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.
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.
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.
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 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.
Thanks in advance for your likes and shares.
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 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.
Artificial intelligence is being celebrated as the innovation that will change the world. And while it undoubtedly has a multitude of applications and uses, it’s worth remembering that one size does not fit all. When a company looks to deploy AI technology, there are many business-specific challenges, so making the right choices can be tricky.
For example, just recently, yet another AI-related breakthrough was announced: A robot dog learned to open a door to allow another robot dog to walk through it. While it is well-acknowledged that the invested research and development for this mission was huge and the commercial potential for some applications is enormous, it is somewhat unclear how this specific innovation or the core models and algorithms of it can serve other industries and verticals. Herein lies the problem.
Gauging AI success in one field in many cases can be meaningless for another. To make things worse, even when trying to go deeper into the technology and attempting to evaluate, for example, which machine learning algorithms are utilized by the product, or what are the number of layers in the deep neural network models mentioned by specific vendors, in the end it will be possibly pointless as it does not directly reflect the technology deployment “success” implications.
Nevertheless, it seems that the market ignores this reality and continues to evaluate AI-based products by buzzword checklists using familiar and related AI terminology (e.g., supervised, unsupervised, deep learning and so on). While checklists are an effective tool for comparative analysis, it still requires the “right” items to be included. Unfortunately, what typically is absent are the items which are important to the customer, from a problem-solution perspective.
Introducing authentic AI
Given all of this, there is a need to change the narrative around AI technology to something meaningful and authentic that reflects the real-life challenges and opportunities that businesses are facing. This is the time to introduce authentic AI.
The Merriam-Webster dictionary defines authentic as both “worthy of acceptance or belief as conforming to or based on fact” and “conforming to an original so as to reproduce essential features.” This is not about fake to be contrasted with real; it’s about the essential features of AI which need to be acknowledged, and hence, redefine the “checklist.” Often, these essential “authentic” features are hidden and only surface when a CIO or CDO is faced with a new problem to be solved. This is seen especially when the AI aspects of a proposed product are fully explored by asking questions such as:
- Is the AI technology utilized by the product aimed specifically for my problem, optimally (e.g., performance, cost, etc.)?
- Is it capable of addressing the complete problem or only a part of it?
- Can it be assimilated into the existing ecosystem without imposing new demands?
- Can it address the compelling environmental conditions of the problem space?
These issues can be grouped into three different “classes:” original, holistic and pragmatic.
Original — How innovative is the solution? This can be quantified by assessing the following:
- The invention of new algorithms or even new models;
- The use of complex orchestration techniques; or
- Through the capability to handle complex data formats and structures.
While there is no need to reinvent the wheel repetitively for any problem, there are distinctive characteristics which require optimizing.
Holistic — How complete is the proposed AI technology? It takes into account the capability of handling the end-to-end aspects of the offering, the competence of harmonizing the operation of the various AI components of the technology and the ability to adapt to ever-changing conditions of the AI application.
Pragmatic — Can the technology solve real-world problems in their actual and natural space in a commercially viable way? This means that, for example, the data sources can be processed in their most native format (unstructured or structured), as well as provide insights or results matching the pragmatic needs of the specific market expectations. In addition, the ability to be quickly deployed and rapid to act are assessed.
All of these elements should be used to systematically assess and evaluate AI-based products and technologies to assess their authenticity and therefore effectiveness in specific use cases.
For example, many home loan mortgage evaluation and recommendation systems utilize a somewhat isolated machine learning-based applicant classification method, one of many processes included within the product. The AI in this system cannot be considered authentic AI to a high degree as it scores low on the original and holistic classes as it isn’t innovative enough (from an AI sense). In addition, the AI component itself does not cover on its own the end-to-end aspects of the technology (hence, affecting the overall performance and precision). It could be considered to be pragmatic to some level if it can handle the required data sources of financial institutions or the customer applications natively, and if the technology’s “outputs” are the explicit results required as a specific recommendation (e.g., loan conditions). However, the deployment timeline (time-to-market) and commercial aspects need to be evaluated as well. This is just one example of many others, covering all kinds of variations.
So, in the case of the door-opening dog, although many people heard about it, its application is fairly limited — in fact, you could say that its bark is definitely worse than its bite.
What is blockchain? And why has blockchain with the supply chain created huge expectations in the industry circles? First, blockchain is a glorified ledger which cannot be hacked to alter any transactions that have happened in the blockchain network. All the nodes in the blockchain know about all the transactions that have happened since the network started. Hence, to compromise a transaction, all the nodes or the majority of the nodes have to be compromised in a reasonable amount of time, which is not practically possible. Blockchain was proposed as a platform on which cryptocurrencies can be generated and distributed, and bitcoin was the first currency to use blockchain. Slowly, people started realizing that blockchain technology can be used for other use cases where a distributed trust or trustless network is needed. For example, contracts can be made smart by using blockchain to register and maintain them. Any change in contract can be tracked and the sanctity of the contract will be secure. The same goes for the supply chain, where orders placed on blockchain can be tracked from start through delivery.
Power of blockchain
Blockchain technology is all about tracking a transaction from the start until the end, and all the nodes in the network register and update themselves with the status of transactions. The power of blockchain comes from the fact that there is no central authority to maintain the record of transactions; the blockchain is distributed and therefore trust is also distributed and not negotiable. All transactions are shared among participating nodes, and any node that gets compromised can be easily isolated since for any transaction to pass muster, the majority of nodes should agree about the said transaction. This feature of blockchain is the key to its strength and can be adopted to variety of use cases. One such use case/domain where blockchain can be adopted is the supply chain, where orders are placed, tracked, delivered, invoiced, returned and so forth.
Blockchain in the supply chain
A supply chain is a business where orders and the fulfilment of orders and delivery of items are all meshed together in a gigantic mesh or, to some, a gigantic mess. Each customer who orders an item and the supplier who supplies them do not see eye to eye for most of the transaction’s phases. The customer who has ordered a certain item might have ordered the item for somebody else who happens to be his customer. The end customer will not have any visibility of his orders unless the first customer updates his system, provided he has a system, with the data from the previous system through which he has ordered items. Cumbersome is the kindest word here.
In short, it can be termed that any supply chain’s digital infrastructure is opaque to anybody except for a few who are direct consumers or producers. The reason for this opaqueness is trust — or lack of it. Except for entities that place orders to the entities that produce the ordered items, the systems are designed to exclude everybody else for the sake of security. It can go along like this, keeping all the other stakeholders guessing.
What if a producer would like to know how much his customer has of his merchandise in stock and predict when his customer will place orders?
What if a producer knows the rate of consumption of his product by a set of his customers, so that he can plan his production accordingly?
Just-in-time production anyone?
At present, these scenarios are heavily dependent on the customer willfully sharing details about his inventory or order history for anybody to see for them to plan accordingly, which is unlikely, for the very reason of security.
What is the answer to one of such many conundrums? Blockchain?
Now, imagine that a customer places an order on a system that has blockchain as underlying platform. In other words, the blockchain is the thing that registers all the transactions, including ones the customer has placed. The producer to whom the order has been placed will see the order and will start servicing the order. If said customer has another customer on behalf of whom the order was placed, the blockchain will enable the other customer to see the status of the order as well. The blockchain enables this kind of transparency since the underlying data of the blockchain is not hackable by any normal means — it would take enormous computing power and time to do any damage to data.
How blockchain can be used in the supply chain
The supply chain with blockchain can be called “supply chain platform with blockchain,” or SCBC. The blockchain has to be the underlying infrastructure for all the data flowing through a supply chain infrastructure. This data needs to be in the form of chunks of individual data that can be compartmentalized easily, such as an order, invoice and so forth. The compartmentalization can help preserve the continuity of the data into next morphing if needed. The SCBC should have a public interface where the interested entities can gain entry with proper credentials. These credentials can be issued in such a way as to enable the entities to see data pertaining only to their interests, and they cannot peek into other entities’ data.
How can access to the underlying data in a blockchain be given? One way — and the most popular — is to use APIs. If the APIs are standardized, any entity that would like to look into the data and act upon it will be able to do so without any hassle. These APIs would be part of any blockchain-based supply chain platform and will allow applications to be developed for which the imagination is the only limit. The use case explained above — an entity performing prediction of his own manufacturing based on his customers’ consumption patterns for the last orders — will be a reality.
In another scenario, an end user of a product can track the inputs of the product he bought. This might be of interest to consumers who are more interested in knowing that they are buying products that are manufactured in an ethical and environmentally friendly manner. This kind of consumer is on the rise, and blockchain will enable these consumers to be aware of their purchasing choices. While it is true that manufacturers themselves certify their products are manufactured in an environmentally friendly manner, there is no transparency in such a declaration and the end user has to take the manufacturer’s word with full trust, which is a difficult proposition.
Even though blockchain was first envisaged for creating, distributing and maintaining cryptocurrencies, lots of other uses are imagined on a daily basis. Use of blockchain in the supply chain is one of them, and will be adopted more or less in due course of time; the benefits outweigh the pitfalls.
The insurance industry is rapidly evolving away from its traditional model of assessing future risks and pricing based on historical records and demographics (age, gender and the like). For decades, underwriters relied on this data to predict everything from an individual’s expected lifespan to the probability of a driver being involved in an accident. But, as it has with so many industries today, technology is disrupting this long-standing practice. In the case of insurance, the internet of things phenomenon has led this revolution.
Typically miniature in size, IoT devices record massive amounts of information on properties, vehicles and even people to which these gadgets are connected. The data then passes to insurers, which enables underwriters to judge potential hazards grounded on a more individual and property-specific picture of risk. Armed with this information, insurers can, in many cases, suggest real-time preventative measures that could avoid a costly claim payout.
IoT has already entered the insurance world. Here are four segments in which this technology is shaping the insurance industry of the future.
For decades, insurers wrote car insurance rates primarily based on demographics and age, which meant teenagers and people in their 20s were charged higher premiums, even though many may be perfectly trustworthy drivers. Today, however, many insurers offer drivers the option of installing a palm-sized IoT tool known as a telematics device into a car’s onboard diagnostics port, or OBD-II, typically located under the steering wheel. (Every car built after 1996 has an OBD-II.)
The telematics device monitors driving habits by recording the auto’s speed, distance traveled, time of day and whether the car has accelerated too quickly or made a hard brake. By analyzing this data, the insurance carrier can then evaluate if a person’s driving patterns make him a safe driver or a road menace. If deemed a safe driver, the insurer rewards the policyholder with a premium discount. Simply having the device in a car can make a person a better driver, too. For example, an alarm sounds if the driver slams on the brakes or speeds. Over time, these devices force drivers into safer road behavior, which then lowers their premiums.
The theory behind telematics is that drivers should pay premiums based on real-life usage rather than age, gender, education and even credit scores. Usage-based insurance bases rates on real-time data, like how far and fast an individual actually drives, instead of purely demographics.
In the near future, more insurers will underwrite auto policies using these tech gadgets. SMA Research predicts by 2020 three-quarters of auto insurers will offer telematics to their policyholders.
Discounts aren’t the only benefits, however. Unfortunately, car accidents happen, and pinpointing fault sometimes depends on the sometimes conflicting accounts of the driver or eyewitnesses. Fortunately, a telematics device delivers correct data of the conditions — speed, braking distance and the like — that may have led to the crash. If the insured wasn’t at fault, the data will support that finding.
The popularity of “smart homes” hasn’t soared simply because homeowners demand the latest in IoT meters that adjust a dwelling’s temperature and energy usage. Home sensors and connected IoT devices have implications for homeowner insurance as well.
Homes outfitted with a water monitoring mechanism, for example, can detect a small water leak before it grows larger and floods a home, causing thousands of dollars in damages. Alerting the homeowner via an email or text of the impending disaster prevents an expensive claim.
Similarly, security cameras and motion-triggered sensors can stop a break-in before a theft occurs. According to Safeguard the World, the chances of a home burglary rise by 300% when a home has no security system.
Fire detectors also signal the homeowner and local fire departments of a blaze that can be quickly put out before it destroys an entire house, thereby reducing a claims payout. Business Insider’s research arm, BI Intelligence, estimates a home equipped with a connected smoke detector that automatically alerts the fire department could potentially cut an insurance payout by an average of $35,000.
Life insurance underwriters have traditionally set life policy premiums based on an individual’s age, medical history and health habits, such as smoking, submitted by the applicant, who may sometimes fudge the truth. But with today’s wearable IoT devices, insurers can more precisely determine a person’s well-being by collecting vital medical data like blood glucose levels, temperature and heart rate.
Instead of grouping a policyholder in an age group that may not indicate that person’s true health, IoT data provides an accurate snapshot of an individual’s overall fitness. That information, in turn, reflects a person’s true health risks and how long she will likely live, which is then used to calculate premiums. Like safer drivers, healthier people will be rewarded with lower premiums.
In addition to premium calculations, data fed into an IoT health monitor allows insurers to suggest lifestyle changes that will improve a person’s health and therefore bring down rates for healthier individuals. Insurers are also able monitor a person’s health as it changes over time, which could have an impact on life insurance rates.
According to the 2017 Liberty Mutual Workplace Safety Index, U.S. employers lost nearly $60 billion due to workers missing six or more days because of workplace injuries or accidents. Although that statistic dates back to 2014 (the most recent report from the Bureau of Labor Statistics and the National Academy of Social Insurance), the amount highlights the enormous cost of workers’ compensation insurance to pay for employees’ medical treatment and wages while they recover.
Yet, IoT offers employers a high-tech way to prevent those costly injuries. IoT instruments designed specifically to monitor a person’s physical condition pick up whether a worker is tired or performing too many repetitive motions. (The Liberty Mutual study ranked overexertion as the leading cause of workplace injuries, directly costing companies $13.8 billion each year.) Since fatigue often leads to injury, the wearable device buzzes a worker when it’s time to take a break and rest.
These IoT health sensors also check a worker’s body temperature, which could indicate an illness. Ill workers are more prone to accidents, so ensuring they recover at home maintains workplace safety.
IoT has emerged as a cutting-edge disruptor within the insurance industry, one that is leading to an exact measurement of risk that will ultimately lead to more precise underwriting not based on history, but on real-life situations and each individual. Correct underwriting means policyholders pay fairer rates, which makes insurance more attractive to potential buyers. Lastly, insurance carriers can switch from paying out a costly claim after the fact to preventing one from happening in the first place.