Why Do We Need Neural Networks for Enterprise IoT?

In recent days, neural networks have become a topic for discussion. The global neural network market's compound annual growth rate (CAGR) is expected to be 26.7 percent from 2021 to 2030. This means that new areas of application for them might appear soon. IoT is today's most fascinating and required technological solution for business. Around 61 percent of companies utilize IoT platforms, and we can anticipate the integration of neural networks into enterprise IoT solutions. This anticipation raises many questions, like what gets such collaboration and how to prepare it. Can we optimize the IoT ecosystem using neural networks, and who will approach such solutions?

"The global neural network market's compound annual growth rate (CAGR) is expected to be 26.7 percent from 2021 to 2030."

-Ritesh Sutaria

Understanding a Neural Network

An artificial neural network (ANN) is a network of artificial neurons striving to simulate the analytical mechanisms taken by the human brain. This type of artificial intelligence includes a range of algorithms that can "learn" from their own experience and improve themselves, which is very different from classical algorithms that are programmed to resolve only specific tasks. Thus, with time, the neural network will remain pertinent and keep on improving. With the proper implementation, enterprise internet of things (eIoT) and ANN can offer businesses the most valuable things: precise analytics and forecasts.

In general, it is not possible to compare both. Enterprise IoT is a system that needs software for data analysis, whereas ANN is a component that needs a large amount of data to be operational. Their team naturally controls the analytical tasks; therefore, high-level business tasks are performed most effectively, reducing costs, automating processes, finding new revenue sources, and more.

In the Internet of Things ecosystem, neural networks help in two areas above all:

1. Data acquisition via ANN-based machine vision
2. Advanced-data analysis

If it needs significant investments to execute ANN in big data analytics solutions, neural network image processing can decrease the cost of the IoT solution. Thus, neural networks improve enterprise IoT solutions, enhance their value, and speed up global adoption.

Enhanced Enterprise IoT Solutions with Neural Networks

IoT-Based Visual Control

The IoT ecosystem begins with data collection. Data quality impacts the accuracy of the ultimate prediction. If you implement visual control in your production processes, neural networks can boost the quality of products by superseding outdated algorithms. Also, they will optimize the eIoT solution. Conventional machine vision systems are pricey as they require the highest resolution cameras to catch minor defects in a product. They come with complex specific software that fails to respond to immediate changes. Neural networks within machine vision systems can diminish camera requirements, self-learn your data, and automate high-speed operations.

Indeed, industrial cameras use large-format global shutter sensors having high sensitivity and resolution to develop the highest quality images. Nevertheless, a well-trained ANN starts to identify images with time. It allows them to reduce the technical needs for the camera and ultimately cuts the final cost of the enterprise IoT implementation. You cannot compromise the quality of images to detect small components like parts in circuit boards; however, it is manageable for printing production, completeness checking, or food packaging.

After training, neural networks use massive amounts of data to identify objects from the images. It enables you to customize the eIoT solution and train the ANN to operate specifically with your product by processing your images. For example, convolutional neural networks are utilized actively in the healthcare industry to detect X-rays and CT scans. The outcome offered by such custom systems is more precise than conventional ones. The capability to process information at high speeds permits the automation of production processes. When the problem or defect is caught, neural networks promptly report it to the operator or launch an intelligent reaction, like automating sorting. Hence, it allows real-time detection and rejection of defective production.

Data Analytics for Enterprise IoT

Today, neural networks allow businesses to grab advantages like predictive maintenance, new revenue flows, asset management, and more. It is possible via deep neural networks (DNN) and the deep Learning (DL) method involving multiple layers for data processing. They detect hidden data trends and valuable information from a significant dataset by employing classification, clustering, and regression. It results in effective business solutions and the facilitation of business applications.

In comparison to traditional models, DL manages with the attributes that are expected for IoT data:

1. Assess the time of taking measurements.
2. Resist the high noise of the enterprise IoT data.
3. Conduct accurate real-time analysis.
4. Determine heterogeneous and discordant data.
5. Process a large amount of data

In practice, this implies that you don't require middle solutions to deliver and sort the data in the cloud or to analyze them in real time.

DNN Model Use Cases

#1: Healthcare

Today, it has become easy to predict disease using AI-based IoT systems, and this technology is developing for further improvements. For instance, the latest invention based on the neural network can detect the risk of heart attacks by up to 94.8 percent. DNN is also helpful in disease detection: the spectrogram of a person's voice received using IoT devices can identify voice pathologies after DNN processing. In general, ANN-based IoT health monitoring systems' accuracy is estimated to be above 85 percent.

#2: Power Consumption

DL systems in eIoT have provided results in power demand prediction based on power price forecasting, consumption data, anomaly, power theft detection, and leak detection. Smart meter data analysis permits you to calculate consumption, determine the unusual usage of electricity, and predict with an accuracy of more than 95 percent, which will help you to adjust energy consumption.

#3: Manufacturing

Neural networks help to use the most in-demand IoT service among manufacturers properly: predictive equipment maintenance. It was ascertained to be a workable practice for mechanical and electrical systems. This network provides accurate real-time status monitoring and predicts proper life rest. Another best example is the recognition of employee activity by taking readings and following in-depth analysis.

#4: Transportation & Logistics

Deep Learning has made smart transportation systems possible. It offers better traffic congestion management by processing travel time, speed, weather, and occupational parking forecasting. Analytical reports based on vehicle data help to discover dangerous driving and possible issues before the failure happens.

ANN for Enterprise IoT

Up until now, research in the field of ANNs been very active, and we cannot foretell all the advantages or pitfalls these solutions will convey. Neural networks find out correlations, models, and trends better than other algorithms. The IoT ecosystem's data will become more extensive, complex, and diverse with time. So, the development of neural networks is the future of IoT. Neural networks suit the IoT ecosystem architecture, substituting alternative solutions with significant advantages and are essential for industrial image processing. Additionally, progressive ANN-based data analytics gets the high-level business value of enterprise IoT solutions.

We cannot conclude it is an affordable solution, but the advantages are priceless if the IoT ecosystem is executed accurately. Therefore, if you are provided with a neural network as one of the opportunities for executing your idea within the IoT ecosystem, give it a chance. You never know, this solution will become a must-have in the coming years.