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Low-Cost Automatic Weather Stations in the Internet of Things

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Low-Cost Automatic Weather Stations in the Internet of Things information Review Low-Cost Automatic Weather Stations in the Internet of Things Konstantinos Ioannou 1,* , Dimitris Karampatzakis 2 , Petros Amanatidis 2, Vasileios Aggelopoulos 2 and Ilias Karmiris 1 1 Hellenic Agricultural Organization “DEMETER”, Forest Research Institute, Vasilika, 57006 Thessaloniki, Greece; [email protected] 2 Industrial and Educational Embedded Systems Lab, Department of Computer Science, International Hellenic University, 65403 Kavala, Greece; [email protected] (D.K.); [email protected] (P.A.); [email protected] (V.A.) * Correspondence: [email protected] Abstract: Automatic Weather Stations (AWS) are extensively used for gathering meteorological and climatic data. The World Meteorological Organization (WMO) provides publications with guidelines for the implementation, installation, and usages of these stations. Nowadays, in the new era of the Internet of Things, there is an ever-increasing necessity for the implementation of automatic observing systems that will provide scientists with the real-time data needed to design and apply proper environmental policy. In this paper, an extended review is performed regarding the technologies currently used for the implementation of Automatic Weather Stations. Furthermore, we also present the usage of new emerging technologies such as the Internet of Things, Edge Computing, Deep Learning, LPWAN, etc. in the implementation of future AWS-based observation systems. Finally, we present a case study and results from a testbed AWS (project AgroComp) developed by our research team. The results include test measurements from low-cost sensors installed on the unit and predictions provided by Deep Learning algorithms running locally. Citation: Ioannou, K.; Keywords: AWS; Internet of Things; Artificial Intelligence; Edge computing; LPWAN; LoRa; AgroComp Karampatzakis, D.; Amanatidis, P.; Aggelopoulos, V.; Karmiris, I. Low-Cost Automatic Weather Stations in the Internet of Things. Information 2021, 12, 146. https:// 1. Introduction doi.org/10.3390/info12040146 The study of weather phenomena as a method for predicting weather changes started from ancient Greece. Aristotle, in his work Meteorologica attempted to explain atmospheric Academic Editor: Willy Susilo phenomena in a philosophical and speculative manner. However, the first weather instru- ments were invented at the end of the sixteenth century: the thermometer in late 1600, Received: 25 February 2021 the barometer in 1643, and the hygrometer (for measuring humidity) in the late 1700s [1]. Accepted: 26 March 2021 As more instruments were developed, weather measurement became more precise and Published: 29 March 2021 reliable. The invention of the telegraph in 1843 allowed the transmission of weather ob- servations. Another major leap forward was made in 1950 with the usage of computers Publisher’s Note: MDPI stays neutral for solving complex mathematical equations describing the atmospheric behavior and the with regard to jurisdictional claims in usage of Doppler Radars, which provided the ability to peer into severe thunderstorms published maps and institutional affil- and unveil the phenomena taking place inside [1]. iations. In a series of International Meteorological Conferences (starting in 1873), instructions were issued regarding meteorological data acquisition, which included measurements as well as the exchange of data between Meteorological Services. Additional guidelines were issued for the analysis, forecasting, and map creation of these data. The International Copyright: © 2021 by the authors. Meteorological Organization was founded in 1878, with main goal the improvement of Licensee MDPI, Basel, Switzerland. the organization between national meteorological services. It was renamed the World This article is an open access article Meteorological Organization in 1950 [2]. distributed under the terms and The usage of the measured data as well as the guidelines for collecting them is conditions of the Creative Commons the responsibility of the World Meteorological Organization (WMO), and during the Attribution (CC BY) license (https:// years, various methods and observing systems (Aeronautical, Marine, Aircraft-based, creativecommons.org/licenses/by/ and Terrestrial) became available to the research community and agencies. WMO is also 4.0/). Information 2021, 12, 146. https://doi.org/10.3390/info12040146 https://www.mdpi.com/journal/information Information 2021, 12, 146 2 of 21 interested in systems capable of retrieving meteorological data as well as environmental observations automatically by Automatic Weather Stations (AWS) and collecting data from a network through various communications channels. The implementation, installation and operation of AWS is a task that is analytically described in the World Meteorological Organization’s publications and guidelines [3,4]. An AWS is defined by WMO as a meteorological station at which observations are made and transmitted automatically. An AWS is used in order to increase the number and reliability of surface observations [4]. According to WMO, there are four (4) categories of AWS: • Light AWS for measurement of few variables (precipitation and/or air temperature). • Basic AWS for the measurement of basic meteorological measurements (air temper- ature, relative humidity, wind speed and direction, precipitation, and atmospheric pressure). • Extended AWS that measure additionally solar radiation, sunshine duration, soil tem- perature, and evaporation. • AWS with automation of visual observations (cloud base height and present weather). All the categories provide the capability of logging data using a proprietary data logger as well as the ability of transmitting data using a variety of methods. Additionally, to the aforementioned categories of AWS, WMO recognizes another type of weather station briefly entitled as Automatic Weather Station—Low Cost (AWS-LC). This type of station is characterized by their low cost of usage and purchase as well as the low power consumption, the capability of data transmission in real time (with or without logging), and finally their size, which is small and compact. However, due to their consumer market orientation and the usage of electronics and sensors produced by vendors without extensive experience in meteorological measurements, the gathered data quality quickly becomes unknown, and AWS-LC stations are not standardized at this moment [4]. Generally, three (3) types of AWS-LC are recognized by WMO: Compact, All in One, and Stand-Alone. Compact and All in One are basic types that are mainly aimed toward hobbyist users who want to gather information regarding the weather locally. These two types sometimes provide the capability to transmit limited volumes of data locally but generally lack the capability of logging data. The third type (Stand-Alone instruments) uses a network of individual intelligent instruments, transmitting information using low-power and low bandwidth interfaces via Wi-Fi and Bluetooth to centralized processing servers [4]. This type of weather stations is optimized for siting and individual measuring instruments selection. The most common layout used to deploy the various instruments is star topology. In this case, every host (in the case of AWS-LC, every measuring device) is connected to a central hub. This hub acts as a conduit to store and transmit the messages [5]. There are numerous advantages using AWS and AWS-LC systems instead of the more traditional manned stations; these advantages include the ability to monitor data in sparse and rural areas, cost reduction, reduction of random errors, increased reliability, measurement accuracy, etc. [3,4]. However, there are also some disadvantages that must be considered prior to the installation of these types of meteorological stations. These disadvantages mainly include the difficulty of installation, the occasional disagreement of professional meteorological observers regarding the automatic interpretation of the measured data (especially in the case of precipitation, cloud cover, and cloud base), trans- mission costs, etc. [3–6]. This paper firstly aims at reviewing the methodologies and technologies used for the implementation of AWS observing systems. Secondly, we make an extensive presentation of new and innovative usages of current computer science trends such as Edge computing, Internet of Things, and Low-Power Wide-Area Networks on the implementation and operation of an AWS-based observation system. Additionally, a case study is presented regarding a patented low-cost AWS and future improvements. All things considered, we discuss AWS in the Internet of Things and future work. Information 2021, 12, 146 3 of 21 2. Automatic Weather Station Systems and Related Work 2.1. WMO Automatic Weather Stations Observing Systems A typical Automated Weather Observing Station (AWOS) is a combination of sensing instruments, interfaces, processing devices, and transmission units. Based on WMO [4], an AWOS, as illustrated in Figure1, is usually part of an integrated system that consists of three (3) main elements: • The AWS units and the sensing instruments attached to or connected to it. • The local modem or interface used to connect AWS to a telecom or computer network. • A Central Processing System fed by data transmitted by all the AWS making up the Observing Network. This system usually connects: • To the WMO Information System (WIS), or • To an Automatic Message Switching System (AMSS) linked to the WIS. Figure 1. An integrated observing system based on World Meteorological
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