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Updated: May 28, 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

Bio-Inspired Energy-Efficient Routing for Wireless Sensor Networks Based on Honeybee Foraging Behavior and MDP-Driven

Fangyan Chen¹, Xiangcheng Wu¹, Weimin Qi¹

¹School of Artificial Intelligence, Jianghan University, Wuhan 430056, China.

Biomimetics (Basel, Switzerland)

|May 26, 2026

Summary

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Optimal Foraging

Optimal Foraging

How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.

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This study introduces a novel hybrid optimization framework for Wireless Sensor Networks (WSNs) inspired by honeybee foraging. It enhances network lifetime and energy balance through adaptive routing and mobile sink trajectory planning.

Area of Science:

Computer Science
Artificial Intelligence
Network Engineering

Background:

Wireless Sensor Networks (WSNs) face challenges with limited node energy and variable data traffic.
Existing solutions struggle to balance energy efficiency and network longevity in dynamic environments.

Purpose of the Study:

To propose a bio-inspired hybrid optimization framework for mobile sink trajectory planning and adaptive routing in WSNs.
To address energy constraints and spatiotemporal data heterogeneity by integrating MILP and MDP with Q-learning.

Main Methods:

A hybrid framework combining Mixed-Integer Linear Programming (MILP) for global planning and Markov Decision Processes (MDP) with Q-learning for local adaptation.
MILP determines optimal cluster head access sequence for the mobile sink, while Q-learning optimizes routing parameters in real-time.

Keywords:

Q-learning adaptive trajectory planning bio-inspired routing energy efficiency honeybee foraging mixed-integer linear programming wireless sensor networks

Related Experiment Videos

Last Updated: May 28, 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

An epsilon-greedy strategy and periodic decision updates balance exploration, exploitation, and computational efficiency.

Main Results:

The proposed MILP-MDP framework significantly improves network lifetime compared to baseline algorithms.
Demonstrates superior energy balance across network nodes.
Effectively manages mobile sink trajectory planning and adaptive routing in dynamic WSNs.

Conclusions:

The integration of bio-inspired foraging strategies and reinforcement learning offers an efficient and robust solution for WSNs.
The hybrid framework effectively tackles the dual challenges of energy constraints and data heterogeneity.
This approach provides a promising direction for optimizing WSN performance in dynamic conditions.