The IoT is making inroads everywhere, from your kitchen to your office (whether at home or in a corporate building) to your supermarket to the factories that make your appliances and the farms that grow your food. The Internet of Things (IoT) promises to touch every aspect of our lives. Just how far into the future that will happen is the question.
For the most part, IoT implementations remain in the pioneering phase. Advances we have anticipated for years — like predictive maintenance, connected vehicles, and telesurgery — are gradually becoming reality.
In five to 10 years, many IoT use cases will be commonplace. For instance, connected vehicles giving you on-the-spot traffic and weather information won’t seem novel anymore. When you go to an ATM, you won’t need a card because the machine recognizes your face and voice.
IoT-enabled changes won’t happen overnight. Rather, they will be incremental as companies invest in advanced technologies and artificial intelligence (AI) algorithms become more sophisticated. Exactly how much impact the IoT will have in five years is hard to say, but we know these developments will play a role:
Advances in AI and robotics
Fog and edge computing
5G networks are in the rollout phase. They vastly increase bandwidth and deliver speeds estimated at 100 times greater than 4G connections. Think of a 5G network as a vast WiFi network that isn’t confined to a building but, instead, transmits data across thousands of miles.
5G and IoT make for a powerful combination that may be the catalyst for autonomous vehicles, delivering data in real time to ensure safe operation. 5G also will play a role in manufacturing the vehicles. Both Porsche and Volkswagen have launched 5G-enabled production pilots involving robots and production tools that are controlled wirelessly.
A whole host of other use cases leveraging the combination of IoT and 5G are under development. They include traffic systems that direct drivers to available parking spots, streetlights that sense pedestrians to turn on and off, and wildfire detection.
For wildfire detection, a handful of companies have introduced cameras and sensors to capture visual and climatic data that can trigger an immediate response to signs of fire. Similar technology is being tested in farming to analyze soil conditions such as moisture, acidity and nutrients to determine which crops to plant.
In recent years, AI has gained prevalence. Going forward, both AI and robotics will have an even greater impact. In the previously mentioned ATM example, AI algorithms will validate users through face and voice recognition. Imagine not having to dig for your debit card in your wallet or pocketbook because the machine knows who you are.
Communications and software companies are working on AI-enabled applications that detect mood in addition to recognizing voices when customers contact a call center or helpline. By detecting a caller’s emotional state, the system can route the call accordingly without making the user navigate through several voice system menus.
On the robotics end, advances we are likely to see include next-gen systems such as the ones car manufacturers are exploring as well as the appearance of robots in places we haven’t seen them before. For instance, a robot may hand you your food at a fast food eatery, a pack of gum at a convenience store, or an aspirin bottle at the drugstore.
Robotics are already in use in manufacturing and logistics, but they will become more sophisticated and take over duties such as cleaning and maintenance. In many settings, such as telesurgery and manufacturing, robots and humans will collaborate—remotely or on site—to accomplish tasks together.
Ultimately, because of advances in both AI and robotics, industrial robots will become even more modular and flexible, and will be able to learn to perform not just repetitive tasks. Manufacturing will be transformed.
With the enormous amounts of data collected by IoT devices, efficient storage and real-time analysis becomes a costly and complicated challenge. That’s what fog and edge computing are meant to solve. The closer to the source of data, the faster, more efficient and scalable will be data insights.
The edge is already a critical component of many digital transformation implementations. In the future, both edge and fog computing will continue to grow. Fog computing works together with edge computing to enable data processing and decision-making close to data’s source (an IoT device), instead of sending it all to the cloud.
Fog computing will ensure seamless processing of data in real-time. It will be helping emergency services respond to a fire within a split second, as sensors won’t be sending data to a server for analysis. Low latency, network bandwidth efficiency, security, and reliability offered by fog and edge computing will support development of more advanced connected devices.
As a business, how should you prepare for the future of IoT? A good start would be to evaluate what areas of the business could benefit from IoT and AI implementations. For that, you need a data strategy to organize and interpret the data captured by the company on a continuous basis from different sources.
A platform such as the Cloudera Data Platform, which works with on-premise, cloud, and hybrid environments, helps manage data and derive insights that inspire smart business decisions. Getting a handle on data helps open the path to further investments in data-driven technologies so the business can leverage IoT and AI to improve operations and the customer experience. Take it slow by implementing one use case at a time, learning lessons along the way that you can apply to the next step of IoT implementation.
IoT investments require vision and realism. Remember that curating high quality training data is an ever-evolving process and technology is always evolving. The most important thing to remember about IoT, and any technology investment, is that changes will keep happening. That’s why investing in a full data lifecycle platform and taking an incremental approach are likely to deliver the best outcomes.
Read more about why full data lifecycle platforms beat point solutions for IoT and beyond.