Optimizing Energy Efficiency in UAV-Based Wireless Communication Networks: A Comparative Analysis of TAODV and DSR Protocols using the Trust Score Algorithm for Signal Processing

This study presents a comprehensive analysis of energy efficiency optimization in signal processing algorithms for UAV-based wireless communication networks. Employing a multifaceted approach that integrates mathematical modeling, game theory analysis, and an array of testing methodologies, the research aims to address the critical challenge of enhancing communication protocol performance while minimizing energy consumption. Central to our investigation is the development and application of the Trust Score Algorithm (TSA), a novel quantitative tool designed to evaluate and compare the efficacy of various signal processing algorithms across multiple dimensions, including energy efficiency, reliability, adaptability, security, and latency. Through detailed comparative analysis and data visualization techniques, the study reveals that the Proposed_TAODV protocol significantly outperforms traditional TAODV and DSR protocols in several key metrics. These include throughput efficiency, end-to-end delay, and packet delivery ratio, particularly as the number of UAV nodes scales up. Such findings underscore the Proposed_TAODV protocol's superior stability and performance, advocating for its potential in improving the sustainability and effectiveness of UAV-based communication networks. The research methodology encompasses both theoretical and empirical testing phases, ranging from simulation-based analysis, to validate the performance of the signal processing algorithms under varied operational conditions. The results not only affirm the superior performance of the Proposed_TAODV protocol but also highlight the utility of the TSA in guiding the selection and optimization of signal processing algorithms for UAV networks

UAV-Empowered Disaster-Resilient Edge Architecture for Delay-Sensitive Communication

The fifth-generation (5G) communication systems will enable enhanced mobile broadband, ultra-reliable low latency, and massive connectivity services. The broadband and low-latency services are indispensable to public safety (PS) communication during natural or man-made disasters. Recently, the third generation partnership project long term evolution (3GPPLTE) has emerged as a promising candidate to enable broadband PS communications. In this article, first we present six major PS-LTE enabling services and the current status of PS-LTE in 3GPP releases. Then, we discuss the spectrum bands allocated for PS-LTE in major countries by international telecommunication union (ITU). Finally, we propose a disaster resilient three-layered architecture for PS-LTE (DR-PSLTE). This architecture consists of a software-defined network (SDN) layer to provide centralized control, an unmanned air vehicle (UAV) cloudlet layer to facilitate edge computing or to enable emergency communication link, and a radio access layer. The proposed architecture is flexible and combines the benefits of SDNs and edge computing to efficiently meet the delay requirements of various PS-LTE services. Numerical results verified that under the proposed DR-PSLTE architecture, delay is reduced by 20% as compared with the conventional centralized computing architecture.Comment: 9,

Enabling Communication Technologies for Automated Unmanned Vehicles in Industry 4.0

Within the context of Industry 4.0, mobile robot systems such as automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) are one of the major areas challenging current communication and localization technologies. Due to stringent requirements on latency and reliability, several of the existing solutions are not capable of meeting the performance required by industrial automation applications. Additionally, the disparity in types and applications of unmanned vehicle (UV) calls for more flexible communication technologies in order to address their specific requirements. In this paper, we propose several use cases for UVs within the context of Industry 4.0 and consider their respective requirements. We also identify wireless technologies that support the deployment of UVs as envisioned in Industry 4.0 scenarios.Comment: 7 pages, 1 figure, 1 tabl

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented