Abstract
Real-time energy consumption is a crucial consideration when assessing the effectiveness and efficiency of communication using energy hungry devices. Utilizing new technologies such as UAV-enabled wireless powered communication networks (WPCN) and 3D beamforming, and then a combination of static and dynamic optimization methodologies are combined to improve energy usage in water distribution systems (WDS).A proposed static optimization technique termed the Dome packing method and dynamic optimization methods such as extremum seeking are employed to generate optimum placement and trajectories of the UAV with respect to the ground nodes (GN) in a WDS.
In this thesis, a wireless communication network powered by a UAV serves as a hybrid access point to manage many GNs in WDS. The GNs are water quality sensors that collect radio frequency (RF) energy from the RF signals delivered by the UAV and utilise this energy to relay information via an uplink. Optimum strategies are demonstrated to efficiently handle this process as part of a zero-power system: removing the need for manual battery charging of devices, while at the same time optimizing energy and data transfer over WPCN.
Since static optimization does not account for the UAV's dynamics, dynamic optimization techniques are also necessary. By developing an efficient trajectory, the suggested technique also reduces the overall flying duration and, therefore, the UAV's energy consumption. This combination of techniques also drastically reduces the complexity and calculation overhead of purely high order static optimizations.
To test and validate the efficacy of the extremum seeking implementation, comparison with the optimal sliding mode technique is also undertaken. These approaches are applied to ten distinct case studies by randomly relocating the GNs to various positions. The findings from a random sample of four of these is presented, which reveal that the proposed strategy reduces the UAV's energy usage significantly by about 16 percent compared to existing methods.
The (hybrid) static and dynamic zero-power optimization strategies demonstrated here are readily extendable to the control of water quality and pollution in natural freshwater resources and this will be discussed at the end of this thesis.
| Date of Award | 21 Nov 2023 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Wenyan Wu (Director of Studies) & Waheb Abdullah (Second Supervisor 1) |