Mobile edge computation offloading based in IOT devices  PhD Dissertation Writing Services - Phdas
                     MOBILE EDGE  
COMPUTATION  
OFFLOADING 
BASED  IN IOT 
DEVICES
An Academic presentation by
Dr. Nancy Agens, Head, Technical Operations, 
Phdassistance  Group www.phdassistance.com
Email: [email protected]
TODAY'S 
DISCUSSION
Outlin In Brief
Introduction  
e Contributions  
System Model  
System Architecture 
 Conclusion
Future Scopes
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.
Introductio
n
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.
To develop the management issues in computation  
offloading across various networks with the aim of  
reducing the power consumption across the network.
Contributio
ns To efficiently solve this management issue, a framework 
 has been developed to obtain transmission power  
allocation approach and computation offloading design.
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.
Figure 1: System Model of a Multi-User Multi-Task Mobile Edge Computing (MEC) Network
System 
Architecture
The MEC system consists of three layers. They are,
1. MANAGEMENT LAYER: The main role of this layer is to provide global resource 
 allocation to assure stability in difficult computation.
2. EDGE LAYER: This layer consists of an access point (AP) and a base station (BS), 
 and supplies the resource needed for computation.
3. 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.
Figure 2: System architecture
Conclusio
n
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.
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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