1,030 research outputs found
Designing a Wireless Sensors Network for Monitoring and Predicting Droughts
Global warming and lack of rain were the main problems that caused increased drought around the world. In New Zealand, according to National Institute of Water and Atmospheric Research (NIWA) the drought in 2012 and 2013 was the worst drought in the last 70 years. Therefore, there is a need for technological intervention to monitor basic information about the weather and soil condition in order to identify and predict drought conditions. Initial experiments have shown that the proposed wireless sensor drought monitoring system is capable of remote real-time monitoring for extended periods. This monitoring can also help identify drought in the early stages and thereby indicate promptly when to take corrective measures. Intelligent sensors in a wireless network monitor the soil condition. These sensors collect various environmental parameters and then send the pre-processed data wirelessly to a base station. From the base station this data uploads every two seconds to the cloud (internet) for further analysis. If a drought condition is identified by the monitoring system then an alert message is sent to the user via text message or email
IoT-Based Alternating Current Electrical Parameters Monitoring System
Energy monitors are indispensable for achieving efficient electrical grids and even more so in the age of the Internet of Things (IoT), where electrical system data are monitored from anywhere in the world. This paper presents the development of a two-channel electrical parameter-monitoring system based on the M5 Stack Core2 kit. The acquisition of variables is done through PZEM 004T V3.0 sensors, and the data are sent to the ThingSpeak cloud database. Local readings are done through the LCD, and data re stored on a micro SD card. Remote monitoring is done through two applications, namely a web application and a mobile application, each designed for different purposes. To validate this proposal, a commercial device with IoT features (Gen 2 Vue Energy Monitor) is used, comparing the active power and active energy readings recorded continuously for 7 days. The results indicate an accuracy of up to 1.95% in power and 0.81% in energy, obtaining a low-cost compact product with multiple features
Enhancing Plug and Play Capabilities in Body Area Network Protocols
This project aimed to create a plug-and-play protocol for Body Area Networks (BANs). This protocol enables communication between a diverse number of devices and a base station, regardless of equipment manufacturer. Previous BANs rely on proprietary software, or protocols that are specialized to the physical device. Our protocol takes a more universal approach, allowing any device to participate in a BAN without introducing any significant overhead or running cost to the operation of that BAN. Unlike previous approaches, any existing motes and the base station will not have to be updated. Only new devices being added to the BAN will have to implement the protocol before connecting. Our protocol introduces overhead that reduced the performance and lifetime of the motes used in our BAN
Development of a Personal Area Network for biomedical measurements for Internet of Things (IoT)
Internet of Things is a set of ever growing technologies and specialized devices that are increasingly influential in our everyday lives. IoT is all about connecting the physical and the digital worlds in one enabling the collection of real world data and the automation of processes. IoT turns your typical device into an smart, programmable one, more capable of interacting with humans and thus enabling users to better understand their surroundings through the data collected. This data collected by the IoT devices can then be used on all kinds of contemporary services and applications. This project aims to implement an IoT application for biomedical measurements, consisting of a WSN(Wireless Sensor Network), where three sensor nodes will collect physical world measurements. This collected information will be transmitted to a routing device, that further send the information to the internet, where the user will be able to access the data in real time through a web browser and schedule some events. In order to carry out the described scenario, a Raspberry Pi and four Zolertia Z1, three working as sensor nodes and one working as a routing node were used. The Z1 mote is powered by a low power MSP430 class microcontroller. Contiki was the operating system chosen to run the sensor nodes. In this scenario, Raspberry Pi plays the role of a router, enabling the connection of the WSN network and the internet. To send the information from the nodes, a high-speed program was developed, aiming to beat the default restrictions that Contiki OS imposes on high-speed networks. The transport protocol chosen is UDP. On the receiving end, an UDP server and a python script were developed with the intent to send the collected data to our ASP.NET web server and mySQL database. Finally connectivity tests and network speed tests of the deployed system are presented
Wireless Data Acquisition For Apiology Applications
Colony Collapse Disorder (CCD), a disease affecting honey bee colonies, is a problem threatening the food security and economy of the entire world. Discovering the cause of CCD is particularly difficult because of the variety of colony locations and environmental variables. In addition, CCD instances do not tend to follow an easily recognizable pattern with respect to apiary conditions, which is exacerbated by the subjective nature of manual apiary data recording methods. Traditional monitoring methods are typically too expensive for wide-scale deployment and often require manual collection of the data, reducing the quantity of data available for analysis. A general wireless data acquisition system was designed to improve the quantity and quality of data and to explore general issues related to wireless data acquisition systems. The system was constructed using off-the-shelf -components to reduce cost. The acquisition system and data management tools were programmed using freely available tools and software. Beehive data are transmitted to the Internet wirelessly through the use of a cellular GSM modem. Results show that it is feasible to build an economical, general purpose wireless data acquisition system that can gather quality data for an Apiology application with similar capabilities to higher-cost contemporary systems
Implicit Study of Techniques and Tools for Data Analysis of Complex Sensory Data
The utility as well as contribution of applications in Wireless Sensor Network (WSN) has been experienced by the users from more than a decade. However, with the evolution of time, it has been found that there is a massive growth of data generation even in WSN. The smaller size of sensor with limited battery life and minimal computational capability cannot handle processive such a massive stream of complex data efficiently. Although, there are various types of mining techniques being practiced today, but such tools and techniques cannot be efficiently used for analyzing such complex and massively growing data. This paper therefore discusses about the generation of large data and issues of the existing research techniques by reviewing the literatures and frequently used tools. The study finally briefs about the significant research gap that calls for need of data analytical tools in extracting knowledge from complex sensory data
Yapay Zeka ve Nesnelerin İnternetine Dayalı Otomatik Sulama Sistemi
It is not hard to see that the need for clean water is growing by considering the decrease of the water sources day by day in the world. Potable fresh water is also used for irrigation, so it should be planned to decrease fresh water wastage. With the development of the technology and the availability of cheaper and more effective solutions, the efficiency of the irrigation increased and the water loss can be reduced. In particular, Internet of things (IoT) devices have begun to be used in all areas. We can easily and precisely collect temperature, humidity and mineral values from the irrigation field with the IoT devices and sensors. Most of the operations and decisions about irrigation are carried out by people. For people, it is hard to have all the real time data such as temperature, moisture and mineral levels in the decision-making process and make decisions by considering them. People usually make decisions with their experience. In this study, a wide range of information from irrigation field was obtained by using IoT devices and sensors. Data collected from IoT devices and sensors sent via communication channels and stored on MongoDB.With the help of Weka software, the data was normalized and the normalized data was used as a learning set. As a result of the examinations, decision tree (J48) algorithm with the highest accuracy was chosen and artificial intelligence model was created. Decisions are used to manage operations such as starting, maintaining and stopping the irrigation. The accuracy of the decisions was evaluated and the irrigation system was tested with the results. There are options to manage, view the system remotely and manually and also see the system’s decisions with the created mobile application.Dünyadaki temiz su kaynaklarının günden güne azalması göz önüne alındığında temiz su ihtiyacının
arttığını görmek zor değildir. Temiz içme suyu aynı zamanda sulama için de kullanılır bu nedenle temiz su israfı
azaltma süreci planlanmalıdır. Teknolojinin gelişmesi, daha ucuz ve daha etkin çözümlerin ortaya çıkması ile
birlikte, sulama verimliliği artmakta ve su kaybı azalmaktadır. Özellikle, Nesnelerin İnterneti cihazları (IoT) tüm
alanlarda kullanılmaya başlanmıştır. IoT cihazlar ve sensörler ile sulama alanından sıcaklık, nem ve mineral
değerlerini kolayca ve hassas bir şekilde toplayabiliriz. Günümüzde sulama ile ilgili işlem ve kararların çoğu
insanlar tarafından yürütülmektedir. Karar verme sürecinde sıcaklık, nem ve mineral seviyeleri gibi birçok gerçek
zamanlı veriye sahip olmak ve bunları dikkate alarak karar vermek insanlar için zordur. İnsanlar genellikle kendi
deneyimleriyle karar alırlar. Bu çalışmada, IoT cihazları ve sensörler kullanılarak sulama alanından geniş bir veri
toplanmıştır. IoT cihazlarından ve sensörlerden toplanan veriler, iletişim kanallarından sunucuya aktarılır ve
MongoDB üzerinde saklanır. Weka yazılımı yardımı ile normalizasyon işlemleri yapılan veriler öğrenme seti
olarak kullanılır. Denemeler sonucunca yüksek başarı oranına sahip karar ağacı (J48) algoritması seçilmiş ve
yapay zeka modeli oluşturulmuştur. Kararlar, sulamayı başlatmak, sürdürmek ve durdurmak gibi işlemleri
yönetmek için kullanılmıştır. Kararların doğruluğu değerlendirilmiş ve sulama sistemi sonuçlarla test edilmiştir.
Oluşturulan mobil uygulama ile sistemi uzaktan ve manuel olarak yönetmek, görüntülemek ve ayrıca sistemin
vermiş olduğu kararları görebilmek için seçenekler vardır
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