The connected car business is booming. According to BI Intelligence, the connected car market is growing at a five-year compound annual growth rate of 45%, which is 10 times as fast as the overall car market. Furthermore, the report estimates that by 2020, 75% of cars shipped globally will be built with the necessary hardware to connect to the internet.
As of today, connected cars come at a high price. Seventy percent of global connected service sales come from premium brands, but by 2022, that number will drop to 50%, allowing more customers to enjoy IoT features with their vehicles. One of the most desired features of a connected car is the safety package.
Currently, newer car models offer safety features such as automatic braking, emergency assistance and collision protection. As technology advances, however, so will the safety features for vehicles. Here is how IoT and big data are changing vehicle safety and what we can expect for the future.
IoT-enhanced safety features for vehicles
Using the OBD2 port of a car, drivers and manufacturing companies can collect a great deal of data, such as speed, seatbelt usage, braking information and fuel usage. Analyzing this data can result in useful information, such as if the driver brakes too harshly or doesn’t wear a seatbelt all of the time. Drivers can then change their behavior based on feedback.
Driver feedback in real time
Soon, connected cars will come with spoken alerts that can verbally tell a driver to slow down if they start to speed, perhaps without realizing it. Sensors can help prevent an accident by monitoring surroundings and alerting drivers when necessary. Furthermore, these sensors are connected to cell phones which ensures the driver does not call or text and drive, causing unnecessary distractions and accidents.
Connected cars will be able to prevent accidents with enhanced collision warning technology. Sensors will be able to recognize when pedestrians are in the street or if there is a dangerous intersection approaching. Vehicle-to-vehicle communication is also on the horizon, which will further help prevent accidents by allowing connected cars essentially to talk to each other.
Predictive maintenance can determine the condition of a car and when a potential problem may occur before it happens. Instead of a car breaking down on a busy highway, which is extremely dangerous for the driver and others on the road, predictive maintenance techniques can forecast the problem so the driver can take action before a breakdown happens. By analyzing data from the vehicle, the onboard telematics system can predict when a defect will happen, reducing the risk of an accident or malfunction for the driver.
The new safety features for connected cars
Most safety features are sold as part of the overall connected car package, but this may change in the future as customers customize which features they will use. Drivers could choose different automation features from their vehicle and personalized packages that offer different voice, motion and feedback functions.
As IoT advances, it will become more attainable for the average person, which means more and more people will drive a connected vehicle. While IoT has made a big splash in fleet management, it might take longer, due in part to expense, to trickle down to the everyday driver. The safety features, however, make it a desirable type of car to own, so when more affordable vehicles hit the market, they will surely be in high demand.
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.
So much of what’s written about IoT is either related to infrastructure, networking or telecom. Why is this? One might think that IoT is driven by these capabilities since it requires hardware, connectivity and management capabilities. I would argue, as would many of the public cloud providers, that it has very little to do with hardware and more to do with software. Folks from the likes of Amazon, Microsoft and Google would agree. They’ve even gone so far as to making it simple to deploy, manage, collect and analyze the data coming off the connected things. The main use cases are the output from these analytics, whether they be manual or automated.
These insights are meant to be consumed by users or other machines. This is one reason that IoT came from the carrier craze behind machine-to-machine (M2M) communications, a way for machines to process data coming from other machines. The M2M trend accelerated in the 2000s and reached its fever pitch in 2010, but since then has largely been replaced by IoT, where the use cases go far beyond the carrier space. Most of M2M vendors ended up shifting to IoT, and largely changed approaches, attempting to find the market that eluded them. This shift doesn’t seem to have materialized into meaningful revenue or growth yet, but that may change — the jury’s still out on that strategy.
M2M’s evolution changed concepts from managing a network of devices or service of a network toward things which can be measured in the real world. This is something that allows computing to have access to far more context, including where a device is, the environment the device is in, how it’s being used and how well it’s running, and allows us to programmatically answer countless questions — the possibilities are endless. The value of this data in isolation is limited, but as we start to collect, measure, analyze and integrate the insights from this data to other computing platforms, it becomes incredibly powerful and allows machine learning to appear magical — or what many call artificial intelligence.
Unfortunately those managing and building capabilities for IoT are met with a high degree of complexity. IoT platforms attempt to simplify things, but each has its own ecosystem due to a lack of standards across platforms and hardware. If that fragmentation isn’t bad enough, the lack of well-formed standards also creates further interoperability and security challenges which show up in news headlines daily. We take standards for granted, assuming they will be there to help us avoid lock-in and to enable computing to work across providers and technologies. In IoT, these are completely missing. This causes major gaps in operating and assuring the services and products built on IoT. Through the years, our customers have expressed their concern over these operational challenges, which is why we’ve been investing in building solutions for these problems. But the requirements are always expanding based on the specific customer environments and use cases — an interesting and expanding problem, indeed.
Thankfully, getting visibility across these disconnected components has been made easier. Several application performance monitoring (APM) companies that specialize in visibility and observability are trying to solve this issue with a combination of APIs and automated instrumentation. These platforms, which are not tightly coupled to specific technology providers, enable a view across these technologies. We’ve built one at AppDynamics, and there are other providers doing similar work. This is a natural extension of what we do in the APM market. One of the key capabilities across APM is providing cross-technology visibility for operations, and then analytics on top of the collected and correlated data. The interconnected nature of things, mobile apps, web apps and back-end services makes it a natural fit for APM technologies.
One great example is the connected car, where we see a large set of complex systems involved which start based on drivers using features and apps in the car. In order to run these apps, the carrier/mobile connectivity is heavily used and, naturally, the back-end services of these apps are critical in making connected car systems function. In most cases, there are several third parties involved in delivering and managing connected car services and apps. When there are issues, such as the unlocking feature not working, understanding where the problem is — and which team or third party can remediate the issue — is a big problem. These problems wind up upsetting users and tarnishing the car brand. Being able to visualize the end-to-end flow is a major win across many organizations, for both those in IT and executives who are betting on the connected car being a differentiator. Interestingly, as with any technology, manufacturers are following each other. Four years ago, our first connected car manufacturer started using AppDynamics’ APM system to solve such challenges and gain the visibility needed to visualize the end-to-end flow and keep drivers happy. Fast-forward to today and many top luxury and premium car brands are using our APM technology to help manage this complex connected car ecosystem.
There are many other IoT use cases where similar situations occur, such as connected factories, energy generation and distribution, smart cities, government and more. Solving this complexity will be an increasingly important issue to satisfy customers and ensure that these organizations can continue to innovate and deliver capabilities that differentiate them from their competitors. It’s an exciting time in software when our technologies can help provide that edge, release more quickly or react rapidly to find new opportunities or resolve brand-affecting issues.
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.
Asset utilization has quickly become a top use case across businesses adopting IoT. It directly contributes to the bottom line by helping businesses confirm whether or not equipment is performing at peak availability, performance and quality. Because operators can tell exactly how equipment is operating, they know where to focus reliability improvement efforts to drive the highest overall equipment effectiveness (OEE). But the range of benefits made available by asset utilization data goes far deeper than many expect. Businesses further along with their implementations have discovered that they can make better long-term decisions, such as asset design, to save on costs and unlock even more ROI and operational performance into the future.
Businesses are often challenged with locating assets in the field and understanding how effectively they are being used. An asset utilization data strategy can provide operations managers real-time visibility into these revenue-generating assets, such as settings and environmental factors. But it can also enable the creation of complex business logic that can take action based on a predefined set of rules. In regulated industries, such as oil and gas, it’s also easy to see which assets are out of compliance.
However, the value of asset utilization data extends far beyond this core group of uses. Here are three additional ways we’re seeing businesses apply their data to reach new goals and improved performance.
Unexpected benefit #1: Defer capital investments
Asset utilization data can be used to extend the useful life of capital equipment for greater return on assets and deferred capital investment. This is especially key in the manufacturing space, where operators want to use technology advances to improve energy consumption to reduce costs and increase profitability.
For example, CNC/milling machines are already instrumented for properties such as chatter, vibration, thermal stability, hydraulic pump flow rate, etc. so as to minimize the power consumption for spindles and peripheral equipment. IoT software that provides detailed asset utilization data can extend equipment performance even further. By collecting machine data and establishing patterns and trends, operators can identify usage behavior and settings for highest throughput at lowest cost.
If you’re thinking more equipment is required for yielding greater volume, asset utilization data can help you do more with what you’ve got and save on future capital investment.
Unexpected benefit #2: Uncover underutilized assets in real time
Asset utilization data can be used to support geofence zones, so unique logic can be applied per zone or asset. Manufacturers of commercial trucks, as well as fleet owners and independent owner/operators, are often challenged with locating assets in the field and understanding how effectively they are being used. IoT systems can collect an asset’s relevant telemetry, contextual data and status against utilization metrics, so underutilized assets can be swiftly reallocated. Operators can even get alerts via email, SMS or other means when an asset is not being used.
If you’re having trouble finding out quickly which assets may be underused and reallocating them accordingly, then asset utilization data can help you.
Unexpected benefit #3: Design better equipment
Hindsight is 20/20 and equipment design is no exception. By having a history of asset utilization data, equipment manufacturers can better understand how their products are functioning in the field and use that information to inform future enhancements.
For example, in the oil and gas space, artificial lifts are being employed in higher volumes to improve recovery rates, maximizing production. It is impossible for the companies that design and build those pumps to foresee all of the operating conditions they will be subject to. They have to engineer based on simulations and a small sample of test wells.
Asset utilization data from real-world customer installations improves that overall dataset, providing a wider range of use cases and ultimately a better designed and performing product.
As the above examples have demonstrated, asset utilization data delivers powerful short- and long-term business value to a broad number of industries. In our recent study (registration required) of more than 300 senior-level personnel at manufacturing, transportation, and oil and gas organizations, it was clear that asset utilization is increasingly becoming a priority for their business. More than 90% of IIoT adopters cite device health as the primary reason for IoT adoption followed by logistics (67%), reducing operating costs (24%) and increasing production volume (18%).
While the immediate goal of your asset utilization deployment might be to ensure OEE, there is a broad range of capabilities to explore beyond this business goal that will unlock additional cost savings and revenue. Its broad versatility has become a favorite of businesses that prefer to start more narrow with their IoT initiative and build on it over time as their needs and priorities change.
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.
Look around an airport, a city or a factory and you will see many opportunities for IoT applications. Look deeper and you’ll see numerous clusters of data, some well used and many others underexploited.
Taking the case of a city, one cluster corresponds to open data. These are published data sets that provide citizens with access to information about local services. Or statistics about crime and performance levels for public services, for example.
A second category of data-driven applications applies to operational challenges. Many cities deploy operational support applications to manage city resources; think of smart street parking or bus and bicycle-sharing services. Other operation-style applications include management of a city’s workforce to optimize waste collection or street maintenance activities.
Data from social media feeds represent a third category of data. By monitoring and responding via such feeds, a city can respond to citizen needs while keeping track of the sentiments expressed by local residents and tourists.
As the design and deployment of IoT matures as a discipline, organizations will consolidate their management of applications and data across these three categories. This will drive economies of scale and cost efficiencies from using application hosting technologies and IT expertise. In due course, this type of consolidation will create new application opportunities. City departments will find it easier to collaborate. And application developers will find new uses from joining up IoT data from different silos.
Smart cities: Data-sharing framework
In a recent white paper, the Alliance for Telecommunications Industry Solutions (ATIS) analyzed the transition that smart cities will experience as they make greater use of city assets and data. ATIS’ report, “Smart Cities: Data Sharing Framework,” explains how cities are likely to consolidate data from the three categories — open, operational and social — into a data exchange. A data exchange is essentially a technical framework for bringing data together from multiple sources. A data exchange makes it easier for application providers to weave together data and applications in order to work across inter-organizational boundaries.
The data exchange concept, however, only goes so far in using the value of data. This is because it satisfies the needs of one group of users, the individual departments with a local government agency, and their smart city application priorities. A far more interesting prospect is to tap into a wider ecosystem of innovators and users. These are individuals and organizations that will approach the challenge from different angles. They will tackle other service needs and look for other sources of value from city data assets. As a proxy for this, consider how some organizations collaborate with service provider partners or academic institutions to inject new ideas, explore new application opportunities and use expertise that they lack in-house.
By extending the multiparty concept to a much larger scale, ATIS identifies the improvements necessary to progress from a data exchange to a data marketplace. The latter is where public and private sector organizations can collaborate to combine and trade data with a view to building new applications. Just like a real work marketplace, the data marketplace provides common rules of engagement for all parties. Ideally, it should have low barriers to entry, encouraging innovators and startups to participate. It should also rely on an agreed set of commercialization and data licensing protocols. Having these elements in place will ensure a smooth running business environment for all concerned.
AI. Overwhelmed. Alexa. Autonomous. Robots.
As in the past few of my now 20 years of attending CES, the question I most hoped to avoid was “Did you see anything cool at the show?” I bristle at the query each year as it is an unfair question.
The question might have been appropriate when the story of CES could be characterized by the debut of the portable computer, HDTV or the flat screen TV. To ask what technology I “saw” at the event reveals misunderstanding that technology today is really more about what cannot be seen — AI, cloud, data analytics — than devices. And, I must say, everything there is cool — from GPS collars for pets to the smart bed, which senses your heart rate, breathing rate and movement and adjusts its firmness accordingly.
This year I felt like I “saw” less of the show than ever, so I listened carefully to comments to gain an essence of the show. What did I hear?
AI. Artificial intelligence, or at least the early implementations of it, was, by far, the most talked about topic. At my company, People Power, we talk about it regularly as the big differentiator that will transform the clutter of smart devices at home into seamless, personalized, intelligent systems that will elegantly and quietly improve security, convenience and economy at home. It seems that most of the industry either understands the transformative potential of AI, or understands that the term better appear in their marketing literature, else their offerings be perceived as “simple technology.”
Overwhelmed. This is a feeling I get every year, and I heard enough people agree. It’s not just the endless sprawl of the show that is overwhelming, but the extent by which microchips and radio waves have penetrated almost every single object and occupation of life. To perceive the current state of technology by attending CES is akin to understanding the depth and breadth of the seven seas by weekending at the beach. One can no longer get their head around technology, and to say that it has permeated “everything” is no exaggeration.
Alexa. Amazon’s voice technology, for the third straight year, is perhaps the most revolutionizing force in the tech world. In three short years, the monolithic towers have transformed to elegant home art items, fueled by respectable competition from Google Home. Now the technology is embedded in cars, thermostats and appliances. With Google Home pushing, the two are accelerating widespread adoption into nearly 25% of internet-connected homes. Apple’s delay in this space may have cost it the opportunity, as these two competitors have driven voice interface technology past the chasm. People Power and many other smart home systems are benefitting from hands-free control.
Autonomous. Few tech power houses have not already made massive investments in autonomous driving technology. Sony, for so many years the king of entertainment devices, dedicated much of its city-block-sized exhibit to demonstrating line-of-sight intelligence required for safe autonomous navigation. Given that some tech giants, such as Intel and Microsoft, suffered greatly by failing to capitalize on the tectonic shift to mobile at the turn of the century, many unlikely players are racing to claim a large stake in autonomous transportation.
Robots. Much of the chatter was about the many offerings of free-standing, human-impersonating robots. While these devices are so far from being replacements for humans or pets, their functionality is impressive. The lack of incremental improvements in these devices from 2017 CES offerings, however, is a reminder of how complex and difficult it is to create versatile, flexible and reliable robotics. Never before, however, have so many companies offered a concept of how robots will, at some point, master certain functions from cleaning floors to surveying secure premises to simply offering companionship.
A final impression of CES is that the pervasiveness of technology, with truly amazing functionality and very low prices, has far outstripped demand cases for electronic products in every part of our lives. We have now entered a time where smart devices that we never thought required intelligence will surround us everywhere we go, at prices that we cannot refuse to try. 2018 will be a very interesting year in consumer electronics.
In spite the fact that the term “internet of things” was coined by Kevin Ashton, executive director at Auto-ID Center, as the title of a presentation he made at Procter & Gamble in 1999, it was only after companies like Pachube, an early leader in the burgeoning IoT field, launched web services that enabled organizations to store, share and discover real-time sensor, energy and environmental data from objects and devices around the world that most of us believed the time for IoT had finally arrived.
Since its founding in 2008, Pachube pretended to be the leading open development platform for the internet of things. In 2011, when the company was acquired by Woburn, Mass.-based LogMeIn, Inc. in a deal that was worth approximately $15 million in cash, the service was rebranded as Cosm, but it was still a beta test version. It was finally launched as Xively and became a division of LogMeIn, which did not want or know how to incorporate the potential of Xively into its business.
Then, in 2017, Xively lost its charm.
On Feb. 15, we woke up to news from Bloomberg that Google will acquire IoT platform Xively from LogMeIn for $50 million to expand its market for connected devices. Here, Google has become the white knight of Xively.
Other white knights
On Dec. 30, 2013, PTC announced it acquired ThingWorx for approximately $112 million, plus a possible earn out of up to $18 million. The ThingWorx acquisition positioned PTC as a major player in the emerging internet of things era. Then in July 2014, PTC acquired Axeda Corporation for approximately $170 million in cash, which Gartner estimated was an acquisition multiple of just over six times revenue.
In Feb. 2016, Cisco acquired Jasper Technologies, Inc. for $1.4 billion in cash; what a wonderful white knight.
In Sept. 2016, software goliath SAP acquired the small IoT startup Plat.One.
Also in 2016, Microsoft did not disclose the sum for its Solair acquisition, an Italian startup that expanded Azure capabilities.
In March 2015, Amazon took another step into the internet of things by acquiring 2lemetry, a startup with a system for sending, receiving and analyzing data from internet-connected devices. 2lemetry had raised at least $9 million from investors, including Salesforce Ventures.
We all know the IoT platform market needs quick consolidation
The M2M/IoT platform market has changed in the last 10 years. The fragmentation is unsustainable, and all I can say is that I do not see a clear IoT platform market leader yet that works as a plug-and-play fix for all kind of connected device creators. Besides, the rush of investors for IoT platform companies triggers rumors of new acquisitions significantly increasing their actual valuation and encourages thousands of entrepreneurs and startups to create new IoT platform copies of each other. Although there is still room for new, innovative IoT platform startups, the decision to trust a company to be able to simplify the complexities of IoT with a scalable and robust infrastructure and drive real results for your business will reduce the choices to a short list. The bad news is that the hundreds of IoT platforms startups out there must now compete with the platforms offered by tech and industrial giant vendors.
Given the confusion that exists around IoT platforms, companies need to approach expert advisors who can recommend which platform(s) is most suitable for their current and future business and technical requirements.
There will not be white knights for everyone
In my recent post “Be careful of The Walking Dead of IoT,” I alerted that in spite of no one having a crystal ball, it is almost inevitable that many of the IoT platforms that exist today will not be around in 10 years — or maybe not even within one, two or three years, given the inflated market.
Some tech giants have been looking for and already found some of the best pieces. But what will happen to the 700+ platforms out there? While there will not be white knights for everyone, at least some IoT platforms have had a happy ending.
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The cyber community is seeing an increase in dialogue between legislators and companies developing IoT devices about the need for regulatory oversight and whether government intervention to secure IoT is needed — or should be feared. Here is a list of six questions that are at the forefront of the debate:
What is government’s role in securing the internet of things?
Government cannot solve the entire problem, so it first needs to understand its role and make the most of it. This means it needs to work strategically and top-down, starting with assuring that the whole system is secured. Call it a “systemic strategy.”
The government needs to identify and protect systems with a cyber-shield — rather than the piecemeal, element-by-element approach that Capitol Hill is discussing.
Government also has an important role when it comes to convening and leading forums to establish best practices for the creation of a secured ecosystem, including infrastructure frameworks that contain IoT within them.
If there’s one thing government can do better than industry, it is to assemble the best brains and talent in the country, from a cross-section of disciplines — software, hardware, manufacturing, AI — to focus their expertise on the challenges we face and to ask the right questions.
Another role government can play is to be the national educator — this will put pressure on vendors to start investing more on the security side of their devices.
Should the cyber community put pressure on lawmakers to legislate data security standards, best practices and processes, or is this a step too far?
This is the $64,000 question. Wisdom and long-range thinking are required here. There is a risk to published standards, because these standards are essentially an instruction manual for attackers in how to avoid detection. We want our “defenders” to innovative and think outside of the box — recognizing that attacks are changing constantly. Top-down standards create a dangerous “check the box” mentality.
That said, if done prudently, best practices and processes should be able to balance the need for cyber-creativity within a rules-based framework that the public can trust. So, no strict standards, but generally accepted best practices and processes should be legislated.
Does IoT need more consumer protection?
Consumers need to have faith in the products and services they buy, whether that is a mortgage, a computer software or a pacemaker. Not every product needs the same level of protection, and not every attacker is after the same thing. I don’t think anyone would disagree that the “cyber murder” — as in accelerating an IoT-connected pacemaker — requires the most sophisticated security we have to offer.
As IoT evolves, and if attackers start to target IoT devices with increasing frequency, then of course more consumer protection will be called for. But I believe that if the industry proactively and effectively addresses the security challenges it faces, then IoT will develop comparable security protection to other internet-connected devices. So, reasonable concern is appropriate, hysteria about IoT teddy bears attacking kids isn’t.
Which IoT devices are the most vulnerable to attack?
General speaking it is the cheaper ones. Because the manufacturers of low-cost IoT devices would rather create new hardware models than patch existing ones. It may seem counterintuitive, but patching can cost more.
Without patches, these devices become more and more exploitable. Vulnerabilities will eventually be discovered and exploited in any IoT device — it’s just a matter of time. So the question really becomes how often vendors are willing to release and update the software with security patches.
Also keep in mind that consumer IoT devices which include audio and/or video functionalities will be more attractive for attackers. That makes sense for a number of reasons, including human nature: It’s interesting to hear and see what is going on in other houses, and this can lead to ransom attacks, as well as industrial and national espionage.
Why are smart homes so vulnerable and what should consumers be aware of?
The smarter your home is, the more vulnerable it is. That’s because each and every IoT control point becomes a potential entry point for attackers. If your house was a business, we would designate each of those as nodes “business critical.” So your threat surface widens as you add smart speakers, doorbells and so on. But unlike businesses, homes don’t typically deploy advanced cybersecurity protections.
Meanwhile, your audio and video camera devices can be used for not just benign privacy violations, but for malignant violations such as ransom and espionage, as I mentioned before.
It’s important to point out that the goal for a frictionless smart home is interoperability. If your refrigerator, doorbell and teapot operate on different systems, that’s painful. But the ultimate consumer experience — interoperability, automation and simplicity — is the ultimate playground for attackers because it widens the cyber-attack surface, making the network more attractive to attacks.
IoT vendors know they cannot succeed without supporting a frictionless, interoperable environment. They will always make that a priority over everything else — including security. This puts a lot of pressure on security vendors to innovate technologies that will allow the IoT industry to grow without exposing consumers to undue risks.
Can we trust the IoT industry to continue improving cybersecurity features?
For the reasons I mentioned above, at this point we cannot, because security is simply not aligned with the business interests.
The industry will need some combination of government pressure, consumer awareness and probably a major event that the media can latch on to — “Cyberattackers take over smart refrigerator; man goes into beer withdrawal and rushed to hospital” — to make security a priority.
Security is currently not aligned with the manufacturer’s business priorities. They will need to feel some kind of a pressure from the government, which will make it a business issue for them. A pressure can start with just making the consumers more aware to the risks.
Despite the attention placed on improving IoT device security, it still remains a weak link. Most of these devices do not have basic security capabilities, and when they do there’s often a configuration problem that renders it vulnerable. The sheer number of IoT devices provides many opportunities for hackers to commit security breaches.
There are now more devices than there are people on the planet, and this number is expected to reach more than 20 billion by 2020 according to Gartner. The wide range of IoT applications run from sensors placed in cornfields to connected cars and even connected sports equipment. This breadth of usage provides hackers with many opportunities to poke around, find security holes and then control the devices and/or steal important data.
To prevent IoT-related threats, firms engaging with such devices should squarely focus on security for the remainder of 2018.
Improving ransomware protections
The WannaCry attack that started in May 2017 is an example of how quickly ransomware can spread. It took advantage of users that did not deploy a Microsoft patch, and was able to spread to more than 230,000 computers and cause hundreds of millions (if not billions) of dollars in damages.
The ransomware threat for IoT devices is growing, as hackers are met with improved security of PCs and networks and they want to find alternative “easy marks.” A group of white hat hackers showed two years ago how they took over a smart thermostat from hundreds of miles away as a demonstration of how hackers could hold such devices ransom. The implications for such attacks are staggering, with the potential for hackers to commandeer IoT-connected machinery, connected cars and other systems. For example, Johnson & Johnson warned in 2016 that one of its insulin pumps was susceptible to hackers, who could conceivably control the pumps and deliver an unauthorized injection.
The aim for many of these hacking attempts will be to control (and ransom) the actual devices, and many firms will be tempted to pay because their businesses rely so heavily on the uninterrupted performance of those very devices. Consider an industrial setting where locking all IoT devices could mean interruption to the power grid, or cessation of all work on a production line. And even if the hacker’s end game is not to control the thermostat, these IoT devices are still all connected to the home Wi-Fi and act as an easy entry point to the network.
Making the case for encryption
Security professionals should also implement encryption protocols for data when it moves between IoT devices, while it’s static, and transitions to back-end systems. Using cryptographic algorithms for IoT data helps firms ensure data integrity and mitigates risk as a target for hackers. The industry challenge for 2018 is how companies will develop encryption protocols and processes that work best for the massive range of IoT devices. For sectors such as healthcare that are being transformed by IoT, encryption is mandatory. Such devices are transmitting very personal and identifiable data about patients, and in some cases the data is exposed in transit.
IoT devices are susceptible to botnet attacks, such as Mirai and JenX, which target routers, digital cameras and other devices that are connected to the internet. These botnets pull together bandwidth, which can then be used for distributed denial-of-service and other forms of attacks.
There are several recommended security improvements for IoT, including the need for a system of regular software updates. Unlike PCs or networks, many IoT devices do not receive any updates, so they’re left in the same security state as they were when they left the manufacturer. The problem is that hackers are always trying to exploit devices through new methods and programs. Without updates, the devices themselves are vulnerable because they aren’t programmed to deflect the very latest hacking attempts. Device manufacturers should ensure their IoT components are set up with regular updating (and users must perform the updates) in order to deter exploit attempts.
Additional IoT cybersecurity initiatives include two-factor authentication between machines, so IoT implementations can have an extra layer of protection with second factor, but without the need for manual human entry. There’s also the need for improved management of multiple user access for single devices through biometrics and advanced digital certificates. Data analytics and machine learning can play a role by analyzing information about IoT security issues and helping to develop the best future protections based on past events. Such analytics can also be used to spot threats in action by detecting anomalies and then automating preventative measures.
As the number of IoT devices continues to rise, and these devices alter how people work and play, there’s a corresponding need for protection. The industry as a whole needs to shift to improving security on the front end by refining security protocols and developing standards. And there needs to be complete security through the device’s entire lifecycle. Companies must focus their attention on all cybersecurity threats for the rest of 2018, with an emphasis on developing plans for IoT security.
Security cameras represent a large global market driven primarily by the increased adoption of video surveillance systems for business or public-sector intelligence, as well as rising threats associated with public safety. As such, surveillance cameras are now common in and around government buildings, military posts, businesses, banks, transportation centers, casinos, shopping malls, sports venues, historic landmarks, schools and many more.
Surveillance isn’t just about security any longer, but in many cases, it’s about extracting value and intelligence from the video captured. This could include retail shopper behaviors, or in managing a parking facility, or when producing manufactured products. Seeing a drone flying through the air capturing images and video nowadays from a construction site or farmland is no longer unusual.
As such, the video surveillance market is experiencing burgeoning growth, and as of 2016, was valued at over $30 billion, with expectations of reaching $75 billion in revenue by 2022, at a compound annual growth rate of 15.4% from 2017 to 2022. What has changed in surveillance is not how data is captured, but how it can be used to drive actions, not only as part of a fast data application that analyzes data as it is captured, but also as part of a big data application that analyzes data when required. It’s no longer about just storing data, but what we can do with it once captured that is fueling a new generation of ‘smart’ video applications.
What is smart video?
Smart video is about this shift from imagery to insights, from simply collecting data for forensic and backwards-viewing to analyzing and understanding the context of the data captured. It uses artificial intelligence and algorithms derived from big data to provide immediate insights and forward-looking predictions. These fast data examples include:
- Parking space management where analytics can be used to determine peak hours of operation, handicap parking use, areas of congestion, average parking durations and unmoved vehicles.
- Machine production analytics can be used to determine yields produced, failures that occurred or about to occur, machine issues and inefficiencies, upcoming maintenance and peak hours of operation.
- Customer retail buying preferences where analytics can be used to determine how many people entered the store, their gender and ages, in-store time spent, average spend and traffic generated by the new kiosk.
- Agricultural drone surveillance where analytics can be used to survey a farm and surrounding land, diagnose vegetation and crop health, determine possible yields, and track livestock and food consumption, as well as insect and pest populations.
- Smart city scenarios where analytics can be used to provide safety and evacuation information, and can coordinate with weather and traffic data to create the fastest evacuation routes out of a city.
The need to provide intelligent capabilities within video surveillance, coupled with the development of cloud-based surveillance systems, has led to the evolution of a smart breed of cameras at the network’s edge. These edge-based cameras have a powerful computing element and capable storage device implemented within that enables local capture and analysis (where the data is generated and lives), providing the valuable insights in real time, without the effects of network availability or latency.
Sample use case
Authorities are looking for a missing senior citizen with mental incapacities who may need help. They believe he entered a store and left. In a big data application, someone would have to review tons of captured video, looking backwards to find evidence of this lost soul in the store, and possibly perform some additional analysis on the data to determine his actions, identify the time he entered or left the store, and take some action. In this example, big data analysis is performed after the event has occurred.
Utilizing AI and algorithms from big data, fast data responds to events as they occur. Once the senior citizen enters the store, a fast data app can perform real-time facial recognition from the video feed, comparing the senior’s face to a database library of facial signatures. If the facial signature is detected, the application can trigger a security alert to help the senior in distress and get him back to his family safely.
The data storage strategy
As big data gets bigger and faster, and fast data gets faster and bigger, the storage strategy is to not funnel all of the video content to the main server, which is expensive and dependent on network availability, but instead, use a combination that stores data locally at the camera-level, as well as an edge gateway that enables video and data to be aggregated at various distances from the edge, and back to the cloud where big data content typically resides. A video surveillance system that uses edge cameras and this storage strategy will reap high system and service reliability, low TCO and the ability to scale without adding expensive recorders or servers to the surveillance system.
Fast data applications for smart video are endless and have only scratched the surface of real-world use. We amass and generate large amounts of information from the increasing number of data points captured by such edge devices as surveillance cameras. Applying analytics to real-time captured data is driving new smart video applications whose video streams extract value and intelligence that drive actionable insights.
Forward-looking statements: This article may contain forward-looking statements, including statements relating to expectations for Western Digital’s embedded products, the market for these products, product development efforts, and the capacities, capabilities and applications of its products. These forward-looking statements are subject to risks and uncertainties that could cause actual results to differ materially from those expressed in the forward-looking statements, including development challenges or delays, supply chain and logistics issues, changes in markets, demand, global economic conditions and other risks and uncertainties listed in Western Digital Corporation’s most recent quarterly and annual reports filed with the Securities and Exchange Commission, to which your attention is directed. Readers are cautioned not to place undue reliance on these forward-looking statements and we undertake no obligation to update these forward-looking statements to reflect subsequent events or circumstances.
Our world is changing faster than ever before. Driverless cars, virtual reality surgery, smart AI assistants — these innovations are no longer science fiction. Technology is evolving to meet our needs and rising to the challenges of our changing world. In particular, the internet of things has expanded immensely, enabling incredible innovation across industries. The past year was a pivotal one for IoT — we saw more applications of IoT come to market than ever before, with Gartner predicting there would be 8.4 billion connected “things” in use by the end of 2017, a 31% increase from 2016.
Looking back on 2017, it’s clear revolutionary new applications of IoT changed our world and helped to improve existing technologies. IoT has had significant impact in industries like healthcare and transportation. Additionally, progress in low-power wide area network (LPWAN) technologies have created potential for connectivity for an entirely new class of objects and systems. While IoT’s impact is far reaching, there were several key developments in 2017 that point to the future of this dynamic technology.
Connectivity helped doctors change lives
IoT is enabling the next generation of healthcare. Since connectivity allows for mass data aggregation, medical professionals have access to more information and insights than ever before. We’ve witnessed the impact firsthand working with Ekso Bionics, a pioneer in wearable exoskeleton technology. Ekso Bionics created the world’s first FDA-cleared connected exoskeleton for rehabilitating patients with stroke and spinal cord injuries. By providing connectivity to Ekso Bionics’ EksoGT exoskeleton suit, patient data can be shared with medical professionals in real time, providing a more complete picture of patient progress. These insights allow therapists to make adjustments accordingly, making rehabilitation safer and more efficient.
These methods proved so successful that in 2017, the company saw a 30% year-on-year increase in utilization of the exoskeleton. Today, there are more than 180 rehabilitation institutions around the world using the EksoGT to help their patients get back on their feet sooner. As this technology advances in the next few years, wearable exoskeletons have extraordinary potential to change the lives of individuals with stroke and spinal injuries.
Our everyday everything got smarter
While many IoT discussions may center on consumer applications like smart lighting and thermostats, in 2017, the industry saw significant focus on LPWAN. Low-power wide area networks are an emerging, high-growth area of the IoT market, designed for low-cost application with low data usage. They are a particularly compelling network option for hard-to-reach-places, as they feature long battery life with low latency and can operate in remote areas. LPWAN offers great potential to connect everyday objects that have never before been connected, like parking spots and garbage cans.
Narrowband IoT is a leading LPWAN technology that has emerged as a driver of innovation in smart city development, an area that saw significant progress this past year. Cities around the world are connecting infrastructure like streetlights and parking lots. These systems provide better traffic control and improve safety. They can also help drive efficiency by keeping energy and maintenance costs low.
Plugging into the ride-sharing economy
We may be on the brink of the autonomous car era, but today, some of the most impactful change IoT is making on the transportation industry is in ride-sharing.
Last year, we partnered with Mobike, the world’s largest smart bicycle sharing service from China, to bring its innovative sharing service to Singapore as a first step outside of China. Each Mobike is equipped with a smart lock embedded with IoT SIM, enabling users to locate an available bike near them through a dedicated app. This is an important aspect of Mobike’s services since there are no dedicated racks for the bikes. Instead, riders can securely park in any authorized location in a city, and the bike is then found by the next rider thanks to its connectivity. Additionally, while the bike is in use, GPS tracks usage information transferred via IoT SIM, enabling the aggregation of transportation data. Mobike has been hugely successful, creating an alternative ride-sharing option that is convenient for users and environmentally beneficial for cities. Looking ahead, the data aggregated from the bikes could be used to better understand city transportation infrastructure.
IoT innovations changed the world in 2017 with progress in healthcare, transportation and LPWAN offerings. We’re excited to explore how these developments will set the course for IoT in 2018 and the years to come.