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Updated: Jun 21, 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

A multiobjective optimization approach to obtain decision thresholds for distributed detection in wireless sensor

Engin Masazade¹, Ramesh Rajagopalan, Pramod K Varshney

¹Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey. enginm@su.sabanciuniv.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|August 14, 2009

Summary

This summary is machine-generated.

Optimizing wireless sensor networks involves balancing error probability and energy consumption. Multiobjective optimization offers design alternatives for better energy savings with minimal error increase.

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Last Updated: Jun 21, 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

Area of Science:

Electrical Engineering
Computer Science
Signal Processing

Background:

Distributed detection in wireless sensor networks (WSNs) requires optimizing local sensor decision thresholds.
The central fusion center relies on sensor decisions for global inference, making threshold determination critical.
Existing methods often focus solely on minimizing error probability, neglecting energy efficiency.

Purpose of the Study:

To investigate and solve the distributed detection problem in WSNs by optimizing sensor decision thresholds.
To formulate and solve a multiobjective optimization problem minimizing both error probability and network energy consumption.
To compare the effectiveness of parallel and serial decision fusion schemes under these objectives.

Main Methods:

Formulation and solution of a multiobjective optimization problem for sensor thresholds.
Application of two multiobjective optimization techniques: Normal Boundary Intersection (NBI) and Nondominating Sorting Genetic Algorithm-II (NSGA-II).
Evaluation of parallel and serial decision fusion schemes.

Main Results:

Multiobjective optimization provides design alternatives, enabling significant energy savings at a slight cost to error probability.
The Normal Boundary Intersection (NBI) method outperformed NSGA-II, yielding better, evenly distributed Pareto optimal solutions faster.
Parallel fusion demonstrated superior error probability, while serial fusion offered greater energy efficiency.

Conclusions:

Multiobjective optimization is crucial for efficient WSN design, balancing performance and energy constraints.
NBI is an effective method for solving complex multiobjective problems in WSNs.
The choice between parallel and serial fusion depends on whether minimizing error or conserving energy is the priority.