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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services.

Yuansheng Luo1, Wenjia Li2, Shi Qiu3

  • 1School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.

Sensors (Basel, Switzerland)
|December 28, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel mathematical programming method to optimize energy consumption in Internet of Things (IoT) continuous data-flow applications by bypassing anomalous nodes. The approach enhances system efficiency and reduces power usage in edge, fog, and cloud computing environments.

Keywords:
E-Health monitoring systemanomaly detectionenergy efficientfog computinginternet of thingslatency awarenessmixed integer nonlinear programming

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Area of Science:

  • Internet of Things (IoT)
  • Distributed Computing
  • Network Optimization

Background:

  • Continuous data-flow applications in IoT, common in E-Health systems, face challenges in energy consumption optimization.
  • Anomalous nodes in IoT networks increase energy usage, necessitating strategies to reroute data flows.
  • Existing research primarily optimizes system architecture and deployment, not runtime performance.

Purpose of the Study:

  • To propose a novel mathematical programming method for optimizing runtime performance of continuous data-flow applications in IoT.
  • To develop a lightweight anomaly detection method for evaluating node reliability.
  • To integrate node reliability into an optimization algorithm for latency-aware energy consumption.

Main Methods:

  • A lightweight anomaly detection method to assess node reliability.
  • Mathematical programming, specifically a mixed integer nonlinear programming model, to optimize latency-aware energy consumption.
  • A block coordinate descend-based max-flow algorithm to solve the optimization problem.

Main Results:

  • The proposed method effectively bypasses anomalous nodes, reducing energy consumption.
  • Node reliability is accurately estimated and incorporated into the optimization process.
  • Numerical simulations using real-life datasets demonstrate superior performance compared to benchmark strategies.

Conclusions:

  • The proposed mathematical programming approach offers a significant advancement in optimizing energy consumption for continuous data-flow IoT applications.
  • The integration of anomaly detection and latency-aware optimization provides a robust solution for improving IoT system efficiency.
  • This work lays the foundation for more energy-efficient and reliable IoT systems, particularly in critical domains like E-Health.