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Energy Efficient SWIPT Based Mobile Edge Computing Framework for WSN-Assisted IoT.

Fangni Chen1,2, Anding Wang3, Yu Zhang1

  • 1College of Information Engineering, Zhejiang University of Technology, Hangzhou 310012, China.

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Summary
This summary is machine-generated.

This study introduces a system combining mobile edge computing (MEC) and simultaneous wireless information and power transfer (SWIPT) to reduce energy consumption in IoT networks. The proposed solution significantly lowers energy use compared to existing methods.

Keywords:
5GIoTenergy minimizationmobile edge computaingsimultaneous wireless information and power transferwireless sensing network

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Area of Science:

  • Wireless Sensor Networks (WSN)
  • Internet of Things (IoT)
  • Mobile Edge Computing (MEC)

Background:

  • The proliferation of IoT devices creates challenges like data overload and energy scarcity.
  • Existing WSN-assisted IoT applications struggle with service supply capabilities due to these limitations.

Purpose of the Study:

  • To propose an integrated system using MEC and SWIPT to enhance IoT service capabilities.
  • To formulate an optimization problem minimizing total system energy consumption.
  • To address data transmission rate and power requirements.

Main Methods:

  • Developed a novel optimization problem to minimize energy consumption.
  • Incorporated joint consideration of power allocation, CPU frequency, and offloading/harvesting factors.
  • Proposed an Alternate Group Iteration Optimization (AGIO) algorithm to solve the non-convex problem by decomposing it into subproblems.
  • Utilized a group interior point iterative algorithm for subproblem optimization.

Main Results:

  • The proposed system demonstrates significantly lower energy consumption compared to two benchmark algorithms.
  • Validated the effectiveness of the AGIO algorithm in optimizing system variables.
  • Established the relationship between system variables and overall energy consumption.

Conclusions:

  • The integration of MEC and SWIPT effectively addresses energy insufficiency and data explosion in IoT networks.
  • The developed AGIO algorithm provides an efficient solution for optimizing energy consumption in complex IoT systems.
  • The findings offer a pathway to more sustainable and capable WSN-assisted IoT applications.