WSN for Traffic Monitoring using Raspberry Pi Board

Kochláň, Michal; Hodoň, Michal; Čechovič, Lukáš; Kapitulík, Ján; Jurečka, Matúš

doi:10.15439/2014f310

Cited by 33 publications

(9 citation statements)

References 1 publication

(1 reference statement)

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“…It runs an ARM11 microcontroller at 700MHz and has a 512 Mega Bytes of RAM memory [6] [7]. The Raspberry Pi is a low cost, low power credit card sized single board computer [8] [9].…”

Section: A Raspberry Pimentioning

confidence: 99%

Automatic Environmental Monitoring System using Wireless Sensor Network

2017

IJIET

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Abstract-Wireless sensor networks provide many solutions for various real time applications. In this paper, a real time environmental monitoring application is presented. Sensors are used to collect details regarding environmental conditions like temperature, humidity, pressure etc. at different locations. The real time data is given to Raspberry Pi which acts as a control station. It stores the data collected from different sensor units and analyzes the stored data. ZigBee protocol is used for communication between different sensor nodes. The system also provides a web interface for the user so that the user can control and monitor the system remotely.

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“…It runs an ARM11 microcontroller at 700MHz and has a 512 Mega Bytes of RAM memory [6] [7]. The Raspberry Pi is a low cost, low power credit card sized single board computer [8] [9].…”

Section: A Raspberry Pimentioning

confidence: 99%

Automatic Environmental Monitoring System using Wireless Sensor Network

2017

IJIET

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“…Raspberry Pi that has been introduced by Eben Upton, where it is a cheap but with a high mobility microprocessor [3], is one example of high potential IOT device, where it enables a Machine-to-Machine Communication using IEEE 802 standard. The developments of CCTV system using Raspberry Pi have also been done in [4] and [5]. In [4], the developed CCTV provides security with low cost smart security camera with night vision capability using Raspberry and Open Source Computer Vision (OpenCV).…”

Section: Introductionmentioning

confidence: 99%

“…The developments of CCTV system using Raspberry Pi have also been done in [4] and [5]. In [4], the developed CCTV provides security with low cost smart security camera with night vision capability using Raspberry and Open Source Computer Vision (OpenCV). The system was designed to be used inside a warehouse facility.…”

Section: Introductionmentioning

confidence: 99%

Smart Surveillance System using Background Subtraction Technique in IoT Application

Harum¹,

Ali²,

Zakaria³

et al. 2018

ijacsa

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This paper presents a development of a security system based on Internet-of-Things (IoT) technology, where an IoT device, Raspberry Pi has been used. In the developed surveillance system, a camera works as a sensor to detect motion, and automatically capture the video of the view of area where the motion is detected. The motion is detected by image processing techniques; background subtraction technique. The technique is applied by comparing two different captured images using Pi NoIR camera. The system can be controlled from anywhere using Telegram application, and users will receive alert message with video using the application. The user can also play a siren from anywhere once detecting suspicious object can access images and videos using Telegram application. This can frighten the thief if the crime is suspected in home or office. Users can also deactivate and activate the system from anywhere at any time using the Telegram. The functionality tests have been done to ensure the developed product can work properly. Besides, tests to identify a suitable video length to be transmitted to the user and to identify the adequate location of the security in order to minimize false detection as well as false alert have been performed. The project is an IoT-based which significantly in line with the Industrial Revolution 4.0, supporting the infrastructure of Cyber-Physical System.

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“…A detailed discussion on its potential use for crowdsourcing applications in climate and atmospheric sciences is provided in Muller et al (2015). The combination of the RPi with the PiCamera module is very popular in home security applications (Prasad et al, 2014 andNguyen at al., 2015), in creating depth estimation systems (Zhuang, 2016) in particular for UAV usage (Neves at al., 2013 andCooper et al, 2017), in traffic monitoring (Kochláň et al, 2014), in agriculture (Jindarat et al, 2015), and also in environmental sciences (Ferdoush et al, 2014). The new photogrammetric system presented in this paper is intended for civil and mining engineering applications, in particular for monitoring rock cuttings along major roads and railway networks and high vertical rock surfaces within mining operations.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Tests of a New Low-Cost Photogrammetric System

Santise

Thoeni

Roncella

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents preliminary tests of a new low-cost photogrammetric system for 4D modelling of large scale areas for civil engineering applications. The system consists of five stand-alone units. Each of the units is composed of a Raspberry Pi 2 Model B (RPi2B) single board computer connected to a PiCamera Module V2 (8 MP) and is powered by a 10 W solar panel. The acquisition of the images is performed automatically using Python scripts and the OpenCV library. Images are recorded at different times during the day and automatically uploaded onto a FTP server from where they can be accessed for processing. Preliminary tests and outcomes of the system are discussed in detail. The focus is on the performance assessment of the low-cost sensor and the quality evaluation of the digital surface models generated by the low-cost photogrammetric systems in the field under real test conditions. Two different test cases were set up in order to calibrate the low-cost photogrammetric system and to assess its performance. First comparisons with a TLS model show a good agreement.

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WSN for Traffic Monitoring using Raspberry Pi Board

Cited by 33 publications

References 1 publication

Automatic Environmental Monitoring System using Wireless Sensor Network

Automatic Environmental Monitoring System using Wireless Sensor Network

Smart Surveillance System using Background Subtraction Technique in IoT Application

Preliminary Tests of a New Low-Cost Photogrammetric System

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