi-causal connection life-cycle to avoid dangling links with the external environment. The proposal is validated in a case study aimed at designing an edge node for smart buildings dedicated to cultural heritage preservation.
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Akanbi, Adeyinka, and Muthoni Masinde. "A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring." Sensors 20, no. 11 (June 3, 2020): 3166. http://dx.doi.org/10.3390/s20113166.
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In recent years, the application and wide adoption of Internet of Things (IoT)-based technologies have increased the proliferation of monitoring systems, which has consequently exponentially increased the amounts of heterogeneous data generated. Processing and analysing the massive amount of data produced is cumbersome and gradually moving from classical ‘batch’ processing—extract, transform, load (ETL) technique to real-time processing. For instance, in environmental monitoring and management domain, time-series data and historical dataset are crucial for prediction models. However, the environmental monitoring domain still utilises legacy systems, which complicates the real-time analysis of the essential data, integration with big data platforms and reliance on batch processing. Herein, as a solution, a distributed stream processing middleware framework for real-time analysis of heterogeneous environmental monitoring and management data is presented and tested on a cluster using open source technologies in a big data environment. The system ingests datasets from legacy systems and sensor data from heterogeneous automated weather systems irrespective of the data types to Apache Kafka topics using Kafka Connect APIs for processing by the Kafka streaming processing engine. The stream processing engine executes the predictive numerical models and algorithms represented in event processing (EP) languages for real-time analysis of the data streams. To prove the feasibility of the proposed framework, we implemented the system using a case study scenario of drought prediction and forecasting based on the Effective Drought Index (EDI) model. Firstly, we transform the predictive model into a form that could be executed by the streaming engine for real-time computing. Secondly, the model is applied to the ingested data streams and datasets to predict drought through persistent querying of the infinite streams to detect anomalies. As a conclusion of this study, a performance evaluation of the distributed stream processing middleware infrastructure is calculated to determine the real-time effectiveness of the framework.
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Xu, Rongxu, Wenquan Jin, and Dohyeun Kim. "Microservice Security Agent Based On API Gateway in Edge Computing." Sensors 19, no. 22 (November 10, 2019): 4905. http://dx.doi.org/10.3390/s19224905.
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Internet of Things (IoT) devices are embedded with software, electronics, and sensors, and feature connectivity with constrained resources. They require the edge computing paradigm, with modular characteristics relying on microservices, to provide an extensible and lightweight computing framework at the edge of the network. Edge computing can relieve the burden of centralized cloud computing by performing certain operations, such as data storage and task computation, at the edge of the network. Despite the benefits of edge computing, it can lead to many challenges in terms of security and privacy issues. Thus, services that protect privacy and secure data are essential functions in edge computing. For example, the end user’s ownership and privacy information and control are separated, which can easily lead to data leakage, unauthorized data manipulation, and other data security concerns. Thus, the confidentiality and integrity of the data cannot be guaranteed and, so, more secure authentication and access mechanisms are required to ensure that the microservices are exposed only to authorized users. In this paper, we propose a microservice security agent to integrate the edge computing platform with the API gateway technology for presenting a secure authentication mechanism. The aim of this platform is to afford edge computing clients a practical application which provides user authentication and allows JSON Web Token (JWT)-based secure access to the services of edge computing. To integrate the edge computing platform with the API gateway, we implement a microservice security agent based on the open-source Kong in the EdgeX Foundry framework. Also to provide an easy-to-use approach with Kong, we implement REST APIs for generating new consumers, registering services, configuring access controls. Finally, the usability of the proposed approach is demonstrated by evaluating the round trip time (RTT). The results demonstrate the efficiency of the system and its suitability for real-world applications.
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Chandra, Sandeep. "Abstract IA20: California Teachers Study (CTS) Data Management Platform: A model for a repeatable turnkey, end-to-end, cloud-based data management and analytics solution for epidemiology cohorts." Cancer Epidemiology, Biomarkers & Prevention 29, no. 9_Supplement (September 1, 2020): IA20. http://dx.doi.org/10.1158/1538-7755.modpop19-ia20.
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Abstract The Health Cyberinfrastructure (CI) Division at the San Diego Supercomputer Center (SDSC) has been deploying secure, compliant, end-to-end solutions to support critical applications including, but not limited to, biomedical research, detecting and preventing medical fraud, enterprise risk management, and medical device data management. These applications have relied on technologies ranging from the traditional data warehouse to the more recent big data platforms leveraging Hadoop, Software as a Service (SaaS) capability leveraging container technology, business intelligence and analytics solutions, and IOT/streaming solutions. Operating these applications and the underlying platforms requires organizations to be agile and visionary in this arena to grow with the ever-changing technological, regulatory, and unique customer requirements, while simultaneously ensuring data security and privacy. To that end, the SDSC Health CI Division is participating in a multi-institution project with City of Hope, a National Cancer Institute (NCI)-designated Comprehensive Cancer Center, and other universities and health organizations to create a research cyberinfrastructure that includes a secure, cloud-based data management platform. The data management platform consolidates all CTS data, including datasets, codes, and accompanying documentation, and operates within Sherlock Cloud, a secure hybrid cloud platform. The innovative platform allows every member of the CTS team to securely access and use all CTS data and information in real time in a consolidated, integrated, and secure manner. Researchers have access to a data warehouse, domain-specific data marts, and analytics platform built on secure cloud technology that greatly enhances the reliability and accuracy of the data collected, and has a more seamless mechanism to access, annotate, input, and transmit data, thereby heightening accuracy and the quality of collaborative analysis performed. This approach not only transforms how CTS data is collected, stored, and shared for high-impact research, but also has the potential to reduce associated costs of ongoing research while increasing efficiency and security. SDSC Health CI Division’s work in building innovative data management and compute solutions for health care researchers over the last decade, and its collaboration with the CTS researchers more recently, has demonstrated a clear need for a unique type of managed services capability that serves the biomedical community. Specifically, the epidemiology cohorts have practiced a more traditional approach consisting of decentralized data management and computing. Even though this approach has served the community well in the past, as we look into the future, it has obvious shortcomings in terms of supporting enterprise-level data management, scale, interoperability, and provenance. SDSC Health CI Division takes a modern approach to information lifecycle management through its Sherlock Cloud platform. Sherlock Cloud Data Management framework captures users’ evolving requirements, and provides centralized, secure cloud-based managed services capability that supports end-to-end data lifecycle management including data integration capabilities, allowing data to be captured, rationalized, homogenized, and managed using best practices and standards. This includes dynamic mappings, transformations, and master data-management techniques utilizing established governance methodologies, with specific focus on data quality, metadata management, and governance. The CTS data management and analytics platform implementation can serve as a model for other epidemiology cohorts and studies. SDSC Health CI Division plans to achieve this by leveraging the existing investment NCI and CTS has already made in building the cloud-based, secure CTS data management platform. Using container technology, Sherlock will package the various components of the data management framework and analytical tools, creating a turnkey offering that can be deployed for other similar studies. This cloud platform agnostic, open-source, turnkey solution provides other cohorts a template they can build on top of, and leverage all the work CTS has performed to develop the core, domain-specific data management and analytics capability, and all that will remain is study specific customizations that cohorts will need to perform. Additionally, APIs built on top of the in-built data model will provide seamless integration with external NCI- and NIH-established Commons. The epidemiology community has a way to go before it is on par with communities in other verticals in their adoption of cutting-edge, enterprise platforms and tools for data management and analytics, but initiatives like CTS can demonstrate that, once designed and developed, these capabilities can be easily packaged and deployed for other studies, providing a framework that a larger research community can leverage. Citation Format: Sandeep Chandra. California Teachers Study (CTS) Data Management Platform: A model for a repeatable turnkey, end-to-end, cloud-based data management and analytics solution for epidemiology cohorts [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr IA20.
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"Modeling of Internet of Things Enabled Sustainable Environment Air Pollution Monitoring System." Global NEST: the international Journal, January 26, 2023. http://dx.doi.org/10.30955/gnj.004707.
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<p>Air quality, radiation pollution, and water pollution were thekey featuresthat possess genuine challenges to the environment. Proper monitoring wasneededin such a way that the world couldaccomplish sustainable development, withkeeping a healthy society. Recently, environment monitoring is become a smart environment monitoring (SEM) scheme, with development of modern sensors and the advancements in the internet of things (IoT). IoT devices were mainly used in WSN for pollution control, vehicle marking, temperature control, and effective waste management. Consequently, modern method of environmental monitoring is called as SEM system, owing to usage of wireless sensors, IoT and AI.This paper leverages IoT devices for sustainable air pollution monitoring. The presented model derives an improved red fox optimizer with deep learning based air pollution monitoring system (IRFODL-APMS) using IoT devices. The presented IRFODL-APMS technique makes use of different IoT devices to collect data. Besides, the IRFODL-APMS model performs prediction process using deep learning based on long short term memory (LSTM). At last, IRFO technique is exploited as a hyperparameter tuning process of the LSTM model to accomplish enhanced prediction performance. The presented IRFODL-APMS model is simulated under distinct measures and the outcomes reported the enhanced predictive outcomes of the IRFODL-APMS approach over other existing models.</p>
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"Clinical laboratory From Automation to the Internet of Things (IoT)." Advances in Bioengineering and Biomedical Science Research 4, no. 2 (May 25, 2021). http://dx.doi.org/10.33140/abbsr.04.02.03.
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Internet of things (IoT), is a network of physical objects, vehicles, machines, appliances, etc. that uses sensors and APIs to connect and exchange data over the internet. IoT relies on a comprehensive set of technologies, such as application programming interfaces (APIs) that connect devices to the internet. Other key IoT technologies are Big Data management tools, predictive analytics, artificial intelligence and machine learning, the cloud, and radio frequency identification, among many others. Cloud-based IoT platforms and architecture connect the real and virtual worlds. They help companies manage the security and connectivity of IoT devices, as well as collect device data, link devices with backend systems, ensure IoT interoperability, and build and operate IoT applications. Smart devices generate an enormous amount of IoT data that must be analyzed and harnessed in real time. Predictive analytics and Big Data appear here. Machine learning is also used to add context to data and generate actions without human intervention. The IoT related to the clinical laboratory, will revolutionize the concept of this, since it will be easier to connect with other laboratories more efficiently, with clinicians and with patients. And the latter will then be at the center of the Health System.
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Gong, Wenwen, Huiping Wu, Xiaokang Wang, Xuyun Zhang, Yawei Wang, Yifei Chen, and Mohammad R. Khosravi. "Diversified and compatible web APIs recommendation based on game theory in IoT." Digital Communications and Networks, February 2023. http://dx.doi.org/10.1016/j.dcan.2023.02.002.
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"Intrusion Detection in IoT based Smart Networks using Fuzzy Brain Storm Optimization Technique." International Journal of Engineering and Advanced Technology 8, no. 6 (August 30, 2019): 3066–71. http://dx.doi.org/10.35940/ijeat.f8651.088619.
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The Internet of Things (IoT) is characterized as an approach where objects are outfitted with sensors, processors, and actuators which include design of hardware board and development, protocols, web APIs, and software systems, which combined to make an associated architecture of embedded systems. This connected environment enables technologies to get associated with different networks, platforms, and devices, making a web of communication which is reforming the manner in which we communicate with the world digitally. These connected embedded systems are changing behaviour and interactions with our environment, networks, and homes, and also with our own bodies in terms of smart devices. Security and privacy are the most significant consideration in the field of real-world communication and mainly on IoTs. With the evolution of IoT the network layer security in the IoT has drawn greater focus. The security vulnerabilities in the IoT system could make security risks based on any application. Therefore there is an essential requirement for IDS for the IoT based systems for avoiding security attacks based on security vulnerabilities. This paper proposed a fuzzy c-means clustering with brain storm optimization algorithm (FBSO) for IDS based on IoT system. The NSL-KDD dataset is utilized to evaluate and simulate the proposed algorithm. The results demonstrate that the proposed technique efficiently recognize intrusion attacks and decrease the network difficulties
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"Iot and Weather Based Smart Irrigation Monitoring and Controlling System for Agriculture." International Journal of Recent Technology and Engineering 8, no. 4 (November 30, 2019): 11431–36. http://dx.doi.org/10.35940/ijrte.d9065.118419.
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Effective and successful agriculture requires effective water management. Irrigation at appropriate periods and at appropriate levels results in profitable yields. Technology can provide an effective solution for this domain. This work presents an IoT based prediction model that can be to create a smart irrigation system for farming. The proposed architecture is composed of three layers; the data collection layer, machine learning based rainfall prediction layer and the rulebased irrigation requirement identification layer. The data collection layer operates in multiple levels using sensors and APIs, obtaining ground based information and also weather information. The machine learning layer performs rainfall prediction based on past data and the final layer uses defined rules to identify the irrigation needs of crops. The major advantage of this model is that it is not fine tuned to a single crop. The model can be used for any crop and can also be used for multiple crops by the same farmer.
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Mahapatra, Tanmaya, and Christian Prehofer. "Graphical Flow-based Spark Programming." Journal of Big Data 7, no. 1 (January 8, 2020). http://dx.doi.org/10.1186/s40537-019-0273-5.
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AbstractIncreased sensing data in the context of the Internet of Things (IoT) necessitates data analytics. It is challenging to write applications for Big Data systems due to complex, highly parallel software frameworks and systems. The inherent complexity in programming Big Data applications is also due to the presence of a wide range of target frameworks, with different data abstractions and APIs. The paper aims to reduce this complexity and its ensued learning curve by enabling domain experts, that are not necessarily skilled Big Data programmers, to develop data analytics applications via domain-specific graphical tools. The approach follows the flow-based programming paradigm used in IoT mashup tools. The paper contributes to these aspects by (i) providing a thorough analysis and classification of the widely used Spark framework and selecting suitable data abstractions and APIs for use in a graphical flow-based programming paradigm and (ii) devising a novel, generic approach for programming Spark from graphical flows that comprises early-stage validation and code generation of Spark applications. Use cases for Spark have been prototyped and evaluated to demonstrate code-abstraction, automatic data abstraction interconversion and automatic generation of target Spark programs, which are the keys to lower the complexity and its ensued learning curve involved in the development of Big Data applications.
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"IOT and Data Research In Industrial Power Management." International Journal of Engineering and Advanced Technology 8, no. 6S (September 6, 2019): 57–60. http://dx.doi.org/10.35940/ijeat.f1011.0886s19.
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IOT plays an important role in collecting data and machine learning for prediction in variety of applications like homecare, healthcare and energy management. In energy management there are various variables such as future power demands, generation status weather conditions and current battery status hard to expect high efficiency. Here, in this proposed idea, for higher efficiency of renewable energy, an IOT system is needed to monitor and collect these Statuses and provide energy management services. Energy will be consumed of passive operation according to hourly variation in price and battery status will be predicted by using machine learning algorithms like Logistic regression, SVM, and k-NN. We trained the system by considering five random variables in datasheet such as Current time, Current cost, predicted time, predicted cost and Solar battery status from the device. This integrated system is used for uploading power related details of Grid and Solar to IBM cloud. Depending on previous datasheet, analytics will be done by resulting which source has to be triggered to drive the load either Solar or Grid. APIs and NodeRed Tool were used for wiring sensor data and Model predicted output. In future power demands, this design will help to predict the price according to hourly variation based on the units and to trigger the source
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Hou, Dongsheng, Wenjing Ma, Wei Zhang, Yixuan Li, Yu Du, and Yukun Hao. "An on‐chain trading model of real world asset backed digital assets." IET Blockchain, September 5, 2023. http://dx.doi.org/10.1049/blc2.12045.
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AbstractMetaverse is a digital value interaction network based on blockchain technology, with an important economic system component. While both traditional financial industries and crypto‐native industries have made significant progress by leveraging blockchain, the value stream of each remains limited to separate ecosystems. To bridge this gap between off‐chain and on‐chain economic systems, an on‐chain trading model was proposed using HD key derivation technique for direct uploading onto chains without going through centralized services for IoT data transmission. To improve the current status of NFTs as static assets, a token protocol binding each NFT with a unique account address was proposed. Additionally, oracle technique was leveraged with a decentralized and distributed trust model spanning across on‐chain and off‐chain components which securely pushes data between smart contracts and Web‐APIs. A decentralized trading model was developed based on smart contracts implementing automated market makers according to CFMM algorithm. Parallel transaction computing was executed based on the DAG model to ensure high operational performance and security standards of underlying blockchain. Finally, the on‐chain trading system of real world asset backed digital assets was developed integrating all the above key techniques that correspond to crucial functions of a complete economic system in Metaverse.
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Guo Zhong-Kai, Li Yong-Gang, Bocheng Yu, Shichao Zhou, Meng Qing-Yu, Xinxin Lu, Huang Yi-Fan, Liu Gui-Peng, and Lu Jun. "Research Progress of Lock-in Amplifier." Acta Physica Sinica, 2023, 0. http://dx.doi.org/10.7498/aps.72.20230579.
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The lock-in amplifier can perform high-precision measurement in both time and amplitude dimensions, so that it becomes a key component of instrumental system for precision measurement and control. This article overviews the concept, technology, and application of phase-locked amplifiers as a guide. It first explains the development and evolution of phase-locked amplifiers of analog, digital, and virtual phase-locked amplifiers, demonstrating their relationship and differences. Then, it classifies phase-locked amplifiers from a mathematical perspective based on the order and type of phase-locked loops. Subsequently, the testing process and metrological calibration progress of the main performance of phase-locked amplifiers, such as amplitude, frequency, and phase noise, are introduced. The conversion relationship between key indicators such as phase noise, time-domain jitter, Allen variance, and the coupling relationship with amplitude noise are discussed. Finally, the application forms and effects of phase-locked amplifiers in the fields of spectral enhancement, impedance analysis, magnetic measurement, microscopic imaging, and space exploration are listed. Through some new applications, the prospects of their transition from scientific instruments to industrial and even civilian products through intelligent computing, precise IoT, and other means are briefly given.
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Vyshnivsʹkyy, V. V. "Development of a prototype of a private cloud based on OpenStack using automatic deployment tools." Connectivity 154, no. 6 (2022). http://dx.doi.org/10.31673/2412-9070.2021.061825.
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The article is devoted to the topical issue of finding effective methods of implementing a private cloud based on the OpenStack cloud platform. The problem of creating a private cloud based on the OpenStack cloud platform with the help of automatic deployment tools to explore the possibility of virtualization of data centers. The article believes that OpenStack is a set of free software modules that can be used to create cloud infrastructure services and cloud storage. It is shown that OpenStack has advantages: flexibility, scalability, cost-effectiveness, which allows for virtualization of data centers based on open source software. Hence, OpenStack is ideal for heterogeneous infrastructures and can work in conjunction with other enterprise and open source technologies. It is shown that OpenStack allows you to solve the most complex IT tasks to date: from processing «Big Data» to «Internet of Things» (IoT, Internet of Things), due to the fact that OpenStack APIs are compatible with Elastic Compute services Cloud (EC2) and Simple Storage Service (S3), so they can be ported to discrete OpenStack environments. Therefore, many businesses and service providers expect OpenStack to help them turn their data centers into scalable automated matrices of physical resources and virtualized services. avoid binding to the supplier, prisk innovate, increase scalability and cost-effectiveness. Therefore, these disparate data centers, controlled by automation and API programming interfaces, can support the application of the DevOps model in providing software for innovative mobility programs, social networks, and many other types of applications based on NFV (Network Function Virtualization). The article presents the following task: to build a private cloud based on the OpenStack cloud platform, it is necessary to determine a set of free software that can be used to create cloud infrastructure services and cloud storage, both public and private. You also need to identify effective methods for deploying a private cloud based on OpenStack using automatic deployment tools, namely: installing and configuring a private cloud based on Openstack on a dedicated server. To solve this problem, you need to create a prototype of an open platform in the cloud based on OpenStack (test models).
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NARAYAN CHANDRA SARKAR, KOUSHIK MONDAL, AYAN DAS, ASIS MUKHERJEE, SUBRATA MANDAL, SOUVIK GHOSH, BIMAL BHATTACHARYA, ROGER LAWES, and SAMSUL HUDA. "Enhancing livelihoods in farming communities through super-resolution agromet advisories using advanced digital agriculture technologies." Journal of Agrometeorology 25, no. 1 (February 17, 2023). http://dx.doi.org/10.54386/jam.v25i1.2080.
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Agricultural production in India is highly vulnerable to climate change. Transformational change to farming systems is required to cope with this changing climate to maintain food security, and ensure farming to remain economically viable. The south Asian rice-fallow systems occupying 22.3 million ha with about 88% in India, mostly (82%) concentrated in the eastern states, are under threat. These systems currently provide economic and food security for about 11 million people, but only achieve 50% of their yield potential. Improvement in productivity is possible through efficient utilization of these fallow lands. The relatively low production occurs because of sub-optimal water and nutrient management strategies. HHaJathrough Historically, the Agro-met advisory service has assisted farmers and disseminated information at a district-level for all the states. In some instances, Agro-met delivers advice at the block level also, but in general, farmers use to follow the district level advice and develop an appropriate management plan like land preparation, sowing, irrigation timing, harvesting etc. The advisories are generated through the District Agrometeorology Unit (DAMU) and Krishi Vigyan Kendra (KVK) network, that consider medium-range weather forecast. Unfortunately, these forecasts advisories are general and broad in nature for a given district and do not scale down to the individual field or farm. Farmers must make complex crop management decisions with limited or generalised information. The lack of fine scale information creates uncertainty for farmers, who then develop risk-averse management strategies that reduce productivity. It is unrealistic to expect the Agro-met advisory service to deliver bespoke information to every farmer and to every field simply with the help of Kilometre-scale weather forecast. New technologies must be embraced to address the emerging crises in food security and economic prosperity. Despite these problems, Agro-met has been successful. New digital technologies have emerged though, and these digital technologies should become part of the Agro-met arsenal to deliver valuable information directly to the farmers at the field scale. The Agro-met service is poised to embrace and deliver new interventions through technology cross-sections such as satellite remote sensing, drone-based survey, mobile based data collection systems, IoT based sensors, using insights derived from a hybridisation of crop and AIML (Artificial Intelligence and Machine Learning) models. These technological advancements will generate fine-scale static and dynamic Agro-met information on cultivated lands, that can be delivered through Application Programming Interface (APIs) and farmers facing applications. We believe investment in this technology, that delivers information directly to the farmers, can reverse the yield gap, and address the negative impacts of a changing climate.