scholarly journals DESIGN AND IMPLEMENT AN IoT CLOUD FOR FIELD SURVEY BASED ON COAP PROTOCOL

2021 ◽  
Vol 25 (Special) ◽  
pp. 1-181-1-188
Author(s):  
Hadeel H. Azeez ◽  
◽  
Mahmood Z. Abdullah ◽  
Keyword(s):  
Real Time ◽  
Smart Cities ◽  
Low Cost ◽  
Raspberry Pi ◽  
New Schools ◽  
Time Data ◽  
Elapsed Time ◽  
Secure Storage ◽  
Cloud Server ◽  
Secure Place

Urban planning for smart cities requires collecting big real-time data, specially geolocation data from GPS sensors to use in many services like finding the best location for new schools so this data must be stored in a secure place with low cost and because the storage services offered from different cloud providers like Google, Amazon Web Service, Azure, etc., is not free. For these reasons, this study proposed Internet of Things (IoT) cloud architecture using Raspberry Pi model B+ as a cloud server with MySQL database services to provide free and secure storage at a low cost. The main contributions of this study lie in the Constrained Application Protocol (CoAP) server hosted in raspberry Pi to offer services in the proposed architecture of the IoT cloud with different scenarios to know the proposed architecture's ability for handling many user requests per second in terms of standard division, average elapsed time, error rate, throughput, and a number of real stored data in the database. AS a result, the proposed architecture handled 150 requests per second in real-time with an elapsed time of 1186 milliseconds without any error or data loss.

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.

scholarly journals DNS-Based Dynamic Authentication for Microservices in IoT

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1233
Author(s):  
Daniel Sánchez ◽  
Andrés López ◽  
Florina Mendoza ◽  
Patricia Arias  Cabarcos
Keyword(s):  
Data Collection ◽  
Ubiquitous Computing ◽  
Real Time ◽  
Low Cost ◽  
Fog Computing ◽  
Time Data ◽  
Elapsed Time ◽  
Real Time Data ◽  
Nomadic Users ◽  
Iot Devices

IoT devices provide with real-time data to a rich ecosystems of services and applications that will be of uttermost importance for ubiquitous computing. The volume of data and the involved subscribe/notify signaling will likely become a challenge also for access and core netkworks. Designers may opt for microservice architectures and fog computing to address this challenge while offering the required flexibility for the main players of ubiquitous computing: nomadic users. Microservices require strong security support for Fog computing, to rely on nodes in the boundary of the network for secure data collection and processing. IoT low cost devices face outdated certificates and security support, due to the elapsed time from manufacture to deployment. In this paper we propose a solution based on microservice architectures and DNSSEC, DANE and chameleon signatures to overcome these difficulties. We will show how trap doors included in the certificates allow a secure and flexible delegation for off-loading data collection and processing to the fog. The main result is showing this requires minimal manufacture device configuration, thanks to DNSSEC support.

scholarly journals Design of an IoT based Real Time Environment Monitoring System using Legacy Sensors

2018 ◽  
Vol 210 ◽  
pp. 03008
Author(s):  
Aparajita Das ◽  
Manash Pratim Sarma ◽  
Kandarpa Kumar Sarma ◽  
Nikos Mastorakis
Keyword(s):  
Air Pollution ◽  
Light Intensity ◽  
Real Time ◽  
Environmental Conditions ◽  
Low Cost ◽  
Environment Monitoring ◽  
Time Data ◽  
Graphical Information ◽  
Real Time Data ◽  
Sun Light

This paper describes the design of an operative prototype based on Internet of Things (IoT) concepts for real time monitoring of various environmental conditions using certain commonly available and low cost sensors. The various environmental conditions such as temperature, humidity, air pollution, sun light intensity and rain are continuously monitored, processed and controlled by an Arduino Uno microcontroller board with the help of several sensors. Captured data are broadcasted through internet with an ESP8266 Wi-Fi module. The projected system delivers sensors data to an API called ThingSpeak over an HTTP protocol and allows storing of data. The proposed system works well and it shows reliability. The prototype has been used to monitor and analyse real time data using graphical information of the environment.

scholarly journals Pengendalian Kualitas Air untuk Tanaman Hidroponik Menggunakan Raspberry Pi dan Arduino Uno

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Ryan Ganesha Calibra ◽  
Irfan Ardiansah ◽  
Nurpilihan Bafdal
Keyword(s):  
Water Quality ◽  
Growth And Development ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Raspberry Pi ◽  
Time Data ◽  
Soil Nutrition ◽  
Remote System ◽  
Total Dissolved Solid

Water quality is very important for plant’s growth and development. Some of the important part of the water qualities are TDS(Total Dissolved Solid), EC(Electrical Conductivity), pH(Acidity). Cultivation inside a greenhouse provides some benefits but also have some deficiency, such as lack of soil nutrition because most plants inside greenhouse uses non soil growing media. To overcome the deficiency, An automated and remote system is needed to ease the controlling of water quality and nutrition feeding to the plant. This study aims to create low-cost greenhouse water quality monitoring that automatically display the real time data on a website. This research is done by using an engineering design methods. This system can be integrated with auto-pot watering system . The result shows that the system can adjust the TDS and pH as programmed, which are TDS= 1000-1200, and pH =5.5-6.5(these are recommended needs for Tomato plant). The TDS sensor in this reseach have the limitation of reading 0~1500ppm.

System for Detecting Driver Alertness while Driving

2021 ◽  
Vol 9 (VI) ◽  
pp. 2095-2099
Author(s):  
Prajwal Chandrakant Sapkal
Keyword(s):  
Aspect Ratio ◽  
Real Time ◽  
Raspberry Pi ◽  
Time Data ◽  
Facial Landmarks ◽  
Remote Location ◽  
Python Language ◽  
Falling Asleep ◽  
Time Basis ◽  
Main Component

In this project, we are going to present a system for sleep detection alarm to monitor the driver, based on the real time surveillance and alert him as well as post it at remote location whenever it’s necessary using cloud platform. This device is to be developed using the Raspberry Pi, Open CV library and camera module. The required coding part of the project will be done using Python language. The main component of the project will be pretrained landmark detector as a software part. It identifies 68 points on the human face. The Dlib’s landmark will detect 68 facial landmarks which enables us to extract the various facial structures using simple Python array slices. The facial landmarks of fully closed eye and a fully opened eye will be first plotted. This data is further processed and tested with some results which will give the information about driver’s alertness. Once the facial landmarks associated with an eye are determined, we can apply the Eye Aspect Ratio (EAR) algorithm. In our case, we’ll be monitoring the eye aspect ratio to see if the values of the facial landmarks, thus implying that the driver/user has closed their eyes or distracted from driving or yawn. Once implemented, our algorithm will start by localising the facial landmarks on real time basis. We can then will be able to monitor the eye aspect ratio to determine if the eyes are close or nearly close which will be the indicator for driver is falling asleep. And then finally raising an alarm if the eye aspect ratio is below a pre-defined threshold for a sufficiently long amount of time. The alarm will be loud enough to wake up the driver and bring back his attention. At the same time data is passed to remote location using cloud whenever it’s necessary.

Cloud-Based Big Data Analysis Tools and Techniques Towards Sustainable Smart City Services

2021 ◽  
pp. 63-90
Author(s):  
Suresh P. ◽  
Keerthika P. ◽  
Sathiyamoorthi V. ◽  
Logeswaran K. ◽  
Manjula Devi R. ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Smart City ◽  
Traffic Management ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Streaming Data ◽  
City Development ◽  
Time Data ◽  
Public And Private

Cloud computing and big data analytics are the key parts of smart city development that can create reliable, secure, healthier, more informed communities while producing tremendous data to the public and private sectors. Since the various sectors of smart cities generate enormous amounts of streaming data from sensors and other devices, storing and analyzing this huge real-time data typically entail significant computing capacity. Most smart city solutions use a combination of core technologies such as computing, storage, databases, data warehouses, and advanced technologies such as analytics on big data, real-time streaming data, artificial intelligence, machine learning, and the internet of things (IoT). This chapter presents a theoretical and experimental perspective on the smart city services such as smart healthcare, water management, education, transportation and traffic management, and smart grid that are offered using big data management and cloud-based analytics services.

scholarly journals Open-Source Electronics Platforms as Enabling Technologies for Smart Cities: Recent Developments and Perspectives

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 404 ◽  
Author(s):  
Daniel Costa ◽  
Cristian Duran-Faundez
Keyword(s):  
Open Source ◽  
Smart City ◽  
Smart Cities ◽  
Low Cost ◽  
Raspberry Pi ◽  
Computational Power ◽  
Different Types ◽  
Recent Developments ◽  
Programmable Devices ◽  
Hardware Platforms

With the increasing availability of affordable open-source embedded hardware platforms, the development of low-cost programmable devices for uncountable tasks has accelerated in recent years. In this sense, the large development community that is being created around popular platforms is also contributing to the construction of Internet of Things applications, which can ultimately support the maturation of the smart-cities era. Popular platforms such as Raspberry Pi, BeagleBoard and Arduino come as single-board open-source platforms that have enough computational power for different types of smart-city applications, while keeping affordable prices and encompassing many programming libraries and useful hardware extensions. As a result, smart-city solutions based on such platforms are becoming common and the surveying of recent research in this area can support a better understanding of this scenario, as presented in this article. Moreover, discussions about the continuous developments in these platforms can also indicate promising perspectives when using these boards as key elements to build smart cities.

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.

Geographic information science III: GIScience, fast and slow – Why faster geographic information is not always smarter

2018 ◽  
Vol 44 (1) ◽  
pp. 129-138 ◽  
Author(s):  
Harvey J. Miller
Keyword(s):  
Real Time ◽  
Information Science ◽  
Smart Cities ◽  
Low Cost ◽  
Geographic Information ◽  
Unintended Consequences ◽  
Navigation Systems ◽  
Rebound Effects ◽  
Geographic Knowledge ◽  
Geographic Data

The growing maturity and deployment of low-cost georeferenced sensors, navigation systems, fast wireless communication, cyberinfrastructure and the Internet of Things (IoT) is accelerating the speed of geographic data flowing from the environment and our capabilities for reacting quickly to geographic information, often automatically and in real-time. This is leading to the rise of real-time GIS and smart cities technologies. While reacting quickly to changing circumstances has value, there are potentials for unintended consequences and rebound effects resulting from our inability to build geographic knowledge quickly and the selective acceleration of societal processes. This report discusses why these unintended outcomes may occur, and suggests technical and scientific approaches for understanding and managing the potential impacts of fast geographic data.

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