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Heterogeneous Wireless Sensor Networks Using CoAP and SMS to Predict Natural Disasters
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-3090-7645
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
University of Chittagong, Bangladesh.ORCID iD: 0000-0002-7473-8185
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. p. 30-35
Series
IEEE Conference on Computer Communications Workshops, ISSN 2159-4228
Keywords [en]
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: urn:nbn:se:ltu:diva-63201DOI: 10.1109/INFCOMW.2017.8116348ISBN: 978-1-5386-2784-6 (electronic)OAI: oai:DiVA.org:ltu-63201DiVA, id: diva2:1092050
Conference
The 2017 IEEE Conference on Computer Communications, Atlanta, GA, 1 May 2017
Projects
BRBWSNAvailable from: 2017-04-29 Created: 2017-04-29 Last updated: 2018-03-26Bibliographically approved
In thesis
1. Wireless Sensor Network Based Flood Prediction Using Belief Rule Based Expert System
Open this publication in new window or tab >>Wireless Sensor Network Based Flood Prediction Using Belief Rule Based Expert System
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
WSN, Belief rule based Expert Systems, Flood prediction
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-66415 (URN)978-91-7790-004-7 (ISBN)978-91-7790-005-4 (ISBN)
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

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