Energy-Efficient Adaptive Sensing Scheduling in Wireless Sensor Networks Using Fibonacci Tree Optimization Algorithm.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless sensor networks (WSNs) offer infrastructure-free deployment but are limited by energy constraints.
• Duty cycle scheduling is a key factor in improving the energy efficiency of WSN sensing operations.
• Existing scheduling strategies often struggle to balance energy conservation with detection accuracy.

Purpose of the Study:

• To develop a novel adaptive sensing scheduling strategy for energy-constrained wireless sensor networks.
• To optimize WSN performance by minimizing energy depletion while maximizing detection accuracy.
• To introduce and evaluate the Fibonacci Tree Optimization Strategy (FTOS) for WSN scheduling.

Main Methods:

• Framed the WSN scheduling problem as an optimization problem with dual objectives: energy reduction and detection accuracy.
• Developed a novel modeling paradigm for wireless sensor networks.
• Employed numerical fitting to determine the explicit form of the objective function, identifying it as a bivariate multimodal function.
• Utilized the Fibonacci Tree Optimization algorithm to optimize scheduling parameters.

Main Results:

• The proposed Fibonacci Tree Optimization Strategy (FTOS) effectively balances energy depletion and detection accuracy.
• FTOS demonstrated superior performance compared to existing methods like LDAS, BS, DSS, and PECAS.
• The Fibonacci Tree Optimization algorithm outperformed ABC, DE, GA, PSO, and CLPSO algorithms in multiple runs.

Conclusions:

• The FTOS provides an effective and deployable solution for adaptive sensing scheduling in energy-constrained WSNs.
• The Fibonacci Tree Optimization algorithm is a robust method for solving complex WSN scheduling optimization problems.
• The proposed strategy offers a consistent and reliable approach to enhancing WSN operational efficiency.