scholarly journals Real-time and Low-cost IoT based farming using raspberry Pi

2020 ◽  
Vol 17 (1) ◽  
pp. 197
Author(s):  
Md. Wahidur Rahman ◽  
Md. Elias Hossain ◽  
Rahabul Islam ◽  
Md. Harun Ar Rashid ◽  
Md. Nur A Alam ◽  
...  
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Ph Sensor ◽  
Sensor Data ◽  
Future Application ◽  
Raspberry Pi ◽  
Moisture Sensor ◽  
Security Issues ◽  
Temperature And Humidity ◽  
Level Sensor

<span>This paper reflects on the implementation of IoT enabled Farming, especially for the people needed a smart way of agriculture. This research focuses on real-time observation with efficient use of cheapest security system. The features of this research including i) Sensor data monitoring using soil moisture sensor which is responsible for measuring moisture of the filed, water level sensor which is liable for detecting flooded water, pH sensor which is accountable for measuring pH of the soil and Temperature and humidity sensor which is responsible for tracking out the present temperature and humidity in the atmosphere ii) Live monitoring of sensor’s value using cloud and a Dashboard iii) Security issues of the farming using Laser shield and IP-Camera through Wi-Fi which is conducted by android application. This paper also assures the analysis of the experimented data through various sensor’s value and gives a momentous way for future application. Result and discussion ensures the contribution in the field of Internet of things</span>

scholarly journals A Portable Fuzzy Driver Drowsiness Estimation System

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4093
Author(s):  
Alimed Celecia ◽  
Karla Figueiredo ◽  
Marley Vellasco ◽  
René González
Keyword(s):  
Real Time ◽  
Traffic Accidents ◽  
Fuzzy Inference ◽  
Low Cost ◽  
Raspberry Pi ◽  
Inference System ◽  
Visual Occlusion ◽  
Computational Performance ◽  
Estimation System ◽  
Definition Of

The adequate automatic detection of driver fatigue is a very valuable approach for the prevention of traffic accidents. Devices that can determine drowsiness conditions accurately must inherently be portable, adaptable to different vehicles and drivers, and robust to conditions such as illumination changes or visual occlusion. With the advent of a new generation of computationally powerful embedded systems such as the Raspberry Pi, a new category of real-time and low-cost portable drowsiness detection systems could become standard tools. Usually, the proposed solutions using this platform are limited to the definition of thresholds for some defined drowsiness indicator or the application of computationally expensive classification models that limits their use in real-time. In this research, we propose the development of a new portable, low-cost, accurate, and robust drowsiness recognition device. The proposed device combines complementary drowsiness measures derived from a temporal window of eyes (PERCLOS, ECD) and mouth (AOT) states through a fuzzy inference system deployed in a Raspberry Pi with the capability of real-time response. The system provides three degrees of drowsiness (Low-Normal State, Medium-Drowsy State, and High-Severe Drowsiness State), and was assessed in terms of its computational performance and efficiency, resulting in a significant accuracy of 95.5% in state recognition that demonstrates the feasibility of the approach.

scholarly journals Semantic Traffic Sensor Data: The TRAFAIR Experience

2020 ◽  
Vol 10 (17) ◽  
pp. 5882
Author(s):  
Federico Desimoni ◽  
Sergio Ilarri ◽  
Laura Po ◽  
Federica Rollo ◽  
Raquel Trillo-Lado
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Smart Cities ◽  
Low Cost ◽  
Semantic Annotation ◽  
Open Data ◽  
Transportation Systems ◽  
Sensor Data ◽  
Time Data ◽  
Future Evolution

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

OBD-II and raspberry Pi technology to diagnose car’s machine current condition: study literature

2017 ◽  
Vol 34 (10) ◽  
pp. 15-21 ◽  
Author(s):  
Sonya Rapinta Manalu ◽  
Jurike Moniaga ◽  
Dionisius Andrian Hadipurnawan ◽  
Firda Sahidi
Keyword(s):  
Real Time ◽  
Mobile Technology ◽  
Design Methodology ◽  
Low Cost ◽  
Raspberry Pi ◽  
Data Link ◽  
Content Type ◽  
History Of ◽  
To Receive ◽  
Technology Comparison

Purpose Low-cost microcomputers such as the Raspberry Pi are common in library makerspaces. This paper aims to create an OBD-II technology to diagnose a vehicle’s condition. Design/methodology/approach An OBD-II scanner plugged into the OBD-II port or usually called the data link connector (DLC), sends diagnostics to the Raspberry Pi. Findings Compared with other microcontrollers such as Arduino, the Raspberry Pi was chosen because it sustains the application to receive real-time diagnostics, process the diagnostics and send commands to automobiles at the same time, rather than Arduino that must wait for another process finished to run another process. Originality/value This paper also represents the history of mobile technology and OBD-II technology, comparison between Arduino and Raspberry Pi and Node.

scholarly journals Artificial vision system for object classification in real time using Raspberry Pi and a Web camera

2021 ◽  
pp. 20-25
Author(s):  
Tomás Serrano-Ramírez ◽  
Ninfa del Carmen Lozano-Rincón ◽  
Arturo Mandujano-Nava ◽  
Yosafat Jetsemaní Sámano-Flores
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Vision System ◽  
Low Cost ◽  
Artificial Vision ◽  
Maintenance Cost ◽  
Raspberry Pi ◽  
Smart Cameras ◽  
Suggested Technique ◽  
Web Camera

Computer vision systems are an essential part in industrial automation tasks such as: identification, selection, measurement, defect detection and quality control in parts and components. There are smart cameras used to perform tasks, however, their high acquisition and maintenance cost is restrictive. In this work, a novel low-cost artificial vision system is proposed for classifying objects in real time, using the Raspberry Pi 3B + embedded system, a Web camera and the Open CV artificial vision library. The suggested technique comprises the training of a supervised classification system of the Haar Cascade type, with image banks of the object to be recognized, subsequently generating a predictive model which is put to the test with real-time detection, as well as the calculation for the prediction error. This seeks to build a powerful vision system, affordable and also developed using free software.

scholarly journals Data Acquisition and Alert System for Recirculation Aquaculture System (RAS) using Fog Computing

2019 ◽  
Vol 3 (6) ◽  
pp. 6-10
Author(s):  
Siti Maryam Zainol ◽  
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Fog Computing ◽  
Processing System ◽  
Raspberry Pi ◽  
Aquaculture System ◽  
Data Collecting ◽  
Level Sensor ◽  
Recirculation Aquaculture System ◽  
Collecting System

This research presents an improved and more effective approach for data acquisition of recirculation aquaculture system (RAS). The previous research, the system uses manual methods to take the important data from RAS and it wastes the time and also gets late response from the fish farmer if the data is not in the good condition. As a result, fog computing technology is applied to overcome all these problems and acts as advance data acquisition system to keep data safely by sharing the processed data in fog computing for every tanks and analyze the data to make an accurate control/decision in the real time. Besides, open source technology plus embedded system based will be integrated for this research because its benefits such as small size, low cost, light weight, portable, high efficiency and low power consumption. This research has achieved the objectives which are design a data collecting system for RAS, design a data processing system using fog computing and integrate, test and validate automatic data collection and processing strategy for recirculation aquaculture system (RAS). The data collecting system for RAS, RaspDAQ is developed by connecting Raspberry Pi 3 to temperature sensor (LM35DT) using analogue digital converter (ADC) MCP3002, water level sensor (HC-SR04), Rpi camera module, LEDs and buzzer. Software and program are built using Python and Apache server to run every functions of RaspDAQ. While third Raspberry Pi 3 is setup as data processing system, RaspFog using PHP, Apache and MySQL server. Both RaspDAQ and RaspFog are based on Raspbian operating system. After that, RaspDAQ1 and RaspDAQ2 are connected to RaspFog using WiFi technology to send sensors data in real time. The received data are stored and plotted using Highcharts.com graph. The data collecting system, RaspDAQ and data server and processor, RaspFog has been tested and validated. At the same time, users can see the graph output in the real time for temperature, water level sensor and real condition using Rpi camera module of RaspDAQ1 and RaspDAQ2 by browsing RaspFog website. From the observation, data has been transferred from RaspDAQ to RaspFog in a short duration which is less than 15 seconds. Consequently, the efficiency of data acquisition process has been improved from manual system to fog computing technology successfully.

scholarly journals WIRELESS REAL TIME PROPORTIONAL CONTROL SYSTEM

2013 ◽  
pp. 15-20
Author(s):  
UJJWALA G. BORATE ◽  
PROF. R.T. PATIL
Keyword(s):  
Low Power ◽  
Real Time ◽  
Low Cost ◽  
Ultra Low Power ◽  
Proportional Control ◽  
Star Network ◽  
Precise Control ◽  
Zigbee Technology ◽  
Temperature And Humidity ◽  
Transceiver Module

This system provides low power consuming and low cost wireless sensor network. This system provides a real time temperature and humidity. It also gives proportional control action. This system consists of TI’s MSP430 microcontroller which consumes ultra low power and improves the overall system performance. The Sensorion’s SHT 11 sensor is used to measure temperature and humidity. Sensor SHT 11 consumes low power and gives the fully calibrated digital output. Zigbee technology is used for wireless communication. Zigbee is low power consuming transceiver module. It operates within the ISM 2.4 GHz frequency band. AT and API command modes configure module parameters. RF data rate is 250 kbps. To achieve the proportional control triac and MOC 3022 are used. The star network topology is implemented. The temperature of earth goes on increasing due to global warming, deforestation, pollution, etc. Due to this the temperature of atmosphere also increases which is harmful and dangerous for many systems. This system provides precise control of temperature and humidity in Green House, Art Galleries and Industries.

scholarly journals Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

2018 ◽  
Author(s):  
Javier Garcia-Guzman ◽  
Lisardo Prieto González ◽  
Jonatan Pajares Redondo ◽  
Mat Max Montalvo Martinez ◽  
María Jesús López Boada
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Low Cost ◽  
Roll Angle ◽  
Measurement Unit ◽  
Raspberry Pi ◽  
State Variables ◽  
Angle Estimation ◽  
Experimental Environment ◽  
Time Operation

Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 - 37€) and GPS (Mtk3339 - 53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

scholarly journals Raspberry Pi for Commercial Applications

2013 ◽  
Vol 11 (2) ◽  
pp. 2250-2255 ◽  
Author(s):  
Chaitanya Bysani ◽  
T. S. Rama Krishna Prasad ◽  
Sridhar Chundi
Keyword(s):  
User Interface ◽  
Real Time ◽  
Low Cost ◽  
Raspberry Pi ◽  
Video Playback ◽  
Automotive Safety ◽  
Internet Connectivity ◽  
Connected Network ◽  
Commercial Off The Shelf ◽  
Commercial Applications

The objective of this paper is to create a low cost commercial off the shelf data analyzer for improving automotive safety and design a user interface infotainment system by using Raspberry Pi.  In this paper we propose Raspberry pi based application that monitor the vehicle ECUs through an OBD-II(On Board Diagnostics) interface, perform Diagnostics with DTCs (Diagnostics trouble codes). Infotainment system having functions such as audio and video playback, games, internet connectivity through either USB Wi-Fi dongles or USB Modems and dashboard camera operation. Raspberry Pi will transmit the data over Wi-Fi in real-time in xml format over Wi-Fi on a DHCP connected network.

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