Bio-mimic optimization strategies in wireless sensor networks: a survey.

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Wireless sensor networks (WSNs) face critical challenges in energy efficiency, quality of service (QoS), and security management.
• Optimization is crucial for WSNs, but achieving simultaneous improvements in multiple areas often involves trade-offs.
• Classical algorithms are often unsuitable for WSNs due to high energy and data processing demands.

Purpose of the Study:

• To provide a comprehensive survey of bio-mimetic optimization techniques applied to WSNs.
• To analyze the application of particle swarm optimization, ant colony optimization, and genetic algorithms in WSN optimization.
• To identify research gaps and suggest future directions for WSN optimization.

Main Methods:

• Literature review of WSN optimization strategies.
• Focus on bio-mimetic algorithms: Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithms (GA).
• Analysis of existing research concerning energy efficiency, QoS, and security in WSNs.

Main Results:

• Bio-mimetic algorithms are increasingly adopted for WSN optimization.
• Most studies focus on optimizing a single WSN issue, neglecting potential conflicts and holistic solutions.
• Particle swarm optimization, ant colony optimization, and genetic algorithms show promise but require further investigation for multi-objective optimization.

Conclusions:

• A holistic approach is needed for WSN optimization, considering the interplay between energy efficiency, QoS, and security.
• Further research into multi-objective bio-mimetic optimization is essential for advancing WSN capabilities.
• Identifying and addressing open research issues will drive future developments in WSN optimization.