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A novel anomaly detection algorithm for sensor data under uncertainty
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0002-3090-7645
Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0003-0244-3561
Rekke forfattare: 32018 (engelsk)Inngår i: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 22, nr 5, s. 1623-1639Artikkel i tidsskrift (Fagfellevurdert) 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. 

sted, utgiver, år, opplag, sider
Springer, 2018. Vol. 22, nr 5, s. 1623-1639
Emneord [en]
Internet of Things; Wireless sensor networks; Anomaly detection; Flood prediction; Belief-rule-based expert systems
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-60360DOI: 10.1007/s00500-016-2425-2ISI: 000426566400022OAI: oai:DiVA.org:ltu-60360DiVA, id: diva2:1046172
Prosjekter
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Forskningsfinansiär
Swedish Research Council, 2014-4251
Merknad

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

Tilgjengelig fra: 2016-11-12 Laget: 2016-11-12 Sist oppdatert: 2018-09-14bibliografisk kontrollert
Inngår i avhandling
1. Wireless Sensor Network Based Flood Prediction Using Belief Rule Based Expert System
Åpne denne publikasjonen i ny fane eller vindu >>Wireless Sensor Network Based Flood Prediction Using Belief Rule Based Expert System
2017 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Luleå University of Technology, 2017. s. 112
Serie
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Emneord
WSN, Belief rule based Expert Systems, Flood prediction
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
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 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2017-11-08 Laget: 2017-11-07 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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