scholarly journals An internet of things framework for real-time aquatic environment monitoring using an Arduino and sensors

2022 ◽  
Vol 12 (1) ◽  
pp. 826
Author(s):  
Md. Monirul Islam ◽  
Mohammad Abul Kashem ◽  
Jia Uddin
Keyword(s):  
Internet Of Things ◽  
Embedded System ◽  
Real Time ◽  
Aquatic Environment ◽  
Environmental Parameters ◽  
Fish Farming ◽  
Effective Control ◽  
Environment Monitoring ◽  
Real Time Monitoring ◽  
The Real

Aquaculture is the farming of aquatic organisms in natural, controlled marine and freshwater environments. The real-time monitoring of aquatic environmental parameters is very important in fish farming. Internet of things (IoT) can play a vital role in the real-time monitoring. This paper presents an IoT framework for the efficient monitoring and effective control of different aquatic environmental parameters related to the water. The proposed system is implemented as an embedded system using sensors and an Arduino. Different sensors including pH, temperature, and turbidity, ultrasonic are placed in cultivating pond water and each of them is connected to a common microcontroller board built on an Arduino Uno. The sensors read the data from the water and store it as a comma-separated values (CSV) file in an IoT cloud named ThingSpeak through the Arduino microcontroller. To validate the experiment, we collected data from 5 ponds of various sizes and environments. After experimental evaluation, it was observed among 5 ponds, only three ponds were perfect for fish farming, where these 3 ponds only satisfied the standard reference values of pH (6.5-8.5), temperature (16-24 °C), turbidity (below 10 ntu), conductivity (970-1825 μS/cm), and depth (1-4) meter. At the end of this paper, a complete hardware implementation of this proposed IoT framework for a real-time aquatic environment monitoring system is presented.

scholarly journals Zigbee and Long-Range Architecture Based Monitoring System for Oil Pipeline Monitoring with the Internet of Things

2021 ◽  
Vol 13 (18) ◽  
pp. 10226
Author(s):  
Rajesh Singh ◽  
Mohammed Baz ◽  
Ch. Lakshmi Narayana ◽  
Mamoon Rashid ◽  
Anita Gehlot ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Monitoring System ◽  
Critical Parameters ◽  
The Internet ◽  
Real Time Monitoring ◽  
The Real ◽  
Oil Pipeline ◽  
Pipeline Monitoring ◽  
The Internet Of Things

Oil pipeline monitoring is having a significant role in minimizing the impact on the environment and humans during pipeline accidents. The real-time monitoring of oil pipelines empowers the authorities to have continuous supervision of the oil pipeline. The Internet of Things (IoT) provides an opportunity for realizing the real-time monitoring system by deploying the IoT-enabled end devices on the oil pipeline. In this study, we propose a hybrid architecture based on 2.4 GHz-based Zigbee and LoRa communication for oil pipeline monitoring. Moreover, customized end devices and LoRa based gateway are designed and implemented for sensing the critical parameters of an oil pipeline. Here, we have performed the simulation of ZigBee communication on the OPNET simulator for evaluating the parameters such as packet delivery ratio (PDR), retransmission attempts, throughput, medium access (MAC) queue size, and queue delay. Furthermore, the distinct evaluation metrics of LoRa such as bit rate, link budget, and receiver sensitivity are also included. Finally, a real-time experiment is implemented with customized end devices and a gateway for evaluating the proposed architecture. In the real-time experiment, the devices and gateway are logging the pressure sensory data into the Cayenne cloud.

Observing the real time formation of phosphine-ligated gold clusters by electrospray ionization mass spectrometry

2017 ◽  
Vol 19 (26) ◽  
pp. 17187-17198 ◽  
Author(s):  
Marshall R. Ligare ◽  
Grant E. Johnson ◽  
Julia Laskin
Keyword(s):  
Mass Spectrometry ◽  
Electrospray Ionization ◽  
Real Time ◽  
Electrospray Ionization Mass Spectrometry ◽  
Gold Cluster ◽  
Gold Clusters ◽  
Ionization Mass Spectrometry ◽  
Ionization Mass ◽  
Real Time Monitoring ◽  
The Real

Real-time monitoring of the gold cluster synthesis by electrospray ionization mass spectrometry reveals distinct formation pathways for Au8, Au9 and Au10 clusters.

scholarly journals AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiang Yu ◽  
Chun Shan ◽  
Jilong Bian ◽  
Xianfei Yang ◽  
Ying Chen ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
Real Time ◽  
Detection Method ◽  
Edge Computing ◽  
Sensor Data ◽  
Computing Environment ◽  
The Real ◽  
Edge Node ◽  
Model Based

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

A Novel Hash Chain-Based Data Availability Monitoring Method for Off-site Disaster Recovery Architecture

2021 ◽  
pp. 2150294
Author(s):  
Neng Huang ◽  
Junxing Zhu ◽  
Chaonian Guo ◽  
Shuhan Cheng ◽  
Xiaoyong Li
Keyword(s):  
Information Systems ◽  
Real Time ◽  
Data Center ◽  
Disaster Recovery ◽  
Distributed Storage ◽  
Data Availability ◽  
Data Consistency ◽  
Real Time Monitoring ◽  
The Real ◽  
Hash Chain

With the rapid development of mobile Internet, there is a higher demand for the real-time, reliability and availability of information systems and to prevent the possible systemic risks of information systems, various business consistency standards and regulatory guidelines have been published, such as Recovery Time Object (RTO) and Recovery Point Object (RPO). Some of the current related researches focus on the standards, methods, management tools and technical frameworks of business consistency, while others study the data consistency algorithms in the cases of large data, cloud computing and distributed storage. However, few researchers have studied on how to monitor the data consistency and RPO of production-disaster recovery, and what architecture and technology should be applied in the monitoring. Moreover, in some information systems, due to the complex structures and distributions of data, it is difficult for traditional methods to quickly detect and accurately locate the first error data. Besides, due to the separation of production data center (PDC) and disaster recovery data center (DRDC), it is difficult to calculate the data difference and RPO between the two centers. This paper first discusses the architecture of remote distributed DRDCs. The architecture can make the disaster recovery (DR) system always online and the data always readable, and support the real-time monitoring of data availability, consistency as well as other related indicators, in this way to make DRDC out-of-the-box in disasters. Second, inspired by blockchain, this paper proposes a method to realize real-time monitoring of data consistency and RTO by building hash chains for PDC and DRDC. Third, this paper evaluates the hash chain operations from the algorithm time complexity, the data consistency, and the validity of RPO monitoring algorithms and since DR system is actually a kind of distributed system, the proposed approach can also be applied to the data consistency detection and data difference monitoring in other distributed systems.

IoT Device Onboarding, Monitoring, and Management

2021 ◽  
pp. 227-255
Author(s):  
Selvaraj Kesavan ◽  
Senthilkumar J. ◽  
Suresh Y. ◽  
Mohanraj V.
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Secure Communication ◽  
Mutual Authentication ◽  
Modern World ◽  
Cloud Platform ◽  
Privacy And Security ◽  
The Real ◽  
Healthy Environment

In establishing a healthy environment for connectivity devices, it is essential to ensure that privacy and security of connectivity devices are well protected. The modern world lives on data, information, and connectivity. Various kinds of sensors and edge devices stream large volumes of data to the cloud platform for storing, processing, and deriving insights. An internet of things (IoT) system poses certain difficulties in discretely identifying, remotely configuring, and controlling the devices, and in the safe transmission of data. Mutual authentication of devices and networks is crucial to initiate secure communication. It is important to keep the data in a secure manner during transmission and in store. Remotely operated devices help to monitor, control, and manage the IoT system efficiently. This chapter presents a review of the approaches and methodologies employed for certificate provisioning, device onboarding, monitoring, managing, and configuring of IoT systems. It also examines the real time challenges and limitations in and future scope for IoT systems.

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