{"id":{"repo_id":"rgu","oai_identifier":"oai:rgu-repository.worktribe.com:3020804"},"canonical_url":"https://search.dev.ndltd.org/etd/rgu/oai:rgu-repository.worktribe.com:3020804","repository":{"repo_id":"rgu","name":"Robert Gordon University","base_url":"https://rgu-repository.worktribe.com/oaiprovider"},"display":{"title":"Investigations into the physical layer of underwater networks to enhance channel capacity and performance.","abstract":"The Underwater Internet of Things (UIoT) needs development to enable usage of modern network applications underwater, particularly regarding sustainable high capacities to enable multimedia transmissions that can monitor industrial or environmental processes. This thesis investigates the nature of the physical layer of underwater networks and investigates how it can be developed to pragmatically increase their channel capacity and general performance according to common networking metrics, so that data can be effectively and efficiently transmitted from the ocean floor to the surface enabling passive harvesting of data. It investigates replacing low-capacity ad-hoc acoustic networks with a smart, co-operative hybrid-architecture that uses high data rate wireless visible light communication to transmit information through the underwater environment towards the floating sink nodes on the surface. This link is supported by optic fibre and artificial intelligence to connect source nodes to each other with smart functionality. This allows for source nodes to share information that enables them to co-ordinate successful transmission of data to the sink whilst avoiding common channel disturbances that would otherwise result in a failed transmission. The thesis shows the investigations and methodology utilised to suggest this as a valid architecture. Firstly, this work investigated how the acoustic network could be optimised through MATLAB simulation using known models from the literature. It was identified that through use of floating repeater nodes at periodic distances to form an optimised multi-hop network, or at least a single link with a signal regeneration mechanism in place, acoustic communication can take place with a prospective maximum capacity increase, from 31.2kbit/sec in a modern modem to 2Mbit/sec, that has the potential to carry a diverse range of multimedia. However, despite the capacity increase, it still lacks the potential to carry high channel capacity multimedia such as video and high-quality images with reasonable quality, firstly due to the relatively low channel capacity but also due to the high latencies associated with acoustics. Thus, it was decided to follow up these results with an investigation into a wireless optical communication-based architecture that has the potential to increase the channel capacity beyond what found in acoustic communications, so that these applications are more feasible. It was found that, according to MATLAB simulations and modelling, an underwater wireless optical network can be developed that increases the possible maximum capacity from the 2Mbit/sec from to 10Mbit/sec and reduces latency, from 188ms to 0.8ms, so that these multimedia applications become more viable. However, it was determined during simulation that this technology is not going to achieve this consistently because of local noise, a variable transmission range due to fluctuating organic matter concentrations and vulnerability to objects. Similarly, it was decided that further investigations were necessary to find methods to enable this consistency, thus, it was decided to investigate the combination of two architectures working in co-ordination to deliver successful outcomes. It was decided that a smart wireless acoustic-optical hybrid network would be compared to a strategically combined smart optic fibre-wireless optical network for capacity and performance comparisons through simulation in MATLAB. It was found that the latter has the potential to successfully maintain consistently high capacity of 10Mbit/sec and low delay jitter of 1ms whereas the former jittered significantly in capacity and latency as the acoustic link acted as a significant bottleneck of 5Mbit/sec as a theoretical maximum, resulting in capacity jitter of 5Mb/sec and delay jitter of 67.7ms. Thus, it would add unpredictability to the network and adversely affect transmission data for applications with high-capacity demands. However, this paradigm with higher capacities is entirely dependent on being able to determine when the forementioned noise, organic matter and objects are likely to interfere with operational certainty. Finally, to research an appropriate manner of mode switching from optic to the deferral mechanism according to the local environment, it was decided to establish an early concept of \"cognitive optics\" which aims to render a network cognitive of the underwater wireless optical channel. It aimed to achieve this through utilisation of an artificial intelligence algorithm that can carry out decision making based on sensed data on noise, local particulate presence and objects that will allow for the transmission method to switch according to local conditions surrounding the node. The investigation utilised a synthetic dataset developed through the utilisation of established models and known oceanic boundaries to train a series of accurate machine learning classifiers, specifically random forest, neural network and support vector machines. In addition, the potential of a fuzzy logic controller was investigated for this purpose. It was found that either fuzzy logic or machine learning classifiers could accurately decide whether to transmit or co-operate with another source node to complete transmission. In terms of machine learning algorithm performance, the experiment showed that a small neural network could achieve this fastest and generally most accurately whilst being least likely to use visible light when it should co-operate with another node to maintain the investigated reliable capacity performance of 10Mbit/sec. For future works, the aim is to create an underwater network that can operate autonomously utilising intelligent modem agents to self-optimise their placement, modulation and power according to circumstances in the network. This would expand on the theories investigated in this thesis by looking at mechanisms that will enable the network to self-optimise in the face of environmental and network conditions in complex manners using artificial intelligence, avoiding the need for costly interventions. The work is concluded with the evaluation that an autonomous underwater multimode network is the best solution to realising the Underwater Internet of Things, most crucially though, that there is potential beyond the dominant acoustic/optical hybrid network topology for other mechanisms to be used such as the investigated co-operative, smart, optical network to transmit data effectively through the water column to the source. There are many future works that can be investigated based on the results, such as further automation and cognitive functionality of the nodes and spatial multiplexing concepts to further raise capacity for transmission in the network.","abstract_html":"The Underwater Internet of Things (UIoT) needs development to enable usage of modern network applications underwater, particularly regarding sustainable high capacities to enable multimedia transmissions that can monitor industrial or environmental processes. This thesis investigates the nature of the physical layer of underwater networks and investigates how it can be developed to pragmatically increase their channel capacity and general performance according to common networking metrics, so that data can be effectively and efficiently transmitted from the ocean floor to the surface enabling passive harvesting of data. It investigates replacing low-capacity ad-hoc acoustic networks with a smart, co-operative hybrid-architecture that uses high data rate wireless visible light communication to transmit information through the underwater environment towards the floating sink nodes on the surface. This link is supported by optic fibre and artificial intelligence to connect source nodes to each other with smart functionality. This allows for source nodes to share information that enables them to co-ordinate successful transmission of data to the sink whilst avoiding common channel disturbances that would otherwise result in a failed transmission. The thesis shows the investigations and methodology utilised to suggest this as a valid architecture. Firstly, this work investigated how the acoustic network could be optimised through MATLAB simulation using known models from the literature. It was identified that through use of floating repeater nodes at periodic distances to form an optimised multi-hop network, or at least a single link with a signal regeneration mechanism in place, acoustic communication can take place with a prospective maximum capacity increase, from 31.2kbit/sec in a modern modem to 2Mbit/sec, that has the potential to carry a diverse range of multimedia. However, despite the capacity increase, it still lacks the potential to carry high channel capacity multimedia such as video and high-quality images with reasonable quality, firstly due to the relatively low channel capacity but also due to the high latencies associated with acoustics. Thus, it was decided to follow up these results with an investigation into a wireless optical communication-based architecture that has the potential to increase the channel capacity beyond what found in acoustic communications, so that these applications are more feasible. It was found that, according to MATLAB simulations and modelling, an underwater wireless optical network can be developed that increases the possible maximum capacity from the 2Mbit/sec from to 10Mbit/sec and reduces latency, from 188ms to 0.8ms, so that these multimedia applications become more viable. However, it was determined during simulation that this technology is not going to achieve this consistently because of local noise, a variable transmission range due to fluctuating organic matter concentrations and vulnerability to objects. Similarly, it was decided that further investigations were necessary to find methods to enable this consistency, thus, it was decided to investigate the combination of two architectures working in co-ordination to deliver successful outcomes. It was decided that a smart wireless acoustic-optical hybrid network would be compared to a strategically combined smart optic fibre-wireless optical network for capacity and performance comparisons through simulation in MATLAB. It was found that the latter has the potential to successfully maintain consistently high capacity of 10Mbit/sec and low delay jitter of 1ms whereas the former jittered significantly in capacity and latency as the acoustic link acted as a significant bottleneck of 5Mbit/sec as a theoretical maximum, resulting in capacity jitter of 5Mb/sec and delay jitter of 67.7ms. Thus, it would add unpredictability to the network and adversely affect transmission data for applications with high-capacity demands. However, this paradigm with higher capacities is entirely dependent on being able to determine when the forementioned noise, organic matter and objects are likely to interfere with operational certainty. Finally, to research an appropriate manner of mode switching from optic to the deferral mechanism according to the local environment, it was decided to establish an early concept of "cognitive optics" which aims to render a network cognitive of the underwater wireless optical channel. It aimed to achieve this through utilisation of an artificial intelligence algorithm that can carry out decision making based on sensed data on noise, local particulate presence and objects that will allow for the transmission method to switch according to local conditions surrounding the node. The investigation utilised a synthetic dataset developed through the utilisation of established models and known oceanic boundaries to train a series of accurate machine learning classifiers, specifically random forest, neural network and support vector machines. In addition, the potential of a fuzzy logic controller was investigated for this purpose. It was found that either fuzzy logic or machine learning classifiers could accurately decide whether to transmit or co-operate with another source node to complete transmission. In terms of machine learning algorithm performance, the experiment showed that a small neural network could achieve this fastest and generally most accurately whilst being least likely to use visible light when it should co-operate with another node to maintain the investigated reliable capacity performance of 10Mbit/sec. For future works, the aim is to create an underwater network that can operate autonomously utilising intelligent modem agents to self-optimise their placement, modulation and power according to circumstances in the network. This would expand on the theories investigated in this thesis by looking at mechanisms that will enable the network to self-optimise in the face of environmental and network conditions in complex manners using artificial intelligence, avoiding the need for costly interventions. The work is concluded with the evaluation that an autonomous underwater multimode network is the best solution to realising the Underwater Internet of Things, most crucially though, that there is potential beyond the dominant acoustic/optical hybrid network topology for other mechanisms to be used such as the investigated co-operative, smart, optical network to transmit data effectively through the water column to the source. There are many future works that can be investigated based on the results, such as further automation and cognitive functionality of the nodes and spatial multiplexing concepts to further raise capacity for transmission in the network.","abstract_has_math":false,"creators":["Stewart, Craig"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["N. Fough and R. 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This thesis investigates the nature of the physical layer of underwater networks and investigates how it can be developed to pragmatically increase their channel capacity and general performance according to common networking metrics, so that data can be effectively and efficiently transmitted from the ocean floor to the surface enabling passive harvesting of data. It investigates replacing low-capacity ad-hoc acoustic networks with a smart, co-operative hybrid-architecture that uses high data rate wireless visible light communication to transmit information through the underwater environment towards the floating sink nodes on the surface. This link is supported by optic fibre and artificial intelligence to connect source nodes to each other with smart functionality. This allows for source nodes to share information that enables them to co-ordinate successful transmission of data to the sink whilst avoiding common channel disturbances that would otherwise result in a failed transmission. The thesis shows the investigations and methodology utilised to suggest this as a valid architecture. Firstly, this work investigated how the acoustic network could be optimised through MATLAB simulation using known models from the literature. It was identified that through use of floating repeater nodes at periodic distances to form an optimised multi-hop network, or at least a single link with a signal regeneration mechanism in place, acoustic communication can take place with a prospective maximum capacity increase, from 31.2kbit/sec in a modern modem to 2Mbit/sec, that has the potential to carry a diverse range of multimedia. However, despite the capacity increase, it still lacks the potential to carry high channel capacity multimedia such as video and high-quality images with reasonable quality, firstly due to the relatively low channel capacity but also due to the high latencies associated with acoustics. Thus, it was decided to follow up these results with an investigation into a wireless optical communication-based architecture that has the potential to increase the channel capacity beyond what found in acoustic communications, so that these applications are more feasible. It was found that, according to MATLAB simulations and modelling, an underwater wireless optical network can be developed that increases the possible maximum capacity from the 2Mbit/sec from to 10Mbit/sec and reduces latency, from 188ms to 0.8ms, so that these multimedia applications become more viable. However, it was determined during simulation that this technology is not going to achieve this consistently because of local noise, a variable transmission range due to fluctuating organic matter concentrations and vulnerability to objects. Similarly, it was decided that further investigations were necessary to find methods to enable this consistency, thus, it was decided to investigate the combination of two architectures working in co-ordination to deliver successful outcomes. It was decided that a smart wireless acoustic-optical hybrid network would be compared to a strategically combined smart optic fibre-wireless optical network for capacity and performance comparisons through simulation in MATLAB. It was found that the latter has the potential to successfully maintain consistently high capacity of 10Mbit/sec and low delay jitter of 1ms whereas the former jittered significantly in capacity and latency as the acoustic link acted as a significant bottleneck of 5Mbit/sec as a theoretical maximum, resulting in capacity jitter of 5Mb/sec and delay jitter of 67.7ms. Thus, it would add unpredictability to the network and adversely affect transmission data for applications with high-capacity demands. However, this paradigm with higher capacities is entirely dependent on being able to determine when the forementioned noise, organic matter and objects are likely to interfere with operational certainty. Finally, to research an appropriate manner of mode switching from optic to the deferral mechanism according to the local environment, it was decided to establish an early concept of \"cognitive optics\" which aims to render a network cognitive of the underwater wireless optical channel. It aimed to achieve this through utilisation of an artificial intelligence algorithm that can carry out decision making based on sensed data on noise, local particulate presence and objects that will allow for the transmission method to switch according to local conditions surrounding the node. The investigation utilised a synthetic dataset developed through the utilisation of established models and known oceanic boundaries to train a series of accurate machine learning classifiers, specifically random forest, neural network and support vector machines. In addition, the potential of a fuzzy logic controller was investigated for this purpose. It was found that either fuzzy logic or machine learning classifiers could accurately decide whether to transmit or co-operate with another source node to complete transmission. In terms of machine learning algorithm performance, the experiment showed that a small neural network could achieve this fastest and generally most accurately whilst being least likely to use visible light when it should co-operate with another node to maintain the investigated reliable capacity performance of 10Mbit/sec. For future works, the aim is to create an underwater network that can operate autonomously utilising intelligent modem agents to self-optimise their placement, modulation and power according to circumstances in the network. This would expand on the theories investigated in this thesis by looking at mechanisms that will enable the network to self-optimise in the face of environmental and network conditions in complex manners using artificial intelligence, avoiding the need for costly interventions. The work is concluded with the evaluation that an autonomous underwater multimode network is the best solution to realising the Underwater Internet of Things, most crucially though, that there is potential beyond the dominant acoustic/optical hybrid network topology for other mechanisms to be used such as the investigated co-operative, smart, optical network to transmit data effectively through the water column to the source. There are many future works that can be investigated based on the results, such as further automation and cognitive functionality of the nodes and spatial multiplexing concepts to further raise capacity for transmission in the network."]},{"key":"dc:title","label":"Title","values":["Investigations into the physical layer of underwater networks to enhance channel capacity and performance."]}]}],"canonical_facts":{"dc:contributor.advisor":["N. Fough and R. Prabhu"],"dc:contributor.sponsor":["No Funder Acknowledged (Outputs)"],"dc:creator":["Stewart, Craig"],"dc:date":["2025-03-31"],"dc:date.issued":["2025"],"dc:description.abstract":["The Underwater Internet of Things (UIoT) needs development to enable usage of modern network applications underwater, particularly regarding sustainable high capacities to enable multimedia transmissions that can monitor industrial or environmental processes. This thesis investigates the nature of the physical layer of underwater networks and investigates how it can be developed to pragmatically increase their channel capacity and general performance according to common networking metrics, so that data can be effectively and efficiently transmitted from the ocean floor to the surface enabling passive harvesting of data. It investigates replacing low-capacity ad-hoc acoustic networks with a smart, co-operative hybrid-architecture that uses high data rate wireless visible light communication to transmit information through the underwater environment towards the floating sink nodes on the surface. This link is supported by optic fibre and artificial intelligence to connect source nodes to each other with smart functionality. This allows for source nodes to share information that enables them to co-ordinate successful transmission of data to the sink whilst avoiding common channel disturbances that would otherwise result in a failed transmission. The thesis shows the investigations and methodology utilised to suggest this as a valid architecture. Firstly, this work investigated how the acoustic network could be optimised through MATLAB simulation using known models from the literature. It was identified that through use of floating repeater nodes at periodic distances to form an optimised multi-hop network, or at least a single link with a signal regeneration mechanism in place, acoustic communication can take place with a prospective maximum capacity increase, from 31.2kbit/sec in a modern modem to 2Mbit/sec, that has the potential to carry a diverse range of multimedia. However, despite the capacity increase, it still lacks the potential to carry high channel capacity multimedia such as video and high-quality images with reasonable quality, firstly due to the relatively low channel capacity but also due to the high latencies associated with acoustics. Thus, it was decided to follow up these results with an investigation into a wireless optical communication-based architecture that has the potential to increase the channel capacity beyond what found in acoustic communications, so that these applications are more feasible. It was found that, according to MATLAB simulations and modelling, an underwater wireless optical network can be developed that increases the possible maximum capacity from the 2Mbit/sec from to 10Mbit/sec and reduces latency, from 188ms to 0.8ms, so that these multimedia applications become more viable. However, it was determined during simulation that this technology is not going to achieve this consistently because of local noise, a variable transmission range due to fluctuating organic matter concentrations and vulnerability to objects. Similarly, it was decided that further investigations were necessary to find methods to enable this consistency, thus, it was decided to investigate the combination of two architectures working in co-ordination to deliver successful outcomes. It was decided that a smart wireless acoustic-optical hybrid network would be compared to a strategically combined smart optic fibre-wireless optical network for capacity and performance comparisons through simulation in MATLAB. It was found that the latter has the potential to successfully maintain consistently high capacity of 10Mbit/sec and low delay jitter of 1ms whereas the former jittered significantly in capacity and latency as the acoustic link acted as a significant bottleneck of 5Mbit/sec as a theoretical maximum, resulting in capacity jitter of 5Mb/sec and delay jitter of 67.7ms. Thus, it would add unpredictability to the network and adversely affect transmission data for applications with high-capacity demands. However, this paradigm with higher capacities is entirely dependent on being able to determine when the forementioned noise, organic matter and objects are likely to interfere with operational certainty. Finally, to research an appropriate manner of mode switching from optic to the deferral mechanism according to the local environment, it was decided to establish an early concept of \"cognitive optics\" which aims to render a network cognitive of the underwater wireless optical channel. It aimed to achieve this through utilisation of an artificial intelligence algorithm that can carry out decision making based on sensed data on noise, local particulate presence and objects that will allow for the transmission method to switch according to local conditions surrounding the node. The investigation utilised a synthetic dataset developed through the utilisation of established models and known oceanic boundaries to train a series of accurate machine learning classifiers, specifically random forest, neural network and support vector machines. In addition, the potential of a fuzzy logic controller was investigated for this purpose. It was found that either fuzzy logic or machine learning classifiers could accurately decide whether to transmit or co-operate with another source node to complete transmission. In terms of machine learning algorithm performance, the experiment showed that a small neural network could achieve this fastest and generally most accurately whilst being least likely to use visible light when it should co-operate with another node to maintain the investigated reliable capacity performance of 10Mbit/sec. For future works, the aim is to create an underwater network that can operate autonomously utilising intelligent modem agents to self-optimise their placement, modulation and power according to circumstances in the network. This would expand on the theories investigated in this thesis by looking at mechanisms that will enable the network to self-optimise in the face of environmental and network conditions in complex manners using artificial intelligence, avoiding the need for costly interventions. The work is concluded with the evaluation that an autonomous underwater multimode network is the best solution to realising the Underwater Internet of Things, most crucially though, that there is potential beyond the dominant acoustic/optical hybrid network topology for other mechanisms to be used such as the investigated co-operative, smart, optical network to transmit data effectively through the water column to the source. There are many future works that can be investigated based on the results, such as further automation and cognitive functionality of the nodes and spatial multiplexing concepts to further raise capacity for transmission in the network."],"dc:identifier":["oai:rgu-repository.worktribe.com:3020804","https://doi.org/10.48526/rgu-wt-3020804"],"dc:identifier.uri":["https://rgu-repository.worktribe.com/3020804/1/STEWART%202025%20Investigations%20into%20the%20physical"],"dc:language":["en"],"dc:relation.isreferencedby":["https://rgu-repository.worktribe.com/output/3020804"],"dc:subject":["Underwater internet of things","Co-operative hybrid-architecture","Sink nodes","Acoustic-optical hybrid networks","MATLAB simulation","Multi-hop networks","Underwater wireless optical networks"],"dc:title":["Investigations into the physical layer of underwater networks to enhance channel capacity and performance."],"dc:type":["Thesis"]},"updated_at":"2026-07-24T04:10:12Z"}