Document Type : Original Article
Abstract
We live in a world where consumer products, vehicles, industrial parts and
tools, sensors and other everyday objects are combined with powerful Internet
connectivity and powerful analysis systems. Through the internet of things, we
are producing a large amount of data. The internet of things is a new concept and
computing that makes our world smarter, with the help of this technology, all
objects will always be able to interact with each other and generate a lot of data
from their own situation. With the growth of the speed of production and the
unprecedented level of data, cloud-based infrastructure alone does not meet the
needs of this technology, and it should be borne in mind that the functionality of
the Internet of objects is reliant on the cloud, which can lead to inefficiencies.
Latency and bandwidth are two important factors in cloud computing
inefficiency, due to the large amount of data and the high processing latency, the
cloud can't respond promptly to incidents and events. The lack of appropriate
bandwidth also the network of internet objects damages, the devices produce a
lot of data in the network, which, if it is not possible to send all these data to the
cloud for processing, can destroy important information for decision making.
Therefore, the internet of things needs a solution to address this challenge. For
this purpose, false computing or edge computing has been focused on removing
these constraints. In this type of computing, which is locally and decentralized,
data processing is done near the data production site. Combining its cloud
computing solution can meet the needs of the internet technology of objects.
Despite the increasing use of cloud computing, there are still unresolved issues
due to inherent problems of cloud computing such as unreliable latency, lack of
mobility support and location. Information is there. Therefore, we need a
computational paradigm that allows us to compute the data before sending it to
the super-batch, which allows us to work on the data before it is lost and lost.
May's computing is a paradigm that expands cloud computing by sending
services to the edge of the network, and dew computing is an organized
computerized computing pattern in the era of cloud computing. Since the launch
of the internet work, it has been continuously serving as the main pillar of the
existing aspects of cloud, fog, and edge computing.
In this paper, we describe the various properties of dew computing and describe
some of its applications, as well as their parametric comparison with cloud
computing paradigms.