The increasing speed of data transmission in networking technology advanced and the growth of cloud storage services has made the adoption of IoT increasingly viable.
For data collected by IoT devices to be useful to businesses, they need to receive it in a timely manner.
This has led to computing power and storage being inserted out on the network edge to lower data transport time and increase availability.
Edge computing brings bandwidth-intensive content and latency-sensitive applications closer to the user or data source.
Edge computing has three primary applications:
Latency is the time between the moment a data packet is transmitted to the moment it reaches its destination (one way) and returns (round trip).
Excessive latency creates traffic jams that prevent data from filling the network to capacity.
The impact of latency on network bandwidth can be temporary (lasting a few seconds) like a traffic light, or constant like a single-lane bridge.
The greatest probability of network congestion is from high bandwidth video content.
In order to relieve network congestion to improve streaming of high bandwidth content now and in the future, service providers are interconnecting a system of computers on the Internet that caches the content closer to the user.
This enables the content to be deployed rapidly to numerous users by duplicating the content on multiple servers and directing the content to users based on proximity.
The technologies that will enable ‘smart’ everything – cities, agriculture, cars, health, etc – in the future require the massive deployment of Internet of Things (IoT) sensors.
An IoT sensor is defined as a non-computer node or object with an IP address that connects to the Internet.
As the price of sensors continues to decline, the number of connected IoT things will skyrocket.
Cisco estimates the IoT will consist of 50 billion devices connected to the Internet by 2020.
IoT can automate operations by:
The Industrial Internet of things (IIoT), which includes the harnessing of sensor data, machine-to-machine communication control and automation technologies, generate large amounts of data and network traffic.
One example of IIoT applications is in the oil and gas sector.
Multiple flying drones called “aerial data collection bots” examining job sites during oil exploration generate large quantities of data in the form of high definition video.
These job sites are difficult to coordinate with fleets of massive trucks, cranes, and rotary diggers.
Legacy methods of traffic management have used manned helicopters for surveillance video.
Now, self-piloted drones can photograph job sites 24 hours a day providing site managers with an up-to-the-minute view of how their resources are deployed.
Relying on edge computing allows the drones to transmit the data in real time and receive instructions in a timely fashion.
The need to maintain or increase availability of IT and its networks is a key concern for IT professionals.
Cloud computing has always been a centralised architecture.
Edge computing transforms cloud computing into a more distributed computing cloud architecture.
The main advantage is that any kind of disruption is limited to only one point in the network
instead of the entire network.
A Distributed Denial of Service (DDoS) attack or a long-lasting power outage, for example, would be limited to the edge computing device and the local applications on that device as opposed to all applications running on a centralised cloud data centre.
Companies that have migrated to off-premise cloud computing can take advantage of edge computing for increased redundancy and availability.
Business-critical applications or applications needed to operate the core functions of the business can be duplicated on-site.