Industrial Monitoring Using Internet of Things -A MQTT Paradigm

Internet of things (IOT) has taken a very pervasive role in our technological advancement. Today we find development in medical, schools, industrial sectors using IOT to enhance their operations. IOT is used in medical to gather information about patient’s health records, in schools’ teachers are able to track attendance of students in the campus, in industries motor controls, maintenance, and predictive fault analysis are some of application. The architecture of IOT is setup in such a way that sensors and actuators are connected to the internet. The devices on which interface to internet are small embedded modules such as microcontroller which have limited resources and processing power at the edge. Hence an efficient and reliable communication protocol is needed which fulfills the design criteria. MQTT is implemented using client and broker network entities. In this paper a hardware system is developed which tracks and monitor the parameters like temperature, RPM, vibration, load current and voltage of induction motor. A Dashboard is developed which illustrates the various parameters on IOT cloud platform which can be accessed remotely. Keywords: MQTT-Message Queuing Telemetry Transport, edge computing, Industrial IOT.

VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance

The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment. In crucial situations, a model that can offer meaningful explanations of its decision-making is essential. In industrial facilities, the equipment's well-timed maintenance is vital to ensure continuous operation to prevent money loss. Using machine learning, predictive and prescriptive maintenance attempt to anticipate and prevent eventual system failures. This paper introduces a visualisation tool incorporating interpretations to display information derived from predictive maintenance models, trained on time-series data.

Elastic O-RAN Slicing for Industrial Monitoring and Control: A Distributed Matching Game and Deep Reinforcement Learning Approach

In this work, we design an elastic open radio access network (O-RAN) slicing for the industrial Internet of things (IIoT). Unlike IoT, IIoT poses additional challenges such as severe communication environment, network-slice resource demand variations, and on-time information update from the IIoT devices during industrial production. First, we formulate the O-RAN slicing problem for on-time industrial monitoring and control where the objective is to minimize the cost of fresh information updates (i.e., age of information (AoI)) from the IIoT devices (i.e., sensors) while maintaining the energy consumption of those devices with the energy constraint as well as O-RAN slice isolation constraints. Second, we propose the intelligent ORAN framework based on game theory and machine learning to mitigate the problem’s complexity. We propose a two-sided distributed matching game in the O-RAN control layer that captures the IIoT channel characteristics and the IIoT service priorities to create IIoT device and small cell base station (SBS) preference lists. We then employ an actor-critic model with a deep deterministic policy gradient (DDPG) in the O-RAN service management layer to solve the resource allocation problem for optimizing the network slice configuration policy under time varying slicing demand. While the matching game helps the actor-critic model, the DDPG enforces the long-term policy-based guidance for resource allocation that reflects the trends of all IIoT devices and SBSs satisfactions with the assignment. Finally, the simulation results show that the proposed solution enhances the performance gain for the IIoT services by serving an average of 50% and 43.64% more IIoT devices than the baseline approaches. <br>

Elastic O-RAN Slicing for Industrial Monitoring and Control: A Distributed Matching Game and Deep Reinforcement Learning Approach

In this work, we design an elastic open radio access network (O-RAN) slicing for the industrial Internet of things (IIoT). Unlike IoT, IIoT poses additional challenges such as severe communication environment, network-slice resource demand variations, and on-time information update from the IIoT devices during industrial production. First, we formulate the O-RAN slicing problem for on-time industrial monitoring and control where the objective is to minimize the cost of fresh information updates (i.e., age of information (AoI)) from the IIoT devices (i.e., sensors) while maintaining the energy consumption of those devices with the energy constraint as well as O-RAN slice isolation constraints. Second, we propose the intelligent ORAN framework based on game theory and machine learning to mitigate the problem’s complexity. We propose a two-sided distributed matching game in the O-RAN control layer that captures the IIoT channel characteristics and the IIoT service priorities to create IIoT device and small cell base station (SBS) preference lists. We then employ an actor-critic model with a deep deterministic policy gradient (DDPG) in the O-RAN service management layer to solve the resource allocation problem for optimizing the network slice configuration policy under time varying slicing demand. While the matching game helps the actor-critic model, the DDPG enforces the long-term policy-based guidance for resource allocation that reflects the trends of all IIoT devices and SBSs satisfactions with the assignment. Finally, the simulation results show that the proposed solution enhances the performance gain for the IIoT services by serving an average of 50% and 43.64% more IIoT devices than the baseline approaches. <br>

Indonesia Network Infrastructures and Workforce Adequacy to Implement Machine Learning for Large-Scale Manufacturing

Technological development prior to industrial revolution 4.0 incentivized manufacturing industries to invest into digital industry with the aim of increasing the capability and efficiency in manufacturing activity. Major manufacturing industry has begun implementing cyber-physical system in industrial monitoring and control. The system itself will generate large volumes of data. The ability to process those big data requires algorithm called machine learning because of its ability to read patterns of big data for producing useful information. This study conducted on premises of Indonesia’s current network infrastructure and workforce capability on supporting the implementation of machine learning especially in large-scale manufacture. That will be compared with countries that have a positive stance in implementing machine learning in manufacturing. The conclusions that can be drawn from this research are Indonesia current infrastructure and workforce is still unable to fully support the implementation of machine learning technology in manufacturing industry and improvements are needed.

GUI based Industrial Monitoring and Control System

The main aim of this project is to develop a Graphical User Interface (GUI) based system to monitor and control the industrial process. The proposed protocol os user-friendly and it is more efficient due to the incorporation of a simple GUI. Moreover, the proposed system is installed to collect the valuable information.

Molecularly Imprinted Polymers for Chemical Sensing: A Tutorial Review

The field of molecularly imprinted polymer (MIP)-based chemosensors has been experiencing constant growth for several decades. Since the beginning, their continuous development has been driven by the need for simple devices with optimum selectivity for the detection of various compounds in fields such as medical diagnosis, environmental and industrial monitoring, food and toxicological analysis, and, more recently, the detection of traces of explosives or their precursors. This review presents an overview of the main research efforts made so far for the development of MIP-based chemosensors, critically discusses the pros and cons, and gives perspectives for further developments in this field.