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

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.

Synchronization-Free Multivariate Statistical Process Control for Online Monitoring of Batch Process Evolution

Synchronization of variable trajectories from batch process data is a delicate operation that can induce artifacts in the definition of multivariate statistical process control (MSPC) models for real-time monitoring of batch processes. The current paper introduces a new synchronization-free approach for online batch MSPC. This approach is based on the use of local MSPC models that cover a normal operating conditions (NOC) trajectory defined from principal component analysis (PCA) modeling of non-synchronized historical batches. The rationale behind is that, although non-synchronized NOC batches are used, an overall NOC trajectory with a consistent evolution pattern can be described, even if batch-to-batch natural delays and differences between process starting and end points exist. Afterwards, the local MSPC models are used to monitor the evolution of new batches and derive the related MSPC chart. During the real-time monitoring of a new batch, this strategy allows testing whether every new observation is following or not the NOC trajectory. For a NOC observation, an additional indication of the batch process progress is provided based on the identification of the local MSPC model that provides the lowest residuals. When an observation deviates from the NOC behavior, contribution plots based on the projection of the observation to the best local MSPC model identified in the last NOC observation are used to diagnose the variables related to the fault. This methodology is illustrated using two real examples of NIR-monitored batch processes: a fluidized bed drying process and a batch distillation of gasoline blends with ethanol.

A discrete event simulation-based methodology for building a digital twin of patient pathways in the hospital for near real-time monitoring and predictive simulation

Background: Discrete Event Simulation (DES) is one of the many tools and methods used in the analysis and improvement of healthcare services. Indeed, DES provides perhaps the most powerful and intuitive method for analyzing, evaluating, and improving complex healthcare systems. This paper highlights the process of developing a Digital Twin (DT) framework based on online DES to run the DES model in parallel with the real world in real-time. Methods: This paper suggests a new methodology that uses DES connected to the Internet of Things (IoT) devices to build a DT platform of patient pathways in a hospital for near real-time monitoring and predictive simulation. An experimental platform that mimics the behavior of a hospital has been used to validate this methodology. Results: The application of the proposed methodology allowed us to test the monitoring functionality in the DT. Therefore, we noticed that the DT behaves exactly as the emulator does in near real-time, we also tested the prediction functionality and we noticed that the DT provides us with a proactive overview for the near future of the patient pathways. The predictive functionality of this DT must be improved depending on the various reasons mentioned in this article. Conclusions: This paper presents a new methodology called HospiT'Win that uses DES and IoT devices to develop a DT of patient pathways in hospitals. This DT consists of two real-time models, a DT for Monitoring (DTM) and a DT for Predicting (DTP). An experimental platform with an emulator of a real hospital was used to validate this methodology before connecting to the real hospital. In the DTP, "dynamic" empirical distributions were used to perform a predictive simulation for the near future. In future research, some additional features and machine learning algorithms will be used to improve the proposed DT models.