Certain Power Management Algorithms for Wireless Sensor Networks by Energy Efficient Data Transmission, Security and Node Deployment

Table of Contents

1 INTRODUCTION

1.1 INTRODUCTION

1.2 TECHNOLOGICAL TRENDS

1.3 WIRELESS SENSOR NETWORKS

1.3.1 Wireless networks

1.3.2 Data-oriented wireless networks

1.3.3 Micro sensor networks

1.4 NODE ARCHITECTURE

1.5 WIRELESS SENSOR NETWORK (WSN)

1.5.1 Sensors in internet

1.5.2 Comparison of traditional networks and wireless sensor networks

1.6 WSN APPLICATIONS

1.6.1 Military applications

1.6.2 Environmental applications

1.6.3 Commercial applications

1.7 CHALLENGES AND CONSTRAINTS

1.7.1 Energy

1.7.2 Ad-hoc deployment

1.7.3 Unattended operation

1.7.4 Security

1.8 ENERGY EFFICIENT DESIGN OF WIRELESS SENSOR NODES

1.9 MOTIVATION FOR RESEARCH WORK

1.10 OBJECTIVE OF THE THESIS

1.11 THE ORGANIZATION OF THESIS

2 LITERATURE SURVEY

2.1 INTRODUCTION

2.2 CHARACTERISTIC OF SENSOR NETWORK

2.3 CHALLENGES

2.4 QUALITY OF SERVICE

2.4.1 Topology management

2.4.2 Localization

2.4.3 Controlled mobility

2.4.4 Data aggregation

2.4.5 Network topology

2.5 APPLICATIONS

2.6 ENERGY MANAGEMENT

2.7 ENERGY CONSUMPTION FACTORS

2.8 COMMON ENERGY SAVING METHODS

2.8.1 Time synchronization

2.8.2 Dynamic power management

2.8.3 Real time support

2.8.4 Transmission power control

2.8.5 Encryption schemes

2.8.6 Data management

2.8.7 Compression techniques

2.9 STUDY ON DATA COMPRESSION AND DEPLOYMENT

2.9.1 Data compression

2.9.2 Deployment and coverage

2.9.2.1 Deployment

2.9.2.2 Coverage

2.10 MIDDLEWARE

2.10.1 Challenges in data gathering in WSN middleware

2.11 SUMMARY

3 ENERGY EFFICIENCY IN WSN USING DATA COMPRESSION TECHNIQUES

3.1 INTRODUCTION

3.1.1 Data compression

3.1.2 Information and entropy

3.1.3 Compression algorithm

3.2 PROPOSED MATRIX - RLE (M-RLE) ALGORITHM

3.2.1 RLE algorithm

3.2.2 Matrix RLE

3.2.3 Pseudo code of the proposed algorithm

3.3 COMPRESSION PERFORMANCES

3.4 SECOND PROPOSED ALGORITHM - QUINE Mc CLUSKEY BOOLEAN REDUCTION METHOD FOR COMPRESSION (QMBRC) ALGORITHM

3.4.1 Proposed algorithm

3.4.2 Analyses

3.4.3 Energy estimation

3.4.4 Compression ratio

3.5 SUMMARY

4 SECURITY ENABLED ENERGY EFFICIENCY IN WSN

4.1 INTRODUCTION

4.1.1 Unreliable communication

4.2 SECURITY REQUIREMENTS

4.3 DEFENSIVE MEASURES

4.4 MIDDLEWARE SECURITY

4.5 ALGORITHM FOR WSN ENERGY EFFICIENT SECURE MIDDLEWARE

4.5.1 WSN middleware

4.6 PROPOSED ALGORITHM : SEEMd SECURITY ENABLED ENERGY EFFICIENT MIDDLEWARE

4.6.1 Algorithm 1- Second chance approach

4.6.2 Algorithm 2- Distance estimation approach

4.7 SECURITY ENHANCEMENT OF WSN DATA USING SYMMETRIC DATA ENCRYPTION THROUGH TABULATION METHOD OF BOOLEAN FUNCTION REDUCTION

4.7.1 Introduction

4.7.2 Privacy measures using encryption

4.8 CRYPTOGRAPHIC TECHNIQUES

4.9 PROPOSED ALGORITHM

4.9.1 Encryption

4.9.2 Decryption

4.10 SUMMARY

5 ENERGY AWARE DATA DEPLOYMENT IN WSN

5.1 INTRODUCTION

5.2 SENSOR DEPLOYMENT METHODS

5.3 CONSTRAINTS

5.4 SLEEP STATE TRANSITION POLICY

5.5 PROPOSED ALGORITHM - ENERGY AWARE NODE DEPLOYMENT IN WSN WITH STRAIGHT LINE TOPOLOGY

5.6 DATA COMPRESSION

5.7 SUMMARY

6 APPLICATION BASED ON WEB ENABLED ENERGY EFFICIENT WSN NETWORK IN AN AGRICULTURE FIELD

6.1 INTRODUCTION

6.2 ARCHITECTURE

6.2.1 Objective of the design

6.2.2 XBee

6.2.3 PIC micro controller

6.3 IMPLEMENTATION OF ALGORITHM FOR ENERGY EFFICIENT DATA COLLECTION

6.3.1 Sleep wake up approach

6.4 WEB BASED MONITORING

6.5 WEB ARCHITECTURE AND DESIGN

6.6 DATABASE SYSTEM AND WEB SEVER

6.7 GRAPHIC USER INTERFACE (GUI)

6.8 SUMMARY

7 CONCLUSION AND FUTURE SCOPE

7.1 CONCLUSION

7.2 FUTURE SCOPE

Research Goals and Themes

The primary goal of this research is to develop energy-efficient algorithms for Wireless Sensor Networks (WSNs), focusing specifically on secure data communication, data compression, and optimized node deployment strategies to enhance network longevity. The study addresses the inherent resource constraints of sensor nodes—such as limited power, processing capability, and storage—and proposes novel methodologies to reduce energy consumption during data transmission and processing.

• Design of energy-efficient compression algorithms (M-RLE and QMBRCA) to minimize transmission overhead.
• Development of secure data transmission and encryption protocols using symmetric key techniques (SEEMd and QMBRC).
• Implementation of energy-aware node deployment strategies using deterministic, straight-line topologies to maximize sensing coverage.
• Creation of a web-enabled WSN application for agricultural monitoring to validate the proposed algorithms in real-world scenarios.

Excerpt from the Book

Summary of Chapters

INTRODUCTION: Provides an overview of Wireless Sensor Networks (WSNs), their architectural components, diverse applications, and the critical challenges regarding energy efficiency and security that motivate the research.

LITERATURE SURVEY: Examines existing research in WSN, focusing on quality of service, energy management, data compression, and deployment strategies to identify current limitations and gaps.

ENERGY EFFICIENCY IN WSN USING DATA COMPRESSION TECHNIQUES: Proposes two compression algorithms, M-RLE and QMBRCA, designed to reduce data size and thus communication energy consumption in WSN nodes.

SECURITY ENABLED ENERGY EFFICIENCY IN WSN: Introduces secure middleware (SEEMd) and a symmetric encryption method using Quine-McCluskey Boolean reduction to ensure data integrity and confidentiality with minimal energy expenditure.

ENERGY AWARE DATA DEPLOYMENT IN WSN: Suggests a deterministic node deployment strategy with a straight-line topology and sleep-wake scheduling to maximize coverage and network lifetime.

APPLICATION BASED ON WEB ENABLED ENERGY EFFICIENT WSN NETWORK IN AN AGRICULTURE FIELD: Demonstrates the practical implementation of the proposed algorithms in a web-based agricultural monitoring prototype using XBee and microcontroller technology.

CONCLUSION AND FUTURE SCOPE: Summarizes the research findings, highlighting the improvements in energy efficiency and security, and suggests future research directions in big data and IoT.

Keywords

Wireless Sensor Networks, Energy Efficiency, Data Compression, Security, Middleware, Node Deployment, Symmetric Key Encryption, Quine-McCluskey, Network Lifetime, Agriculture Monitoring, Data Aggregation, Quality of Service, Topology Management, Sensor Nodes, Web-Enabled WSN.

Frequently Asked Questions

What is the core focus of this doctoral thesis?

The thesis focuses on developing energy-efficient algorithms for Wireless Sensor Networks (WSNs), specifically targeting data security, data compression, and optimized node deployment to extend network lifetime.

What are the primary challenges addressed by the author?

The author addresses resource constraints such as limited battery power, processing limitations, and the necessity for secure, reliable data communication in unattended, often hostile environments.

What is the main objective of the proposed compression algorithms?

The objective is to reduce the volume of data transmitted over the network, thereby significantly lowering communication energy consumption and extending the operational lifespan of the battery-powered sensor nodes.

Which scientific methods are applied in this work?

The work utilizes mathematical techniques like Boolean function reduction (Quine-McCluskey method), statistical analysis, and simulation tools like MATLAB and Prowler to design and evaluate the proposed algorithms.

What is the significance of the web-based monitoring application in Chapter 6?

This chapter validates the research by providing a real-world prototype for agricultural monitoring, demonstrating how the proposed energy-efficient algorithms perform in an integrated sensing and data visualization system.

How are the keywords defining this research?

Keywords such as "Energy Efficiency," "Data Compression," "Symmetric Key Encryption," and "Node Deployment" encapsulate the primary research areas aimed at solving WSN resource constraints.

What is the "Second chance" approach mentioned in the middleware algorithm?

The "Second chance" approach is a feature of the SEEMd middleware that prevents a sensor node from unnecessarily entering sleep mode during critical data sensing periods, ensuring high reliability for time-sensitive applications.

How does the proposed "straight-line topology" improve node deployment?

By using a deterministic straight-line deployment pattern addressed via gray codes, the research minimizes redundancy and coordination errors, leading to optimized energy savings and better overall network coverage compared to random deployment.