Fog computing, also known as fog networking or fogging, is a decentralized computing infrastructure in which data, compute, storage and applications are distributed in the most logical, efficient place between the data source and the cloud. Fog computing essentially extends cloud computing and services to the edge of the network, bringing the advantages and power of the cloud closer to where data is created and acted upon.
The OpenFog Consortium was organized to develop a cross-industry approach to enabling end-to-end IoT deployments by creating a reference architecture to drive interoperability in connecting the edge and the cloud. The group has identified numerous IoT use cases that require edge computing including smart buildings, drone-based delivery services, real-time subsurface imaging, traffic congestion management and video surveillance. The group released a fog computing reference architecture in February 2017.
Helder Antunes, OpenFog Consortium chairman and senior director of the corporate strategic innovation group at Cisco, said the release will drive IoT adoption by providing a “universal framework. While fog computing is starting to be rolled out in smart cities, connected cars, drones and more, it needs a common, interoperable platform to turbocharge the tremendous opportunity in digital transformation.”
Another group that was formed to drive edge interoperability is the Edgex Foundry, an open source consortia approach managed by The Linux Foundation and seeded with some 125,000 lines of code developed internally by Dell Technologies.
Where does fog computing work best?
“The ideal use cases require intelligence near the edge where ultra low latency is critical, run in geographically dispersed areas where connectivity can be irregular, or create terabytes of data that are not practical to stream to the cloud and back,” said Vasey. “Fog computing works well in a cloud-based control plane to provide control and broader insight across a large numbers of nodes. These include transportation, agriculture, wind energy, surveillance, smart cities and buildings.”
Smart cities and fog computing :
Large cities face challenges from traffic congestion, public safety, high energy use, A lack of broadband bandwidth and connectivity is a major issue in establishing smart Cities. While most modern cities have one or more cellular networks providing adequate coverage, these networks often have capacity and peak bandwidth limits that barely meet the needs of existing subscribers. This leaves little bandwidth for the advanced municipal services envisioned in a smart city. Deploying a fog computing architecture allows for fog nodes to provide local processing and storage. This optimizes network usage.
Smart cities also struggle with safety and security, where time-critical performance requires advanced, real-time analytics. Municipal networks may carry sensitive traffic and citizen data, as well as operate life-critical systems such as emergency response. Fog computing addresses security, data encryption and distributed analytics requirements.
Smart buildings and fog computing
Building automation demonstrates the need for edge intelligence and localized processing. A commercial building may contain thousands of sensors to measure various building operating parameters: temperature, keycard readers and parking space occupancy. Data from these sensors must be analyzed to see if actions are needed, such as triggering a fire alarm if smoke is sensed. Fog computing allows for autonomous local operations for optimized control function.
Each floor, wing or even individual room could contain its own fog node that is responsible for performing emergency monitoring and response functions, controlling climate and lighting, and providing a building-resident compute and storage infrastructure to supplement the limited capabilities of local smartphones, tablets and computers.
Fog computing works with cloud computing, so the long-term history of building operational telemetry and control actions can be aggregated and uploaded to the cloud for large-scale analytics to determine operational aspects of buildings. The stored operational history can then train machine learning models, which can be used to further optimize building operations by executing these cloud-trained machine learning models in the local fog infrastructure.
Visual security and fog computing
Video cameras are now used in parking lots, buildings and other public and private spaces to increase public safety. The sheer bandwidth of visual (and other sensor) data being collected over a large-scale network makes it impractical to transport all of the data to the cloud to obtain real-time insights. Imagine a busy airport or city center with many people and objects moving through an area at a time. Real-time monitoring and detection of anomalies pose strict low-latency requirements on surveillance systems. Timeliness is important for both detection and response.
Privacy concerns must be addressed when using a camera as a sensor that collects image data so that the images do not reveal a person’s identity or reveal confidential contextual information to any unauthorized parties. Fog computing allows for real-time, latency-sensitive distributed surveillance systems that maintain privacy.
Through a fog architecture, video processing is intelligently partitioned between fog nodes co-located with cameras and the cloud. This enables real-time tracking, anomaly detection, and collection of insights from data captured over long intervals of time.
How does fog computing reduce security risks?
Security is a fundamental concern of any deployment that uses IoT, network and cloud technologies. The OpenFog architecture specifies a secure end-to-end compute environment between the cloud and the fog nodes that connect to IoT devices. These devices use a hardware-based immutable root of trust, which can be attested by software agents running throughout the infrastructure.
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.
Benefits of Fog Computing :
Extending the cloud closer to the things that generate and act on data benefits the business in the following ways:
- 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. Your 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.
How fog computing works :
While edge devices and sensors are where data is generated and collected, they don’t have the compute and storage resources to perform advanced analytics and machine-learning tasks. Though cloud servers have the power to do these, they are often too far away to process the data and respond in a timely manner. In addition, having all endpoints connecting to and sending raw data to the cloud over the internet can have privacy, security and legal implications, especially when dealing with sensitive data subject to regulations in different countries.
In a fog environment, the processing takes place in a data hub on a smart device, or in a smart router or gateway, thus reducing the amount of data sent to the cloud. It is important to note that fog networking complements — not replaces — cloud computing; fogging allows for short-term analytics at the edge, and the cloud performs resource-intensive, longer-term analytics.
FOG COMPUTING CHARACTERISTICS :
Fog computing is a distributed paradigm that provides cloud-like services to the network edge. It leverages cloud and edge resources along with its own infrastructure, as Figure 1 shows. In essence, the technology deals with IoT data locally by utilizing clients or edge devices near users to carry out a substantial amount of storage, communication, control, configuration, and management. The approach benefits from edge devices’ close proximity to sensors, while leveraging the on demand scalability of cloud resources. Fog computing involves the components of data-processing or analytics applications running in distributed cloud and edge devices. It also facilitates the management and programming of computing, networking, and storage services between datacenters and end devices. In addition, it supports user mobility, resource and interface heterogeneity, and distributed data analytics to address the requirements of widely distributed applications that need low latency.
FOG-COMPUTING COMPONENTS :
In fig.presents a fog-computing reference architecture.Sensors stream data to IoT networks,
applications running on fog devices subscribe to and process the information, and the obtained insights are translated into actions sent to actuators. Fog systems dynamically discover and use APIs to build complex functionalities.
Fig : Fog-computing architecture. In the bottom layer are end devices—including sensors and actuators—along with applications that enhance their functionality. These elements use the next layer, the network, for communicating with edge devices, such as gateways, and then with cloud services. The resource-management layer runs the entire infrastructure and enables quality-of-service enforcement. Finally, applications leverage fog-computing programming models to deliver intelligent services to users.
Components at the resource-management layer use information from the resource monitoring service to track the state of available cloud, fog, and network resources and identify the best candidates to process incoming tasks. With multitenant applications, the resource-management components prioritize the tasks of the various participating users or programs. Edge and cloud resources communicate using machine-to-machine (M2M) standards such as MQTT (formerly MQ Telemetry Transport) and the Constrained Application Protocol (CoAP). Software-defined networking (SDN) helps with the efficient management of heterogeneous fog networks.
FOG-COMPUTING SOFTWARE SYSTEMS :
There are four prominent software systems for building fog computing environments and applications
Cisco IOx provides device management and enables M2M services in fog environments.5 Using device abstractions provided by Cisco IOx APIs, applications running on fog devices can communicate with other IoT devices via M2M protocols.
Cisco Data in Motion (DMo) enables data management and analysis at the network edge and is built into products that Cisco Systems and its partners provide.
LocalGrid’s fog-computing platform is software installed on network devices in smart grids. It provides reliable M2M communication between devices and data-processing services without going through the cloud.
Cisco ParStream fog-computing platform enables real-time IoT analytics.
FOG-COMPUTING APPLICATIONS :
Various applications could benefit from fog computing.
Healthcare and activity tracking:
Fog computing could be useful in healthcare, in which real-time processing and event response are critical. One proposed system utilizes fog computing to detect, predict, and prevent falls by stroke patients. The fall-detection learning algorithms are dynamically deployed across edge devices and cloud resources. Experiments concluded that this system had a lower response time and consumed less energy than cloud only approaches. A proposed fog computing–based smart-healthcare system enables low latency, mobility support, and location and privacy awareness.Smart utility services Fog computing can be used with smart utility services,8 whose focus is improving energy generation, delivery, and billing. In such environments, edge devices can report more fine grained energy-consumption details (for example, hourly and daily, rather than monthly, readings) to users’ mobile devices than traditional smart utility services. These edge devices can also calculate the cost of power consumption throughout the day and suggest which energy source is most economical at any given time or when home appliances should be turned on to minimize utility use.
Augmented reality, cognitive systems, and gaming :
Fog computing plays a major role in augmented-reality applications, which are latency sensitive. For example, the EEG Tractor Beam augmented multiplayer, online brain–computer interaction game performs continuous real-time brain-state classification on fog devices and then tunes classification models on cloud servers, based on electroencephalogram readings that sensors collect. A wearable cognitive-assistance system that uses Google Glass devices helps people with reduced mental acuity perform various tasks, including telling them the names of people they meet but don’t remember. In this application, devices communicate with the cloud for delay-tolerant jobs such as error reporting and logging. For time-sensitive tasks, the system streams video from the Glass camera to the fog devices for processing. The system demonstrates how using nearby fog devices greatly decreases end-to-end latency.