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dc.contributor.authorAbedi Khoozani, Parisaen
dc.date2013-03-04 12:39:24.502
dc.date.accessioned2013-03-04T21:46:02Z
dc.date.available2013-03-04T21:46:02Z
dc.date.issued2013-03-04
dc.identifier.urihttp://hdl.handle.net/1974/7842
dc.descriptionThesis (Master, Electrical & Computer Engineering) -- Queen's University, 2013-03-04 12:39:24.502en
dc.description.abstractRapid advances in hardware technology are making it possible to manufacture different types of Sensor Nodes (SN) that results in fast growing heterogeneous Wireless Sensor Networks (WSNs). These WSN’s are applicable for a wide range of applications relevant for military, industry and domestic use. However, WSNs have particular features such as scarce resources which can affect their performance. In addition, WSNs are subject to experience changes that can occur both within the condition of the network, due to factors such as node mobility or node failure (prevalent in harsh environments), and with regards to user requirements. Consequently, it is vital for WSNs to sense the current network conditions and user requirements to be able to perform efficiently. Cognition is necessarily introduced in WSNs as a response to this need. Cognition in the context of WSNs deals with the ability to be aware of the environment and user requirements and to proactively adapt to changes. This thesis proposes a hierarchical architecture along with a cognitive network management protocol capable of enabling cognition in WSNs. Specifically; this research introduces Cognitive Nodes (CN) into WSNs so that they can manage the cognitive network. The cognitive network management process is composed of three sub-processes: 1) scanning the network, 2) decision-making, and 3) updating the nodes from taken decisions.Scanning the network process aims to provide an awareness of current network conditions. Therefore, at the first execution, each CN creates a profile table for each node in its purview and updates the tables periodically during the network operation. In decision making process, CNs make necessary decisions in terms of the working state of SNs (active/sleep), the duration of this state, and the Frequency of Sensing (FoS). Decision making process uses an optimization scheme to find the optimal number of active SNs in order to prolong the lifetime of the network. Finally, the nodes will be informed of the taken decisions. Based on the simulation and implementation results, the proposed cognitive WSN shows a significant enhancement in terms of the network’s longevity, its ability to negotiate competing objectives, and its ability to serve users more efficiently.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectCognitionen
dc.subjectWireless sensor networken
dc.subjectHeterogeneousen
dc.subjectArchitectureen
dc.titleA Low-Complexity Architecture and Framework for Enabling Cognition in Heterogenous Wireless Sensor Networksen
dc.typethesisen
dc.description.degreeM.A.Sc.en
dc.contributor.supervisorIbnkahla, Mohameden
dc.contributor.supervisorKim, Il-Minen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.degree.grantorQueen's University at Kingstonen


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