Perancangan Sistem Peringatan Dini Tanah Longsor Menggunakan Metode Fuzzy Berbasis Android

Tanah longsor merupakan salah satu bencana alam yang sering terjadi di kawasan Indonesia. Bencana ini biasanya sering terjadi di daerah pegunungan, bukit, lereng yang curam, maupun tebing. Tak jarang tanah longsor juga terjadi di lahan pertanian dan perkebunan yang posisinya terletak di tanah miring. Oleh karena itu, perlu diciptakan sistem peringatan dini tanah longsor. Kemiringan tanah, pergeseran tanah, dan kadar air yang berlebihan dalam tanah merupakan penyebab utama terjadinya tanah longsor. Untuk mengukur parameter tersebut, digunakan sebuah sistem berbasis Internet of things (IoT) yang terhubung dengan berbagai macam sensor. Pada penelitian ini nilai fuzzy didapat dari hasil pengukuran sensor accelerometer dan gyroscope MPU6050 dan sensor Soil Moisture yang dikirim ke server antares menggunakan LoRa. Dalam hal ini Fuzzy Logic digunakan untuk menganalisis hasil deteksi sensor tersebut berupa tiga keputusan akhir, yaitu aman, waspada, dan awas yang dapat dilihat pada perangkat android dengan nilai akurasi 90%. Kata Kunci : Sistem Peringatan Dini, Tanah Longsor, Antares, LoRa, Fuzzy Logi

IoT-based air quality monitoring systems for smart cities: A systematic mapping study

The increased level of air pollution in big cities has become a major concern for several organizations and authorities because of the risk it represents to human health. In this context, the technology has become a very useful tool in the contamination monitoring and the possible mitigation of its impact. Particularly, there are different proposals using the internet of things (IoT) paradigm that use interconnected sensors in order to measure different pollutants. In this paper, we develop a systematic mapping study defined by a five-step methodology to identify and analyze the research status in terms of IoT-based air pollution monitoring systems for smart cities. The study includes 55 proposals, some of which have been implemented in a real environment. We analyze and compare these proposals in terms of different parameters defined in the mapping and highlight some challenges for air quality monitoring systems implementation into the smart city context

Evaluation of arima and ann stream analytics for air quality monitoring system

There are many environmental monitoring systems available in the market with Internetof-Things (IoT) enabled technology. However, the existing system is not equipped with online data analytics. Some of them provide analytics but are done in offline mode through third-party software or devices known as batch analytics. Pricewise, the existing monitoring system alone is expensive even though none of them are furnished with stream analytics. The thesis presents the design and development of an accurate air quality monitoring system equipped with streaming machine learning predictive analytics called Smart Environmental System (SES). The developed SES is divided into two sections End-Node Unit (ENU) and Gateway Unit (GWU). ENU consisted of calibrated sensors of NO2, CO, CO2, PM2.5, PM10, O3, temperature humidity integrated with Raspberry Pi Single-Board Computer (SBC) and Long-Range (LoRa) Transmitter (Tx) module. Meanwhile, GWU consisted of Raspberry Pi SBC, LoRa Receiver (Rx) and 4G module. The ENU transferred the data wirelessly to the GWU through LoRa communication, and GWU stored the data immediately in MySQL, which was installed in the Linux Apache MySQL PHP (LAMP) server. Investigation on evaluating senso rs’ accuracy is executed by comparing the collected data by SES vs data from the Department of Environment (DoE). The SES’s accuracy percentage error of CO, NO2, O3, PM10 are 5.1%, 7%, 6.1% and 6% correspondingly compared to DoE. Such accuracy of sensors is acceptable with an accuracy below 10%. Once accuracy has been validated, the data stored in MySQL database is successfully exported to the R query table in R-Server by using dbGetQuery() command, checked and aligned with the MySQL database. It is observed that the data in MySQL are successfully exported to the R query table based on the similar number of variables between those two tables. The data stored in the query table act as input to the analytics algorithm, which runs in R-server as well. In this thesis, two algorithms have been implemented and compared. Auto -Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN). It is identified that ARIMA has better prediction accuracy (PA) percentage of 99.45%, 99.87%, 99.75%, 98.92% for CO, NO2, O3 and PM10 over ANN thus chosen as a predictive analytics algorithm for SES. Once embedded in SES, ARIMA performances are evaluated based on Mean Absolute Percentage Error (MAPE) and Prediction Accuracy (PA). It is observed that ARIMA MAPE is 1.64%,9.67%, 9.59%, 7.09%, for CO, NO2, O3 and PM10, respectively which led PA to achieve 96.78%, 90.33%, 90.41% and 92.91% correspondingly. The results proved that the proposed SES is able to precisely predict those gases for the next 24 hours above the 90% prediction accuracy. It can be concluded the proposed SES could be implemented as a future for the Air Pollutant Index (API) system

Predicting lorawan behavior. How machine learning can help

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases

CityIOT: Air quality in the city supported by an IoT ecosystem

Air pollution is a worldwide problem, which affects millions of people and can have serious consequences for public health, economy and for social issues. Constant emissions of gaseous pollutants, as a result of industrial operations, combustion of fossil fuels and forest fires, affect air quality and contribute to an increase in this type of pollution. Internet of Things (IoT) has emerged as a response to the creation of intelligent monitoring systems that can be used for management and identification of pollution sources, and for health protection. The development of new equipment and communication technologies has allowed the creation of real-time air quality monitoring applications. This dissertation presents an implementation of an IoT system for monitoring outdoor air quality, based on a multisensory system with LoRa communication capabilities. The system incorporates low-cost sensors capable of detecting different air pollutants, as well as atmospheric parameters, such as temperature and relative humidity. To achieve a long-distance transmission of readings with low energy consumption, the use of LoRa technology was adopted. The frequency of sending readings to the network is based on the level of urban traffic in a certain location. Furthermore, a web/mobile application is presented, which allows the user to monitor in real time and to carry out a temporal analysis of the measurements taken by the system.A poluição do ar representa um problema global, que afeta milhões de pessoas, podendo trazer graves consequências para a saúde pública, para a economia e também sociais. A constante emissão de poluentes em estado gasoso, resultante de operações industriais, combustão de combustíveis fósseis e fogos florestais, afetam a qualidade do ar e contribuem para o aumento deste tipo de poluição. A Internet das Coisas (IoT) emergiu como resposta para a criação de sistemas inteligentes de monitorização que podem ser usados para a gestão e identificação de fontes de poluição e para a proteção da saúde pública. O desenvolvimento de novos equipamentos e tecnologias de comunicação, têm vindo a permitir a criação de aplicações de monitorização da qualidade do ar, em tempo real. Esta dissertação apresenta uma implementação de um sistema IoT de monitorização de qualidade do ar exterior, baseado num sistema multisensor com capacidade de comunicação LoRa. O sistema incorpora sensores de baixo custo capazes de detetar diferentes poluentes atmosféricos, como também parâmetros atmosféricos, como temperatura e humidade relativa. De modo a ser obtida uma transmissão de leituras em longa distância e com baixo consumo energético, o uso da tecnologia LoRa foi adotado. A frequência do envio de leituras para a rede, é feita com base no nível de tráfego urbano, numa certa localização. Ainda é apresentada uma aplicação web/móvel, que permite ao utilizador acompanhar em tempo real e proceder a uma análise temporal, das medições efetuadas pelo sistema