Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.651314
Title: Agent-based decentralised adaptive dynamic data gathering from large-scale wireless sensor networks
Author: Haghighi, M.
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2013
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Abstract:
Wireless Sensor Networks (WSNs) have become a mainstream technology for environmental monitoring, for observing dynamic changes in variables of interest, over extended periods of time via large-scale networks of sensors. In recent years, many engineering disciplines have also become reliant on WSNs to detect and track certain events by monitoring various parameters through a number of spatially distributed sensor/ actuator nodes. WSNs have a wide range of applications including healthcare, military, and monitoring both of built structures and of natural environments and habitats. In all such fields, gathering and then relaying captured data to a central unit is often considered the primary task of the network; and many application domains also require WSNs to capture and store data for long periods of time. The storing and management of flows of data tends to be a challenging issue because WSNs often consist of nodes with limited processing, memory, and power resources. Therefore WSN software layers need to implement efficient data storage allocation mechanisms in order to provide sufficient memory space for multiple applications. Due to the hard-limit resource constraints in WSN nodes, and lack of support for high-level development environments, application developers tend to avoid concurrent object-oriented (00) models when designing WSN applications. Traditional WSN applications have involved exchanging an excessive amount of data, usually in an offline mode, between sensor nodes and a central unit that then analyses the captured data. In recent years however, a number of new WSN node designs have been released, with resource-rich capabilities that enable the running of multiple applications on individual nodes or on groups of them collectively; and that allow computational data analysis to be implemented online, in the WSN nodes, often in a distributed fashion.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.651314  DOI: Not available
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