Machines function much like organs in the human body. The brain is the command center, and the central nervous system is responsible for connecting information received from the five senses: sight, smell, touch, taste and hearing. Your smarts aren’t worth much without the awareness of your environment and your internal condition.
In facilities, the network that connects different machines and sensors is called the industrial internet of things. As evidenced in the consumer IoT marketplace with smart thermostats that “feel” temperature, doorbells that “see” who’s outside and other connected sensor-based devices, brain-to-sensor connectivity is one of the most important facets of IoT technology today.
The problem is in the software brain, which today has a host of disconnected sensory inputs that do not relate to one another. Many consumers buy home IoT devices only to find that each one uses a different protocol, requires a new app to operate and often won’t speak with other devices unless explicitly designed to do so.
Disjointed dashboards such as these only hamper operations and lead to frustrated and disillusioned users. IIoT is experiencing similar growing pains, and that’s why focusing on building the central nervous system for IIoT is the next critical step forward.
Where we are in the IIoT evolution
Although we have a sense of what it should be, IIoT doesn’t really exist yet.
That statement may come as a surprise if you take a look at recent headlines, but the current iteration of IIoT is a collection of varied and incompatible protocols, sensors and companies with proprietary information that has to be forcefully integrated together. There is a lot that history can teach us from previous tectonic shifts as new platforms emerged, such as the internet and smartphone ecosystems. We can’t afford to repeat the same mistakes, as there is a lot at stake here — critical infrastructure, manufacturing, hospitals and data-centers will all be affected by decisions we make today.
Right now, the market has yet to get crowded, reach its inflection point and have a shakeout. But even at the beginning of the hype cycle, there is a compelling future to consider if and when the industry works together to connect everything. The winners in this future will have the most overreaching AI with the best interoperability.
So where do we go from here?
Essential traits for IIoT
A central nervous system for IIoT is an ever-present layer that constantly runs in the background. It connects to multiple data sources and communication channels, and is able to provide the right insight to the right person at the right time — pulling in the relevant data and people to quickly address the issue at hand. In building this central nervous system, we will need to focus on two primary functions.
First, we need to focus on the very basics of network connectivity and interoperability. We need to use a common language, so that everything can operate on the same network and share information. The pump, sensing weakness, needs to be able to communicate with the maintenance tech to let him know of a developing malfunction and automatically order the required spare parts for the repair. At the same time, that same pump, sensing its own temperature rising, needs to be able to communicate with the facilities manager and notify him to reduce the load. This command center will employ AI that is aware of various inputs and act on that information to make changes, notify where necessary and generally connect the dots.
Second, we need to concentrate on optimization. Keeping systems up and running as efficiently as possible requires an overarching layer — a central nervous system — to relay information to the brain for interpretation and action. In the examples above, the pump needs to be aware of its internal health and operating condition in order to notify the right person to take the corrective actions.
Now is the time for a shift in mindset
This is no easy feat. It requires multiple entities working in unison, building bridges between silos that have existed for decades, and finding the right business models to enable this cooperation. New technologies and technology vendors will play a critical role in building the infrastructure, but it’s up to the incumbents — the facility managers, the services providers, the OEMs and the insurance companies — to come to the table with a fresh mindset. Our market is changing rapidly, and we can either be surprised or be proactive and control its trajectory to a better outcome. Let’s build the central nervous system that enables our assets to truly speak across boundaries.
All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.
The TEE is no longer an emerging technology. If you’ve ever used apps like Samsung Pay or WeChat Pay, device features like Samsung KNOX/Secure Folder, or many of the leading Android device makers’ flagship phones, then you’ve been protected by one. But it is not a technology that is confined to high-end devices.
The proliferation of the internet of things is expanding the need for trusted identification to new connected devices, and the TEE is one technology helping manufacturers, service providers and consumers to protect their devices, IP and sensitive data.
But what is it, how does it work and why should we care?
What is a TEE?
The trusted execution environment, or TEE, is an isolated area on the main processor of a device that is separate from the main operating system. It ensures that data is stored, processed and protected in a trusted environment. TEE provides protection for any connected “thing” by enabling end-to-end security, protected execution of authenticated code, confidentiality, authenticity, privacy, system integrity and data access rights.
It is already used widely in complex devices, such as smartphones, tablets and set-top boxes, and also by manufacturers of constrained chipsets and IoT devices in sectors such as industrial automation, automotive and healthcare, who are now recognizing its value in protecting connected things.
How does it work?
The fundamental concepts of a TEE are trust, security and isolation of sensitive data. The most advanced TEE implementations embed devices with unique identities via roots of trust. These enable key stakeholders in the value chain to identify whether the device they’re interacting with is authentic. It also cryptographically protects both data and applications stored inside it. Applications that sit within the TEE are known as trusted applications. The data stored on and processed by trusted applications is protected and interactions made (whether between applications or the device and end user) are securely executed.
This is because a TEE enables:
- Secure peripheral access — It has the unique capability of being able to directly access and secure peripherals such as the touchscreen or display (i.e., the user interface), offering protection for fingerprint sensors, cameras, microphones, speakers and so on.
- Secure communication with remote entities — It can secure data, communications and cryptographic operations. Encryption keys are only stored, managed and used within the secure environment, with no opportunity for eavesdropping. This is particularly relevant for IoT as secure cloud enrollment of things like sensors is central to scalability.
- Trusted device identity and authentication — Some TEEs inject a root of trust that enables the legitimacy of the device to be verified by the connected service which it is trying to enroll with.
Why should we care?
Our world is driven by data and we need to get better at protecting it
TEE technology solves a significant problem for anyone concerned about protecting data. Take manufacturers and service providers for example; the TEE is increasingly playing a central role in preventing high-profile hacking, data breaches and use of malware, all of which can result in significant brand damage.
As devices become more complex so do their security requirements
It is clear that a smart heart rate monitor or insulin pump will not have the same capabilities as a connected car. Nevertheless, they all embed critical software and handle highly sensitive data and functions that are crucial to protect.
But it is not just the data that is key — secure connectivity and communication are also fundamental. Smart devices increasingly rely on connectivity to function (whether to pair with other devices or enroll with cloud services). This, however, makes them highly vulnerable. The TEE tackles this problem by allowing a trusted application to securely share secrets with a remote entity, such as a server or a secure element, in order to establish a secure communication channel.
IoT needs trust and scalability
The IoT value proposition is very desirable — cost savings, new/faster/better services, increased revenue, improved operational efficiency, enhanced digital lives. The IoT landscape is a diverse and ever-expanding space of possibility — and some of the best benefits haven’t even been imagined yet!
To fully take advantage of the current and future benefits that IoT offers, devices need to be scalable. This can only be achieved if their underlying technology is built on a foundation of security that can provide robust protection long into the future.
The TEE enables scalability in IoT by embedding hardware-backed protection at the heart of the device. New technologies, like Digital Holograms, are also coming forward to solve problems like device attestation, protection from overproduction, cloning and tampering, supply chain integrity from start to in-field operation, and trusted, autonomous cloud enrollment.
The trusted execution environment is already bringing value to a range of device types and sectors, which we’ll explore in greater detail in upcoming blogs. What’s really exciting though, is not the technology itself, but the options and possibilities it opens up. Whether it’s for developers to add additional value to their services by utilizing the hardware isolation, or the complementary technologies like Digital Holograms that sit alongside to add value for service providers and device makers, this is a technology that is only just gaining momentum. For example, our open TEE is already embedded into more than 1.5 billion devices worldwide, a number that has grown by more than 50% in less than a year, and as the IoT ecosystem and its security requirements expand even further, we can expect that growth rate to continue to rise.
All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.
The use of big data has seeped into many industries, changing the way we work. Big data enables IoT platforms to function, connecting devices to each other, revolutionizing machines. While the fleet management industry is not typically synonymous with the word innovation, big data has changed that.
With the introduction of big data, fleet management is rapidly evolving. Platforms automate fleet management technologies, connected devices monitor vehicle and driver performance, and managers have remarkable insight into how the fleet is performing in real time. Not only are the cost savings high, but the safety and risk mitigation capabilities are making safer roads all around the world.
The most prominent changes are in the ability to monitor driver safety and driver behavior, the better performing and more reliable vehicles, and the cost savings for companies that rid themselves of legacy systems and invest in big data platforms. Here’s a look at how big data is upgrading fleet management.
Big data improves driver safety and driver behavior
Onboard telematics sensors installed in fleet vehicles can collect data on driver behavior. From braking too harshly to speeding, patterns are sent back to managers who can then coach drivers on how to operate the vehicle better. Sensors will soon be able to give real-time feedback to drivers to correct behavior. Additionally, these systems connect to cell phones and block drivers from texting and driving.
Fleet managers can view all the aggregated data to find patterns and correct any overarching problems in the fleet. Furthermore, managers can see data in real time and alert a driver if, for example, the vehicle’s tire pressure is too low. The driver can then stop and fix the problem, rather than risking a breakdown, which endangers many lives.
Big data means better performing fleet vehicles
Fleet managers having access to fleet vehicle data means an overall better-performing fleet thanks to predictive maintenance. In addition to real-time alerts about problematic engines, low batteries and inspection reminders, managers can use data to predict when a vehicle will need maintenance.
Instead of waiting for a problem to happen and fixing it, managers can use big data sets and connected devices to calculate when a part of a vehicle will break down. Automated maintenance models can take vehicles part by part and assess when that part might need to be replaced or tuned up, instead of wearing down the part beyond repair. In addition to lengthening the lifetime value of vehicles, this also improves driver safety by reducing breakdowns on the road. Predictive analytics makes fleets more efficient by limiting, even eliminating, downtime, which results in significant cost savings.
Big data brings about huge cost savings for fleet management
Predictive analytics and safety features mean fewer accidents. Since accidents account for roughly 14% of fleet expenses, this in itself saves resources for companies in fleet management. Predictive maintenance also allows fleet managers to understand the lifetime value of parts and vehicles, which saves resources in that area as well.
Both of those aspects also mean less downtime, which can be a substantial financial strain on fleets. The cost of late deliveries, replacing vehicles, paying drivers overtime, and so forth can add up; but by reducing downtime, companies can avoid all of that. If fleets have fewer accidents and less downtime, this shows insurance agents responsibility and may end up resulting in lower premiums. For companies that have to pay high premiums on each fleet vehicle, the savings here skyrocket.
GPS data too can mean savings. Imagine combining GPS data with gas prices along fleet routes. If a driver knows when to stop and fuel at a low-cost provider, the savings would be in the tens of dollars. Now imagine giving that information to thousands of drivers — the savings would be in the thousands of dollars for companies that automate that process.
Taking advantage of the big data benefits
Fleet management companies that invest in big data will find great rewards. From a more connected fleet to high financial savings, the advantages are plentiful. Big data platforms are a somewhat significant investment, but they can make for a safer road and more efficient fleet. Seventy-eight percent of maintenance managers are not happy with their current maintenance approach, most likely because they do not have the tools and resources necessary.
The technology is now available to decrease accidents, drastically reduce downtime and make a more efficient fleet. Managers can make real-time decisions about their fleet using big data, so companies should consider investing in this technology.
All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.
In today’s world, customer experience is the only real differentiator between a company and its competitors. Dissatisfied customers will not just leave, they’ll tell their friends and entire social networks about it too. Brand trust is essential for increasing engagement numbers and turning your occasional customer into a long-term and loyal one.
As I’ve written about before, the internet of things brings massive opportunity to engage consumers and deliver a great customer experience. But there’s another form of IoT that’s gaining market traction: the industrial internet of things.
According to a study by Statista, global IoT spending for the industrial manufacturing industry is predicted to reach $890 billion by 2020. Industrial manufacturing impacts industries such as manufacturing, logistics, healthcare, agriculture, automotive and industrial markets. While the difference between consumer IoT and IIoT mainly lies in the types of devices and applications, the technologies that power them and their purpose, in the end, are all IoT and there’s as many overlaps as there are differences.
As the IIoT market continues to grow, customer experience needs to be top of mind. One of the biggest concerns with IIoT adoption occurs during the initial setup process. Since IIoT is specific to industrial use cases, the common IoT adoption challenges are similar across the board with a large focus on three core areas, all of which have huge impacts on the customer experience:
- Compatibility or integration: Many companies are working with older systems that can be costly to upgrade or replace. The implementation of IoT technology can get complicated when trying to set up these old systems with new ones. If customers can’t set up or use the device, they’ll likely become frustrated, which leads to poor customer experiences.
- Security: According to a recent IEEE survey, security is the top concern for companies looking to adopt IoT technology. Addressing security in the IoT realm requires a multifaceted approach, and one that often requires human support. If information is hacked or leaked, it’s important companies are reassured manufacturers are doing everything they can to keep their information safe. Nothing erodes trust quite like security concerns.
- Lack of data understanding: A lack of knowledge by untrained users can be challenging when operating sophisticated IoT sensors. Training on how best to utilize this rich data is the most beneficial offering a customer support team can provide.
Additionally, a poor integration strategy or severe service interruption can lead to larger, longer term effects for the company. Take Tesla’s “thinking” algorithms, for example. They help run the company’s autopilot software, enabling Tesla vehicles limited levels of autonomous driving capability. These algorithms sync with other sensors and systems throughout the car to provide a holistic view. If something goes down with another part of the car, the sensors track it and send this information to the autopilot software, notifying the driver that it’s time to step in.
While this may all seem fairly straightforward, like any device, sensors can go down. When that happens, it’s important that the driver feels safe and reassured. This is where a human element in customer care offers value for both parties. The customer support operator, using the data available through the car’s IoT sensors and neighboring devices, offers real-time insight and assistance for the best support. The operator can jump in immediately with access to an up-to-date driver profile, easing the driver’s concerns through a personalized approach.
However, while some companies are making strides in providing a seamless IIoT experience, concerns around the overall customer experience have been challenged. According to survey by Statista, 55% of its respondents said that the biggest challenge in slowing down IoT adoption is understanding the technology.
To combat this, customer experience teams need to be prepared to support IIoT in many ways, including:
- On-demand assistance: In-the-moment customer support and IIoT sensors offer little communication error due to real-time analytics feeding the support agent live information. Agents can offer deep insight with short turnaround time, offering the best support when working on industrial sized concerns.
For example, Nest’s smart thermostat technology provides the ability to control temperatures in the home. In an industrial setting, this technology is useful for maintaining consistency among offices and larger campus settings. The technology’s ability to self-adjust and optimize energy efficiently offers customers flexibility and ease of use. Partnered with a live customer support agent, the setup process is user-friendly and requires minimal training for in-house commercial use.
- Predictive maintenance support: IIoT sensors track maintenance concerns and examine historical records to map product functionality. Back in 2016, AirAsia turned to IoT to reduce its ecological footprint while also boosting its own savings. By partnering with GE for its Flight Efficiency Services, the company reduced fuel use and saved money. This technology helped the airline follow precise navigation routes and analyze flight data to optimize aircraft utilization.
IoT goes a step further, equipping airlines and their ecosystem partners with the potential to turn sunken costs into drivers of incremental revenue. From curb to gate to destination, networks of sensors have the power to gather data, interpret it and trigger actions that could increase revenue while improving the overall passenger experience. Having a live-agent support team to work in parallel with this technology gives its users the ability to operate more efficiently and get ahead of any troubleshooting downtime.
- Better accountability: Utilizing a blend of human engagement with the data provided from IIoT sensors offers businesses a true look into product challenges and successes.
AT&T, one of the world’s largest telecom providers, helps its mobile users gain access to real-time information about city services and traffic issues by using the data collected across a variety of connected devices from streetlights to utility meters. Adding in customer support teams provides mobile users with another layer of defense should anything go down. This also provides utility organizations an opportunity to gain valuable insight to what applications are most necessary for integration success. Support teams are able to offer regional feedback on unforeseen issues and ultimately provide the organization with a chance to prioritize for customer needs and product shortcomings.
As IIoT continues to grow, companies need to use the data at hand and equip their CX teams with that information to provide the best customer experience. Rather than avoiding IIoT adoption to prevent customer challenges, businesses should embrace these concerns by using customer support to correct them. Customers want more ways to contact a company, faster resolutions and more personal interactions. In return, they will stay loyal and promote the trust they have in a brand.
In IoT, a tiny circuit used for one purpose accumulates data from sensors and transmits it to the server in a uniform time interval. Once upon a time, specialized embedded processors required electronics knowledge and restrictive languages. Nowadays, you can just pick up an Arduino and write a few lines of script in almost any programming language and empower the IoT device.
Potential of the internet of things
Every electronic gadget features computing power as well as connectivity. Thermostats, security devices, locks and lights, vacuum cleaners and air conditioners — all of them contain small computers, and all are interconnected via the internet. Given the ubiquity of IoT technologies, it can achieve an unbelievable level of coverage in both space and time for observing any events with a complex or simple programming language.
Predictions for 2020 promise many connected devices per person and terabytes of data per second to process — without including IoT-connected dogs and cats. The goal of IoT is to fully integrate people into smart systems that are self-seeking, self-controlling and self-optimizing.
Main programming languages for the internet of things
The internet of things is still a mystery to many, even though it has been around since 1982. The concept of securing up physical devices and making them talk to each other is great.
IoT is an entirely different platform for developers and engineers, where the only one thing has remained consistent: the programming language. Developers seem to be using the same languages for their projects, while also integrating some specific settings for IoT.
IoT consists basically of the devices doing some job and the web servers these devices are talking to. As a conclusion, to “cover” both sides you need programming language used for embedded development (C or C++) and programming language used for web server development (Python, Go, Java, C#).
Speaking to industry professionals that have worked on IoT devices and systems before, I have found C, C++ and Java are the most popular choices for general-purpose projects.
Overview of programming languages
C is a popular programming language for low-level projects. It considered the most useful for IoT devices because it doesn’t need a lot of processing power.
C++ is an optional programming language if the IoT device requires more complex tasks. For example, think of water level detectors and smart lighting rather than devices that detect heat.
Java is useful for IoT devices that require a lot of computation, since it is more manageable than C++ and more commonly practiced.
Python is commonly mentioned by IoT developers. It has become a valuable resource for web application developers. But it comes with a huge baggage — the entire Python interpreter has to be installed on the tiny computer. Some IoT devices do not have enough RAM memory for this. Another trouble with the Python programming language is that it is super slow. Many IoT devices are real-time things — they produce data quickly on relatively slow CPUs.
C# is a robust and powerful object-oriented programming language, but not many developers practice it. Mastering C# is not a trivial task, but it is not quite as challenging as other programming languages. However, it is very easy to use with Raspberry Pi.
Perspectives on the internet of things
IoT adds value to the products we already own and the services we already use. The data extracted from IoT devices is meant to tell us important insights about the market. The potential for highly individualized services are endless and will dramatically change the way people live.
The main goal of developing applications is to increase the level of communication with the help of one of the main programming language. In this way, the implicit goal of developing IoT technologies is simplifying data collection in a more secure and economical way, increasing the general well-being of individuals and society as a whole.
New, complex networks or interconnected devices that can share infinite amounts of information are being created right now, and more companies will allocate money to IoT development in the coming years. It is not a science fiction anymore; these innovations will be borne of technologies and cover the globe.
According to Gartner, IIoT devices are projected to reach 3.17 billion units in 2020. This includes manufacturing field devices, process sensors for electrical generating plants and real-time location devices for healthcare. Companies taking advantage of these IP-connected business devices are already benefiting from new revenue opportunities, operational efficiencies and substantial cost savings.
However, technology developments in this space are fast outpacing industry standards — earning them an unwelcome reputation for exposing sensitive data to security risks. Developments in IIoT data protection are currently failing to keep up with the rapid rate of innovation and demand. Securing the confidentiality and integrity of data passing between all these devices remains a major challenge for many businesses as IT professionals have to familiarize themselves with multiple IIoT designs, often with immature security features, that present clear data breach risks. Data from IIoT and M2M systems is especially prized by cybercriminals as they seek to intercept and sell intellectual property and personally identifiable information.
Recent research by Forrester found that the top three challenges for IT professionals are IIoT integration, migration/installation risks and privacy concerns. In the study, 92% of C-level respondents reported that they implemented security policies for managing IoT devices, yet less than half (47%) reported that they did not have enough tools in place to enforce those policies. Undeterred, businesses are continuing to invest in IP-connected devices — 49% of respondents expect to increase spending on IIoT security this year.
While business spending on cybersecurity is projected to amount to $134 billion by 2022, the majority of industry experts agree that built-in security is the answer to establishing a trusted standard of IIoT security. Incorporating security into the initial IIoT design process will maintain the privacy and integrity of highly sensitive data from the beginning.
Built-in security properties
Security should never be an afterthought. Device manufacturers must adopt a security-by-design approach and build better security into the initial development of IIoT devices. Being proactive with cybersecurity practices can save a business from a widespread data breach or prevent a hacking incident that results in revenue loss and customer mistrust. The following security measures are recommended for built-in IIoT protection:
- In-depth protection: Device software should have multiple defense layers;
- Automated security patching: The ability to automatically patch and update IIoT device software that is in line with prevailing threat developments;
- Unique hardware identity: Every device should be assigned a unique identifier inextricably linked to its hardware that marks it out as trustworthy;
- Independently tested trusted computing base: Device operating systems and security mechanisms including access control, authorization and authentication, virus protection and data backup are verified according to recognized industry standards;
- Compartmentalization: Applying network security segregation within the device hardware to prevent attacks from spreading;
- Software failure alerts: Software failures should be automatically reported to the manufacturer; and
- Authentication with certificates: Device authentication should always use certificates rather than passwords.
Virtual private networks
Even when the above properties are built into IIoT devices, there is one major security measure that businesses must implement. All remote connections and monitoring of IIoT devices should be secured with industry-proven encryption technology such as virtual private network (VPN) software. VPNs can secure the IP-connection of every IIoT device so that data traffic is encrypted as it passes between individual devices and the remote central management point over the internet. When combined with remote access controls and certified authentication measures, VPNs form an effective barrier that shields company confidential data from the unwanted attention of unauthorized parties.
In summary, the phenomenal growth in development and adoption of IIoT devices is rapidly outpacing manufacturers’ ability to make them completely secure. In the next few years, we should see more manufacturers building best-practice security measures into devices. Though there are several recommended properties for built-in security, such as security patching and authentication with certificates, encrypting communications with VPNs is essential. Centrally managed VPN software provides vital data encryption for the many thousands of remote connection points that make up an IIoT environment. In combination with built-in security features and processes, VPNs provide robust protection for maintaining the privacy and integrity of highly sensitive IIoT data.
In a world that is increasingly digital and virtual, the use of embedded SIMs (eSIM) and soft SIMs are starting to gather pace. Simply put, a device with an eSIM comes with a SIM chip built in, straight out of the box. A device with a soft SIM doesn’t have any SIM hardware at all; the SIM functionality is delivered onto the device virtually, or over the air (OTA), once the user switches it on.
Both these technologies make the process of starting to use a new device, such as a smartphone, simpler and quicker. And they help break down traditional (and costly) geographical barriers when it comes to mobile connectivity between different countries. What’s also exciting is that they open up a new world of possibilities when it comes to form factors: to date, having to squeeze in a SIM or even a micro SIM has limited manufacturers’ options when it comes to items that can be equipped with connectivity. That’s no longer an issue, spurring innovation by bringing connectivity to previously unconnected “things.”
Truly borderless mobility
Imagine you are on a round-the-world trip of a lifetime — starting from the U.S. East Coast, stopping in the UK, then venturing down to South Africa, moving on to discover the Asia-Pacific region, and then flying back to Europe before returning home to the U.S. Currently, most of us would buy local SIM cards when travelling — they are cost-effective and offer good network connectivity. The issue is that a device may be locked for a specific MNO’s SIM — and it’s a pain having to buy a new SIM the moment you step off the plane in a new country. Now, thanks to global Wi-Fi hotspot devices, people can stay connected while roaming, without the hassle of changing SIM cards or fearing bill shock due to high roaming charges.
Soon, billions of IoT devices will be connected worldwide — and just like when people travel from country to country, eSIMs and soft SIMs will play a central role in how quickly and seamlessly all those things connect too.
The challenge for IoT device manufacturers is that the standard method of soldering or inserting IoT SIMs by hand into devices is time-consuming and expensive. eSIMs enable device manufacturers to not only embed connectivity into anything without a physical chip, but to also reprogram SIMs OTA for millions of devices simultaneously.
Take the manufacturing industry for an example. Imagine a global steel plant, where all workers wear an IoT-enabled activity tracker type bracelet to monitor key variables, such as air quality and heart rate, to help ensure their health and safety in tough conditions. Being able to reprogram eSIMs inside all the wearable devices OTA simplifies their management and helps to make global IoT deployment more practical and affordable.
Another example is a logistics business with hundreds of vehicles in its fleet, each vehicle equipped with IoT-enabled sensors collecting huge amounts of telemetry data every day. The challenge for the company is how to harness the full power of all this data for maximum insights and efficiencies, without breaking the bank due to roaming charges as its vehicles cross from one country to the next. eSIMs and soft SIMs can help slash roaming costs in this scenario by automatically switching mobile networks, or reprogramming the SIM OTA with a new profile to ensure best rates and coverage. It doesn’t stop there though — an eSIM-enabled managed service can also include other value-add services for logistics, such as localization, scheduling, asset management, temperature sensing, video monitoring and vehicle diagnostics — all integrated with the core supply chain management systems of the business.
Adapt and thrive
As eSIMs and soft SIMs start to become mainstream, we may well see a shift in the competitive landscape for not just MNOs, but for the whole mobile ecosystem. New device form factors and IoT applications will emerge, and businesses will be able to unleash the full potential of truly global mobile and IoT services.
So, rather than the end of an era, these new SIM technologies should be considered as a new beginning — one that gives all businesses in the mobile world the chance to renew and reinvigorate what they bring to the sector today and tomorrow. This evolution will favor those who are ready, willing and able to adapt and become more agile in response to new competitive pressures and opportunities.
During all my years evangelizing the internet of things, I have been explaining to customers, partners and friends that IoT can positively change the way we do business and the way we live our lives. I have been asked if IoT is a new revolution in our society or if it is just one step in the technological evolution of the digital revolution. Today, the debate continues, but whether evolution or revolution, the internet of things is here to stay.
I definitely think it’s an evolution. The development of the internet of things is a bold move. IoT is not just a leap from the internet. The internet of things brings with it an evolutionary force that we rarely see in technology.
It is important to not scare conservative enterprises. It is not about ripping out current automation systems to replace them with new technologies. End users will resist rapid and radical change because of the increased risk of downtime and associated costs.
Therefore, I think this debate should be framed in a more general question: What Age period are we living?
The Connected Age
I consider the start of the Connected Age when the term “internet of things” was coined by Kevin Ashton in 1999. Kevin probably envisioned the move to sensorization would transform every industry in the world.
The main driving force behind the Connected Age is data: data that can be collected, data that can be analyzed, data can be shared and data can be used to improve service offerings. Data is the new oil in this Age.
Global sensorization is driving new ideas and thoughts that will ultimately drive innovation in our personal, business and working lives. Sensors data is opening up new opportunities, driving new business models and taking innovation to new levels.
We are still living in the Connected Age. I expect this Age to end in 2025, not because there will not be more things to connect, but because it is when most “things” will become intelligent and start being controlled by robots.
The Robotic Age
Reading “Genesis of AI: The first hype cycle,” I rediscovered how artificial intelligence was born and evolved until now. But it wasn’t until after I read “Your data is crucial to a Robotic Age. Shouldn’t you be paid for it?” that I realized we are entering before 2025, not without a certain fear, the Robotic Age.
According to IDC, “By 2019, 40% of digital transformation initiatives — and 100% of IoT initiatives — will be supported by AI capabilities.”
Qualcomm also envisions a world where edge AI makes devices, machines, automobiles and things much more intelligent, simplifying and enriching our daily lives.
AI has emerged as the most exciting capability in today’s technology landscape. Its potential is rich for large, complex organizations that generate massive amounts of data that can be fed into AI systems. AI-driven companies will represent the future of broader parts of the economy, and we may be headed for a world where labor’s share falls dramatically from its current roughly 70% to something closer to 20-30%. At the same time, the number of robots will increase.
Robotics and artificial intelligence have reached a crucial point in their evolution. A robot is no longer just a mechanical device capable of interacting with its environment and carrying out an assigned task. At present, research laboratories all over the world are developing and implementing sophisticated robots in technical, practical and even philosophical tools. Nevertheless, we cannot forget that there are still problems in the land of AI.
The Cognitive Age
For those of you who believe the mind is the center of all things, New York Times columnist David Brooks wrote two editorials that point to wider transformations that are shaping the world in which we live.
We could consider the start of Cognitive Age being when Facebook abandoned an experiment after two artificially intelligent programs appeared to be chatting to each other in a strange language only they understood. The two chatbots came to create their own changes to English that made it easier for them to work — but which remained mysterious to the human.
Are we sure Facebook shut down its artificial intelligence program? Facebook is not the only company — or government — running secret AI programs. Are you scared?
There are many myths about Cognitive. This article, published by Deloitte Consulting, may help dispel five of the most persistent myths.
“Start thinking how to prepare yourself and your business for the Cognitive Age.”
As I explain in “Bring your own cyber human (BYOCH) part 1: Augmented humans,” we are on the path to becoming cyber humans. To live in the Cognitive Age, I encourage companies to invest in how to enhance our senses and increase our intelligence to compete with and win over robots.
The Connected Age is a fact. Arm predicts 1 trillion IoT devices will be built by 2035. For those who think IoT is a revolution, don’t be worried because we are just living in an evolutionary process.
With the introduction of AI and machine learning, enterprises will be able to embark on projects never thought possible before. The Robotics Age is going to be a great challenge for humanity. The fear of being inferior to our creation, not being able to control them, competing with machines for jobs and having to obey them will really mean the beginning of a revolution.
What does AI mean for the future? What will the implications and the risks be? Will AI really understand humans? With its current skills, humanity will be inferior to the cognitive systems that will populate the Cognitive Age. That is why I encourage governments, private laboratories and researchers to work on Augmented Human projects so we do not become slaves to our uncontrolled inventions.
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Artificial intelligence has moved from science fiction to science fact, and the market for AI functionality in business is growing by leaps and bounds. In fact, innovative companies worldwide are mining considerable business value with the implementation of AI capabilities, such as natural language processing, across a broad spectrum of industries. Yet, despite the popularity of AI among early adopters, myths and misunderstandings make it challenging for businesses to integrate and deploy AI effectively.
Let’s take a look at four enduring myths about AI and address why businesses need to reboot their thinking …
1. If you build it, they will come … or will they?
It’s no longer enough to just build and sell a product for the sake of it. The market is crowded and fast-paced as digital startups and legacy enterprises vie for innovative ways to realize their digital future. Forward-looking companies are putting plans in place to generate predictable demand for new sources of digital revenue.
Analyst firm IDC predicts that by 2020, 50% of global 2000 companies will see the majority of their business being dependent on the creation of digitally enhanced products, services and experiences. Pioneering companies that have already made the shift are reporting faster growth than their competition. In this new world, data fuels new and predictable revenue streams on the back of AI, connected things and other digital technologies.
While any company can embark on a digital platform strategy, a differentiated value proposition is essential — and it is required to build at scale. The customer experience is critical. For example, the latency for refreshing images on a virtual reality (VR) headset shouldn’t exceed 10 milliseconds. To accomplish this today, VR gaming headsets require bulky hardware to control sensors and run compute-intensive software.
However, with multi access edge computing (MEC) and AI-based technologies, a mobile edge computing platform would sit close to the point of consumption, taking over the heavy lifting while avoiding unwanted latency. By using MEC and AI, a number of mobile operators, including Deutsche Telekom and Verizon, have recently made announcements related to applications and devices where millisecond response times and customer usage patterns and preferences matter. In other words, by placing AI at the core of their offering and using edge computing, network service providers can make the digital transformation to generate new sources of revenue based on consumer and business demands.
2. One AI size surely fits all, right?
It sounds cliché, but the possibilities with AI are limitless. AI will eventually find its way into just about every product and service. However, not every new product or service will succeed. Success or failure will be based on the business case, and for a number of companies, the right AI use case continues to remain a mystery. In a recent global study conducted by the McKinsey Global Institute, only 20% of C-level executives surveyed said they currently use any AI-related technology at scale, and many firms noted they are uncertain of the business case or the ROI.
Sure, Alexa and Siri seem to have all the answers, but AI for its own sake is not the answer. AI technology is a means to an end, and it should be applied thoughtfully to achieve a real customer value proposition. To achieve revenue growth, operational efficiency and market differentiation, the key question to ask is: What problem are you trying to solve and how can AI play a role?
For example, an industrial equipment manufacturer will benefit from agile machine learning to improve predictions about the onset of failure, allowing maintenance to be scheduled in advance. Likewise, better usage predictions can help a mobile operator reduce the time it takes to resolve network congestion and isolate instability. Failure pattern analysis can enable a software developer to root out priority bugs faster.
Today, an estimated 1,800-plus AI companies have captured more than $16 billion in funding — yet, not all of them will survive. To maximize your product’s value, first and foremost be sure to focus on delivering the best user experience balanced with the best possible business case.
3. AI is revolution not evolution
As digital penetrates every industry segment in varying degrees, there is a sense of urgency for legacy enterprises to go digital fast. Real fast. Even luxury carmaker Porsche is shifting value from horsepower to digital power, with services such as finding nearby parking spots or warning drivers of hazardous road conditions. Soon, it is anticipated that software-based offerings will account for about 10% of the $89,400 sticker price of the new Porsche 911 sports car.
It’s important for companies to map out a digital transformation roadmap — yes, it is evolution — for their mature products before they near the end of life, in order to reduce the negative impacts on the business. The objective is to ensure the core business remains profitable for as long as possible, while successfully incubating fresh product lines shaped by strategic priorities. But be sure to design products around people — the focus should be on how to go above and beyond what the customer expects to meet their digitally focused future expectations. When compared to their peers, revenue growth for companies focused on customer experience outperforms competitors by a wide margin.
Many established companies have already embarked on this digital transformation journey. A prime example is a partnership between GE and AT&T to deploy networked sensors in streetlights. The smart lights can dim or brighten, monitor air quality, keep an eye on parking spots, and even detect and report gunshots. To move digital transformation efforts further and faster, companies need to ensure their R&D organizations have the right business focus, incentives, culture and processes for growth. Ultimately, businesses need to see AI as an evolutionary technology that can deliver outstanding digital experiences for customers.
4. Technology trumps design
Today, the old adage “form follows function” can more accurately be rephrased “form follows emotion.” Designing for the “average user” is outdated. The more personal the experience, the more valuable it will be to the customer — and the more successful your product or service is likely to become. Whether it is a developer console, business productivity application or application for a consumer device, personalization matters.
While AI can be a powerful force for disruption, it is important to focus on what really matters most. From qualitative to end-user participatory research and concept evaluation, the goal of human-centered design is to find new ways to engage with the target audience on their terms. And the stakes couldn’t be any higher. According to a recent study by Salesforce Research, 64% of customers now expect real-time responses and interactions with brands. Half of those surveyed are likely to switch brands if companies do not anticipate their needs.
AI-based machine learning allows companies to collect and analyze data in real-time for a better understanding of the user’s experience with a product. It provides insight and intelligence on how customers are interacting with other users and services. In other words, AI offers the perfect opportunity to create digital growth opportunities, provided that you reimagine products from the end-user’s perspective. This means looking beyond the assumptions and misconceptions of using AI as a technology to using AI-specific use cases with true focus on understanding the needs, preferences, attitudes and motivations of a wide range of customers.
It is a disruptive time in the automotive industry. The industry that famously proclaimed customers could purchase a car in “any color, as long as it’s black” has been turned on its collective head. Henry Ford, the legendary automotive pioneer who uttered those words, was adamant in his belief that demands for more colors and options represented only 5% of the customer base and automakers should focus on the other 95%.
What a difference a few decades make. Automotive manufacturers who not so long ago only worried about styling and horsepower must now anticipate the demands of an ever-more sophisticated customer base; a customer base that often values connectivity and convenience over aesthetics and performance. These customers will no longer settle for two or three option packages.
This insistence on an abundance of options and the flexibility to change the mix of those options at any time places increased pressure on margins for automakers — especially considering they are already dealing with competition from nontraditional automotive companies, an unpredictable regulatory environment and the natural, cyclical nature of the industry.
Despite the uncertainty and increased demand for customization, one constant remains: metal must be bent, poured or ground, plastic must be injected, and components must be assembled into a finished vehicle. Manufacturing is the one constant in an otherwise volatile industry and therefore it is extremely important that automakers make their manufacturing processes as efficient as possible.
Support for more customization and fast reaction to customer requests is important in today’s automotive landscape, but it comes at a significant cost. For decades, manufacturing processes achieved efficiency through standardization; fewer variables meant higher economies of scale, lower labor costs and fewer defects. Increased customization and flexibility requires more sophisticated, costly manufacturing processes. Factor in the lower sales volumes to spread those costs across and it is easy to understand the push for more efficiency.
It is no surprise then that auto manufacturers are investing heavily in industrial internet of things.
Fortunately, the automotive industry has a head start with IIoT. Automotive manufacturers were automating and connecting manufacturing equipment on the plant floor long before the term “internet of things” was coined. That experience makes it easier to see the value in collecting and analyzing data being generated by plant floor equipment.
The advanced shop floor management techniques of IIoT rely on this data to calculate actual machine performance versus planned machine performance in real time. This immediate feedback detects and predicts breakdowns or inefficiencies — in both processes and equipment — and allows operators to take corrective action if a deviation from target is detected.
At a macro level, plant managers can compare manufacturing performance between plants, lines or machines to ensure all processes are running at peak efficiency.
Data collected from plant floor equipment can also eliminate unscheduled downtime by assessing the health of critical equipment and predicting equipment failure to schedule repairs before a breakdown occurs. This paradigm shift from preventive to predictive is enabled through analysis of plant floor equipment data and seamless integration to enterprise asset management applications.
In a typical high-volume automotive manufacturing environment, it can be difficult to trace with precision the production lineage of a given product. But with IIoT technologies, access to historical data collected from manufacturing operations enables both backward and forward end-to-end traceability to the root cause of a product defect.
In the automotive industry, niche products are becoming more and more important and as automakers struggle to meet the demands of more sophisticated and demanding consumers. IIoT technology provides an effective way to offset the increased costs associated with lower volumes and highly customized vehicles.