Implementation of Transmission Line Fault Detection System using Long Range Wireless Sensor Networks

This paper proposes a fault detection system designed for transmission lines using Long-Range Wireless Sensor Network (LoRAWSN). The system is designed to detect and locate faults across transmission lines in real-time, which can significantly improve the reliability and efficiency of power transmission systems. A WSN will be built across transmission lines over an area. The faults identified by these sensor nodes is then transmitted to a central control unit, which analyses and displays the data. The LoRaWAN technology enables the WSN to cover long distances while consuming minimal power, making it ideal for monitoring transmission lines. The proposed fault detection system is evaluated through real world experiments, which demonstrate the feasibility and effectiveness of the proposed system. Overall, this paper presents a novel and practical approach for fault detection on transmission lines, which has the potential to improve the reliability and efficiency of power transmission systems

IOT Based Continuous Glucose Monitoring for Diabetes Mellitus using Deep Siamese Domain Adaptation Convolutional Neural Network

The phrase "Internet of Things" (IoT) refers to the forthcoming generation of the Internet, which facilitates interaction among networked devices. IoT functions as an assistant in medicine and is critical to a variety of uses that monitor healthcare facilities. The pattern of observed parameters can be used to predict the type of the disease. Health experts and technologists have developed an excellent system that employs commonly utilized technologies like wearable technology, wireless channels, and other remote devices to deliver cost-effective medical surveillance for people suffering from a range of diseases. Network-connected sensors worn on the body or put in living areas collect large amounts of data to assess the patient's physical and mental wellbeing. In this Manuscript, IoT -based Continuous Glucose Monitoring for Diabetes Mellitus using Deep Siamese Domain Adaptation Convolutional Neural Networ k (CGM-DM- DSDACNN) is proposed. The goal of the work that has been described to investigate whether Continuous Glucose Monitoring System (CGMS) on the basis of IoT is both intrusive also secure. The job at hand is for making an architecture based on IoT that extends from the sensor model to the back-end and displays blood glucose level, body temperature, and contextual data to final users like patients and doctors in graphical and text formats. A higher level of energy economy is also attained by tailoring the Long range Sigfox communication protocol to the glucose monitoring device. Additionally, analyse the energy usage of a sensor device and create energy collecting components for it. Present a Deep Siamese Domain Adaptation Convolutional Neural Network (DSDACNN) as a last resort for alerting patients and medical professionals in the event of anomalous circumstances, like a too -low or too-high glucose level

The 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks ( EUSPN 2020)

The potential of IoT in contributing towards sustainable economic development in Sub-Saharan Africa (SSA) through digital transformation and effective service delivery is widely accepted. However, the unreliability/unavailability of connectivity and power grid infrastructure as well as the unaffordability of the overall system hinders the implementation of a multi-layered IoT architecture for rural societal services in SSA. In this work, affordable IoT architecture that operates without reliance on broadband connectivity and power grid is developed. The architecture employs energy harvesting system and performs data processing, actuation decisions and network management locally by integrating a customized low-cost computationally capable device with the gateway. The sharing of this device among the water resource and quality management, healthcare and agriculture applications further reduces the overall system cost. The evaluation of LPWAN technologies reveals that LoRaWAN has lower cost with added benefits of adaptive data rate and largest community support while providing comparable performance and communication range with the other technologies. The relevant results of the analysis is communicated to end-users’ mobile device via 2G/3G GPRS. Hence, the proposed IoT architecture enables the implementation of IoT systems for improving efficiency in three key application areas at low cost.</p

LoRaWAN Solutions in the COVID-19 Aspect

The rapid growth in the number of solutions using the Internet of Things (IoT) technologies is not new. The number of products using LoRaWAN technology is also growing. The spread of COVID-19 has, among other things, led to the evolution of IoT devices related to virus control. Several LoRaWAN products related to COVID-19 are being developed. LoRaWAN COVID-19 solutions are mainly grouped around the following topics: contact tracing/proximity detection, health equipment or device data transmission, and signalling/calling systems. Contact tracing has a crucial role in healthcare institutions, government agencies, and vital factories and among these institutions' workers. In the case of a medical device, data transmission simplifies administration, reduces the volume of contact, and allows contactless monitoring. In critical situations, healthcare institutions need to create temporary nursing areas. Mobile signalling or call systems may be required in these areas. The research examines LoRaWAN COVID-19 solutions and their relationship to critical infrastructures

The 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2020)

 Ensuring food security has become a challenge in Sub-Saharan Africa (SSA) due to combined effects of climate change, high population growth, and relying on rainfed farming. Governments are establishing shared irrigation infrastructure for smallholder farmers as part of the solutions for food security. However, the irrigated farms often failed to achieve the expected crop yield. This is partly due to lack of water management system in the irrigation infrastructure. In this work, IoT-based irrigation management system is proposed after investigating problems of irrigated farmlands in three SSA countries, Ethiopia, Kenya, and South Africa as case studies. Resource-efficient IoT architecture is developed that monitors soil, microclimate and water parameters and performs appropriate irrigation management. Indigenous farming and expert knowledge, regional weather information, crop and soil specific characteristics are also provided to the system for informed-decision making and efficient operation of the irrigation management system. In SSA, broadband connectivity and cloud services are either unavailable or expensive. To tackle these limitations, data processing, network management and irrigation decisions and communication to the farmers are carried out locally, without the involvement of any back-end servers. Furthermore, the use of green energy sources and resource-aware intelligent data analysis algorithm is studied. The intelligent data analysis helps to discover new knowledge that support further development of agricultural expert knowledge. The proposed IoT-based irrigation management system is expected to contribute towards long term and sustainable high crop yield with minimum resource consumption and impact to the biodiversity around the case farmlands.</p

TD-DAQ : a low-cost data acquisition system monitoring the unsaturated pore pressure regime in tailings dams

Tailings dams are large, often self-contained, storage facilities of mine residue. On self-contained tailings dams the tailings material itself is used to raise the containment embankments holding newly deposited residue. To develop the necessary strength, it is essential that material must dry out sufficiently. Despite substantial advancements in the field of instrumentation, these parameters are rarely measured on tailings dams and their evolution over time is poorly understood. Understanding the role of pore water suction and water content evolution over time can benefit from the installation of sensors and data acquisition systems (DAQ) capable of continuously monitoring these parameters. Such monitoring remains difficult and expensive owing to the challenges of measuring negative water pressures and the often-remote locations and harsh operating environments typical of mining operations. This paper describes the development, testing and validation of a low-cost DAQ for the measurement of the unsaturated pore pressure regime in a platinum tailings dam located in the Limpopo province of South Africa. The Tailings Dam DAQ (referred to as TD-DAQ) is designed to measure the negative pore pressure, moisture content and temperature in fine-grained material over extended periods of time. These measurements are stored on the DAQ and transmitted in parallel using new wireless network communications technologies (Sigfox) suited to remote, battery powered applications. The successful deployment of the TD-DAQ presents a real-time, low-cost instrumentation solution to improve the efficiency of condition monitoring of tailings storage facilities, contributing to a reduction in the probability of failure events.Marula Platinum (Pty) Ltd, a subsidiary of the Implats Group.http://www.elsevier.com/locate/ohxCivil Engineerin

Improvement of the algorithm ADR in an Internet of Things network LoRaWAN by using Machine Learning

El Internet de las Cosas es un paradigma habilitador de la Industria 4.0, donde sensores y actuadores se conectan a Internet. El protocolo LoRaWAN (Long Range Area Network) es uno de los más empleados, y es usado para transmitir información a largas distancias con mínimo consumo energético. Este protocolo implementa el esquema Adaptative Data Rate para mejorar la energía consumida por nodo, que al ser evaluado a través de simulaciones exhaustivas en Omnet++, ha exhibido posibilidades de mejora en el tiempo de convergencia. El presente trabajo muestra una propuesta para el mejoramiento del algoritmo ADR de tal forma que se optimice el consumo energético en redes LoRaWAN. Dentro de la propuesta se comparan diferentes modelos paramétricos y no paramétricos. Los resultados indican que los métodos basados en Máquinas de Vectores de Soporte y en Redes Neuronales Artificiales presentan la mayor exactitud, con un porcentaje por encima del 90% en las estimaciones.The Internet of Things (IoT) is an enabling paradigm for Industry 4.0, where sensors and actuators connect to the Internet. The protocol LoRaWAN (Long Range Area Network) is one of the most used in the IoT, and its primary objective is to transmit sensor information over long distances with minimal energy consumption. This protocol implements Adaptive Data Rate scheme to optimize the energy consumed per node, which, when evaluated through exhaustive simulations in Omnet ++, has exhibited opportunities for improvement in convergence time. The present work shows machine learning models based on parametric and nonparametric methods based on Support Vector Machines (SVM) and Artificial Neural Networks (ANN). The results indicate that the SVM and ANN methods have a success rate greater than 90% in the estimated parameters.Fil: González-Palacio, Mauricio. Universidad de Medellín; ColombiaFil: Sepúlveda-Cano, Lina María. Universidad de Medellín; ColombiaFil: Quiza-Montealegre, Jhon. Universidad de Medellín; ColombiaFil: D'amato, Juan Pablo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentin

LoRa Enabled Smart Inverters for Microgrid Scenarios with Widespread Elements

The introduction of low-power wide-area networks (LPWANs) has changed the image of smart systems, due to their wide coverage and low-power characteristics. This category of communication technologies is the perfect candidate to be integrated into smart inverter control architectures for remote microgrid (MG) applications. LoRaWAN is one of the leading LPWAN technologies, with some appealing features such as ease of implementation and the possibility of creating private networks. This study is devoted to analyze and evaluate the aforementioned integration. Initially, the characteristics of different LPWAN technologies are introduced, followed by an in-depth analysis of LoRa and LoRaWAN. Next, the role of communication in MGs with widespread elements is explained. A point-by-point LoRa architecture is proposed to be implemented in the grid-feeding control structure of smart inverters. This architecture is experimentally evaluated in terms of latency analysis and externally generated power setpoint, following smart inverters in different LoRa settings. The results demonstrate the effectiveness of the proposed LoRa architecture, while the settings are optimally configured. Finally, a hybrid communication system is proposed that can be effectively implemented for remote residential MG management

Suitability of LoRa, Sigfox and NB-IoT for Different Internet-of-Things Applications

The large-scale implementation of the internet of things (IoT) technologies is becoming a reality. IoT technologies benefit from low-power wide area network (LPWAN) systems. These technologies include Long Range (LoRa), Sigfox, and Narrowband IoT (NB-IoT). Numerous networks have already been deployed around the world, which is expected to accelerate the growth of IoT. This thesis discusses the performance of these three prominent LPWAN technologies in the market that have been specifically designed for IoT use. The main idea of LPWAN technologies is to provide wide coverage area using only small amount of base stations and to serve large amount of low-power and low-cost IoT devices. The main purpose of this thesis work is to compare LoRa, Sigfox, and NB-IoT and evaluate their suitability to various IoT applications. The appropriate technology selection is possible through in-depth analysis and technological comparison of LPWAN systems. There are many technological differences among these LPWAN technologies. A single technology may not be able to meet all requirements of all IoT applications. Therefore, some IoT applications can benefit from one technology more than others. The right selection helps in fulfilling the need of IoT application to save cost, time and improve efficiency. In addition to the literature-based suitability evaluation of the aforementioned technologies some practical measurements are performed using commercial off-the-shelf hardware. These measurements consider LoRa and Sigfox user devices in both outdoor and indoor locations. The key performance indicators obtained from the measurements are signal-to-noise ratio (SNR) and received signal strength indicator (RSSI). In addition, also penetration loss from outdoor to indoor is derived. The obtained measurement results were in line with the ones found from the literature