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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

Camera scheduling and energy allocation for lifetime maximization in user-centric visual sensor networks.

Chao Yu¹, Gaurav Sharma

¹Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA. chyu@ece.rochester.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|March 31, 2010

Summary

We developed strategies for camera scheduling and energy allocation to maximize the operational lifetime of image sensor networks. These methods optimize sensor coverage and energy distribution for extended network functionality.

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Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Wireless, battery-powered image sensors are deployed for visual coverage of monitored regions.
A central processor coordinates sensors to gather visual data, forming image sensor networks (ISNs).

Purpose of the Study:

To maximize the operational lifetime of image sensor networks.
To address camera scheduling and energy allocation problems within ISNs.

Main Methods:

Modeling network lifetime as a stochastic variable dependent on coverage geometry and data request distribution.
Utilizing asymptotic analysis to develop lifetime-maximizing strategies.
Simulating proposed camera scheduling and energy allocation strategies.

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Last Updated: Jun 14, 2026

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

Main Results:

Proposed strategies effectively maximize network lifetime.
The effectiveness of the strategies was validated through simulations.
The study highlights the importance of coverage geometry and data request distribution in ISN lifetime.

Conclusions:

Optimized camera scheduling and energy allocation are crucial for extending ISN operational lifetime.
The developed strategies offer a robust approach to lifetime maximization in ISNs.
Further research can explore dynamic adaptations of these strategies.