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Updated: Jan 28, 2026

Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver

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A New Ant Colony Optimization-Based Dynamic Path Planning and Energy Optimization Model in Wireless Sensor Networks

Fangyan Chen¹, Xiangcheng Wu¹, Zhiming Wang¹

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

Biomimetics (Basel, Switzerland)

|January 27, 2026

Summary

This study introduces an Ant Colony Optimization (ACO)-inspired model using mixed-integer linear programming (MILP) to optimize wireless sensor networks (WSNs). The model balances energy consumption, delay, and network lifetime for enhanced performance.

Keywords:

dynamic path planning mixed-integer linear programming wireless sensor networks (WSNs)

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Area of Science:

Computer Science
Network Engineering
Optimization Theory

Background:

Wireless Sensor Networks (WSNs) are crucial for environmental monitoring and industrial control.
Energy limitations in sensor nodes necessitate balancing energy consumption, transmission delay, and network lifetime to prevent energy holes.

Purpose of the Study:

To develop a novel optimization model inspired by biological foraging strategies (herbivores and ants) for WSNs.
To address the challenge of multi-objective optimization in WSNs, specifically energy efficiency, delay, and network longevity.

Main Methods:

Formulation of an Ant Colony Optimization (ACO)-conceptualized model using Mixed-Integer Linear Programming (MILP).
Integration of discrete path selection and continuous dwell time optimization for mobile sinks.
Mapping pheromone intensity and evaporation to MILP energy constraints and cost functions for dynamic path planning.

Main Results:

Achieved flexible trade-offs between data transmission delay and energy consumption via adjustable weight coefficients.
Transformed complex WSN path planning and scheduling into deterministic optimization models with global optimality guarantees.
Demonstrated significant improvements in energy efficiency, real-time performance, and extended network lifetime through experimental validation.

Conclusions:

The proposed ACO-conceptualized MILP model effectively optimizes WSN performance.
The model provides a robust framework for balancing competing objectives in energy-constrained networks.
This approach enhances overall network efficiency and prolongs operational lifespan.