Technology catches on quickly as way of reducing costs, but big challenges remain.
One of the benefits touted by IoE proponents is that smart cities will improve the quality of life and make cities more “livable.” The concept is appealing, and if it comes to pass as visionaries hope, the smart city of the future will be a virtual cornucopia of convenience and efficiency.
Residents and vistors will never be lost with the proliferation of location technologies, which also will keep them informed about where they are and what is around them. They will have a myriad of choices for food, entertainment, shopping, and other amenities, all just a tap away on their smart devices. And this information will be based on data that will lead to an understanding and anticipation of individual needs and desires. And one of the city’s biggest nightmares, traffic, will be tamed with both public and personal autonomous vehicles guided by the smart city’s AI traffic management system.
These are enormous plans with lots of moving parts, and what’s interesting here isn’t the scope of the projects. It’s the speed of acceptance and the implementation of at least some parts of this grand vision.
“Normally, you expect governments to be slow adopters of technology,” said Wally Rhines, chairman and CEO of Mentor Graphics. “That’s not the case here. Local and state governments are using this as a way to alleviate their financial problems and provide services without the high labor costs.”
He said the rapid adoption of smart meters, smart traffic lights and automated ticketing for speeders is unexpected. What’s also rather surprising is that it’s driving more than half of the semiconductor spending for the IoT.
But along with this utopian plan comes a big challenge—how to protect it.
Rob Coombs, security marketing director at ARM, said that attacks come at three levels. At the lowest level are communications attacks that exploit code vulnerabilities, the so-called “man in the middle” attack where the attacker slips into communication between two parties, and weak random number generation. Those are also the easiest and cheapest to secure. One rung up from that are software attacks using buffer overflows, interrupts or malware. And at the high end of the spectrum are hardware attacks, which target JTAG testing and debugging, buses or I/O pins. Those typically are well-funded attacks requiring time, money and equipment such as electronic scanning microscopes, and they are the most expensive to secure.
Smart cities increase the level of challenge just by sheer size. “One of, if not the biggest challenge to smart cities, is that such entities will have a very large attack surface,” said Chowdary Yanamadala vice president of business development at ChaoLogix.
The reason for that is the cornucopia of platforms that will be part of it. The Internet of Everything will include the cloud, smart devices, and billions of sensors, smart and dumb. It will integrate social media, various renditions of data (data analytics, big data, smart data, open data, etc.), and unprecedented mobility, all of which have multiple vectors for encroachment.
The good and the bad will be possible because cities, and infrastructures, are migrated from analog to digital. It is the digital domain that enables AI. Digital technologies open the infrastructure to information communications technology, which will enhance the quality and performance of urban services via unprecedented access to ubiquitous data. Such data will reduce costs and resource consumption, and allow the infrastructure to engage more effectively, and actively with its citizens.
“One of the great enablers for all of this will be the huge number of sensors,” notes Ted Marena, director of FPGA/SoC marketing for Microsemi. “When you combine them with advanced analytics and algorithms, as well as advancements in processing power and capabilities, it will be able to offer a much better predictive environment.”
That predictive environment will empower the AI to do a much better job of keeping smart cities smart.
Today, the IoE is mostly a networked infrastructure connecting physical objects. As it evolves, it will synthesize new capabilities such as context awareness, increased processing, AI, and command and control of various infrastructures. It will become a network of networks where billions or even trillions of connections create unprecedented, deep wells of information for use by billions of smart applications. It will be a blend of emerging connected technologies that meld together, open APIs, engineering, algorithms – and culture – a true renaissance for the 21st century.
For example, the IoE will enable such activities as people swallowing pills that sense and report the health of their digestive tract, blood chemistry, organ health, and more, to a doctor via a secure connection. Sensors placed on the skin or sewn into clothing will collect and disseminate information about a person’s vital signs to any number of authorized parties. Eventually, people themselves will become nodes on the Internet, with both static and active activity being sent and analyzed by applications that will use the data to custom tailor activities for them.
All of this will integrate into the smart city and optimize the interaction of medical facilities, and users, for example, to choose the closest facility, or the one with the shortest wait time. It also will optimize other first responder services and provide them with real time, interactive data to minimize route time and deliver the exact solution. The same can be said for other consumer services, as well.
But making this happen will require conquering a lot of challenges, both technically and psychologically.
The integration of smart cities and the IoE will bring a new level of innovation for things that have been difficult to manage on a micro scale. Such things include the power grid, transportation, agriculture, and city services.
One of the benefits is that better management of these areas will impact the verticals that are related to them, creating new opportunities for growth and revenue, as well as optimizing existing channels and their services.
A good example of that is the “blue revolution,” or aquaculture—farming in fresh or salt water. It has a widespread application base throughout the food chain, farming, fishing, ranching, which all benefit from smart cities and the IoE.
For example, in agricultural ecosystems, farmers are benefiting from the integration of these emerging technologies by developing vertical farms. These facilities are built up, rather than out, and are operated indoors. That allows them to be located in, or near metropolitan areas and sold directly to consumer or local markets.
These vertical farms are integrated via small sensors, either in the soil or attached to the plants, and connected to other business networks verticals, such as trucking and stores. This allows best-in-class supply and demand checking, allowing delivery to be optimized, waste minimized, and costs tightly controlled. Such edge-of-the-envelope technology and interconnect allows a high level of automation for watering, which has a byproduct of reducing water usage.
This is only one avenue that points to underlying connections between the IoE and smart cities. The impact is profound, especially when applied across virtually every ecosystem. Eventually, the fully integrated and connected IoE smart city ecosystem will flow from the individual to the industrial sector.
The potential is limitless. Take pipelines, as another example. By incorporating sensors, and connecting the them to the IoE, the flow of oil and gas can be precisely regulated, based upon usage feedback by the entities that control the distribution. Cities will have precise knowledge of how much fuel is going where. This can be applied to the energy infrastructure, as well. This is one of the big promises of the integration of smart cities and the IoE.
This is just the tip of the iceberg. The potential is staggering. But so are the challenges.
One of the biggest challenges that smart cities face is how to connect billions of devices and sensors, thousands of servers, multidimensional processing, not to mention the transmission, streaming and analysis of Big Data. And all of it will be done with decentralized control. Moreover, the infrastructure must be intelligent enough to use analysis of past experiences to make intuitive and insightful decisions in real time.
Where this gets particularly challenging is at the edge. That is a topic of debate among various entities. Some see the edge as almost a separate network with smart nodes that will be able to both determine content and how to handle it, and interface back to the central processing core. They believe that long paths to centralized processors just cannot work, simply because of the sheer scale and complexity of the networks. Paths must be short and direct, with sufficient bandwidth to enable data to flow unimpeded.
Other see the edge as an enhancement of core processing capabilities, citing advancements in technology that will lead to fatter pipes so I/O to the core won’t be as bottlenecked as it is today in many cases.
“There will be a certain amount of processing required at the edge,” says Microsemi’s Marena. “But, especially in developed countries where the infrastructure is well developed, you will have faster and faster pipes because the network bandwidth growing at a phenomenal pace.”
How this ultimately will shake out is still one of the great unknowns of network stratification. Both camps are pretty steadfast in their positions and there are good arguments on both sides. But no matter how this plays out, it will require, at a minimum,
a horizontal multi-structure encompassing at least these elements:
• The interconnect of sensors, machines, people, and clouds.
• Managing big data efficiently and effectively by various levels of access rights, semantic links and next-generation analytic algorithms.
• Efficiently storing only relevant data. Analytics must be able to intelligently assess what data is relative, and for how long.
• Use of advanced analytics to interpret and correlate use patterns, such as sales trends, for example. Additionally, the technical architecture must have the capability to, using AI, develop effective predictive modeling by historical data analysis.
• Have as secure an infrastructure as possible to protect the petabytes of data, billions of devices and server farms on a global scale.
• Be able to move data in and out and across multiple layers for use in multiple services and applications — the epitome of open data available where and when needed.
While these are very defined objectives, achieving them will take a lot of cooperation among all the entities involved. The technical sector will likely have no issue doing so, but the political, economic and social sectors are not as like to just fall into lock step to accomplish this.
How to make it happen
In the past, a standard approach is to throw more resources at the problem. That can be faster and better hardware or better applications, among other things. But for this new ecosystem, that will not work. More hardware and more software will only bog the I/O pipeline. The one thing that this ecosystem must have is free-flowing data. This will require a fundamental redesign of the city’s network architecture.
One approach is to use layers—separate the components and let them work in isolated environments, then send them into the pipeline. In this model, the first layer would use mesh technologies. This would be used for sensors and other context-aware devices. Data acquired by such devices would be analyzed at this layer and AI used to determine what goes where, once the analysis is complete.
A second layer would address data. Data here is captured, stored, and analyzed. It is then distributed across points within the city, via intelligent analytics. Such an architecture is also scalable and extensible. Another advantage of this is reduction of network lag times. Such a layer can offer real-time, context aware processing and analytics.
The next layer would address global storage and mining of data. Once the data is processed and analyzed at the edge, relevant data is shipped off to a central repository for archiving. More timely data can be stored here, as well, but must be treated as big data so it doesn’t become relic data and simply forgotten. This layer must also be able to, either constantly or periodically, analyze stored data for current relativity and clean house regularly.
The last layer would be for applications and innovation. Isolating this platform will allow for a free-flowing development platform where things can be tried out. New services can be launched that can be tested at both the user and city levels.
Figure 1 is an example of what the city of Nice, France has done with the concept.
Figure 1. Initial application of a smart city concept in Nice, France. Source: Cisco Consulting Services
Of course, this is only one possible scenario, although it is being implemented. There are others. As the evolution of the smart city unfolds, so will the evolution of the platforms and concepts around it.
As this new ecosystem evolves, one of the things that is an inevitable is that the more complex a network or system becomes, the more vulnerable it is to hacking.
“There’s a bumpy road ahead for the Internet of Things and smart cities initiatives if we can’t find solutions to these security problems,” said Paul Kocher, president and chief scientist for Rambus’ Cryptography Research division. “It’s also one where the value proposition for an end user depends on having the connected device be better than the disconnected one.”
Others agree. “This new paradigm will have a very large attack surface. There will be multiple points of entry at multiple layers, and multiple levels,” says Yanamadala. That makes this a very complicated to defend against.
Adds Marena: “The security aspect will continue to be a major challenge as we move forward. It will be like a constant game of chess where the security segment puts together new infrastructure and tools and the hackers will, eventually, find backdoors and ways around the security.”
For example, let’s take a smart city where everything is connected – traffic, power, utilities. All of these platforms are vulnerable. An attack vector at any point, potentially, has access to the entire ecosystem. Everything is connected, and that is a problem that has not been given a lot of thought.
“If somebody has figured out how to hack into the one of the traffic computers and hijacks the traffic control system, you can image the kind of disruption that can cause,” notes Yanamadala. It is quite possible that not only is the traffic system now compromised, but now there is a vector into the rest of the network. “Such a hack can cause a lot of critical damage to the smart city before it can be stopped.”
So how does the city deal with such a potentially catastrophic intrusion? As it turns out, the major challenge isn’t technical. “The real challenge here is political, organizational, and regulatory,” says Yanamadala. “There are multiple suppliers coming into a particular ecosystem,”
That also tends to make for some strange partnerships. One of the biggest issues is that, at this point, there are very few if any guidelines. That makes it nearly impossible to blanket the city or the network with a global security blanket. What security is out there is based primarily on secure overlays, which involve multiple separate solutions built as a patchwork. Since the comingling of the IoE and smart cities is still a few years out, there is time to work on establishing this methodology, from the ground up. If everyone gets on the same page, security solutions for this next paradigm can be truly effective, which they must be, considering the scope of the application.
Smart cities integration with the IoE is yet to be realized. There is some history with M2M, but the breadth and variety of devices that will be part of the IoE makes this, for all intents and purposes, untested territory.
Early adopters, such as Nice, France, New York, Chicago, Amsterdam and others have started down that path with some narrow implementations of smart city platforms. They also are integrating some of what will become the IoE when it becomes native. It is very limited, in both scope and application, but is a glimpse of things to come.
Evolving paradigms like the IoE and smart cities start small. Experience, mistakes, trial and error, success and failure are all part of progress.
—Ed Sperling contributed to this report.