Wireless Sensor Networks for Environmental Monitoring Applications.
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
DECLARATION ……….. iii
ABSTRACT ………….. iv
ACKNOWLEDGMENT ………….. v
DEDICATION …………… vi
LIST OF FIGURES ………… xi
LIST OF TABLES …. xi
CHAPTER 1
1.0 INTRODUCTION …1
1.1 STATEMENT OF PROBLEM ……… 2
1.2 GOAL …… 2
1.3 SPECIFIC OBJECTIVES …. 2
CHAPTER 2
2.0 CONCEPTUAL ANALYSIS ..
2.1 WIRELESS SENSOR NETWORK …. 3
2.2 TYPES OF WSN ………. 5
2.2.1 TERRESTRIAL …… 5
2.2.2 UNDERGROUND …….. 5
2.2.3 UNDERWATER ……. 5
2.2.4 MULTIMEDIA ………….. 5
2.2.4 MOBILE ….. 5
2.3 MOBILE AD HOC NETWORK …….. 6
2.4 WSN VERSUS MANET …. 6
2.5 SENSOR NODES ………. 6
2.5.1 SENSOR NODES MECHANISM ……. 7
2.6 TCP/IP …………………… 7
2.7 DATA AGGREGATION… 8
2.8 DATA AGGREGATION TECHNIQUES …. 9
2.9 CLUSTERING …………. 9
2.9.1 CENTRALIZED APPROACH ………… 10
2.9.2 IN-AGGREGATION …….. 10
2.9.3 TREE-BASED APPROACH …. 10
2.10 802.15.15.4 IEEE STANDARD ……. 10
2.11 ZIGBEE SUITE ……… 10
2.12 XBEE …………………. 11
2.12.1 XBEE MODULE ………… 12
2.13 ARDUINO ………. 12
2.14 RASPBERRY PI B ………………. 12
2.15 FAULT TOLERANCE ……13
2.16 ECOLOGICAL SYSTEMS/BIODIVERSITY …….. 14
2.17 REMOTE MONITORING ……….. 14
2.18 CLOUD COMPUTING ….. 15
2.18.1 SERVICE MODELS ……. 15
2.19 EMPIRICAL STUDIES …………………… 17
2.20 ENVIRONMENTAL MONITORING ……….. 17
2.21 DATA PREDICTION, COMPRESSION AND RECOVERY IN CLUSTERED WSN. …. 17
2.21.1 CHALLENGES ……….. 19
2.22 BIODIVERSITY FOR ENVIRONMENTAL MONITORING ….. 19
2.22.1 CHALLENGES: ………………….. 20
2.23 WSN FOR BOREHOLE MONITORING: ………. 20
2.23 AUTOMATIC REMOTE METER READING SYSTEM …….. 21
2.24 HEALTH CARE REMOTE MONITORING APPLICATION. …… 21
2.25 SUMMARY …………….. 22
CHAPTER 3
3.0 METHODOLOGY (ANALYSIS AND SOLUTION DESIGN) ……. 24
3.1 SPECIFICATIONS……………………….. 24
3.2 SCOPE / AREA OF STUDY …………. 25
3.3 HARDWARE/SOFTWARE DESIGN ………….. 25
3.4 XCTU/XBEE SERIAL 2 (DEVICE COMMUNICATION) ….. 25
3.5 WIRELESS SENSOR NODE (WSNd) … 27
3.5.1 ARDUINO UNO …… 28
3.5.2 DHT11 TEMPERATURE-HUMIDITY SENSOR …….. 30
3.5.3 BREADBOARD ……. 31
3.5.4 JUMPER CABLES/ WIRE ……………… 32
3.5.5 USB CABLE: ……… 32
3.5.6 SPARKFUN ARDUINO EXPLORER….. 32
3.6 BS (RASPBERRY PI). ……………. 33
3.6.1 SPECIFICATION ……….. 33
3.6.2 BS DESIGN ….. 34
3.7. XBEE S2 (CONFIGURED AS A ROUTER AT MODE), SPARKFUN AND USB CABLE ………… 35
3.8 ETHERNET NETWORK CABLE ………………….. 35
3.9 AMAZON EC2 CLOUD SERVICE (AWS) …… 35
3.10 WSNd SOFTWARE DESIGN / METHODOLOGY ………….. 36
3.11 BS SOFTWARE DESIGN ……………… 37
3.12 DATABASE DESIGN ………… 38
CHAPTER 4
4.0 IMPLEMENTATION …………….. 39
4.1 WIRELESS SENSOR NODE (WSNd) …….. 39
4.1.1 DHT11 Library …………….. 39
4.1.2 The void setup() Function ……. 41
4.1.4 void loop() Function …………… 41
4.3 REMOTE BS …………. 42
4.4 REMOTE BS (RMBS) DATA COMMUNICATION ………. 43
4.5 SER.FLUSH() FUNCTION …….. 44
4.6 BASH SCRIPT ……. 44
4.7 CRON …………. 45
4.8 BS MEMORY MANAGEMENT …………… 45
4.9 ONLINE DATABASE SCHEMA … 46
4.10 DATA PRESENTATION CHART ………. 46
CHAPTER 5
5.0 TESTING ……… 47
5.1 WIRELESS SENSOR DATA COLLECTION …… 47
5.2 REMOTE BS DATA OUTPUT ………… 48
5.2.1 Humidity/Temperature ……. 48
5.3 LINE GRAPH …………. 49
CHAPTER 6
6.0 CONCLUSION … 51
6.1 KNOWN LIMITATION ….. 51
6.2 FUTURE WORK … 51
6.3 SUMMARY ……….. 51
APPENDIX A …………. 53
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.
BIBLIOGRAPHY
B. M. G. Jennifer Yick, “Wireless Sensor Network survey,” Elsevier B.V. or its licensors or contributors. ScienceDirect® is a registered trademark of Elsevier B.V., pp. Volume 52, Issue 12, Pages 2292–2330, 2016.
O. Thiare, “MSc Project topic proposal,” unpublished, Senegal, 2016.M. B.-A. Rachid Souissi, “An Intelligent Wireless Sensor Network Temperature Acquisition System with an FPGA,” Open Access Article of Scientific Research, 2014.
E. a. Arne Bröring, “New Generation Sensor Web Enablement,” 2011. [Online]. Available: file:///C:/Users/Chris/Downloads/sensors-11-02652.pdf. [Accessed 21 March 2011].
e. a. Shu Yinbiao, “International Electrotechnical Commission,” Switzerland, CH-1211 Geneva 20, 2014.W. S. Y. S. ,. E. C. I.F. Akyildiz, “Wireless sensor networks: a survey Computer Networks,” Published by Elsevier Science B.V, vol. 38, no. 4, p. 393–422, 2002.