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Fog computing tutorial | Applications, Architecture, Pros, Cons

The Fog computing tutorial covers basics, architecture, applications, Pros and Cons (i.e. challenges). This Fog computing tutorial also mentions necessity of fog computing in IoT (Internet of Things) as well as its working operation.

Introduction:
Fog computing is the next generation computing which extends the cloud computing to the edge of the network. It is also known as edge computing. It has been developed to address the issues faced by cloud computing.

As we are aware that use of internet is booming due to proliferation of wired and wireless technologies. As a result billions of devices connected on the internet are generating huge amount (i.e. exa bytes) of data daily. As per one study there will be about 250 million connected vehicles and 30 billion IoT devices by 2020. The current cloud model is not capable to handle this huge bandwidth of data generated by private companies, factories, airplanes, healthcare devices etc. Moreover certain time sensitive data can not be handled by cloud network due to its higher latency.

What is Fog Computing?

Fog computing

Fog computing extends the cloud to be closer to the things which generate data. This minimizes latency requirement as fog nodes directly act on the data. It processes and analyzes the data generated by IoT devices. Following are the major functions of fog computing architecture.
• Analyzes most time sensitive data at the edge of the network, where they are generated by devices. This avoids sending huge amount of data to the cloud.
• It acts on IoT data in milliseconds as per policy.
• Fog computing sends only selected data to the cloud for analysis and long term storage.

Why Fog Computing?

Fog computing addresses following three requirements of todays networks.
• Data Volume
• Latency
• Bandwidth

Moreover use of IoT devices is increasing due to wide availability of low cost sensors. As per one study, huge amount of data are generated from various sensors. Following requirements of IoT should be met for its successful implementation.
• Reduction in latency of data
• High data security
• Data reliability
• Processing of data at respective suitable place based on type of data
• Monitoring of data across large geographical area

Fog computing addresses all the above requirements of IoT networks. The figure-1 depicts three layers viz. device, fog layer and cloud. As shown fog is intermediate layer between cloud and devices which generate the data as shown.

Fog Computing Applications

Following are the list of fog computing applications.
• Smart Homes: Fog computing addresses home security applications with use of smart sensors, cameras, wireless technologies and so on.
• Smart Cities: Large cities face more challenges in public safety, traffic handling, sanitation, energy utility etc. Single IoT network with network of fog nodes can address these challenges. Refer advanages and disadvantages of Smart city >> for more information.
• Smart Buildings: Various sensors are installed and are monitored continuously to take action in certain situations. Fog computing helps in monitoring and control in such smart buildings.
• Smart Grid: In this network, smart meters are deployed at various consumer locations to measure real time status information on electricity usage etc. Refer Smart grid architecture and its working >>.
• Smart Vehicle: Fog computing can be integrated into vehicular networks. It is categorized into two types viz. infrastructure based and autonomous. Fog nodes are responsible for sending and receiving information to/from vehicles.
• Rail Monitoring: Fog nodes are installed along side of the railway track. These nodes help in real time monitoring of track conditions. Moreover sending data to cloud from high speed train is difficult and complex task which can be easily addressed by fog computing architecture based network.
• Visual Security: Video cameras are installed at public places, residential plots, parking lots, gardens, shopping malls etc. Bandwidth of visual data generated from these cameras make it impossible to send them to cloud for storage and analysis. Fog computing takes care of this situation to provide safety and security to the people by fast analyzing data near to the video camera end itself. • Health Data Management: Using fog computing patients can have their health related data available locally. Moreover these data are analyzed locally to provide fast treatment by doctors and health experts.

Fog Computing Architecture and its working

Let us understand architecture of fog computing as part of our basic fog computing tutorial. The Fog computing architecture consists of physical and logical elements in the form of hardware and software to implement IoT (Internet of Things) network. As shown in figure-2, it is composed of IoT devices, fog nodes, fog aggregation nodes with the help of fog data services, remote cloud storage and local data storage server/cloud. Let us understand fog computing architecture components.

Fog computing architecture

• IoT devices: These are devices connected on IoT network using various wired and wireless technologies. These devices produce data regularly in huge amount. There are numerous wireless technologies used in IoT which include Zigbee, Zwave, RFID, 6LoWPAN, HART, NFC, Bluetooth, BLE, NFC, ISA-100.11A etc. IoT protocols used include IPv4, IPv6, MQTT, CoAP, XMPP, AMQP etc.
• Fog Nodes: Any device with computing, storage and network connectivity is known as fog node. Multiple fog nodes are spread across larger region to provide support to end devices. Fog nodes are connected using different topologies. The fog nodes are installed at various locations as per different applications such as on floor of a factory, on top of power pole, along side of railway track, in vehicles, on oil rig and so on. Examples of fog nodes are switches, embedded servers, controllers, routers, cameras etc. High sensitive data are processed at these fog nodes.
• Fog aggregate nodes: Each fog nodes have their aggregate fog node. It analyzes data in seconds to minutes. IoT data storage at these nodes can be of duration in hours or days. Its geographical coverage is wider. Fog data services are implemented to implement such aggregate node points. They are used to address average sensitive data.
• Remote Cloud: All the aggregate fog nodes are connected with the cloud. Time insensitive data or less sensitive data are processed, analyzed and stored at the cloud.
• Local server and cloud: Often fog computing architecture uses private server/cloud to store the confidential data of the firm. These local storage is also useful to provide data security and data privacy.
Refer Fog computing architecture working >> for more information.

Pros and Cons of Fog Computing

Following are the Pros (i.e. advantages or benefits) of Fog Computing.
• It offers better security.
• It saves network bandwidth and hence reduces operational costs.
• It reduces latency.
• It offers better privacy.
• It is easy to develop fog applications.
• Fog nodes are mobile in nature.
• Fog nodes can withstand any harsh environment conditions.
Following are the Cons (i.e. disadvantages or challenges) of Fog Computing.
• It is difficult for any arbitrary devices to exchange the data on fog computing networks.
• There are security concerns due to wide use of IoT based wireless networks, IP address spoofing etc.
• Data consistency and data management in fog computing is a challenge.
• Trust and authentication are major concerns.
• Scheduling is complex as tasks can move between clients, fog nodes and back end servers.
• Power consumption is higher due to de-centralized architecture.
• Also refer advantages and disadvantages of fog computing >> for more information.


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