Edge Computing is the use of a network of data centers that can store critical information and
eventually send the stored data to a central data repository which may be a physical storage
location or a cloud-based repository. Edge computing basically involves the collection of data on
certain technology-driven devices and thus works on the phenomenon of Internet of Things.
Miller (2018) states that “The IoT is a network of physical objects (such as wearable devices,
home appliances, security systems, personal and commercial vehicles, nanotechnology,
manufacturing equipment, and more) embedded with smart components (such as
microprocessors, data storage, software, sensors, actuators, and more) and connected to other
devices and systems over the Internet” (p. 3).
An example of an Edge Computing use case is the use of mobile applications to gather relevant
information for development and enhancement of services in the financial sector. Banking
applications on mobile devices like smart phones are used to collect information on customer
behavior and customer preferences, and the data sent eventually to a repository where the
financial institutions can analyze and draw valuable insights from them. The financial
institutions also use Automated Teller Machine (ATM) systems to accomplish similar tasks by
storing relevant customer data using internet of things processes to get the most accurate and up
to date real-time information.
Miller, L.C. (2018). IoT Solutions for Dummies, Arm Special Edition. USA: John Wiley &
Edge computing is the network infrastructure which most companies begin to work
where the organizations start to realize the potential and also affect the business. The
autonomous vehicle is one of the industries which benefit greatly in the latest
developments in edge computing.
The automotive industry has invested so many billions of dollars in developing the
technology for driverless cars which are not expected to take over the highways
anytime soon. Further, for operating safely, the vehicles need to gather and analyze
huge amounts of data according to the surroundings, directions, and weather
conditions. Also, further not to mention communicating with other vehicles on the
road. Moreover, it needs to feed data back to manufacturers to track usage and
maintenance alerts which interface with local municipal networks. Also, the
transmitted data goes in the same flow for the traffic produced by cellular phones,
personal computers, and a range of other connected devices. Also, the additional
vehicles gathering and transmitting data, bandwidth strains are inevitable if
manufacturers don’t adopt new computing solutions. Moreover, it could be one way
where office computer to experience inconvenient lag when accessing a network.
Also, for another self-driving car to lag where it’s traveling at 65 mph on an open
Additionally, the Edge computing architecture makes it possible for autonomous
vehicles for collecting, processing, and sharing data between vehicles. Also, it makes
to broader networks in real time with almost no latency. When the combined network
of edge data centers geographically positioned to collect and relay critical data to
municipalities as well as emergency response services, and auto manufacturers, edgeenabled vehicles offers unparalleled reliability where crippling network
infrastructures. For example, Hyundai is working on self-driving vehicles which is
more of a focus on affordability. Further, the Hyundai claims for developing its own
autonomous vehicle of the operating system, where the goal of using a lot less
computing power. Finally, as the result in a low-cost platform, it can be installed in
future Hyundai models for the average consumer can afford.
Blair Felter. (2018) 5 Edge Computing Use Cases With Huge Potential Retrieved
Jon Walker. (2019) The Self-Driving Car Timeline – Predictions from the Top 11
Global Automakers Retrieved from https://emerj.com/ai-adoption-timelines/selfdriving-car-timeline-themselves-top-11-automakers/
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