2017 nd 2018 are characterized by an explosion of urbanization with as IoT enables smart cities to optimize services to their residents. According to the recent World Cities report of the United Nations over 3.7 billion people are now living in urban areas, while this number is expected to double by 2050. Urbanization trends are accompanied by a rise of the aging population and the emergence of entirely new lifestyle work patterns (e.g., telecommuting).
All these changes are putting extreme pressures on modern cities, which have to cope with the depletion of natural resources (e.g., water, energy) and the support of new lifestyles in a way that ensures sustainable development. In this context, the concept that IoT enables smart cities is a reality, such as vendors such as SIGFOX covers more than 10 million objects registered on its network which currently spans 26 countries.
IoT networks are able to leverage both advanced technologies and a city’s human capital in order to optimize urban operations, improve environment performance, create new sustainable business opportunities and improve the citizens’ quality of life. Smart cities are based on advanced ICT infrastructures and technologies such as high-speed broadband connectivity, multi-purpose low power sensors and actuators, as well as cloud computing infrastructures that facilitate scalable collection and processing of large volumes of data about the urban context. Most of these technologies are underpinning the Internet-of-Things (IoT) paradigm, which explains the close affiliation between smart cities and IoT.
Overall, as IoT enables smart cities becomes saturated in terms of sensors and mobile devices (e.g., smartphones used by citizens), they provide umbrella environments for the development of many different smart city applications. The latter can be classified according to two major (yet orthogonal) criteria:
Given the multitude of IoT technologies and applications in smart cities, policy makers need to prioritize the development of their IoT projects and infrastructures in-line with their urban development strategy. The latter strategy defines the city’s goals and substantiates them based on tangible KPIs (Key Performance Indicators), such as improvements in CO2 emissions and environment performance, reductions in urban traffic and the average time of urban trips in the city, increase in GDP (Goss Domestic Product) of the city, quality of life indexes and more.
With this strategy at hand, technology advisors and city CIOs (Chief Information Officers) can work towards preparing a comprehensive strategy for the tasks that IoT enables smart cities requires in terms of the infrastructures to be developed and the IoT projects to be implemented. The selection of projects should consider the application domains that need to be targeted in order to meet the specified performance indicators. The development of a city’s IoT strategy is usually a complex task, as it should consider multiple factors and trade-offs, including financial, business and technology factors at the same time. For instance, as most cities operate on quite constrained budgets it’s always important to define projects with realistic budgets, which could be financed either by the city’s budget or as part of public-private partnerships. The latter is a very popular paradigm for financing the usually costly IoT infrastructure development projects.
As a prominent example, the LinkNYC project, which provides New Yorkers with super-fast WiFi for free, is a result of a public private partnership between the city and the CityBridge consortium where Intersection, Qualcomm, CIVIQ Smartscapes and other companies participate.
In terms of technological factors, IoT enables smart cities a strategy that should specify key technological choices, including:
A vertical applications development phase, where applications in vertical areas (such as energy and urban mobility) are developed. An applications integration and interoperability phases, where different vertical applications are integrated in order to monitor or achieve city wide KPIs such as sustainability KPIs based on a combination of transport, energy, mobility and water management projects. An open innovation and citizens’ engagement phase, where citizens and innovators engage with existing infrastructures and applications in order to provide additional social and innovation capital, as a means of expanding and optimizing the operation of integrated applications.
In this landscape, we are witnessing a proliferation of smart city projects in many cities of the developed world. Nevertheless, there are also on-going efforts to improve existing smart city projects and broaden the scope and capabilities of new projects. These efforts concern both technological and non-technological developments and include:
Overall, the vision that IoT enables smart cities is gradually realized, but much more is yet to come. In this evolving landscape city authorities, technologies providers and other stakeholders are expected to collaborate to develop and execute effective IoT strategies for urban development.
The line is blurring between information technology (IT) and operational technology (OT). As more industrial robotics equipment is connected to the industrial internet of things (IIoT), the vulnerabilities increase. Among the many devices being added to networks are robotic machines. That’s raising red flags for some experts. And it has many people worried. What are the risks associated with connecting an army of robots? It’s the stuff of science fiction.
The World Robotics Report 2016 gives us some insight into the scope of global automation growth: “The number of industrial robotics deployed worldwide will increase to around 2.6 million units by 2019.” It says that the strongest growth figures are for Central and Eastern Europe. The report cites China as the market for growth, and says that North America is on the path to success. “The USA is currently the fourth largest single market for industrial robots in the world,” according to the report.
TechCrunch contributor Matthew Rendall says “Industrial robotics will replace manufacturing jobs — and that’s a good thing”. He writes that the “productivity growth” behind 85% of job losses is all about machines replacing humans. Luddite and famous poet Lord Byron would not have been pleased. But Rendall is not bothered. He says that “more is getting done” by industrial robotics that are safer and more reliable than human beings. And he believes that this robotics revolution will be beneficial to workers and society in the long run.
All this rush to automation might be the best thing since jelly doughnuts. But one question could make all the difference between abysmal failure and glorious success: Can we keep them secure?
We probably don’t need to worry about robots taking over the world any time soon. (Let’s hope, anyway.) What concerns security experts is that our computer-based friends can be hacked. Wired Magazine reports how one group of researchers was able to sabotage an industrial robotics arm without even touching the code. That’s especially worrying when you think that most industrial robotics have a single arm and nothing else. These devices are made to make precise movements. Hackers can change all that.
German designer Clemens Weisshaar addressed the issue in a form at Vienna Design Week in 2014. “Taking robots online is as dangerous as anything you can put on the web,” he said. In a video from the forum, Weisshaar talked about how even his company’s robot demonstration in London had been hacked within 24 hours. They even tried to drive his robots into the ground. “If everything is on the internet,” he said, “then everything is vulnerable to attack.”
Industrial robotics cyber security challenges are only one part of what many are calling Industry 4.0. It’s a trending concept -- especially in Germany -- and it’s another way of referring to the Fourth Industrial Revolution. To understand what this is about, we should first reach back in the dim recesses of our minds to what we learned in history class in school.
The Industrial Revolution, as it was originally called, took place in the 18th and 19th centuries. It started in Great Britain and involved the harnessing of steam and tremendous advances in production methods - the 1st. Next came the 2nd roughly from 1870 until World War I in the USA. This involved the use of electricity to develop mass production processes. Th 3rd brought us into the digital age. Part four is upon us now.
A video from Deloitte University Press introduces us to the Fourth Industrial Revolution -- Industry 4.0. It gives a good summary of the four “revolutions”, and it talks about some of the new technologies that now define our age:
“These technologies,” says the narrator, “will enable the construction of new solutions to some of the oldest and toughest challenges manufacturers face in growing and operating their business.” They also make up the environment in which hackers flourish.
Industrial Robots Cyber Security Challenges for IoT Data and Devices
In this space we have already discussed the security vulnerabilities of IoT devices. We told you how white hat hackers proved that they could commandeer a Jeep Cherokee remotely by rewriting the firmware on an embedded chip. Imagine what hackers with more sinister motives might be planning for the millions of robotic devices taking over the manufacturing shop floor -- supposing they are all connected.
Some researchers tackled the issue in a study called “Hacking Robots Before Skynet”. (You will remember from your science fiction watching that Skynet is the global network that linked robots and other computerized devices in the Terminator movie franchise.) The authors had a lot to say about the current state of cybersecurity in the industrial robotics industry. We can borrow directly from the paper’s table of contents to list what they call “Cybersecurity Problems in Today’s Robots”:
Each of these topics could probably merit a full article on its own. The researchers explained further: “We’re already experiencing some of the consequences of substantial cybersecurity problems with Internet of Things (IoT) devices that are impacting the Internet, companies and commerce, and individual consumers alike, Cybersecurity problems for industrial robotics could have a much greater impact.”
What might that impact be? Well, to start with, robots have moving parts. They tell how a robot security guard knocked over a child at a shopping mall. A robot cannon killed nine soldiers and injured 14 in 2007. And robotic surgery has been linked to 144 deaths. It’s not Skynet yet, but connecting robots has its risks.
How we communicate with machines and how they communicate with each other are matters that require significant attention. Arlen Nipper of Cirrus Link Solutions talks about MQTT, which is a protocol for machine-to-machine (M2M) messaging. Manufacturing designers and operators send instructions to one-armed industrial robotics, who work in a variety of industries from automotive to aerospace to agriculture to packing and logistics. All this talking back-and-forth with industrial robotics cyber security has to be regulated. NIST’s Guide to Industrial Control Systems (ICS) Security has a few references to robots. But maybe not enough.
The threat of IoT Botnets describes a network of devices that have been compromised by a cybercriminal and are being used to conduct a coordinated attack. Typically these devices are a mixture of computers and mobile devices.
IoT Botnets can be utilized for many different types of cyber attack. Spam email campaigns and Distributed Denial of Service (DDOS) attacks are two common uses for botnets. A DDOS attack involves overwhelming a target with requests and causing the service to fail.
In both scenarios, using a network of compromised machines spreads an attacker's point of origin. This means that DDOS attacks cannot be thwarted simply by blocking a single IP address, and makes it tough for spam filters to identify the source of malicious email. The most commonly discussed examples of these smart devices tend to be consumer-focussed, such as the capacity to control your home heating from your smartphone. But increasingly, there are more and more uses of IoT devices in the business world. Many industries now use a connected network of sensors and cameras to capture data and automate decisions based on that information.
Gartner has predicted that there will be approximately 20 billion IoT devices by the year 2020. And because these devices have an IP address and the ability to share data, they are susceptible to cyber criminals. In fact, many of these devices are far easier to hack than traditional computers and smartphones. In a rush to catch early adopters, manufacturers are overlooking security in favor of product features and speed to market. Additionally, as the industry is still quite new, there are no standardized methods for detecting and fixing a compromised IoT appliance.
One of the main threat of IoT botnets pose is that they make DDOS attacks easier to conduct. Many of these appliances may be connected via the same router, and with their low security levels, it’s straightforward for hackers to compromise multiple devices very quickly. For businesses, this means that even the many consumer IoT gadgets are a threat. Back in 2014, cyber security experts Proofpoint discovered an attack that utilized consumer devices in a sustained cyber breach.
The Internet of Things offers many easy-to-reach IP addresses with which to conduct an attack. And while DDOS attacks aimed at a particular company can be damaging to business and reputation, a coordinated attack of this nature aimed at a country’s critical infrastructure could have even more damaging effects.
The second major threat of IoT Botnets is compromised IoT devices is related to the nature of the work they do. As mentioned previously, the role these appliances play tends to involve the collection of data and subsequent decision-making process. This data itself could be valuable to criminals via ioT botnets. The heating and lighting habits of a particular building could indicate the times when it’s staffed, for example.
But even more dangerous is the prospect that hackers could manipulate this data. For industries that use sensors to indicate that equipment components have exceeded their designed wear thresholds, an inaccuracy in this information could have life-threatening repercussions.
All of this concern inevitably leads us onto the question of what users can do to protect themselves against these attacks. And the honest answer at the moment is that it’s tricky to take definitive action. Much of the progress to secure these devices needs to be made by the manufacturers themselves.
But there a few things that businesses can do to mitigate the risk. The first thing to do is to review your cyber security processes. If you assume that your email filters would not be able to stop a coordinated spam attack using IoT botnets, then how well educated are your users? Do they know how to identify a suspect email?
Secondly, if you’re a business that uses IoT devices, ensure that you have strong login credentials and that you have a robust process for installing any manufacturer updates and patches. You may also be able to segment these appliances onto a separate network to reduce the risk of lateral infection into the rest of your organization.
Probably the biggest thing users can do to threat of IoT botnets is to petition the vendors to take it more seriously. Practices such as digital firmware signatures and anomaly detection could begin to make these devices more secure. It’s clear that the Internet of Things poses a security risk to consumers and businesses alike. There’s no easy answer when it comes to protecting your business from attack. All companies should be reviewing their security policies, having recognized the increased threat of an attack that has multiple origins.
And for businesses that utilize IoT appliances, it’s a case of understanding there is an element of danger and ensuring that the competitive edge offered by these devices outweighs that risk. What does this decision process look like in your business? Are you using IoT appliances and has the phrase ‘Internet of Things’ started to work its way into your general security policy conversations? Are you prepared to face IoT botnets?
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Industrial IoT automation dictates that all predictive maintenance systems hinge on the processing of data from many IoT devices, which renders predictive maintenance one of the most common IIoT applications.
Moreover, as predictive maintenance leads to improved OEE, reduced labor for performing the maintenance and better planning of related supply chain operations, it is increasingly considered one of the killer applications for IIoT.
With IIoT reconfigurations take place at the cyber world based on digital technologies rather than at the physical world where processes are much more tedious and time consuming. IIoT automation systems provide a seamless link between the cyber and physical worlds, which ensures that changes in the IT configurations are properly reflected on the field.
Industrial IoT predictive maintenance is expected to generate the large scope of B2B transactions that require data analysis. Indeed, IIoT is on such a growth pattern many of the billions of connected things in the coming years will be industrial assets, which will be deployed in settings like factories, agricultural, oil refineries and energy plants.
According to McKinsey the Industrial Internet has the potential to deliver up to $11.1 trillion on an annual basis by 2025 and 70% of this is likely to concern industrial and business-to-business solutions i.e. the Industrial IoT is expected to be worth more than twice the value of the consumer internet.
The Industrial IoT is at the heart of the fourth industrial revolution (Industry 4.0), which is driven by the interconnection of all industrial assets and the ability to collect and analyze data from them. In the scope of the Industrial IoT, assets are cyber-physical systems, which enable the control of physical devices through their cyber representations and the processing of digital data about them.
The applications of cyber-physical systems span a very broad range, including production control, process optimization, asset management, integration of new technologies (such as 3D printing & additive manufacturing), as well as various industrial automation tasks. Nevertheless, the most prominent application is the ability to continually monitor, predict and anticipate the status of assets, with emphasis on industrial IoT predictive maintenance using predictions about when a piece of equipment should be maintained or repaired.
Maintenance and Repair Operations (MROs) are at the heart of industrial operations, as they involve repairing mechanical, electrical, plumbing, or other devices as a means of ensuring the continuity of operations. Nowadays, the majority of MRO operations are carried out on the basis of a preventive maintenance paradigm, which aims at replacing components, parts or other pieces of equipment, prior to their damage that could catastrophic consequences such as low production quality and cease of operations for a considerable amount of time. However, in most cases preventive maintenance fails to lead to the best usage of equipment (i.e. optimal Operating Equipment Efficiency (OEE)), as it maintenance is typically scheduled earlier than actually required.
In industrial IoT predictive maintenance (PdM) alleviates the limitations of preventive approaches. PdM is based on predictions about the future state of assets, with particular emphasis on anticipating the time when an asset will fail in order to appropriately schedule its maintenance.
PdM is empowered by models that estimate when the cost of maintenance becomes (statistically) lower that the cost that is associated with the risk of equipment failure.
Based on an optimal scheduling of maintenance, PdM leads to improved OEE, enhanced employee productivity, increased production quality, reduced equipment downtime, as well as a safer environment where failures are anticipated and repairs proactively planned. McKinsey & Co. estimates that the economic savings of predictive maintenance could total from $240 to $630 billion in 2025.
Nevertheless, there are still many industries that dispose with preventive maintenance, since they have no easy way to integrate and analyze data sets from thousands of heterogeneous sensors that are typically available in their plant floors. As a result only a fraction (i.e. 1% according to McKinsey & Co) of the available data is used, which is a serious setback to unlocking the potential of predictive maintenance applications, such as maintenance as a service, on-line calculation of OEE risk, maintenance driven production schedules and more.
The advent of Industrial IoT predictive maintenance is gradually unlocking the potential of PdM technologies facilitate the collection and integration of data from thousands of different sensors, while at the same time providing the means for unifying the semantics of the diverse data sets. Furthermore, IoT analytics technologies (notably predictive analytics) facilitate the processing of IoT data streams with very high ingestion rates based on machine learning and statistical processing techniques that can predict the future condition of components and equipment.
In several cases, IoT data are processed by Artificial Intelligence based techniques such as deep learning, in order to identify hidden patterns about the degradation of assets. Deep learning techniques are capable of leveraging (multimedia) data from multiple maintenance modalities such as vibration sensing, oil analysis, thermal imaging, acoustic sensors and more. Moreover, advanced deployments of industrial IoT predictive maintenance are not limited to deriving predictions about the future state of assets. Rather, they are able to close the loop down to the plant floor, through for example changing configurations in production schedules, altering the operational rates of machines or even driving automation functions.
PdM is looming as one of the killer applications for the Industrial IoT, which is evident not only on its potential savings but also on the rise of relevant IoT-based products and services. Most vendors have been recently releasing IoT-based solutions for PdM. In addition to empowering data collection and analytics, vendors are striving to enhance their products with added-value functionalities that help them stand out in the market. For example:
Recently, the DataRPM platform has been also established by a consortium of different vendors and manufacturers. DataPRM claims ability to deliver Cognitive Predictive Maintenance (CPdM) for Industrial IoT, based on the use of Artificial Intelligence for automating predictions of asset failures and closing the loop to ERP, CRM, and other business information systems.
Other major players in industrial engineering and automation, such as SIEMENS and BOSCH are offering their own platforms, while all major IT consulting enterprises have relevant services in their portfolio. Nevertheless, it is indicative of the market momentum of PdM and its positioning as one of the most prominent applications in the growing Industrial IoT predictive maintenance market.
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