Energy Optimization of Smart Water Systems using UAV Enabled Zero-Power Wireless Communication Networks

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 GNsin 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

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the recent technological advancements and manufacturing cost reduction opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting their relevance to 5G networks and essentially, how it will play a pivotal role in the development of 6G ecosystem. We discuss important features of NTNs integration into TNs and the synergies by delving into the new range of services and use cases, various architectures, technological enablers, and higher layer aspects pertinent to NTNs integration. Moreover, we review the corresponding challenges arising from the technical peculiarities and the new approaches being adopted to develop efficient integrated ground-air-space (GAS) networks. Our survey further includes the major progress and outcomes from academic research as well as industrial efforts representing the main industrial trends, field trials, and prototyping towards the 6G networks

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the recent technological advancements and manufacturing cost reduction opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting their relevance to 5G networks and essentially, how it will play a pivotal role in the development of 6G ecosystem. We discuss important features of NTNs integration into TNs and the synergies by delving into the new range of services and use cases, various architectures, technological enablers, and higher layer aspects pertinent to NTNs integration. Moreover, we review the corresponding challenges arising from the technical peculiarities and the new approaches being adopted to develop efficient integrated ground-air-space (GAS) networks. Our survey further includes the major progress and outcomes from academic research as well as industrial efforts representing the main industrial trends, field trials, and prototyping towards the 6G networks

IoT Applications Computing

The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing

Resource Allocation in Drone-Assisted Emergency Communication Systems

Due to low cost and high mobility, drones are considered important in emergency communications. In this thesis, we consider a unique drone assisted emergency communication system used in disaster scenarios, where the drone with limited power acts as a relay to improve the downlink sum rate through rational resource allocation. The wireless channel model between drones and ground users in emergency communications is different from conventional relay networks, while drones have their coverage area and data rate limits. Considering these specific characteristics, we formulate a joint power and subcarrier allocation problem to maximize data rate of users, which is limited by the transmit power budget per drone and the number of users on each subcarrier in emergency communications. However, resource allocation in a unique drone assisted emergency communication system is a nondeterministic polynomial time (NP)-hard problem requiring brute force search, which has prohibitive computational complexity. Instead, efficient algorithms that provide a good trade-off between system performance and implementation practicality are needed. The contributions of this thesis are proposing two different resource allocation schemes. Both schemes divide users into high-priority(HP) users and low-priority(LP) users and both guarantee minimum guaranteed rate for HP users. The first scheme is an adaptive algorithm with low complexity. In this scheme, a suboptimal solution is proposed by dividing users into two priority groups: HP users (rescuers) and LP users (affected people). This procedure achieves quasi-linear complexity in terms of the number of users. Finally, the data of the brute force search method and this method were collected through simulation experiments. The data shows that the data rate of the proposed scheme was very close to the optimal data rate when there was a lack of resources. The second scheme is an adaptive algorithm. In the proposed scheme, we formulate a joint power and subcarrier allocation problem to maximize data rate of users, which is limited by the transmit power budget per drone and the number of users on each subcarrier in emergency communications. Due to the intractability of the formulated problem, it is decomposed into two sub-problems: power allocation optimisation and subcarrier allocation optimization. Then a joint resource allocation algorithm is proposed. The simulation results show that the performance of the proposed method is close to that of the optimal solution but with much lower complexity

Energy Efficiency of Ultra-Dense Small Cell Radio Access Networks for 5G and Beyond

Small cell base station (BS) densification in the radio access network (RAN) is an effective solution to improve the RAN capacity. However, small cell BS densification by adding more non-zero energy-consuming BSs increases energy consumption, compromising energy efficiency, which can be mitigated by adopting sleep mode. A comprehensive evaluation framework is applied in this research to analyse the capacity, energy consumption, and energy efficiency performance of the ultra-dense small cell RANs as a complete energy efficiency assessment, which is lacking in the literature. The impact of advanced techniques millimetre wave (mmWave), antenna array beamforming, and integrated access and backhaul (IAB) on RAN energy efficiency are also investigated. MATLAB- based simulation results show that the ultra-dense small cell RANs, where the number of BSs greatly exceeds the number of active user equipment (UEs), can only be energy efficient if all the empty cells without UE association are turned off completely. Energy efficiency enhancement comes from capacity improvement and energy consumption constraint. Specifically, the ultra-dense small cell RANs can achieve maximum performance improvement of 7.56-fold and 2.35-fold regarding capacity, 3780.11-fold and 32.38-fold regarding energy consumption using the current power model, and 28591.53-fold and 75.97-fold regarding energy efficiency in homogeneous and heterogeneous infrastructures, respectively, comparing the cases with and without the sleep mode. In addition, mmWave and IAB trade energy consumption and energy efficiency for capacity improvement and backhaul cost reduction. With mmWave and IAB, dense small cell RAN can achieve a maximum of 2.55-fold and 1.70-fold for capacity improvement, 2.46-fold and 2.89-fold for energy consumption reduction using the current power model, and 6.27-fold and 8.34-fold energy efficiency enhancement for UE densities of 900 and 300 UEs/km2, respectively, comparing the cases with and without the sleep mode

Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read