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Related Experiment Video

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Joint Congestion Control and Resource Allocation in Cache-Enabled Sensor Networks.

Yuan Ren¹, Guangyue Lu², Changyin Sun³

¹Shaanxi Key Laboratory of Information Communication Network and Security, Xi'an University of Posts and Telecommunications, Xi'an 710121, China. renyuan@xupt.edu.cn.

Sensors (Basel, Switzerland)

|July 10, 2019

Summary

This summary is machine-generated.

This study optimizes beamforming for cache-enabled sensor networks, balancing congestion control and energy efficiency. Wireless caching effectively reduces network congestion and queue backlogs, improving overall performance.

Keywords:

Internet of Things beamforming cache-enabled sensor networks congestion control resource allocation successive convex approximation

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Area of Science:

Wireless communication networks
Sensor networks
Network optimization

Background:

Cache-enabled sensor networks face challenges in congestion and energy efficiency.
Dynamic buffering queues are crucial for reflecting network congestion and service delay.

Purpose of the Study:

To investigate optimal beamforming design for joint congestion control and energy-efficient resource allocation.
To maximize the time-averaged sum rate under various network constraints.

Main Methods:

Lyapunov optimization to trade off queue backlogs and sum rate maximization.
Successive convex approximation to convert the problem into a solvable second-order cone program.

Main Results:

Wireless caching significantly alleviates network congestion by reducing buffering backlogs.
The proposed scheme effectively balances average queue length and time-averaged sum rate.

Conclusions:

Optimal beamforming design is key for efficient resource allocation in cache-enabled sensor networks.
The developed method provides a computationally efficient solution for complex network optimization problems.