Mobile edge computation offloading based in IOT devices | PhD Dissertation Writing Services - Phdassistance.com

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MOBILE EDGE COMPUTATION OFFLOADING BASED IN
IOT DEVICES
An Academic presentation by Dr. Nancy Agens,
Head, Technical Operations, Phdassistance
Group www.phdassistance.com Email
info_at_phdassistance.com
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TODAY'S DISCUSSION
Outline
In Brief Introduction Contributions System
Model System Architecture Conclusion Future
Scopes
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In Brief
Internet of Things has shown great increase in
the development that lead to the evolution of
the delay-sensitive and computation-intensive
functions. As the cloud computing technology is
time delaying, and there is a limitation of
resources at end devices, mobile edge computing
is well-thought-out as a most promising method
that could solve the time- delaying issues of
such challenging applications. Mobile-edge
computing can be applied to the Internet of
Things (IoT) devices to provide the better
quality for computation intensive applications
and to extend the life of battery.
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Introduction
Mobile-edge Computing network implements a binary
offloading strategy, so that the computation is
either executed locally or offloaded completely
to a mobile-edge computing server in the
Wireless Devices (WD). With the advancement in
the wireless power transfer (WPT) technology, we
can charge the battery of the wireless devices
continuously over the air without replacing
battery in these devices. In the meantime, the
computing power of the device can be enhanced to
a great extent due to the advancement of
mobile-edge computing (MEC) technology.
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To develop the management issues in computation
offloading across various networks with the aim
of reducing the power consumption across the
network.
Contributions

• To efficiently solve this management issue, a
  framework has been developed to obtain
  transmission power allocation approach and
  computation offloading design.

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System Model
The figure below shows a mobile-edge computing
network possessing one cloud server with K edge
servers, and N wireless devices. Assuming that
each wireless device has M independent tasks
where each task of the device is computed by the
wireless device itself or be offloaded to, and
they are processed by the cloud server.
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Figure 1 System Model of a Multi-User Multi-Task
Mobile Edge Computing (MEC) Network
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System Architecture

• The MEC system consists of three layers. They
  are,
• MANAGEMENT LAYER The main role of this layer is
  to provide global resource allocation to assure
  stability in difficult computation.
• EDGE LAYER This layer consists of an access
  point (AP) and a base station (BS), and supplies
  the resource needed for computation.
• DEVICE LAYER There are a set of IoT devices
  which lack local computing capacity, and
  additionally they are expected to perform some
  similar tasks with the same latency constraint,
  such as sensors used for tracking on bicycles.

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Figure 2 System architecture
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Conclusion

• IoT has evolved as a renowned technology for
  building mobile applications.
• With the progress of the technology, the
  intricacy and balance of the data for processing
  also increases.
• EC model help to control the issue to a great
  extent by offloading computing tasks to the
• cloud server.
• To reduce the implementation time and the power
  consumption of mobile devices, a
• computation offloading method (COM) has been
  proposed.

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Future Scopes
For future work, the proposed method would be
extended in a real world situation of
IoT. Additionally, it is proposed to resolve
different time requirements needed for
execution, and to find an offloading approach to
reduce the energy consumption of the IoT devices.
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