WIRELESS SENSOR NETWORKS FOR ENVIRONMENTAL MONITORING APPLICATIONS

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ABSTRACT


After many years of rigorous research and development in wireless sensor network (WSN) technology with numerous responses to innovative applications, WSNs still have some interesting unanswered questions. In this thesis we explain the challenges of the state of art in WSN for environmental monitoring applications using open-source hardware platforms, Arduino UNO, DHT11 temperature-humidity sensor, XBee and Raspberry Pi. The system is not only low cost but scalable enough to accept multiple sensor nodes associated with environmental monitoring.

Leveraging on WSN & cloud technologies, we present the design and implement a cloud-based online real-time environmental data (temperature-humidity) collection platform to avoid memory conflicts of the BS that already has small storage capacity.

We implement a robust sensor node failure detector to enable user know the status of the network at every point in time under an online real-time basis. In this thesis we have detailed the overall construction architecture of the hardware and software design.

Some samples of the deployment and measurement data obtained are presented using various charts to validate the practicality of the system.


We emphasize the importance of having a generic system in which its ZigBee network configuration function set (router and coordinator) mode of data transmission will be implemented in API mode to accommodate any sensor node for better packet reception (RX) and transmission (TX).


TABLE OF CONTENTS

ABSTRACT
LIST OF FIGURES
LIST OF TABLES

CHAPTER 1
1.0 INTRODUCTION
1.1 STATEMENT OF PROBLEM
1.2 GOAL
1.3 SPECIFIC OBJECTIVES

CHAPTER 2
2.0 CONCEPTUAL ANALYSIS
2.1 WIRELESS SENSOR NETWORK
2.2 TYPES OF WSN
2.2.1 TERRESTRIAL
2.2.2 UNDERGROUND
2.2.3 UNDERWATER
2.2.4 MULTIMEDIA
2.2.4 MOBILE
2.3 MOBILE AD HOC NETWORK
2.4 WSN VERSUS MANET
2.5 SENSOR NODES
2.5.1 SENSOR NODES MECHANISM
2.6 TCP/IP
2.7 DATA AGGREGATION
2.8 DATA AGGREGATION TECHNIQUES
2.9 CLUSTERING
2.9.1 CENTRALIZED APPROACH
2.9.2 IN-AGGREGATION
2.9.3 TREE-BASED APPROACH
2.10 802.15.15.4 IEEE STANDARD
2.11 ZIGBEE SUITE
2.12 XBEE
2.12.1 XBEE MODULE
2.13 ARDUINO
2.14 RASPBERRY PI B
2.15 FAULT TOLERANCE
2.16 ECOLOGICAL SYSTEMS/BIODIVERSITY
2.17 REMOTE MONITORING
2.18 CLOUD COMPUTING
2.18.1 SERVICE MODELS
2.19 EMPIRICAL STUDIES
2.20 ENVIRONMENTAL MONITORING
2.21 DATA PREDICTION, COMPRESSION AND RECOVERY IN CLUSTERED WSN
2.21.1 CHALLENGES
2.22 BIODIVERSITY FOR ENVIRONMENTAL MONITORING
2.22.1 CHALLENGES
2.23 WSN FOR BOREHOLE MONITORING
2.23 AUTOMATIC REMOTE METER READING SYSTEM
2.24 HEALTH CARE REMOTE MONITORING APPLICATION
2.25 SUMMARY

CHAPTER 3
3.0    METHODOLOGY (ANALYSIS AND SOLUTION DESIGN)
3.1 SPECIFICATIONS
3.2 SCOPE / AREA OF STUDY
3.3 HARDWARE/SOFTWARE DESIGN
3.4 XCTU/XBEE SERIAL 2 (DEVICE COMMUNICATION)
3.5 WIRELESS SENSOR NODE (WSNd)
3.5.1 ARDUINO UNO
3.5.2 DHT11 TEMPERATURE-HUMIDITY SENSOR
3.5.3 BREADBOARD
3.5.4 JUMPER CABLES/ WIRE
3.5.5 USB CABLE
3.5.6 SPARKFUN ARDUINO EXPLORER
3.6 BS (RASPBERRY PI)
3.6.1 SPECIFICATION
3.6.2 BS DESIGN
3.7. XBEE S2 (CONFIGURED AS A ROUTER AT MODE), SPARKFUN AND USB CABLE
3.8 ETHERNET NETWORK CABLE
3.9 AMAZON EC2 CLOUD SERVICE (AWS)
3.10 WSNd SOFTWARE DESIGN / METHODOLOGY
3.11 BS SOFTWARE DESIGN
3.12 DATABASE DESIGN

CHAPTER 4
4.0 IMPLEMENTATION
4.1 WIRELESS SENSOR NODE (WSNd)
4.1.1 DHT11 Library
4.1.2 The void setup() Function
4.1.4 void loop() Function
4.3 REMOTE BS
4.4 REMOTE BS (RMBS) DATA COMMUNICATION
4.5 SER.FLUSH() FUNCTION
4.6 BASH SCRIPT
4.7 CRON
4.8 BS MEMORY MANAGEMENT
4.9 ONLINE DATABASE SCHEMA
4.10 DATA PRESENTATION CHART

CHAPTER 5
5.0 TESTING
5.1 WIRELESS SENSOR DATA COLLECTION
5.2 REMOTE BS DATA OUTPUT
5.2.1 Humidity/Temperature
5.3 LINE GRAPH

CHAPTER 6
6.0 CONCLUSION
6.1 KNOWN LIMITATION
6.2       FUTURE WORK
6.3       SUMMARY
APPENDIX A
APPENDIX B GUI CLOUD CODE
BIBLIOGRAPHY


CHAPTER 1

1.0        INTRODUCTION


The quest for a healthy environment and the survival of the human race has led researchers to undertake a fight against global warming and desert encroachment. This became imperative because they have a high negative impact on ecological life and the global economy. The resultant effect may be difficult to control if there are no perfect ways to determine their realistic rate of increase and moderation. In line with contemporary challenges in monitoring biodiversity and climate change, it has become imperative to mitigate the prevailing pressure on ecological systems. Due to the interwoven relationships between ecosystems and human activities, it is very significant to improve quality of life by using existing technologies to monitor biodiversity activities. In addressing the challenges that it imposes to ecological life, a very sensitive and reliable technology such as sensors is needed to obtain real-time data situation of a given environment. However, such dynamic sensors for data collection have to be linked up in a network for easy communication to a central data repository (base station, or BS) in order to check for redundancy and aggregation based on similarities to void error reading analysis. WSN can be used as platform to collect data and study the behaviour of ecological data.

WSN has a vital application like remote monitoring and target tracking [1]. It is usually characterized by a dense deployment and is large scale in environments limited in terms of resources [2]. The systems are powered with batteries and configured to carry out a processing capabilities like collection and storage of data and energy sensor optimization.


Energy limitation is a serious concern in the design of WSNs as it largely determines the success of network operations. Researchers over the years have applied WSNs in various fields, like military surveillance, energy management, earthquake detection and ecological data mining. But for effective environmental monitoring, guaranteed quality of service (QoS) and fast accessibility of real-time online data in WSNs are still open questions.

1.1        STATEMENT OF PROBLEM


The need to access real-time ecological data remotely cannot be overemphasized. Consequently, considering the limited storage space of nodes in WSNs, the traditional ways of gathering high volumes of biodiversity data and storage of such data are ineffective. The current state of the art in real-time biodiversity data collection methods fails to incorporate real-time synchronization of data to a cloud service. Due to the limited storage capacity over time as data grows, the mode of data visualization, management and analysis of data become complex and easily prone to errors. Failure detection and fault tolerance are currently not implemented and information on failed sensor nodes is not available remotely.

1.2        GOAL



The aim of this project is to set up a robust WSN for Environmental Monitoring Application of ecological data (biodiversity).....


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