Wireless Sensor Networks (WSNs) are highly integrated technologies applying sensors, microcontrollers and wireless networks technologies. Wireless sensor networks (WSNs) is a promising technology that has a large spectrum of applications such as, battlefield reconnaissance, border protection and security surveillance, preparing forecasts, severe environment detection, volcano monitoring, disaster management. WSNs operate unattended in harsh environments with limited energy supplies that can’t be practically changed or recharged. Thus energy efficiency is a critical design issue which must be addressed.
Clustering plays an effective role in judicious use of dwindling energy resources of the deployed sensor nodes. Nodes are grouped into clusters and a specific designated node, called the cluster head is responsible for collecting data from the nodes in its cluster, aggregating them and sending to the BS, where data can be retrieved later. Besides energy efficiency, clustering has many other advantages like reduced routing overhead, conservation of communication bandwidth, stabilized network topology, network stability etc
In this research, we study the energy efficiency of two clustering algorithms, S-Web and LEACH and compare them for network lifetime. Simulation results show that the S-Web clustering mechanism achieves a noticeable improvement in the network lifetime.
Table of Contents
CHAPTER 1 INTRODUCTION
CHAPTER 2 LITERATURE SURVEY
2.1 Clustering ad hoc networks
2.2 Advantages of Clustering
2.3 Challenges for Clustering Algorithms
• Limited Energy:
• Constrained resources:
• Secure Communication:
• Cluster formation and CH selection:
• Load balancing:
• Minimal cluster count:
• Synchronization:
• Data Aggregation:
• Fault-tolerance:
• Quality of Service (QoS):
2.4 Clustering Schemes for Sensor Networks
2.4.1 Optimizing Cluster Organization
2.4.2 Averaging Power Consumption
2.4.3 Scheduling Active and Non-Active Nodes
2.5 Clustering Algorithms
2.5.1 Low-Energy Adaptive Clustering Hierarchy (LEACH):
2.5.2 Sensor Web or S-WEB:
2.5.3 Energy Efficient Clustering Scheme (EECS):
2.5.4 Hybrid Energy Efficient Distributed Clustering (HEED):
2.5.5 Energy-efficient unequal clustering (EEUC):
2.5.6 Power-efficient and adaptive clustering hierarchy (PEACH):
CHAPTER 3 Problem Definition and Implementation
3.1 Problem Definition
3.2 Implementation
CHAPTER 4 Results
4.1 First scenario (Normal Node to Normal Node)
4.2 Second scenario (Normal Node to Cluster Head)
4.3 Third scenario (Cluster Head to Normal Node)
4.4 Fourth scenario (Cluster Head to Cluster Head)
4.5 Fifth scenario (Random)
CHAPTER 5 Conclusion and Future Work
Research Objectives and Themes
This thesis aims to evaluate and compare the energy efficiency and network lifetime of two specific clustering algorithms, S-Web and LEACH, in Wireless Sensor Networks (WSNs). The research seeks to identify how different clustering mechanisms influence data transmission overhead and overall network longevity, particularly in scenarios where communication occurs between random pairs of nodes or cluster heads.
- Analysis of energy consumption patterns in WSN clustering protocols.
- Evaluation of network lifetime improvements using the S-Web mechanism compared to LEACH.
- Implementation of simulation models using JAVA for various network communication scenarios.
- Investigation into the impact of cluster head routing and topology awareness on energy preservation.
Excerpt from the Book
4.1 First scenario (Normal Node to Normal Node)
In the first scenario, we consider communication between any random pair of normal nodes. We know in a clustering scheme when a normal node, that is not a cluster head, has any data to send it routes its traffic through the cluster head of the cluster to which it belongs. Also a node receives its data through its cluster head. Thus, cluster heads act as routers sending and receiving data for respective normal nodes. The following result is the average of the number of hops and consumed energy per message.
As can be seen from the table 1, S-Web has a lower average number of hops and energy consumption per message than LEACH does. The average energy consumption for this scenario in LEACH is observed to be 3812.28 µJ whereas in case of S-Web it is 1932.86 µJ. The reason for high energy consumption in LEACH is that the cluster heads are only aware of the nodes in their own cluster. Also the BS does not have global network knowledge. Hence when a node needs to communicate to a node belonging to other cluster, its cluster head has to query the BS to know addresses of other cluster heads. Communication with BS is an energy intensive task as it is usually far away from the sensing field. This frequent communication with BS accounts for high energy consumption. However, in S-Web, the cluster heads in addition to maintaining the local cluster information also contain limited global topology information. Thus, frequent communication with BS is avoided and energy saved.
Summary of Chapters
CHAPTER 1 INTRODUCTION: This chapter provides an overview of wireless sensor networks, their applications, and the critical challenge of energy efficiency in unattended environments.
CHAPTER 2 LITERATURE SURVEY: This section reviews existing clustering methodologies for ad hoc networks and identifies key challenges like energy constraints, load balancing, and fault tolerance.
CHAPTER 3 Problem Definition and Implementation: This chapter outlines the research problem and details the simulation environment, assumptions, and tool selection (JAVA) for testing the algorithms.
CHAPTER 4 Results: This chapter presents the comparative performance data of S-Web and LEACH across five different communication scenarios, highlighting energy efficiency and lifetime metrics.
CHAPTER 5 Conclusion and Future Work: This chapter summarizes the research findings, confirming the superior performance of S-Web, and suggests future improvements like incorporating mobility models.
Keywords
Clustering, Energy Efficiency, SWEB, Wireless Sensor Networks, WSN, LEACH, Network Lifetime, Data Aggregation, Routing Protocols, Simulation, JAVA, Topology, Cluster Head, Multi-hop, Network Scalability
Frequently Asked Questions
What is the core focus of this research?
The research focuses on evaluating the energy efficiency of clustering algorithms in Wireless Sensor Networks (WSNs) to extend the overall operational lifespan of the network.
Which clustering algorithms are compared in this study?
The thesis compares the performance of the LEACH (Low-Energy Adaptive Clustering Hierarchy) and the S-Web (Sensor Web) clustering algorithms.
What is the primary objective of this work?
The primary objective is to compare these algorithms based on energy consumption, number of hops, and network lifetime to determine which approach is more efficient.
What research methodology was employed?
The author utilized a simulation-based approach, implementing the algorithms in JAVA and analyzing their performance across five distinct communication scenarios.
What are the key themes addressed in the main body?
The study covers the challenges of clustering in WSNs, such as limited energy and resource constraints, and discusses the advantages of effective cluster formation and routing.
How are the key results defined?
The results are defined by comparing the average energy consumption in microjoules (µJ) and the average number of hops required to complete communication rounds for each algorithm.
Why does LEACH consume more energy than S-Web in the simulations?
LEACH requires cluster heads to query the Base Station (BS) for routing information due to a lack of global topology knowledge, leading to energy-intensive communications that S-Web avoids.
What unique feature does S-Web provide that benefits energy saving?
S-Web cluster heads maintain limited global topology information, which decouples the Base Station from frequent routing decisions, thereby significantly reducing energy consumption.
- Citar trabajo
- Sahil Sholla (Autor), 2013, Performance Evaluation of Clustering Algorithms in Wireless Sensor Networks (WSN). Energy Efficiency of S-Web and LEACH, Múnich, GRIN Verlag, https://www.grin.com/document/293888
