Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth

Ein neuartiges autonomes Echtzeit-Lastüberwachungs- und -steuerungssystem in Mikronetzen auf der Grundlage von Fuzzy-Logik-Steuerung und drahtloser LoRa-Kommunikation

Die dezentralen Mikronetze auf der Grundlage erneuerbarer Energiequellen (EE) bieten eine Lösung für den massiven Strommangel in den afrikanischen Ländern südlich der Sahara (SSA), insbesondere in abgelegenen Regionen, die nicht an die nationalen Stromnetze angeschlossen sind. Die auf erneuerbaren Energien basierenden Microgrids leiden jedoch unter Zuverlässigkeitsproblemen und häufigen Stromausfällen, die meist auf die Instabilität der Microgrids zurückzuführen sind. Dies resultiert aus der intermittierenden Natur der genutzten erneuerbaren Energiequellen. Das Fehlen einer angemessenen Lastüberwachung und -steuerung führt zu einem Verlust an Zuverlässigkeit und zu häufigen Stromausfällen. Um dieses Problem zu lösen, müssen die Mikronetze an dezentralen Standorten durch kosteneffiziente, hochpräzise und reaktionsfähige Systeme in Echtzeit überwacht werden, um schnell auf Ungleichgewichte zwischen Erzeugung und Verbrauch reagieren zu können. Fortschritte bei drahtlosen Sensornetzwerken, insbesondere Long-Range Wide Area Networks (LoRaWAN), werden genutzt, um ein kostengünstiges und bidirektionales drahtloses Sensornetzwerk zu entwickeln. In dieser Forschungsarbeit wird ein neuartiges sensorisches Echtzeit-Lastüberwachungs- und -steuerungssystem für Mikronetze vorgeschlagen. Das System basiert auf der Fuzzy-Logic-Controller-Technik (FLC) und der drahtlosen Long-Range-Kommunikationstechnologie (LoRa). Es verwaltet Ungleichgewichte zwischen Stromerzeugung und Energiebedarf, indem es auf der Grundlage der momentanen PV-Erzeugung und des Ladezustands des Batteriespeichersystems eine Belastungsgrenze für ein Mikronetz festlegt. Das vorgeschlagene System besteht aus einem drahtlosen, wartungsarmen Netzwerk verteilter Energiesensormodule zur Überwachung und Steuerung der Last auf der Nachfrageseite.The distributed microgrids based on Renewable Energy Sources (RES) offer a solution to the massive electricity shortage in Sub-Saharan Africa (SSA), especially in remote regions that are not connected to national grids. Nevertheless, RES-based microgrids suffer some reliability issues and experience frequent blackouts mainly due to microgrid instability. This is due to the intermittent nature of RES. Lack of proper load monitoring and control leads to loss of reliability and frequent power outages. To address this problem, the microgrids must be real-time monitored at distributed locations by cost-effective, high-accuracy, responsive systems to respond quickly to generation-consumption imbalances. Advancements in wireless sensor networks, particularly Long-Range Wide Area Networks (LoRaWAN), are leveraged to design an affordable and bidirectional wireless sensory network. This research work proposes a novel real-time load monitoring and control sensor system in microgrids. The system is based on Fuzzy Logic Controller (FLC) technique and Long-Range (LoRa) wireless communication technology. It manages imbalances between power generation and energy demand by setting a loading limit to a microgrid, based on the instant PV generation and State of Charge (SoC) of the battery energy storage system. The proposed system comprises a wireless low-power, a low-maintenance network of distributed power sensory modules for demand-side load monitoring and management. This study developed the prototypes of the system, and deployed it in a solar PV microgrid, in Silale village in Dodoma, Tanzania. The findings after the implementation of the developed system in the microgrid show that it is evidently possible to use a FLC-based traffic light for the load control to ensure a proper and achieve a real-time DSM, to avoid overloading in the microgrids. However, this research has ...Ibrahim A. Mwammenywa ; [First reviewer: Prof. Dr.-Ing. Ulrich Hilleringmann, Second reviewer: Prof. Dr.-Ing. habil. Stefan Krauter]Tag der Verteidigung: 23.05.2024Universität Paderborn, Dissertation, 202

A SURVEY ON DEVICES EXPLOITING LORA COMMUNICATION

Information and Communication Technologies (ICT) have experienced a large application in many fields, such as smart homes, health monitoring, environmental monitoring, and a great number of studies is present in literature. In particular, it is expected that the Internet of Things (IoT) will become increasingly pervasive in everyday life. Among different technologies, devices based on Long Range (LoRa) and LoRaWAN stand out due to their relative low cost, low power consumption and large cover range. In this survey, recent papers investigating applications of LoRa modules have been selected. The different usecases are presented with a comparison between communication parameters and results obtained

Low-Cost IoT Remote Sensor Mesh for Large-Scale Orchard Monitorization

Population growth and climate change lead agricultural cultures to face environmental degradation and rising of resistant diseases and pests. These conditions result in reduced product quality and increasing risk of harmful toxicity to human health. Thus, the prediction of the occurrence of diseases and pests and the consequent avoidance of the erroneous use of phytosanitary products will contribute to improving food quality and safety and environmental land protection. This study presents the design and construction of a low-cost IoT sensor mesh that enables the remote measurement of parameters of large-scale orchards. The developed remote monitoring system transmits all monitored data to a central node via LoRaWAN technology. To make the system nodes fully autonomous, the individual nodes were designed to be solar-powered and to require low energy consumption. To improve the user experience, a web interface and a mobile application were developed, which allow the monitored information to be viewed in real-time. Several experimental tests were performed in an olive orchard under di erent environmental conditions. The results indicate an adequate precision and reliability of the system and show that the system is fully adequate to be placed in remote orchards located at a considerable distance from networks, being able to provide real-time parameters monitoring of both tree and the surrounding environment.info:eu-repo/semantics/publishedVersio

Research advancements in ocean environmental monitoring systems using wireless sensor networks: a review

The ocean environment monitoring system is of great significance to the researchers because the ocean is the storehouse of natural resources. It is critical to comprehend and assess the ocean’s environmental conditions. Several studies have been conducted over the last several decades that use sophisticated information and communication techniques to ensure the ocean ecosystem. Wireless sensor networks (WSNs) are a promising technology to monitor the ocean environment, which delivers significant benefits such as enhanced accuracy and real-time observations. The advancements in sensor technology such as micro electromechanical systems (MEMS), integrated systems, distributed processing, wireless communications, and wireless sensor applications have contributed to the development of WSNs. This paper describes the utilization of WSN and analyzes the previous and existing project works and technologies used for ocean environment monitoring through WSNs, and also includes the MEMS sensor technology used for monitoring various ocean parameters such as ocean wave monitoring, water conductivity, temperature, and depth of ocean

Designing a low-cost wireless sensor network for particulate matter monitoring: Implementation, calibration, and field-test

Poor air quality can provoke severe impacts on health, necessitating environmental monitoring of atmospheric particulate matter (PM) to assess potential threats to human well-being. However, traditional continuous air quality monitoring systems are often costly and time-consuming in data treatment. Lately, there is a growing trend towards the use of low-cost wireless PM sensors, providing more detailed information than standard systems. This paper presents a system designed to measure air quality, specifically, a wireless sensor network composed of a distributed sensor network linked to a cloud system. The proposed system can efficiently measure air quality as it is cost-effective, small-sized, and consumes little power. Sensor nodes based on low-power long range (LoRa) motes transmit field measurement data to the cloud via a gateway, and a cloud computing system is implemented to store, monitor, process, and visualise the data. Advanced techniques were included in our cloud for data processing and analysis to optimise the detection of PM. Laboratory and field tests in the historic Riotinto mine validate the system's viability, offering real-time air quality information for nearby populations. Once calibrated, sensors demonstrate high accuracy, presenting mean error of −0.3% and low deviation (R2 = 0.96) when compared to regulatory systems for both low (<10 μgPM10/m3) and hazardous concentrations (300 μgPM10/m3), which makes them perfect as early warning systems for atmospheric pollution in mining.Funding for this research was provided by the project “68/83 Contribucion ´ de fuentes del material particulado atmosf´erico en el entorno del distrito minero de Riotinto (2023).” The authors express their appreciation to the Atalaya Mining Company for granting permission to conduct this research on their premises and for their wholehearted support. Funding for open access charge: Universidad de Huelva / CBUA, Spain.CIQSOCIQS

Energy Efficient IoT-Sensors Network for Smart Farming

The experience of smart farming can be improved using IoT-based applications. Still, the performance of IoT networks may be degraded due to different factors, i.e., the coverage area of the farm/location (surface or underwater)/environmental conditions etc. Network operations over heterogeneous environments may cause excessive resource consumption and thus may reduce the IoT sensor’s lifespan. To optimise energy consumption, in this paper, an energy-efficient method will be introduced for smart farming, and its performance will be analysed using different parameters (i.e., Throughput/energy consumption/residual energy etc.) using two different IoT standards (Long Range Low powered technology (LoRa)/SigFox)

Delivering IoT Services in Smart Cities and Environmental Monitoring through Collective Awareness, Mobile Crowdsensing and Open Data

The Internet of Things (IoT) is the paradigm that allows us to interact with the real world by means of networking-enabled devices and convert physical phenomena into valuable digital knowledge. Such a rapidly evolving field leveraged the explosion of a number of technologies, standards and platforms. Consequently, different IoT ecosystems behave as closed islands and do not interoperate with each other, thus the potential of the number of connected objects in the world is far from being totally unleashed. Typically, research efforts in tackling such challenge tend to propose a new IoT platforms or standards, however, such solutions find obstacles in keeping up the pace at which the field is evolving. Our work is different, in that it originates from the following observation: in use cases that depend on common phenomena such as Smart Cities or environmental monitoring a lot of useful data for applications is already in place somewhere or devices capable of collecting such data are already deployed. For such scenarios, we propose and study the use of Collective Awareness Paradigms (CAP), which offload data collection to a crowd of participants. We bring three main contributions: we study the feasibility of using Open Data coming from heterogeneous sources, focusing particularly on crowdsourced and user-contributed data that has the drawback of being incomplete and we then propose a State-of-the-Art algorith that automatically classifies raw crowdsourced sensor data; we design a data collection framework that uses Mobile Crowdsensing (MCS) and puts the participants and the stakeholders in a coordinated interaction together with a distributed data collection algorithm that prevents the users from collecting too much or too less data; (3) we design a Service Oriented Architecture that constitutes a unique interface to the raw data collected through CAPs through their aggregation into ad-hoc services, moreover, we provide a prototype implementation