How AR-IoT Helps Businesses Rethink Data Management and Generate New Value

As of 2021, there're almost 14 billion active IoT devices. According to Forbes, IoT solutions could bring $4 to $11 trillion in economic value by 2025. By that time, 200+ zettabytes of data are expected to be in cloud storage around the globe.

IoT has already become a trailblazing and proven way to collect data from the physical world.

However, as IoT-collected data volumes are growing rapidly, many companies fail to generate business value from it. As a result, they risk lagging behind tech-savvy competitors and new and swift market entrants able to bring much more innovative solutions to the market faster and at a lower cost due to their ability to visualize and manage data more efficiently.

This is where AR comes to the rescue. IoT devices collect data from the physical world, whereas AR devices take digital data and return it to the physical world for people to view and interact with.

This article explores how the convergence of AR and IoT technologies can be a trailblazing way for organizations to get the most out of their IoT-collected data, what challenges and issues stand in the way of AR-based IoT solutions development, and how businesses can effectively overcome them to generate new value and data-based insights for robust business intelligence (BI).

Contents

What's IoT data collection and why does it matter?

IoT data collection is the process of using sensors to track the state of physical things. 

IoT data collection is invaluable in many domains and verticals as it allows remote systems to be monitored and managed in real-time. For example, IoT devices can monitor patients in a ward or home, remotely control manufacturing systems, and track shipments and vehicles from a distance. The data collected by IoT devices makes businesses more productive and efficient.

With large-scale data collection, there is a need to manage it efficiently. Collecting massive amounts of data is useless if there are no processes to clean it up, organize, and process it.

IoT data management is essential as it enables organizations to access their IoT devices’ data and extract the information they need from it.

Types of IoT-collected data

Automation data

This type of data helps IoT systems manage devices inside the home, vehicles on the road, and other moving parts of any system. Processing automation data is complex as the costs of errors are extremely high – from accidental system blocking to traffic accidents.

Location data

Tracking the movement of an object or person is another important function of IoT devices and sensors. Connected systems use location data for fleet management, asset tracking, employee monitoring, and other management tasks. IoT can provide faster data processing speed and accuracy compared to GPS, which is why many business owners choose to use motion sensors instead of GPS trackers.

Status data

Status data is the baseline metric for most IoT applications. This is the most basic type of information collected –  whether the device is turned on, whether there’s free space in the area, etc. This data is useful for any decision-making, planning, and maintenance. However, it can be of little value if not used in conjunction with other IoT data types.

Actionable data

These types of IoT datasets are extensions of status data. In addition to receiving clean and objective information, the system processes it and transforms it into easy-to-follow instructions. Actionable data is often used to predict future behavior/performance, optimize energy consumption and workplace efficiency, and make long-term decisions. With actionable data, business owners can better leverage other insights from the IoT system.

IoT data collection challenges

Although the IoT market has grown dramatically in recent years, manufacturers and consumers are still facing significant challenges in collecting and managing IoT data. That’s just to name a few.

Data security & privacy

Some IoT devices collect sensitive information. CCTV cameras, voice assistants, and similar tools can track user activities and conversations. IoT devices employed in manufacturing have access to sensitive data about manufacturing processes and procedures.

Securing and protecting this data is a common issue for IoT devices. These devices are often designed to be accessed from the public Internet due to the need to send data to cloud servers for processing and are controlled by mobile devices and web portals.

In addition to securing sensitive data from cyber-attacks, manufacturers and users of IoT devices must protect it in accordance with applicable laws and regulations, which can be challenging.

Some common IoT security and privacy issues that can compromise the sensitive data they contain include poor password security, unpatched vulnerabilities, poor encryption, lack of compliance, etc.

Data volume

With almost 14 billion devices connected through IoT in 2021, the sheer volume of IoT-collected data makes data storing, transferring, and processing a major problem. IoT devices are oftentimes deployed in remote locations with limited bandwidth, making it difficult and often costly to manage collected data. In the cloud, servers must quickly process and analyze growing volumes of data to extract critical information and send any necessary alerts or commands to IoT devices.

Data complexity

Many IoT devices are built with a big data mindset. Having collected as much data as possible, these devices transfer it to cloud servers for processing. Besides generating huge amounts of data, this approach also leads to complex datasets being created. The data produced by IoT devices is often unstructured and presents a limited perspective. This data must be carefully time-stamped, indexed, and correlated with other data sources to create the context necessary for effective decision-making.

The combination of this amount of data and complexity makes it difficult to efficiently manage IoT data. Many tools designed to manage complex datasets are unable to handle the amount of data that IoT devices produce nowadays. On the other hand, solutions that can cope with huge amounts of data may not offer the required level of in-depth analysis, preventing companies from generating appropriate insights.

Good news is that these challenges, while significant, are solvable.

 

Why AR is a sweet spot for IoT

IoT solutions using AR are most useful in business contexts where some or all decisions should be based entirely on data-enabled insights and trends. This is especially relevant to cases where users need to leverage digital information to interact more effectively with physical objects and surroundings in real-time.

AR-based IoT applications are also handy for aggregating large volumes and different kinds of contextual data and consolidating it in a single view. 

Unlike virtual reality (VR) applications, which require the user to be completely immersed in a virtual environment, AR applications overlay digital information on top of the user’s physical environment. As an example, an AR app can add digital content to live camera feeds, making it appear as part of the physical world. This capability allows users to interact with the data more intuitively.

AR-IoT use cases

Let’s look at cases where AR-based IoT has already become an indispensable element of new value generation.

Remote control and monitoring 

With multiple surveillance integrations and the massive IoT bandwidth, infrastructures are already in place to enable remote monitoring of facilities, devices, equipment, etc.

Powered with AR overlays, IoT apps can make it easier for users to view critical operational information. For example, visual warning icons can be used on a head-up display to direct personnel to the location of faulty equipment or to help them navigate situational hazards.

You can connect AR to any data source as long as it provides the correct information and contextual mappings to your headset, phone, or other devices.

Imagine your employees need help with complex tasks such as repairing network gateways, checking controllers, or calibrating environmental sensors. With the help of IoT and AR, you can provide the necessary control without going to the production floor.

Fleet management facilitation and simplification 

One of the historical challenges facing fleet management is accurately visualizing the spatial relationships between vehicles and other assets. The driver’s position can change at any time, as can road conditions. While modern mapping tools make it a little easier to put things in perspective, fleet managers can face a mismatch: How do the dots on the screen relate to real assets?

AR headsets make data from your mobile IoT device more immersive. By allowing you to interact with data streams and model assets in simulated 3D space, AR fleet dashboards can give you a more accurate understanding of the key drivers of action. Some companies have even suggested equipping trucks with reinforced windshields, allowing long-haul drivers and commercial operators to focus on traffic data, weather alerts, and cargo status readings.

Car maintenance provides another interesting use case. Not all drivers are mechanics. However, with AR glasses and IoT apps, they can connect to their car’s onboard IoT networks and detect problems before they become critical.

More careful handling of parcels

Shipping companies are experimenting with ideas such as providing logisticians and movers with smart clothes that can help them identify the contents of a package to handle it properly.

How will it work? Imagine that a customer expects a fragile batch of antiques to be delivered. A parcel handler in a warehouse is about to throw a box into a truck. At this moment, their clever glove buzzes, telling them that the item they have picked up requires special care. The package is loaded safely, which helps improve customer satisfaction and loyalty.

Wearable AR devices linked to the IoT could be the key to seamless object tracking and supply chain digitalization. 

How AR-IoT solutions benefit enterprises and SMB

Enhance and extend human capabilities

Companies use AR-IoT solutions to help employees complete complex tasks more easily. For example, an AR-IoT application can help organizations train factory workers to assemble equipment faster and more smartly by modeling products and overlaying IoT data on them. Likewise, hospitals can use these solutions to train healthcare technicians to optimize their work.

OEMs can help their employees repair complex equipment by creating a custom AR app that overlays IoT data on a visual representation of machine components. This view allows workers to “look” inside the machine, even into hard-to-reach areas, and diagnose issues more easily. 

In workshops, employees can wear AR devices to visualize IoT data in the context of their work. For instance, when equipment is fitted with IoT sensors in a production line, technicians wearing AR glasses can walk across the floor to understand how the equipment works. The closer the technician is to the equipment, the more accurate the information will be.

AR-IoT applications also make it easier to spot problems on assembly lines. In hardware design, companies can inject IoT data into digital models and use AR to interact with those models at any scale, giving designers a significantly more accurate understanding of how a project will perform in the real world. For example, a view can help designers find the most commonly used features to improve machine design and performance.

Likewise, remote IoT devices such as cameras and temperature sensors installed in and around buildings can collect data that can help companies create digital twins. When such virtual models are combined with AR devices, architects, contractors, and builders can interact with the data to provide better baseline data and, among other things, improve plumbing and electricity placement.

Manage and control Objects and Spaces more efficiently

IoT and augmented reality technologies can help companies make sense of disaggregated data gathered from physical spaces such as factories, warehouses, or retail stores. Understanding disaggregated data is critical for functional areas management. An AR-IoT application can help a company optimize warehouse utilization by determining the amount of space allocated to high-turnover products, monitoring inventory status, and identifying aging inventory that needs to be removed.

AR apps can also use IoT data to guide employees through the warehouse using the most efficient route. Such an application can reduce the number of resources required, lower energy costs, and increase labor productivity. Similarly, companies can use AR to overlay traffic data collected from beacons, mobile apps, and CCTV analytics onto retail store configurations to determine the best store setup.

In addition, AR headsets can use real-time IoT data from in-store sensors to allow managers to walk through store aisles and identify problems. Alerts can appear, for example, when items are out of stock, in the wrong location, or if they have the wrong barcode.

Are you looking to leverage custom AR-IoT solutions to get the most out of your data and generate new value?

Reduce Costs 

Companies can lower costs by leveraging IoT and AR technologies to improve productivity, reduce material waste and asset downtime, and optimize working capital — just to get started. Even a simple AR-IoT solution that displays context-sensitive data in real-time to help workers repair equipment can improve first-time fix rates, reduce errors and material costs, and eliminate repeat technician visits.

Create Strategic Value

AR-IoT solutions can add value in various ways, including increasing brand equity, improving customer satisfaction, and mitigating risk. For example, using AR to escort workers around a warehouse can improve safety, generally improving the work environment.

Increase Revenue

Enterprises can deploy IoT solutions leveraging AR to increase the speed of operations and, as a result, business results. Companies can also use these technologies to build new products and services that they can monetize. For example, creating a premium service experience based on IoT and AR can help retain customers and reduce churn.

Hot AR-IoT trends

Digital twins

A digital twin is a virtual copy of a physical product or asset. This replica is updated as often as possible or in real-time, depending on business-specific requirements and goals.

Constant updating helps keep up with real-life counterparts and any changes that may occur down the road. A digital twin is built as similar to its physical counterpart as possible. Hence, it can be used successfully for many purposes, including analyzing previous iterations and testing new strategies and approaches.

The digital twins help companies change existing production equipment while reducing downtime to an absolute minimum. They use identical data and layout as the original one, allowing specialists to remotely access critical information about a physical asset simply by working with its digital twin.

Digital twins use real data to make real-time decisions, and IoT devices can provide real-time data all the time. On top of that, they provide a great opportunity to visualize data in real-time, especially when IoT devices are being integrated into larger systems. For instance, AI development teams can use these connected devices to optimize ML or DL algorithms. Current and historical data can be combined into a digital twin to improve automated systems.

For example, manufacturing teams can see a single asset move along a production pipeline and answer the following questions with a high level of accuracy: Where does it linger in the process? How does this relate to products delivered to our customers? By observing the entire system, industries can figure out how to drive change that can have the greatest positive impact.

Digital twins act as a repository of data and a hub to contextualize it. Because the connected data serves the same digital purpose as its physical counterpart, a digital twin features an inherent order and structure. And since everything points to the same place (twin), it’s easy to integrate software, applications, and processes into the same repository to deploy that data. 

A digital twin acts as a source of truth – a true representation of diverse data collected from disparate sources.

As IoT continues to grow in scale and complexity for businesses, digital twins are becoming more important. They help anchor sensors and beacons. They are the backbone of corporate data management systems. Without digital twins, IoT involves many more networks between data points of origin and data use points. As such, digital twins help centralize all of the existing IoT-collected data.

 

We’ve been experimenting with AR-based IoT software development in our R&D Embedded Center for quite a while. One of the results is a HoloLens-enabled digital twin of the robotic arm.

 

XR Streaming

Mobile devices’ limited computing and graphics power poses challenges for both software developers and end-users. Limited performance means rendering 3D content in AR/VR is also limited. Sometimes data needs to be processed in a complex way, and the quality of perception (for example, graphic effects) is also much lower than what we’re used to in modern PC programs.

However, further implementation of immersive technologies requires mobile devices and form factor optimization. Once AR and VR hit the mainstream, mobile usage will increase as a direct result.

XR streaming brings data-intensive AR apps to mobile devices in real-time. This opens up new areas of application of immersive technologies in the IoT environment.

One of the greatest advantages of XR streaming is that the computing power to run applications doesn’t have to come from the mobile device. It comes from a powerful on-premises server or the cloud. XR streaming allows you to view AR/VR content such as 3D CAD models or BIM data in unprecedented detail and complexity.

Moreover, the CPU (central processing unit) and GPU (graphics processing unit) of the end device are required only to a small extent, which in the long term allows the size of the end devices to be reduced, and the form factors – optimized.

AR-IoT challenges

Nevertheless, several issues can derail the implementation of the AR-IoT solutions and hold back their mass adoption. Among them are:

Costs

IoT and AR systems can be pretty expensive to design and deploy. This is especially true for companies that embark on such projects development without having the right expertise, deep domain knowledge or appropriate software development bandwidth.

Therefore, companies must carefully evaluate their opportunity to achieve target ROI through AR-IoT solution development before making a substantial investment.

An effective way to do so is indulging in a proof-of-concept (PoC) or pilot project to validate a business idea’s feasibility and create innovative use cases.

Software engineering talent

Developing AR-IoT solutions is usually outside the purview of most in-house IT departments. Moreover, it requires specialists well-versed in both physical devices (hardware) and digital technologies (software). Globally, the talent pool with the right expertise is limited. As such, most companies should partner with external technology or IoT team augmentation providers to build custom AR-based IoT solutions fast and cost-effectively, even if they develop the required skillsets and expertise internally.

They can easily get stymied without a significant ramp-up that expert tech partners can help with.

Data quality

AR-IoT solutions are only as good as the quality of data that companies use. Gaining access to data and combining it meaningfully can be challenging. Organizations should create a digital thread — the communication structure that enables data flow — to link interrelated data across all workflows, products, and applications.

Devices

Each AR-IoT solution should be designed with the device type in mind to ensure a unique and unparalleled UX. AR head-mounted devices can be bulky, have a limited field of view, and inconvenient to use over long periods. While technology is improving rapidly, businesses need to learn to deal with today’s constraints and develop an AR strategy compatible with a plethora of devices.

Communication and security

Creating a seamless AR experience with live IoT data requires high connection speeds and low latency. This can be challenging in remote areas and underground, so companies need to consider these conditions when developing applications. Over time, 5G networks can solve these problems. However, AR device management, in addition to IoT devices, increases the number of assets connected to the IoT system, which increases the likelihood of security vulnerabilities. “AR plus NB-IoT” can be an effective solution to address this issue.

Companies should involve security experts in designing and developing AR-IoT solutions to minimize cyber risks.

Speed

IoT and AR are relatively new technologies. As such, implementing solutions based on them may take longer than expected. Certain applications, such as those that manage interactions with medical IoT, will require certifications, regulatory compliance and approvals, and even new standards. These steps can increase the time it takes to deploy AR-IoT.

Cross-functional collaboration and tech partnerships as a way to solve AR-IoT challenges

Cross-functional collaboration between traditional business units is critical to AR-IoT project success. Companies must engage stakeholders from all relevant groups – not only from core business departments but also from IT and R&D teams – to support AR-IoT experiments.

Since developing an AR-based IoT solution must be a business-level initiative, getting management support is very important. This support from the outset can ensure that these projects gain momentum quickly.

Companies must be careful when choosing technology partners, custom software development providers and system integrators for the AR-IoT ecosystem; they must be able to support the reorganization of operational processes and the implementation of technological solutions.

On top of that, companies must optimize the user experience and ensure that AR-powered IoT devices are usable and intuitive in the physical environments in which they’re going to be used. Doing this right can speed up implementation and ensure regular and meaningful use of this technology.

As mentioned above, the best way to optimize UX is to encourage IoT teams to develop PoC and MVPs, launch pilot projects, run tests for end-users, and quickly iterate solutions.

And that’s where tech partnerships with custom software development providers running robust IoT R&D Centers and having access to the right talent and expertise come to play a crucial role.

Final thoughts

The combination of IoT and augmented reality technologies expands the opportunities for people to interact with the world around them. Yet, to harness this power, companies must choose where to deploy technology, build an ecosystem that supports IoT and AR, and develop a talent pool that can leverage both technologies correctly.

Companies that find the ways to do it right will not only be able to cut costs, increase revenues, improve CX and strengthen their brand reputation, but also use contextual data to generate new strategic value they’ve never seen before.

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