scholarly journals Internet of Robotic Things in Surveillance

2021 ◽  
Vol 9 (12) ◽  
pp. 2174-2178
Author(s):  
Gaurang Waghela
Keyword(s):  
Physical World ◽  
Sensor Data ◽  
Raspberry Pi ◽  
Robotic Control ◽  
Distributed Intelligence ◽  
Course Of Action ◽  
Intelligent Devices ◽  
And Robotics

Abstract: These days a new field has emerged known as IoRT which is a combination of IOT and Robotics and known as Internet of Robotic Things. Through IORT, intelligent devices can monitor events, fuse sensor data from a variety of sources, use local and distributed intelligence to determine a best course of action, and then act to control or manipulate objects in the physical world and physically moving through that world. This paper mainly focuses on application of IoRT as a surveillance robot with audio and video features in the domain of security. Keywords: IOT, Robotics, Surveillance Robot, Ardino, Sensors, Raspberry Pi, Robotic control.

Ambient Communication Experience (ACE), Information Interaction in Design Space

2011 ◽  
pp. 139-146
Author(s):  
Rosaleen Hegarty ◽  
Tom Lunney ◽  
Kevin Curran ◽  
Maurice Mulvenna
Keyword(s):  
Information Systems ◽  
Design Space ◽  
Contextual Information ◽  
Physical World ◽  
Sensor Data ◽  
Data Discovery ◽  
Information Interaction ◽  
Virtual Services ◽  
Computing Landscape

A changing computing landscape is expected to sense the physical world yet remain concealed within its very infrastructure to provide virtual services which are discreetly networked, omnipresent yet non-intrusive. Ambient Information Systems (AIS), permit a mode of expression that can easily exist at the level of subconscious realisation. This research focuses on the development of an Ambient Communication Experience (ACE) system. ACE is a synchronisation framework to provide co-ordinated connectivity across various environmentally distributed devices via sensor data discovery. The intention is to facilitate location-independent and application-responsive screening for the user, leading to the concept of technologically integrated spaces. The aim is to deliver contextual information without the need for direct user manipulation, and engagement at the level of peripheral perception.

scholarly journals Smart garbage collection management system

2018 ◽  
Vol 7 (2.7) ◽  
pp. 193
Author(s):  
Mounica Gaddam ◽  
Venkata Dileep Thatha ◽  
Srinivas Ravi Kavuluri ◽  
Gopi Krishna Popuri
Keyword(s):  
Waste Management ◽  
Solid Waste Management ◽  
Threshold Level ◽  
Truck Driver ◽  
Sensor Data ◽  
Raspberry Pi ◽  
Health Issues ◽  
The People ◽  
Collection Management System ◽  
Current Location

Waste management is necessary in today's world because, with the growing population, waste generated by the human is also increasing. Million Tons of waste is being produced by the people all over the world every day. If waste is not properly disposed off, it may lead to huge health issues and it may have adverse effects on our environment also. Among all, waste collection and transportation are one of the costliest stages in solid waste management. As the truck driver must go to each bin every single day and check whether the bin is full or not. If the bin is not full, it is not only waste of time but also wastes of fuel for truck and it also increases pollution due to smoke released from trucks, needs more men for checking all the bins in different routes. In this paper, we are going to propose a smart solution for this problem using the Internet of Things. We use an ultrasonic sensor to measure the size of the bin, and raspberry pi to process the information further. This sensor data will be sent to the cloud using Wi-Fi module of raspberry pi, from the cloud the data is sent to android app. When the trash inside the bin crosses the certain threshold level, that bin and its location are shown in the App using google maps, and the current location of the truck driver is detected, and shortest path is shown. By this the garbage bins can be emptied before the dustbin overflow.

scholarly journals An Automated IoT Visualization BIM Platform for Decision Support in Facilities Management

2018 ◽  
Author(s):  
Kai-Ming Chang ◽  
Ren-Jye Dzeng ◽  
Yi-Ju Wu
Keyword(s):  
Energy Saving ◽  
Automatic Transmission ◽  
Work Productivity ◽  
Spatial Relationship ◽  
Information Modeling ◽  
Sensor Data ◽  
Raspberry Pi ◽  
Multiple Perspectives ◽  
Fifth Dimension ◽  
Electric Equipment

Building information modeling (BIM) is the digital representation of physical and functional characteristics (such as geometry, spatial relationship, and geographic information) of a facility to support decisions during its life cycle. BIM has been extended beyond 3D geometrical representations in recent years, and now includes time as a fourth dimension and cost as a fifth dimension, as well as such other applications as virtual reality and augmented reality. The Internet of Things (IoT) has been increasingly applied in various products (smart homes, wearables) to enhance work productivity, living comfort, and entertainment. However, research addressing the integration of these two technologies (BIM and IoT) is still very limited, and has focused exclusively on the automatic transmission of sensor information to BIM models. This paper describes an attempt to represent and visualize sensor data in BIM with multiple perspectives in order to support complex decisions requiring interdisciplinary information. The study uses a university campus as an example and includes several scenarios, such as an auditorium with a dispersed audience and energy saving options for rooms with different functions (mechanical/electric equipment, classrooms, and laboratory). This paper also discusses the design of a common platform allowing communication among sensors with different protocols (Arduino, Raspberry Pi), the use of Dynamo to accept sensor data as input and automatically redraw visualized information in BIM, and how visualization may help in making energy-saving management decisions.

scholarly journals TEACHING STUDENTS TO WTHE USE OF MICROCOMPUTER IN TEACHING STUDENTS

2020 ◽  
Vol 69 (1) ◽  
pp. 387-397
Author(s):  
S.A. Nugmanova ◽  
◽  
М. Erbolat ◽  
Keyword(s):  
High School ◽  
High School Students ◽  
Visual Programming ◽  
Raspberry Pi ◽  
School Students ◽  
Computing Platform ◽  
Technical Specifications ◽  
Arduino Uno ◽  
Visual Programming Language ◽  
And Robotics

This article discusses the prerequisites for using the Arduino Uno brand of hardware and software, which are necessary when creating simple automation and robotics systems for non-professional users in teaching the basics of microcontroller robotics. The article discusses the capabilities of the Arduino hardware computing platform as applied to mechatronic complexes. A functional description and technical specifications are given using the Arduino UNO board as an example. A comparative analysis of the hardware of the most relevant boards has been compiled. The prospects for the use of the Arduino microprocessor platform for training and design in the field of physical process control are determined. The article compares various microcontrollers Arduino, Raspberry Pi, Lego Mindstroms. based on the analysis, it is concluded that Lego Mindstroms microcontrollers are the most convenient for teaching younger students, and for teaching middle and high school students - Arduino microcontrollers. Mindstorms microcontrollers are sold complete with instructions, peripherals, parts, and sensors. Their body protects against damage, and familiar to many children LEGO allows you to create various mechanisms and robots using a visual programming language. This set is easily mastered by primary and secondary school students. High school students can develop programming skills in the integrated Arduino environment.

scholarly journals DESENVOLVIMENTO DE UM DISPOSITIVO PARA APOIO AO ENSINO DE COMPUTAÇÃO E ROBÓTICA

2014 ◽  
Vol 6 (2) ◽  
pp. 71-85
Author(s):  
Rafael de Oliveira Maia ◽  
Francisco Assis da Silva ◽  
Mário Augusto Pazoti ◽  
Leandro Luiz de Almeida ◽  
Danillo Roberto Pereira
Keyword(s):  
Image Processing ◽  
Computer Science ◽  
Programming Language ◽  
Raspberry Pi ◽  
Writing Programs ◽  
Python Programming Language ◽  
Python Programming ◽  
And Robotics

In this work we proposed the development of an alternative device as a motivating element to learn computer science and robotics using the Raspberry PI and Arduino boards. The connections of all hardware used to build the device called Betabot are presented and are also reported the technologies used for programming the Betabot. An environment for writing programs to run at Betabot was developed. With this environment it is possible to write programs in the Python programming language, using libraries with functions specific to the device. With the Betabot using a webcam and through image processing search for patterns like faces, circles, squares and colors. The device also has functions to move servos and motors, and capture values returned by some kindsof sensors connected to communication ports. From this work, it was possible to develop a device that is easy to be manipulated and programmed, which can be used to support the teaching of computer science and robotics.

scholarly journals Smart Assistance for Asperger Syndrome using Raspberry Pi

2019 ◽  
Vol 9 (1) ◽  
pp. 2680-2683
Keyword(s):  
Internet Of Things ◽  
Asperger Syndrome ◽  
Weather Conditions ◽  
Raspberry Pi ◽  
And Robotics

This paper focuses on autism people who need smart assistance for their everyday survival. One of the most common Autism categories is Asperger syndrome. The motive of this project is to promulgate their tasks efficiently by detecting and recognizing their faces which will be stored in the database using Raspberry Pi.The “Smart Assistant for Asperger Syndrome using Raspberry Pi” involves IoT (Internet of Things) and Robotics. This mirror not only allows the users to plan their daily schedules, but also helps them to be updated with the environment such as weather conditions.

scholarly journals Elemental: An Open-Source Wireless Hardware and Software Platform for Building Energy and Indoor Environmental Monitoring and Control

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4017 ◽  
Author(s):  
Akram Syed Ali ◽  
Christopher Coté ◽  
Mohammad Heidarinejad ◽  
Brent Stephens
Keyword(s):  
Environmental Monitoring ◽  
Open Source ◽  
Environmental Quality ◽  
Printed Circuit Boards ◽  
Operating Conditions ◽  
Sensor Data ◽  
Raspberry Pi ◽  
Software Platform ◽  
Energy Usage ◽  
Efficient Operation

This work demonstrates an open-source hardware and software platform for monitoring the performance of buildings, called Elemental, that is designed to provide data on indoor environmental quality, energy usage, HVAC operation, and other factors to its users. It combines: (i) custom printed circuit boards (PCBs) with RFM69 frequency shift keying (FSK) radio frequency (RF) transceivers for wireless sensors, control nodes, and USB gateway, (ii) a Raspberry Pi 3B with custom firmware acting as either a centralized or distributed backhaul, and (iii) a custom dockerized application for the backend called Brood that serves as the director software managing message brokering via Message Queuing Telemetry Transport (MQTT) protocol using VerneMQ, database storage using InfluxDB, and data visualization using Grafana. The platform is built around the idea of a private, secure, and open technology for the built environment. Among its many applications, the platform allows occupants to investigate anomalies in energy usage, environmental quality, and thermal performance via a comprehensive dashboard with rich querying capabilities. It also includes multiple frontends to view and analyze building activity data, which can be used directly in building controls or to provide recommendations on how to increase operational efficiency or improve operating conditions. Here, we demonstrate three distinct applications of the Elemental platform, including: (1) deployment in a research lab for long-term data collection and automated analysis, (2) use as a full-home energy and environmental monitoring solution, and (3) fault and anomaly detection and diagnostics of individual building systems at the zone-level. Through these applications we demonstrate that the platform allows easy and virtually unlimited datalogging, monitoring, and analysis of real-time sensor data with low setup costs. Low-power sensor nodes placed in abundance in a building can also provide precise and immediate fault-detection, allowing for tuning equipment for more efficient operation and faster maintenance during the lifetime of the building.

scholarly journals Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1064
Author(s):  
I Nyoman Kusuma Wardana ◽  
Julian W. Gardner ◽  
Suhaib A. Fahmy
Keyword(s):  
Deep Learning ◽  
Air Quality ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Model ◽  
Sensor Data ◽  
Raspberry Pi ◽  
Learning Models ◽  
Proposed Model ◽  
Deep Learning Model

Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In this article, we address this challenge by: (1) designing a novel hybrid deep learning model for hourly PM2.5 pollutant prediction; (2) optimising the obtained model for edge devices; and (3) examining model performance running on the edge devices in terms of both accuracy and latency. The hybrid deep learning model in this work comprises a 1D Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to predict hourly PM2.5 concentration. The results show that our proposed model outperforms other deep learning models, evaluated by calculating RMSE and MAE errors. The proposed model was optimised for edge devices, the Raspberry Pi 3 Model B+ (RPi3B+) and Raspberry Pi 4 Model B (RPi4B). This optimised model reduced file size to a quarter of the original, with further size reduction achieved by implementing different post-training quantisation. In total, 8272 hourly samples were continuously fed to the edge device, with the RPi4B executing the model twice as fast as the RPi3B+ in all quantisation modes. Full-integer quantisation produced the lowest execution time, with latencies of 2.19 s and 4.73 s for RPi4B and RPi3B+, respectively.

scholarly journals An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4153
Author(s):  
Gabriel Signoretti ◽  
Marianne Silva ◽  
Pedro Andrade ◽  
Ivanovitch Silva ◽  
Emiliano Sisinni ◽  
...  
Keyword(s):  
Data Compression ◽  
Signal To Noise Ratio ◽  
Sensor Data ◽  
Wide Area Networks ◽  
Compression Algorithms ◽  
New Approach ◽  
Intelligent Devices ◽  
Real World Datasets ◽  
The Internet Of Things ◽  
Very High

Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.

scholarly journals IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS

2020 ◽  
Vol 2 (1) ◽  
pp. 13-25
Author(s):  
M.RAMANA REDDY
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Monitoring System ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Pollution Monitoring ◽  
Sensor Data ◽  
Raspberry Pi

Air pollution is the largest environmental and public health challenge in the world today. Air pollution leads to adverse effects on human health, climate and ecosystem. Air is getting polluted because of release of Toxic gases by industries, vehicular emissions and increased concentration of harmful gases and particulate matter in the atmosphere. In order to overcome these issues an IoT based air and sound pollution monitoring system is designed. To design this monitoring system, machine learning algorithms K-NN and Naive Bayes are used. K-Nearest Neighbour and Naive Bayes are machine learning algorithms used to predict the status of pollution present in the environment. In this system, analog to digital converter, global service mobile communication, temperature sensor, humidity sensor, carbon monoxide and sound sensors are interfaced with raspberry pi using serial cable. The sensor data is uploaded in thinkspeak (IoT) and webpage. This data is compared with the trained data to check accuracy. To calculate the accuracy of both algorithms, Python code is developed using python software tool.

Sign in / Sign up

Export Citation Format

Share Document