Fog Computing and the Internet of Things
The Internet of Things (IoT) is generating an unprecedented volume and variety of data. But by the time the data makes its way to the cloud for analysis, the opportunity to act on it might be gone. High-speed data processing, analytics and shorter response times are becoming more necessary than ever before. The term fog computing, coined by Cisco, refers to the need for bringing the advantages and power of cloud computing closer to where the data is being generated and acted upon. Fog computing reduces the amount of data that is transferred to the cloud for processing and analysis, while also improving security.
- Analyzes the most time-sensitive data at the network edge, close to where it is generated instead of sending vast amounts of IoT data to the cloud.
- Acts on IoT data in milliseconds, based on policy.
- Sends selected data to the cloud for historical analysis and longer-term storage.
What IoT Means to Business
The IoT speeds up awareness and response to events. In industries such as manufacturing, oil and gas, utilities, transportation, mining, and the public sector, faster response time can improve output, boost service levels, and increase safety.
Imagine it: On a factory floor, a temperature sensor on a critical machine sends readings associated with imminent failure. A technician is dispatched to repair the machine in time to avoid a costly shutdown. In oil and gas exploration, sensors on oil pipelines register a pressure change. In response, pumps automatically slow down to avert a disaster. In utilities, ruggedized cameras at remote field substations detect an intruder and alert security officers. Almost instantaneous analysis reveals similar events at other substations, automatically raising the alert to the highest level.
Connecting new kinds of things to the Internet also creates new business opportunities. Examples include pay-as-you-drive vehicle insurance, lighting-as-a-service, and machine-as-a-service (Maas).
What IoT Means to Infrastructure
Capitalizing on the IoT requires a new kind of infrastructure. Cloud models are not designed for the volume, variety, and velocity of data that the IoT generates. Billions of previously unconnected devices are generating more than two exabytes of data each day. Moving all data from the network edge to the data center for processing adds latency. Traffic from thousands of devices soon outstrips bandwidth capacity. Moreover, having every device connected to the cloud and sending raw data over the internet can have privacy, security and legal implications, especially when dealing with sensitive data that is subject to separate regulations in different countries.
Some are machines that connect to a controller using industrial protocols, not IP. Before this information can be sent to the cloud for analysis or storage, it must be translated to IP.
Compounding the challenge, IoT devices generate data constantly, and often analysis must be very rapid. This is especially true in scenarios such as telemedicine and patient care, where milliseconds can have fatal consequences. The same can be said about vehicle to vehicle communications, where the prevention of collisions and accidents can't afford the latency caused by the roundtrip to the cloud server. The cloud paradigm is like having your brain command your limbs from miles away - it won't help you where you need quick reflexes.
Handling the volume, variety, and velocity of IoT data requires a new computing model. The main requirements are to:
- Minimize latency: Milliseconds matter when you are trying to prevent manufacturing line shutdowns or restore electrical service. Analyzing data close to the device that collected the data can make the difference between averting disaster and a cascading system failure.
- Conserve network bandwidth: Offshore oilrigs generate 500 GB of data weekly. Commercial jets generate 10 TB for every 30 minutes of flight. It is not practical to transport vast amounts of data from thousands or hundreds of thousands of edge devices to the cloud. Nor is it necessary, because many critical analyses do not require cloud-scale processing and storage.
- Address security concerns: IoT data needs to be protected both in transit and at rest. This requires monitoring and automated response across the entire attack continuum: before, during, and after.
- Operate reliably: IoT data is increasingly used for decisions affecting citizen safety and critical infrastructure. The integrity and availability of the infrastructure and data cannot be in question.
- Collect and secure data across a wide geographic area with different environmental conditions: IoT devices can be distributed over hundreds or more square miles. Devices deployed in harsh environments such as roadways, railways, utility field substations, and vehicles might need to be ruggedized. That is not the case for devices in controlled, indoor environments.
- Move data to the best place for processing: Which place is best depends partly on how quickly a decision is needed. Extremely time-sensitive decisions should be made closer to the things producing and acting on the data. In contrast, big data analytics on historical data needs the computing and storage resources of the cloud.
Fog Computing
The fog extends the cloud to be closer to the things that produce and act on IoT data. These devices, called fog nodes, can be deployed anywhere with a network connection: on a factory floor, on top of a power pole, alongside a railway track, in a vehicle, or on an oil rig. Any device with computing, storage, and network connectivity can be a fog node. Examples include industrial controllers, switches, routers, embedded servers, and video surveillance cameras.
Analyzing IoT data close to where it is collected minimizes latency. It offloads gigabytes of network traffic from the core network, and it keeps sensitive data inside the network.
Examples of Fog Applications
Fog applications are as diverse as the Internet of Things itself. What they have in common is monitoring or analyzing real-time data from network-connected things and then initiating an action. The action can involve machine-to-machine (M2M) communications or human-machine interaction (HMI). Examples include locking a door, changing equipment settings, applying the brakes on a train, zooming a video camera, opening a valve in response to a pressure reading, creating a bar chart, or sending an alert to a technician to make a preventive repair. In transportation, it's helping semi-autonomous cars assist drivers in avoiding distraction and veering off the road by providing real-time analytics and decisions on driving patterns. It also can help reduce the transfer of gigantic volumes of audio and video recordings generated by police dashboard and video cameras. Cameras equipped with edge computing capabilities could analyze video feeds in real time and only send relevant data to the cloud when necessary. The possibilities are endless.
Production fog applications are rapidly proliferating in manufacturing, oil and gas, utilities, transportation, mining, and the public sector.
When to Consider Fog Computing
- Data is collected at the extreme edge: vehicles, ships, factory floors, roadways, railways, etc.
- Thousands or millions of things across a large geographic area are generating data.
- It is necessary to analyze and act on the data in less than a second.
How Does Fog Work?
Developers either port or write IoT applications for fog nodes at the network edge. The fog nodes closest to the network edge ingest the data from IoT devices. Then-and this is crucial-the fog IoT application directs different types of data to the optimal place for analysis:
- The most time-sensitive data is analyzed on the fog node closest to the things generating the data.
- Data that can wait seconds or minutes for action is passed along to an aggregation node for analysis and action.
- Data that is less time sensitive is sent to the cloud for historical analysis, big data analytics, and long-term storage.
What Happens in the Fog and the Cloud
Fog nodes:
- Receive feeds from IoT devices using any protocol, in real time
- Run IoT-enabled applications for real-time control and analytics, with millisecond response time
- Provide transient storage, often 1-2 hours
- Send periodic data summaries to the cloud
The cloud platform:
- Receives and aggregates data summaries from many fog nodes
- Performs analysis on the IoT data and data from other sources to gain business insight
- Can send new application rules to the fog nodes based on these insights
Benefits of Fog Computing
- Greater business agility: With the right tools, developers can quickly develop fog applications and deploy them where needed. Machine manufacturers can offer MaaS to their customers. Fog applications program the machine to operate in the way each customer needs.
- Better security: Protect your fog nodes using the same policy, controls, and procedures you use in other parts of your IT environment. Use the same physical security and cybersecurity solutions.
- Deeper insights, with privacy control: Analyze sensitive data locally instead of sending it to the cloud for analysis. IT team can monitor and control the devices that collect, analyze, and store data.
- Lower operating expense: Conserve network bandwidth by processing selected data locally instead of sending it to the cloud for analysis.
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