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Amplify the Internet of Things in Smart and Connected Cities with Fog

When it comes to the Internet of Things (IoT), cities have enormous potential. A city needs to manage many different processes and priorities ranging from trash collection to traffic management, for hundreds of thousands to millions of people distributed over a large area. Many of these processes can be enhanced through the use of IoT.

During the past several years we have seen more and more technology solutions being deployed to help cities optimize these processes and provide additional value to its citizens. Smart parking is perhaps one of the best known and perhaps the most visible for city inhabitants.

Typically an IoT solution stack for these processes is build on several layers: (1) sensors to measure, (2) for each type of sensor there is typically a compute infrastructure at the edge of the network near the sensors, to perform simple aggregation, protocol/access technology conversion and local processing/analytics, (3) connectivity (wireless, backbone) to transfer data to the cloud, (4) cloud for deeper analytics, business processes and long term data access.

No two cities however are the same and each city has its own unique challenges. Backbone network infrastructures are managed in different ways, and local views on privacy and security can differ substantially. Light poles form a natural infrastructure to connect sensors with the edge of the network as it provides power and physical security. However cities upgrade their physical infrastructures with different time tables, and upgrades take time (and money), which leads to different alternative IoT infrastructure deployments at the network edge. Each city is organized differently which means budgets are managed differently. The latter can be especially challenging if new services touch multiple departments.

But cities also face common IoT challenges. Different types of sensors typically come with their own edge hardware and service management software. If a city deploys multiple sensor platforms this leads to so-called box proliferation and service management siloes (what some people call the vertical approach to IoT). This is not only undesirable from an aesthetics point of view, but makes it harder (more costly) to manage the whole city IoT infrastructure.

Challenges edge services

Challenges edge services

More sensors also means an increased security risk. Certain sensors have little processing power (to save money and battery life) which can make them targets for security attacks. While network security can filter out a lot of attacks there is still an increased risk of infecting the whole city infrastructure, specifically on an application level through data obfuscation. Sensor platforms do offer a certain level of security, but it is not always the primary focus within the solution and dealing with multiple platforms leads to multiple different security solutions.

The above challenges make it harder to scale up IoT in cities. Therefore a paradigm shift is needed towards a hyper distributed architecture of smart nodes: The heterogeneous edge hardware and software platforms should be replaced by a platform on which the virtualized services of the providers can be deployed. This so-called fog platform has hardened security as well as common libraries, features and hardware that can be used by the service providers to deploy their virtualized edge services. The fog platform also provides uniform edge service life-cycle management, policy based data and service access, as well as multi tenancy, reducing the cost for a city to manage such an infrastructure.

Fog: a platform to reduce cost, increase security and amplify IoT

Fog: a platform to reduce cost, increase security and amplify IoT

The fog platform is a win-win for the city and its edge service providers. Service  providers can focus on their core competence of sensors, data aggregation/processing and business logic while leveraging standard security and processing features from the fog platform as well as the possibility to easier share and combine data (sometimes referred to as the horizontal approach to IoT) between different services.

For the city, the fog platform will make it easier to manage and deploy new edge services, without adding new boxes on the network edge for each new service, thereby reducing the capital and operational cost for managing the city IoT infrastructure.

The value of the fog platform for a city is not only the direct operational and capital cost savings as well as hardened security,  but equally important is the shorter deployment cycle of new edge services, and easier data sharing between the traditional service siloes. With such an approach the city can become a large distributed test-bed to incubate new innovative ideas on data fusion and processing and develop new services that contribute to the overall quality of life for its citizens.

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A Unified Platform Beyond only Cloud as Driver for IoT

The Internet of Things (IoT) has been among us for a while, but in recent years we have seen a change in scale, in part due to cheaper sensors that are emerging. Cities are deploying sensors to improve the quality of life for their citizens, while factories are connecting more and more machines and collecting more data about the production processes. Supply chains are being revolutionized by tracking in real-time not only position but also movement (shaking, dropping), humidity, etc… In almost every industry you can see the impact of IoT.

Due to this change in scale new challenges are starting to emerge that are demanding a rethink of the Cloud only paradigm, and the silo approach to IoT.

Challenges

IoT typically means deploying application intelligence and analytics at the edge (the area between Cloud/data centers and end points such as sensors, factory robots, etc…), or pushing data directly to the Cloud for processing. Both approaches have their advantages as well as potential drawbacks.

IoT painpoints

IoT painpoints

The network between the edge and the Cloud can be relatively expensive (especially if you send all data to the Cloud) or has limited capacity (capacity is of course correlated with price). Latency to the Cloud can also be relatively high, and often lacks determinism. For example changing the color of traffic lights via the Cloud might not be optimal.

More and more solutions are being deployed at the edge to address the challenges Cloud faces. But these solutions have their drawbacks too. Many different solutions (hardware and software) make it more challenging to manage these edge services in a consistent and coherent manner.

IoT deployment is typically not confined to the traditional enterprise IT domain (au contraire). This means that traditional security solutions do not always apply, resulting in potential high risk security breaches: it is not only about stealing data, but also about controlling machines For example manufacturing robots, location of vehicles, …

One of the trends that we are seeing is that providers of edge services want to focus on their service (application) as this is where their expertise is. Today however many providers also need to provide the hardware (not always a good source of revenue), a certain level of security (not always their primary level of expertise), and a way to manage their services and devices (which can pose a challenge if a customer deploys multiple silos of IoT services).

 A Unified platform beyond Cloud only

To address the challenges described above, a rethink is needed. On one hand the Cloud only paradigm is not sufficient, yet such a new platform needs to support a Cloud like methodology for the edge.

Fog, a driver for IoT

Fog, a driver for IoT

The emphasis here is on “like”, as the edge differs from a Cloud/data center in several important aspects such as: limited resources, limited network capacity, security challenges, and resource distribution. However, such a platform will also have things in common with Clouds. Just like in a Cloud environment it needs to manage the (edge) service life cycle and orchestrate deployment.

With such a platform in place, edge service providers can focus on their core business as this new platform provides them with hooks to develop, deploy, scale, monitor, and manage their services in a secure and safe environment while seamlessly connecting to the Cloud.

Moving to Cloud and beyond

The vision of such a unified platform has been described by Bonomi et.al. and labeled  Fog Computing. We are now seeing this vision unfold in several distinct stages.

Unified (IP based) connectivity is typically the first stage. For example, cities offering free Wi-Fi in the city center, or factories that are consolidating their different networks.

Once unified connectivity is in place, it becomes easier to deploy services at the edge by connecting hardware to this IP network. This can lead to service silos, which are sometimes difficult to avoid due to legacy applications and hardware.

The next stage is the deployment of a unified platform (Fog platform) between Cloud and the endpoints to enhance service deployment beyond the Cloud but also to spur innovation by making it easier to share data between these services. This stage is where there is a true added value, as service management is unified and hardware platforms can become more consolidated.

This paradigm shift to think beyond Cloud towards a unified platform, will lead to new products, services and business models, but can also increases the risk of fragmentation due to lack of standards, architectural vision and abstraction. In order for this paradigm shift to truly succeed it is therefore important to have a continuous conversation between the IT and OT industry.

To ensure companies capture the value of IoT, it is important to start the thought process on a Fog and IoT vision early on: service deployments, connectivity capacity beyond Cloud, data filtering and analytics at the edge, device consolidation, real-time requirements, etc…

Such an IoT vision will enable companies to better prepare and understand the risks and opportunities in an increasingly connected world.

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Connected Analytics: Capturing the Value of the Internet of Everything

Ten large oil refineries produce about 10 terabytes of data each day, which equates to the entire printed collection of the U.S. Library of Congress.

One modernized city the size of Singapore can generate about 2.5 petabytes of data every day, which translates to all U.S. academic research libraries combined.

And with more than 14 billion, data-transmitting devices connected to the Internet today, growing to 50 billion by 2020, it is little wonder that most of us are overwhelmed by this mind-boggling explosion of data.

Wim 1

Turning this flood of raw data into useful information and even wisdom for better business decisions and quality of life experiences is what the Internet of Everything (IoE) is all about. This is a daunting task. According to IDC Research, just .5% of all data is used or analyzed, and online data volumes are doubling every two years from a combination of mobile devices, videos, sensors, M2M, social media, applications and much more.

Connected Analytics Portfolio

Last Thursday, however, Cisco unveiled our Connected Analytics portfolio for the Internet of Everything, a unique approach that includes software packages to bring analytics to the data, regardless of its location or whether it is in motion or at rest. This new generation of analytics tools for IoE can convert more and more data into valuable intelligence — from the inter cloud, to the data center to the network’s edge.

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Fog – A Clear Vision

This week at the Internet of Things World Forum we are challenging the industry to accelerate the adoption of the Internet of Things (IoT). IoT leverages many ‘off the shelf’ technologies but also has some unique requirements, which must be met. How do we make sure that the right critical information is being processed while conserving bandwidth and having a resilient network? Here at Cisco, “fog computing” is a clear technology vision, with the means to provide greater visibility and control- having the network and applications process the critical data in concert with the cloud. With today’s announcement at IoT World Forum, Cisco continues to deliver on its vision for fog computing with an increase in the number of platforms supporting Cisco IOx and the addition of application management capabilities.

Earlier this year we announced the availability of Cisco IOx, as part of the Cisco Fog portfolio of technologies. Cisco IOx allows customers and solution providers across all industries to develop, manage and run software applications directly on Cisco industrial networked-devices, including hardened routers, switches, and other devices. We have seen tremendous market traction of Cisco IOx in the last few months along with the accelerating IoT market growth. As IoT transitions from early adoption to wide deployment, Cisco IOx is enabling solutions providers across many industries to create innovative software solutions. Today’s announcement of the second phase of the IOx platform builds on the continuing momentum of Cisco’s vision for Fog computing. Read More »

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Analytics at the Edge: Where the Network Becomes the Database

In 1984, John Gage of Sun Microsystems coined the phrase “the network is the computer” as computing functions started to become increasingly distributed across the network. Today, boundaries that once separated individual computers have disappeared and application processing is enabled—and managed—by the network. We are now at the forefront of a new market transition, as eloquently explained by Rick van der Lans in his paper, “The Network Is the Database.”

The network is indeed becoming the database. Big Data and the related approach to database management are moving away from a centralized data warehouse model and literally starting to flow across the network. We are virtualizing data management by leaving data in the network, instead of copying it into a data center. Data stays in motion wherever and whenever it’s needed across the network, instead of being at rest.

What does this mean for business value? A distributed—and virtualized—data management approach solves the three major issues of Big Data: volume, variety, and velocity.

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