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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Energy-Optimal Latency-Constrained Application Offloading in Mobile-Edge Computing.

Xiaohui Gu1, Chen Ji1, Guoan Zhang1

  • 1School of information science and technology, Nantong University, Nantong 226019, China.

Sensors (Basel, Switzerland)
|June 3, 2020
PubMed
Summary
This summary is machine-generated.

Mobile-edge computation offloading (MECO) optimizes battery life and reduces latency by offloading tasks from mobile devices to nearby servers. This strategy analytically determines optimal resource allocation for energy efficiency.

Keywords:
channel conditionenergy-latency trade-offmobile application offloadingmobile-edge computingpartial offloading

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Telecommunications

Background:

  • Mobile devices face limitations in battery life and processing power for demanding applications.
  • Mobile-edge computation offloading (MECO) offers a solution by leveraging nearby network resources.
  • Balancing energy consumption and execution latency is crucial for effective offloading.

Purpose of the Study:

  • To develop an offloading strategy for joint optimization of communication and computational resources.
  • To minimize total energy consumption while adhering to execution delay constraints.
  • To analyze the trade-offs between energy efficiency and latency in MECO.

Main Methods:

  • Formulating the offloading strategy as an optimization problem.
  • Analytically deriving optimal transmission power, rate, and task offloading fractions.
  • Investigating conditions for optimal full-offloading and no-offloading decisions.

Main Results:

  • The proposed strategy analytically determines optimal parameters for resource allocation.
  • Conditions for binary offloading decisions (full or none) were established.
  • System parameters like latency constraints and task complexity were shown to influence the offloading strategy.

Conclusions:

  • The developed MECO strategy effectively balances energy consumption and latency.
  • Simulation results validate the energy efficiency and performance of the proposed strategy.
  • The study provides insights into optimizing resource utilization in mobile-edge computing environments.