{"id":{"repo_id":"strathclyde","oai_identifier":"oai:strathclyde:th83kz38r"},"canonical_url":"https://search.dev.ndltd.org/etd/strathclyde/oai:strathclyde:th83kz38r","repository":{"repo_id":"strathclyde","name":"University of Strathclyde","base_url":"https://stax.strath.ac.uk/catalog/oai"},"display":{"title":"Cognitive wireless sensor networks (CogWSNs)","abstract":"Cognitive Wireless Sensor Networks (CogWSNs) are an adaptive learning based wireless sensor network relying on cognitive computational processes to provide a dynamic capability in configuring the network. The network is formed by sensor nodes equipped with cognitive modules with awareness of their operating environment. If the performance of the sensor network does not meet requirements during operation, a corrective action is derived from stored network knowledge to improve performance. After the action is implemented, feedback on the action taken is evaluated to determine the level of improvement. Example functions within CogWSNs can be as simple as to provide robust connectivity or as complex as to negotiate additional resources from neighbouring network groups with the goal of forwarding application-critical data. In this work, the concept of CogWSNs is defined and its decision processes and supporting architecture proposed. The decision role combines the Problem Solving cognitive process from A Layered Reference Model of the Brain and Polya Concept, consisting of Observe, Plan, Implement, and Evaluate phases. The architecture comprises a Transceiver, Transducer, and Power Supply virtual modules coordinated by the CogWSN's decision process together with intervention, if necessary, by a user. Three types of CogWSN modules are designed based on different implementation considerations: Rule-based CogWSN, Supervised CogWSN, and Reinforcement CogWSN. Verification and comparison for these modules are executed through case studies with focus on power transmission and communication slot allocation. Results show that all three modules are able to achieve targeted connectivity and maintain utilisation of slots at acceptable data rates.","abstract_html":"Cognitive Wireless Sensor Networks (CogWSNs) are an adaptive learning based wireless sensor network relying on cognitive computational processes to provide a dynamic capability in configuring the network. The network is formed by sensor nodes equipped with cognitive modules with awareness of their operating environment. If the performance of the sensor network does not meet requirements during operation, a corrective action is derived from stored network knowledge to improve performance. After the action is implemented, feedback on the action taken is evaluated to determine the level of improvement. Example functions within CogWSNs can be as simple as to provide robust connectivity or as complex as to negotiate additional resources from neighbouring network groups with the goal of forwarding application-critical data. In this work, the concept of CogWSNs is defined and its decision processes and supporting architecture proposed. The decision role combines the Problem Solving cognitive process from A Layered Reference Model of the Brain and Polya Concept, consisting of Observe, Plan, Implement, and Evaluate phases. The architecture comprises a Transceiver, Transducer, and Power Supply virtual modules coordinated by the CogWSN's decision process together with intervention, if necessary, by a user. Three types of CogWSN modules are designed based on different implementation considerations: Rule-based CogWSN, Supervised CogWSN, and Reinforcement CogWSN. Verification and comparison for these modules are executed through case studies with focus on power transmission and communication slot allocation. Results show that all three modules are able to achieve targeted connectivity and maintain utilisation of slots at acceptable data rates.","abstract_has_math":false,"creators":["Goh, Hock Guan"],"institution":"University of Strathclyde","degree_name":"phd","degree_level":"doctoral-pg","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014","date_published":"2014","updated_at":"2026-07-24T04:52:18Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.48730/45ms-pr19"],"render_values":[{"text":"10.48730/45ms-pr19","href":"https://doi.org/10.48730/45ms-pr19","code":true}]},{"key":"dc:identifier","label":"Identifier","values":["T13812"],"render_values":[{"text":"T13812","href":null,"code":true}]}]},"links":{"outbound_url":"https://stax.strath.ac.uk/concern/theses/th83kz38r","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Goh, Hock Guan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014"]},{"key":"dc:date.issued","label":"Date","values":["2014"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Electronic and Electrical Engineering"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Strathclyde"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral-pg"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["phd"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["T13812"]},{"key":"dc:identifier.doi","label":"DOI","values":["10.48730/45ms-pr19"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://stax.strath.ac.uk/concern/theses/th83kz38r"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Cognitive Wireless Sensor Networks (CogWSNs) are an adaptive learning based wireless sensor network relying on cognitive computational processes to provide a dynamic capability in configuring the network. The network is formed by sensor nodes equipped with cognitive modules with awareness of their operating environment. If the performance of the sensor network does not meet requirements during operation, a corrective action is derived from stored network knowledge to improve performance. After the action is implemented, feedback on the action taken is evaluated to determine the level of improvement. Example functions within CogWSNs can be as simple as to provide robust connectivity or as complex as to negotiate additional resources from neighbouring network groups with the goal of forwarding application-critical data. In this work, the concept of CogWSNs is defined and its decision processes and supporting architecture proposed. The decision role combines the Problem Solving cognitive process from A Layered Reference Model of the Brain and Polya Concept, consisting of Observe, Plan, Implement, and Evaluate phases. The architecture comprises a Transceiver, Transducer, and Power Supply virtual modules coordinated by the CogWSN's decision process together with intervention, if necessary, by a user. Three types of CogWSN modules are designed based on different implementation considerations: Rule-based CogWSN, Supervised CogWSN, and Reinforcement CogWSN. Verification and comparison for these modules are executed through case studies with focus on power transmission and communication slot allocation. Results show that all three modules are able to achieve targeted connectivity and maintain utilisation of slots at acceptable data rates."]},{"key":"dc:description.abstract","label":"Abstract","values":["Cognitive Wireless Sensor Networks (CogWSNs) are an adaptive learning based wireless sensor network relying on cognitive computational processes to provide a dynamic capability in configuring the network. The network is formed by sensor nodes equipped with cognitive modules with awareness of their operating environment. If the performance of the sensor network does not meet requirements during operation, a corrective action is derived from stored network knowledge to improve performance. After the action is implemented, feedback on the action taken is evaluated to determine the level of improvement. Example functions within CogWSNs can be as simple as to provide robust connectivity or as complex as to negotiate additional resources from neighbouring network groups with the goal of forwarding application-critical data. In this work, the concept of CogWSNs is defined and its decision processes and supporting architecture proposed. The decision role combines the Problem Solving cognitive process from A Layered Reference Model of the Brain and Polya Concept, consisting of Observe, Plan, Implement, and Evaluate phases. The architecture comprises a Transceiver, Transducer, and Power Supply virtual modules coordinated by the CogWSN's decision process together with intervention, if necessary, by a user. Three types of CogWSN modules are designed based on different implementation considerations: Rule-based CogWSN, Supervised CogWSN, and Reinforcement CogWSN. Verification and comparison for these modules are executed through case studies with focus on power transmission and communication slot allocation. Results show that all three modules are able to achieve targeted connectivity and maintain utilisation of slots at acceptable data rates."]},{"key":"dc:title","label":"Title","values":["Cognitive wireless sensor networks (CogWSNs)"]}]}],"canonical_facts":{"dc:creator":["Goh, Hock Guan"],"dc:date":["2014"],"dc:date.issued":["2014"],"dc:description":["Cognitive Wireless Sensor Networks (CogWSNs) are an adaptive learning based wireless sensor network relying on cognitive computational processes to provide a dynamic capability in configuring the network. The network is formed by sensor nodes equipped with cognitive modules with awareness of their operating environment. If the performance of the sensor network does not meet requirements during operation, a corrective action is derived from stored network knowledge to improve performance. After the action is implemented, feedback on the action taken is evaluated to determine the level of improvement. Example functions within CogWSNs can be as simple as to provide robust connectivity or as complex as to negotiate additional resources from neighbouring network groups with the goal of forwarding application-critical data. In this work, the concept of CogWSNs is defined and its decision processes and supporting architecture proposed. The decision role combines the Problem Solving cognitive process from A Layered Reference Model of the Brain and Polya Concept, consisting of Observe, Plan, Implement, and Evaluate phases. The architecture comprises a Transceiver, Transducer, and Power Supply virtual modules coordinated by the CogWSN's decision process together with intervention, if necessary, by a user. Three types of CogWSN modules are designed based on different implementation considerations: Rule-based CogWSN, Supervised CogWSN, and Reinforcement CogWSN. Verification and comparison for these modules are executed through case studies with focus on power transmission and communication slot allocation. Results show that all three modules are able to achieve targeted connectivity and maintain utilisation of slots at acceptable data rates."],"dc:description.abstract":["Cognitive Wireless Sensor Networks (CogWSNs) are an adaptive learning based wireless sensor network relying on cognitive computational processes to provide a dynamic capability in configuring the network. The network is formed by sensor nodes equipped with cognitive modules with awareness of their operating environment. If the performance of the sensor network does not meet requirements during operation, a corrective action is derived from stored network knowledge to improve performance. After the action is implemented, feedback on the action taken is evaluated to determine the level of improvement. Example functions within CogWSNs can be as simple as to provide robust connectivity or as complex as to negotiate additional resources from neighbouring network groups with the goal of forwarding application-critical data. In this work, the concept of CogWSNs is defined and its decision processes and supporting architecture proposed. The decision role combines the Problem Solving cognitive process from A Layered Reference Model of the Brain and Polya Concept, consisting of Observe, Plan, Implement, and Evaluate phases. The architecture comprises a Transceiver, Transducer, and Power Supply virtual modules coordinated by the CogWSN's decision process together with intervention, if necessary, by a user. Three types of CogWSN modules are designed based on different implementation considerations: Rule-based CogWSN, Supervised CogWSN, and Reinforcement CogWSN. Verification and comparison for these modules are executed through case studies with focus on power transmission and communication slot allocation. Results show that all three modules are able to achieve targeted connectivity and maintain utilisation of slots at acceptable data rates."],"dc:identifier":["T13812"],"dc:identifier.doi":["10.48730/45ms-pr19"],"dc:identifier.uri":["https://stax.strath.ac.uk/concern/theses/th83kz38r"],"dc:publisher.department":["Department of Electronic and Electrical Engineering"],"dc:publisher.institution":["University of Strathclyde"],"dc:title":["Cognitive wireless sensor networks (CogWSNs)"],"dc:type.qualificationlevel":["doctoral-pg"],"dc:type.qualificationname":["phd"]},"updated_at":"2026-07-24T04:52:18Z"}