Energy Efficient Node Scheduling for Conservation of Energy in Wireless Sensor Network

摘要

Wireless Sensor Networks have seen tremendous advancement in design and applications in the recent years. Wireless sensor networks, which consist of many sensor devices communicating with each other in order to sense the environment, is an emerging field in the area of wireless networking. Sensors monitor the changes in a physical attribute of the surroundings, say temperature, and observed data is collected and analyzed. These sensors are mostly unattended, and their limited battery life makes energy a precious resource that must be utilized wisely. Since these networks consist of a large number of sensors, allowing some of the nodes to sleep intermittently can greatly increase the network lifetime.
　　With the rapid development of WSN technology, focus is being given into solving the challenges that this field has to face. One such challenge is to maximize the network lifetime of the network while the target nodes remain monitored constantly. This problem of maximizing the network lifetime while satisfying the coverage and also energy constraints (sensors are equipped with battery as the only power source and hence the energy constraint) is known as the Target Coverage Problem in Wireless Sensor Networks.
　　In this research, Target Coverage Problem has been discussed and existing algorithm for node scheduling has been explored, which has been modified later on. The modified version of algorithm is found to give better performance over the existing one. With the introduction of Greatest_Contribution function in modified algorithm, the performance of proposed algorithm is found to be better than the existing one.

目录

封面

声明

英文摘要

List of Abbreviations

List of Figures

目录

1 Introduction

1.1 Background

1.2 Objective

1.3 Thesis Organization

2 Literature Review

2.1 Sleep Scheduling in Wireless Sensor Network

2.2 Basic Concepts

2.3Sleep Scheduling Considering Only Coverage

2.4 Coverage problem and its types

2.5 Sleep Scheduling Considering Only Connectivity

2.6 Sleep Scheduling Considering both Coverage and Connectivity

2.7 Other Works on Sleep Scheduling

3 System Model

3.1 Assumptions

3.2 Target Coverage Problem

4 Greedy Algorithms

4.1 Greedy-MSC Heuristic

4.2 Enhancement of the Existing Algorithm

5 Evaluations

5.1Simulation

5.2 Simulation results

6 Conclusion

References

Acknowledgement