Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Wireless Sensor Network Based Flood Prediction Using Belief Rule Based Expert System
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. (Pervasive and Mobile Computing)
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Flood is one of the most devastating natural disasters. It is estimated that flooding from sea level rise will cause one trillion USD to major coastal cities of the world by the year 2050. Flood not only destroys the economy, but it also creates physical and psychological sufferings for the human and destroys infrastructures. Disseminating flood warnings and evacuating people from the flood-affected areas help to save human life. Therefore, predicting flood will help government authorities to take necessary actions to evacuate humans and arrange relief for the people.

This licentiate thesis focuses on four different aspects of flood prediction using wireless sensor networks (WSNs). Firstly, different WSNs, protocols related to WSN, and backhaul connectivity in the context of predicting flood were investigated. A heterogeneous WSN network for flood prediction was proposed.

Secondly, data coming from sensors contain anomaly due to different types of uncertainty, which hampers the accuracy of flood prediction. Therefore, anomalous data needs to be filtered out. A novel algorithm based on belief rule base for detecting the anomaly from sensor data has been proposed in this thesis.

Thirdly, predicting flood is a challenging task as it involves multi-level factors, which cannot be measured with 100% certainty. Belief rule based expert systems (BRBESs) can be considered to handle the complex problem of this nature as they address different types of uncertainty. A web based BRBES was developed for predicting flood. This system provides better usability, more computational power to handle larger numbers of rule bases and scalability by porting it into a web-based solution. To improve the accuracy of flood prediction, a learning mechanism for multi-level BRBES was proposed. Furthermore, a comparison between the proposed multi-level belief rule based learning algorithm and other machine learning techniques including Artificial Neural Networks (ANN), Support Vector Machine (SVM) based regression, and Linear Regression has been performed.

In the light of the research findings of this thesis, it can be argued that flood prediction can be accomplished more accurately by integrating WSN and BRBES.

Place, publisher, year, edition, pages
Luleå University of Technology, 2017. , p. 112
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords [en]
WSN, Belief rule based Expert Systems, Flood prediction
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-66415ISBN: 978-91-7790-004-7 (print)ISBN: 978-91-7790-005-4 (electronic)OAI: oai:DiVA.org:ltu-66415DiVA, id: diva2:1155122
Presentation
2017-12-12, A193 Campus Skellefteå, Forskargatan 1, 931 62, Skellefteå, 08:00 (English)
Opponent
Supervisors
Available from: 2017-11-08 Created: 2017-11-07 Last updated: 2018-01-13Bibliographically approved
List of papers
1. Heterogeneous Wireless Sensor Networks Using CoAP and SMS to Predict Natural Disasters
Open this publication in new window or tab >>Heterogeneous Wireless Sensor Networks Using CoAP and SMS to Predict Natural Disasters
2017 (English)In: Proceedings of the 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS): The 8th IEEE INFOCOM International Workshop on Mobility Management in the Networks of the Future World (MobiWorld'17),, Piscataway, NJ: IEEE Communications Society, 2017, p. 30-35Conference paper, Published paper (Refereed)
Abstract [en]

Even in the 21 st century human is still handicapped with natural disaster. Flood is one of the most catastrophic natural disasters. Early warnings help people to take necessary steps to save human lives and properties. Sensors can be used to provide more accurate early warnings due to possibilities of capturing more detail data of surrounding nature. Recent advantages in protocol standardization and cost effectiveness of sensors it is possible to easily deploy and manage sensors in large scale. In this paper, a heterogeneous wireless sensor network is proposed and evaluated to predict natural disaster like flood. In this network CoAP is used as a unified application layer protocol for exchanging sensor data. Therefore, CoAP over SMS protocol is used for exchanging sensor data. Furthermore, the effectiveness of the heterogeneous wireless sensor network for predicting natural disaster is presented in this paper.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2017
Series
IEEE Conference on Computer Communications Workshops, ISSN 2159-4228
Keywords
WSN, CoAP, IEEE 802.15.4, 6LoWPAN, SMS, Belief Rule Base Expert System
National Category
Computer Sciences
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-63201 (URN)10.1109/INFCOMW.2017.8116348 (DOI)978-1-5386-2784-6 (ISBN)
Conference
The 2017 IEEE Conference on Computer Communications, Atlanta, GA, 1 May 2017
Projects
BRBWSN
Available from: 2017-04-29 Created: 2017-04-29 Last updated: 2018-03-26Bibliographically approved
2. A novel anomaly detection algorithm for sensor data under uncertainty
Open this publication in new window or tab >>A novel anomaly detection algorithm for sensor data under uncertainty
2018 (English)In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 22, no 5, p. 1623-1639Article in journal (Refereed) Published
Abstract [en]

It is an era of Internet of Things, where various types of sensors, especially wireless, are widely used to collect huge amount of data to feed various systems such as surveillance, environmental monitoring, and disaster management. In these systems, wireless sensors are deployed to make decisions or to predict an event in a real-time basis. However, the accuracy of such decisions or predictions depends upon the reliability of the sensor data. Unfortunately, erroneous data are received from the sensors. Consequently, it hampers the appropriate operations of the mentioned systems, especially in making decisions and prediction. Therefore, the detection of anomaly that exists with the sensor data drew significant attention and hence, it needs to be filtered before feeding a system to increase its reliability in making decisions or prediction. There exists various sensor anomaly detection algorithms, but few of them are able to address the uncertain phenomenon, associated with the sensor data. If these uncertain phenomena cannot be addressed by the algorithms, the filtered data into the system will not be able to increase the reliability of the decision-making process. These uncertainties may be due to the incompleteness, ignorance, vagueness, imprecision and ambiguity. Therefore, in this paper we propose a new belief-rule-based association rule (BRBAR) with the ability to handle the various types of uncertainties as mentioned.The reliability of this novel algorithm has been compared with other existing anomaly detection algorithms such as Gaussian, binary association rule and fuzzy association rule by using sensor data from various domains such as rainfall, temperature and cancer cell data. Receiver operating characteristic curves are used for comparing the performance of our proposed BRBAR with the aforementioned algorithms. The comparisons demonstrate that BRBAR is more accurate and reliable in detecting anomalies from sensor data under uncertainty. Hence, the use of such algorithm to feed the decision-making systems could be beneficial. Therefore, we have used this algorithm to feed appropriate sensor data to our recently developed belief-rule-based expert system to predict flooding in an area. Consequently, the reliability and the accuracy of the flood prediction system increase significantly. Such novel algorithm (BRBAR) can be used in other areas of applications. 

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Internet of Things; Wireless sensor networks; Anomaly detection; Flood prediction; Belief-rule-based expert systems
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-60360 (URN)10.1007/s00500-016-2425-2 (DOI)000426566400022 ()
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251
Note

Validerad;2018;Nivå 2;2018-03-05 (andbra)

Available from: 2016-11-12 Created: 2016-11-12 Last updated: 2018-09-14Bibliographically approved
3. A Web Based Belief Rule Based Expert System to Predict Flood
Open this publication in new window or tab >>A Web Based Belief Rule Based Expert System to Predict Flood
2015 (English)In: Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services (iiWAS2015) / [ed] Maria Indrawan-Santiago; Matthias Steinbauer; Ismail Khalil; Gabriele Anderst-Kotsis, New York: Association for Computing Machinery (ACM), 2015, p. 19-26, article id 3Conference paper, Published paper (Refereed)
Abstract [en]

Natural calamity disrupts our daily life and brings many sufferings in our life. Among the natural calamities, flood is one of the most catastrophic. Predicting flood helps us to take necessary precautions and save human lives. Several types of data (meteorological condition, topography, river characteristics, and human activities) are used to predict flood water level in an area. In our previous works, we proposed a belief rule based flood prediction system in a desktop environment. In this paper, we propose a web-service based flood prediction expert system by incorporating belief rule base with the capability of reading sensor data such as rainfall, river flow on real time basis. This will facilitate the monitoring of the various flood-intensifying factors, contributing in increasing the flood water level in an area. Eventually, the decision makers would able to take measures to control those factors and to reduce the intensity of flooding in an area.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2015
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing; Enabling ICT (AERI)
Identifiers
urn:nbn:se:ltu:diva-27246 (URN)10.1145/2837185.2837212 (DOI)2-s2.0-84967203069 (Scopus ID)09c47bac-5b82-43aa-a855-c478efcdbf60 (Local ID)978-1-4503-3491-4 (ISBN)09c47bac-5b82-43aa-a855-c478efcdbf60 (Archive number)09c47bac-5b82-43aa-a855-c478efcdbf60 (OAI)
Conference
International Conference on Information Integration and Web-based Applications & Services : 11/12/2015 - 13/12/2015
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Note

Godkänd; 2015; 20151014 (karand)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-07-10Bibliographically approved

Open Access in DiVA

fulltext(6131 kB)785 downloads
File information
File name FULLTEXT01.pdfFile size 6131 kBChecksum SHA-512
0ce97793fd1de300ce6c6cdfb0f863311dd1c4c5aa5c73545c8490e65635cf67cd91dd9cd09138992350c8b7a7a25796ee08511d6a7091f4389ae9fc2f30d56b
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Islam, Raihan Ul
By organisation
Computer Science
Media and Communication Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 785 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 425 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf