Routing and Energy Efficiency is regarded as highly challenging area of Sensor networks. Significant advancements in Wireless Sensor Networks (WSNs) opens doors for wide implementation in real-time applications like Industrial Monitoring, Smart Cities development, Underwater monitoring operations, tracking objects and many more. Energy Efficient routing is regarded as the most challenging task. Sensor networks mostly operate in complex and dynamic environments and routing becomes tedious task to maintain as the network size increases. Lots of routing protocols- Reactive, Proactive and Hybrid are proposed by researchers but every protocol faces some limitations in terms of energy, routing, packet delivery ratio and security. Therefore, to overcome all the routing issues, the trend has shifted to Biological based Algorithms like Swarm Intelligence based techniques. Ant Colony Optimization based routing protocols have demonstrated exceptional results in terms of performance when applied to WSN routing.
This thesis outlines routing protocols in sensor networks, highlight the concept of Swarm Intelligence and presents various Ant Colony Optimization based routing protocols for sensor networks. In addition to this, we present Ant Colony based Energy Efficient routing protocol (IEEMARP = Improvised Energy Efficient Multipath Ant Based Routing Protocol) for sensor networks. The proposed protocol takes into consideration various performance metrics like Packet Delivery Ratio, Throughput, Energy Efficiency, Routing Overhead and End-to-End delay. Proposed protocol is simulated and tested using NS-2.35 simulator. Simulation based results stated that IEEMARP routing protocol is overall 16% more efficient in terms of Packet delivery ratio, Energy Efficiency, Throughput, Routing Overhead and End-to-End delay as compared to other ACO based routing protocols. In addition to this, IEEMARP is highly reliable protocol to ensure timely delivery with acknowledgement packet exchange between source node to sink node and vice versa and also combats the issue of congestion and packet dropping to large extent.
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)
1.12.4 Ant Colony Multicast Trees (ACMT)
1.12.5 Improvised Ant Colony Routing (IACR)
1.12.6 ACO Router Chip
1.12.7 Energy Balanced Ant Based Routing Protocol (EBAB)
1.12.8 Adaptive Clustering for Energy Efficient WSN based on ACO (ACO-C)
1.12.9 Ant Colony Clustering Algorithm (ACALEACH)
1.12.10 Ant Colony Optimization based- Energy-Aware Multipath Routing Algorithm (ACO-EAMRA)
1.12.11 Energy Efficient ACO Based QoS Routing (EAQR)
1.12.12 Comprehensive Routing Protocol (CRP)
1.13 Organization of Thesis
Chapter – 2 : Literature Review
Chapter – 3 : Research Methodology
3.1 Motivation
3.2 Research Problem
3.3 Research Objectives
3.4 Research Methodology
3.5 Research Contributions
3.6 Scope of Research
3.7 Research Gaps Identified
Chapter – 4 : IEEMARP: Improvised Energy Efficient Multipath Ant Colony Optimization based Routing Protocol for Sensor Networks
4.1 Problem Definition and Background
4.2 Protocol Design Choices
4.2.1 Energy Efficiency
4.2.2 Reliability
4.2.3 Dynamic Network and Scalability
4.2.4 Throughput and Routing Overhead
4.3 Assumptions
4.4 IEEMARP Protocol- Operation
4.4.1 Neighborhood Discovery via Link Knowledge
4.4.2 Forwarding of Packets / Fault Localization
4.4.3 Reliable End-to-End Communication from Source to Destination
4.5 IEEMARP Routing Protocol- Properties
4.6 IEEMARP Protocol- Algorithm
4.7 IEEMARP Routing Protocol- Algorithm
Chapter – 5 : Simulation and Performance Analysis of IEEMARP Routing Protocol
5.1 Introduction to NS-2 Simulator
5.2 Performance Metrics
5.3 Simulation and Performance Comparison of Basic Ant Colony Optimization (ACO) Routing Protocol with AODV, DSR and DSDV Routing Protocols for Wireless Sensor Networks
5.3.1 Flowchart of Simple Ant Net Based Routing
5.3.2 Simulation Parameters
5.3.3 Simulation Scenarios
5.3.4 Simulation Results
5.4 Simulation and Performance Comparison of Basic Ant Colony Optimization based Routing Protocols: ACEAMR, AntChain, EMCBR and IACR
5.4.1 Simulation Parameters
5.4.2 Simulation Results
5.4.3 Overall Analysis and Best Protocol Suitability
5.5 Simulation and Performance Comparison of Proposed Routing Protocol
5.5.1 Simulation Parameters
5.5.2 Simulation Scenarios and IEEMARP Routing Protocol working
5.5.3 Performance Results of IEEMARP Routing Protocol with ACEAMR, AntChain, EMCBR and IACR routing protocols on performance metrics
5.5.4 Performance Comparison of IEEMARP routing protocol with Traditional WSN routing protocols: DSR, DSDV and Basic ACO
Conclusion and Future Scope
References
Objectives and Topics
The main objective of this thesis is to address energy efficiency challenges in Wireless Sensor Networks (WSNs) by developing a novel routing protocol. The research focuses on identifying existing limitations in current protocols and utilizing Swarm Intelligence, specifically Ant Colony Optimization (ACO), to create an energy-efficient, reliable, and multipath routing solution.
- Energy efficiency and network lifetime maximization in WSNs.
- Analysis and performance comparison of existing ACO-based routing protocols.
- Development of the Improvised Energy Efficient Multipath Ant Colony Optimization based Routing Protocol (IEEMARP).
- Simulation-based validation and testing using the NS-2.35 simulator.
- Performance analysis against traditional and existing swarm-based routing protocols.
Excerpt from the Book
4.4.1 Neighbourhood Discovery via Link Knowledge
A sensor node sends a Hello message via one-hop broadcast to make its presence known to present nodes in its radio range. Nodes which are in the radio range are called neighbor nodes. A node may go to sleep mode to conserve energy level. When a node wakes up, it sends new hello packet is sent to all the neighbor nodes along with the duration of time it will be in active state in the message. If a node does not wish to be used for packet forwarding then it need not send a Hello message to its neighbors. Hello message allows the node to track of all its one-hop neighbors that are willing to forward packets and the duration the node is in active or awake state. The protocol doesn’t enforce the node to monitor entire one-hop neighbors.
Summary of Chapters
Chapter – 1 : Introduction: Outlines the concepts of Wireless Sensor Networks, their evolution, types, design challenges, and the fundamental principles of Swarm Intelligence and Ant Colony Optimization.
Chapter – 2 : Literature Review: Provides a comprehensive survey of existing research, methodologies, and routing protocols within the domain of WSNs and bio-inspired algorithms.
Chapter – 3 : Research Methodology: Details the motivation, research objectives, and the structured methodology used to analyze, design, and validate the proposed routing protocol.
Chapter – 4 : IEEMARP: Improvised Energy Efficient Multipath Ant Colony Optimization based Routing Protocol for Sensor Networks: Introduces the proposed IEEMARP protocol, covering its design parameters, algorithmic structure, and operational phases.
Chapter – 5 : Simulation and Performance Analysis of IEEMARP Routing Protocol: Presents the evaluation of the proposed protocol using the NS-2.35 simulator and compares its performance metrics against various existing state-of-the-art protocols.
Keywords
Wireless Sensor Networks, WSN, Energy Efficiency, Ant Colony Optimization, ACO, Swarm Intelligence, Routing Protocol, IEEMARP, Network Lifetime, Multipath Routing, NS-2, Performance Metrics, Throughput, Packet Delivery Ratio, Reliability.
Frequently Asked Questions
What is the core focus of this research?
The research is primarily focused on improving energy efficiency and overall performance in Wireless Sensor Networks (WSNs) through the development of an advanced routing protocol.
Which specific algorithms are analyzed?
The research analyzes various bio-inspired algorithms, primarily focusing on Ant Colony Optimization (ACO) and its derivatives such as Ant System, Ant Colony System, and MAX-MIN Ant System.
What is the primary goal of the IEEMARP protocol?
The primary goal of IEEMARP is to enhance WSN performance by increasing energy efficiency, maintaining a high packet delivery ratio, optimizing throughput, and reducing end-to-end delay through a multipath routing approach.
What research methodology was employed?
The methodology involved a detailed literature review, comparison of existing protocols, design and development of the new IEEMARP protocol, and rigorous validation through simulation using the NS-2.35 network simulator.
What metrics are used to evaluate the routing protocols?
The protocols are evaluated based on key performance indicators including Packet Delivery Ratio, Throughput, Routing Overhead, Energy Consumption, and End-to-End Delay.
What distinguishes IEEMARP from other ACO-based protocols?
IEEMARP is designed as a continuous learning multipath protocol that specifically incorporates energy verification, packet validation, and reliable acknowledgment mechanisms (ACK/ACKR) to ensure accurate data delivery in heterogeneous sensor environments.
How does the acknowledgement mechanism enhance reliability?
The acknowledgement mechanism (ACK/ACKR) ensures that packets are successfully received from source to sink, providing a feedback loop that validates the chosen path and allows for route re-establishment if necessary.
What are the main simulation findings?
Simulation results indicate that the proposed IEEMARP protocol outperforms several existing ACO-based protocols by approximately 15-22% across key metrics like packet delivery ratio and throughput while significantly reducing energy consumption.
- Arbeit zitieren
- Anand Nayyar (Autor:in), 2017, Improvised Energy Efficient Routing Protocol based on Ant Colony Optimization (ACO) for Wireless Sensor Networks, München, GRIN Verlag, https://www.grin.com/document/385058
