Improvised Energy Efficient Routing Protocol based on Ant Colony Optimization (ACO) for Wireless Sensor Networks

Inhaltsverzeichnis (Table of Contents)

• Chapter 1: Introduction
• 1.1 Sensor Networks- Evolution and Introduction
• 1.2 Model of Wireless Sensor Network
• 1.3 Wireless Sensor Networks- Design Principles and Challenges
• 1.4 Wireless Sensor Networks - Types
• 1.5 Wireless Sensor Networks- Classifications
• 1.6 Wireless Sensor Network Architecture: Protocol Stack
• 1.7 Routing in Wireless Sensor Networks
• 1.7.1 Challenges connected to Routing in Wireless Sensor Networks
• 1.7.2 Classifications of Routing Algorithms/Protocols
• 1.8 Swarm and Swarm Intelligence (SI)
• 1.9 Ant Colony Optimization (ACO)
• 1.9.1 Introduction
• 1.9.2 Ants in Nature
• 1.9.3 Ants Stigmergic behavior
• 1.9.4 Real Ants v/s. Artificial Ants
• 1.9.5 Ant Colony Optimization Metaheuristic
• 1.9.6 Mathematical Model of Ant Colony Optimization
• 1.9.7 Components of Ant Colony Optimization (ACO)
• 1.9.8 Ant Colony Optimization Algorithms
• 1.9.8.1 Ant System Algorithm
• 1.9.8.2 Ant Colony System (ACS)
• 1.9.8.3 MAX-MIN Ant System
• 1.9.8.4 Ant Lion Optimizer
• 1.9.9 Ant Colony Optimization- Working and Algorithm
• 1.10 Suitability of Ant Colony Optimization (ACO) based approach for Developing Energy Efficient Routing Protocols for Wireless Sensor Networks
• 1.11 ACO Based Routing Protocols for Wireless Sensor Networks
• 1.11.1 Sensor Driven Cost-Aware Ant Routing (SC)
• 1.11.2 Energy Efficient Ant Based Routing (EEABR)
• 1.11.3 Flooded Forward Ant Routing (FF)
• 1.11.4 Flooded Piggyback Ant Routing (FP)
• 1.11.5 Energy-Delay Ant Based (E-D Ants)
• 1.11.6 Ant Colony Based Reinforcement Learning Algorithm (AR and IAR)
• 1.11.7 Basic Ant Based Routing (BABR) for Wireless Sensor Networks (WSN)
• 1.11.8 Ant Based Quality of Service Routing (ACO-QoSR)
• 1.11.9 Ant Colony Optimization based Location-aware Routing (ACLR)
• 1.12 Energy Efficient Routing Protocols based on Ant Colony Optimization for Wireless Sensor Networks
• 1.12.1 Ant Chain Protocol
• 1.12.2 Ant Aggregation
• 1.12.3 Pheromone Based Energy Aware Directed Diffusion (PEADD)

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This thesis focuses on the challenging area of routing and energy efficiency in Wireless Sensor Networks (WSNs). It explores various routing protocols, highlighting the benefits of Swarm Intelligence, particularly Ant Colony Optimization (ACO), for addressing energy efficiency challenges in these networks.

• Examining the evolution and design principles of WSNs
• Investigating the challenges related to routing and energy efficiency in WSNs
• Exploring the concepts of Swarm Intelligence and ACO, including their suitability for WSN routing
• Presenting and analyzing existing ACO-based routing protocols for WSNs
• Proposing a novel energy-efficient routing protocol based on ACO for WSNs.

Zusammenfassung der Kapitel (Chapter Summaries)

Chapter 1 provides a comprehensive introduction to Wireless Sensor Networks, covering their evolution, design principles, types, classifications, architecture, and the challenges associated with routing. The chapter also delves into the concepts of Swarm Intelligence and Ant Colony Optimization, exploring their potential for addressing WSN routing issues. It further presents a review of existing ACO-based routing protocols for WSNs.

Schlüsselwörter (Keywords)

The primary keywords and focus topics of this work include Wireless Sensor Networks (WSNs), routing protocols, energy efficiency, Swarm Intelligence, Ant Colony Optimization (ACO), packet delivery ratio, throughput, routing overhead, and end-to-end delay. The research explores the application of ACO-based algorithms for developing efficient and reliable routing solutions for WSNs.