In recent days, neural networks have become a topic for discussion. But the question still needs to be solved- How can it affect our world today and tomorrow?

The global neural network market's compound annual growth rate (CAGR) is expected to be 26.7% from 2021 to 2030. This means that new areas of application for them might appear soon. The Internet of Things that is IoT, is today's most fascinating and required technological solution for business. Around 61% 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?

What do you understand by a neural network, and how is it beneficial for enterprise IoT?

An artificial neural network that is 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 the business 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, etc.

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

• Data acquisition via ANN-based machine vision
• 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.

Which solutions within enterprise IoT can be enhanced using 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. Besides this, 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 on your data
• 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.

An exclusive example of how ANN is utilized for edge and fog computing. As per PSA, a neural network executed in a machine vision system permits lowering the number of defects by 90% in half a year, whereas production costs are decreased by 30%. Prospective areas for ANN in IoT visual control are quality assurance, sorting, production, collecting, marking, traffic control, and ADAS.

Big data advanced analytics for enterprise IoT:

Today, neural networks allow businesses to grab advantages like predictive maintenance, new revenue flows, asset management, etc. 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. For example, full-cycle metallurgical enterprises can execute one solution to analyze the variable and unstructured data from metal mining, smelting, and final manufacturing products. Airplanes generate about 800TB of data per hour, making it impossible to process it all ideally using conventional analytical systems.

Today, DNN models are successful in the following enterprise IoT applications. 

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%. 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%.

Power consumption:

DL systems in the enterprise Internet of Things 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%, which will help you to adjust energy consumption.

Manufacturing:

Neural networks help to use the most demanded 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.

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.

As we know, the previous industries generate heterogeneous data. Therefore, the potential of ANN analytics within EIoT will be unlocked for multiple complicated systems.

When to consider ANN for enterprise IoT:

Till now, research in the field of ANNs been very active, and we cannot foretell all the advantages or pitfalls these solutions will convey. No doubt, 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.

For now, we can look into the following features of neural networks for enterprise IoT:

• They suit the IoT ecosystem architecture, substituting alternative solutions with significant advantages.
• Essential for industrial image processing.
• Progressive ANN-based data analytics gets the high-level business value of the enterprise IoT solutions – improves productivity, and exactness, boosts sales, and produces informed business decisions.
• Training the ANN requires time and expenditure but will become fully customizable.
• 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.

Your home security system. Air condition system. Your car. Why, even your coffee maker. Almost every imagine digital appliance is now connected to the Internet. The era of connected things has arrived.

IoT is no longer a science project that businesses are putting off for the future. It is a promise to a future that must be leveraged now. In fact, today, it is more difficult to find a coffee-maker or any home appliance without Wifi or Bluetooth connectivity. Not just at homes, even at corporations, connected devices has become a serious boardroom topic. According to DigiCert’s State of IoT Security survey 2018, 83% of organizations say the Internet of Things (IoT) is important to business today, and 92% say it will be in two years.

IoT can bring to businesses several benefits like improved operational efficiency, new revenue channels, business agility, and enhanced customer experience.

However, there are enterprise concerns that dwarf the possibility of gaining these benefits.

Among the top 4 enterprise concerns for IoT are security and privacy.

Source: DigiCert’s State of IoT Security survey 2018

How the Internet of Things can become the Internet of ‘Threats’

If not controlled, secured and monitored, the Internet of Things can go from smart connected things to a web of connected threats. Here are some ways how connected devices can go rogue.

#1 The connected risk of BYOD

Global corporations are losing no time in enabling their employees with BYOD (Bring Your Own Device) and WFH Work From Home working models. Although these working models amplify productivity, they also carry with them the risk of IoT.

For instance, an insecure connected device at an employee’s home can be hacked into by a hacker thereby gaining access to the office system. If the employee has failed to take adequate security measures for the office gadgetry, then it leaves the ground open for the hacker to seed an infectious malware, virus or anything malicious into the office network. That is the connected risk of BYOD which IoT creates.

#2 DDoS attacks

Source: DigiCert’s IoT Security Infographic

Do you know that insecure IoT devices can take down cities? IoT botnets combined with DDoS attacks can bring connected urban infrastructure to a grinding halt. This is not any sci-fi or fictional scenario. Hackers can track down IoT sensors, hack into their weak interfaces and run commands to shut down services or to hijack their functioning.

To cite a real-world example, cities like New York, Singapore, Barcelona, etc. are already running extensive public utilities with the help of IoT. IBM’s white paper - The Dangers of Smart City Hacking found more than 17 security vulnerabilities that make it “painfully easy” to take down large IoT-based urban networks. The security vulnerabilities included public default passwords, SQL injection, authentication bypass and so on.

#3 Premise Intrusion

Home security device shipments worldwide is expected to touch 700 Millions by 2019. According to Alarms.org, three-fourth of homeowners buy security systems that can be monitored through their mobile devices. While these systems saves time and provide convenience, they also become easy targets that hackers can infiltrate easily.

By hacking into the smartphone or a weak smart device, the hacker can take down the home security system thereby gaining access to the entire household. The same scenario applies to corporate offices as well, which makes IoT a certain Internet of Threats.

So, do these security threats mean that it is the end of the road for IoT app development? Not so. There are best practices that enterprises can embrace to insulate their IoT networks from vulnerabilities.

Best practices to establish security in IoT app development

IoT is a relatively new concept. The IT industry as a whole is yet to attain widespread knowledge and authority on its usage, maintenance and security. Here are some best practices that can help thwart the security risks involved in IoT app development.

#1 Review the risk involved

Having a brief idea of the risk landscape will help device a strategic security policy specifically for IoT devices. Penetration testing can be carried out to identify key vulnerabilities that should be addressed on high priority. For example, default public passwords is a vulnerability that can be resolved quickly without much ado.

#2 Setup device identity

Each device in the IoT network must be identified and tagged to grant secure access. Use secure over-the-air updates to keep the device security intact and in tune with the latest development.

#3 Encryption

More than the connected device, it is the data that it creates and exchanges that is of value. Every data exchange by the devices in the network should be secured with end-to-end encryption, code signing or with SSL certificates.

#4 Public Key Infrastructure

Public Key Infrastructure (PKI) can help create the basic framework required for authenticating device identities and for establishing the integrity of security patches. It also facilitates easier management of public-key encryption thus making it a perfect choice for establishing IoT security.

#5 Plan long-term

IoT is going to be here for the long-term. It is not any short-term fad that can be easily replaced. It is got a strong hardware presence which cannot be removed easily. Hence, any security measures made for IoT networks should be planned for the long-term.

What’s next?

With the promise of IoT comes several perils as well. IoT botnets can take down large-scale and sensitive connected networks, including urban infrastructure, home security systems, etc. McKinsey Global Institute estimates the economic impact that IoT can create to be in the range of $3.9 trillion to $11.1 trillion worldwide by 2025. But, the true economic benefit of IoT can be attained only if it is secured and insulated from security threats. To sum it up, security should be the bottom line of IoT app development. Without security, IoT can create more damage than the benefits that it can provide.

An early theme of digital transformation was the notion of selling services rather than products. A contract with the “thing maker” to circulate cooling fluid throughout my factory rather than a purchase order for me to buy the pumps and filters needed to do it myself, for example. The contract lets me focus on creating products for my customers rather than maintaining the machines making this possible. I don’t want to spend time on the process (pumps and filters), I just need the outcome (properly cooled machines) in the least distracting way possible to my core business of producing goods, medicine, energy, etc. The contract lets you, purveyor of the connected pumps and filters, build a closer relationship with me, streamline your business, and avoid competing in an increasingly commoditized space.

The fundamental shift happening today goes beyond providing guaranteed services rather than just hardware. Ensuring my lights stay on rather than selling me light bulbs solved your commodity hardware problem, but over time service offerings will face similar pressure as your competitors follow your connected product path and undergo digital transformations of their own. Your long term return on investment in IoT depends on more than keeping my lights on and water flowing. The value your IoT system creates for you depends on your IoT system’s ability to generate more business for me. There’s no such thing as a cheaper “good enough” replacement part when it comes to generating new revenue.

In healthcare for example, when your IoT system enables me to perform procedures in 24% less time, my clinics can perform 24% more procedures each day, increasing my revenue by 24% and delivering a 24% better patient experience. That’s what I’m looking for when I’m buying medical equipment. Depending on my corporate agility, the adoption and rollout of your connected machines may be a phased approach, following a progression of business outcomes. Asset Management means knowing the status of each device at all times and controlling them accordingly. This first step helps me see the potential value of incoming data and better understand my current utilization. Workflow Integration is connecting this information with my enterprise systems, which enables Predictive Maintenance and automatically alerts service technicians when a machine shows signs of impending failure. Where everything comes together and bonds me securely to your connected product service is Yield Optimization.

At this point your IoT system is collecting data from machines in my facilities as well as external data like weather and information from my other enterprise systems, correlating this information and uncovering patterns and ways for me to achieve more with less. Your “things” are now more than hardware installed in my facility performing physical tasks. They’re active components in a new System of Intelligence engaged in a loop of continuous learning and improvement.

This is true digital transformation, the creation of business value out of data collected and processed by your IoT solution.

To paraphrase Geoffrey Moore, smart “thing makers” are investing in IoT solutions for their customers today in order to generate more revenue for themselves tomorrow. Traditional hardware vendors are being commoditized and replaced whenever a cheaper “good enough” option comes along. To thrive in the long run, your value must be “sticky”, embedded in your customer’s business, providing benefit to their customers as well. The “things” you sell now simply enable your customers to run their basic operations. Whenever a part breaks, customers make a decision to order a new one either from you or a competitor. How differentiated is your equipment from the rest of the market? Your business is constantly at risk.

What we’re seeing as a result are “thing makers” creating smart systems that empower their customers to not just operate, but to *optimize* their operations. These devices still perform their physical functions as before, but also collect and share a stream of data about their status and conditions in the world around them. It’s the data they produce, and the insights your system derives from this data, that enable your organization to offer far more valuable products and services to your customers that are not so easily replaced.

If you know the state of your machines at all times, you can build predictive maintenance and service models enabling guaranteed uptime and automatic replenishment. If your equipment never breaks or runs empty, your customer is unlikely to replace it with a competitor’s version.

If your products provide not just lighting and temperature control but also insights correlating usage patterns with time, weather, and utility data that reduce your customer’s costs, you can sell them this information for a percentage of these savings.

It’s the future. Your connected product system is part of your customer’s operating procedures, continuously generating insights for maximizing productivity. Improved asset utilization, faster turnarounds, synchronized workflows, and more. Smoother operations and reliable performance deliver better experiences for their customers, further expanding your customer’s business, because of your IoT solution. You don’t just sell “things.” You sell outcomes, which is what your customers really wanted in the first place.

That’s pretty smart.