Climate-Resilient UAVs: Enhancing Energy-Efficient B5G Communication in Harsh Environments

This paper explores the crucial role of Unmanned Aerial Vehicles (UAVs) in advancing Beyond Fifth Generation (B5G) communication networks, especially in adverse weather conditions like rain, fog, and snow. The study investigates the synergy between climate-resilient UAVs and energy-efficient B5G communication. Key findings include the impact of weather elements on UAV coverage and communication dynamics. The research demonstrates significant enhancements in energy efficiency, reduced interference, increased data transmission rates, and optimal channel gain under various weather conditions. Overall, this paper emphasizes the potential of climate-resilient UAVs to improve energy-efficient B5G communication and highlights technology's role in mitigating climate change's impact on communication systems, promoting sustainability and resilience

An overview of robotics and autonomous systems for harsh environments

Across a wide range of industries and applications, robotics and autonomous systems can fulfil the crucial and challenging tasks such as inspection, exploration, monitoring, drilling, sampling and mapping in areas of scientific discovery, disaster prevention, human rescue and infrastructure management, etc. However, in many situations, the associated environment is either too dangerous or inaccessible to humans. Hence, a wide range of robots have been developed and deployed to replace or aid humans in these activities. A look at these harsh environment applications of robotics demonstrate the diversity of technologies developed. This paper reviews some key application areas of robotics that involve interactions with harsh environments (such as search and rescue, space exploration, and deep-sea operations), gives an overview of the developed technologies and provides a discussion of the key trends and future directions common to many of these areas

Remotely piloted aircraft systems and a wireless sensors network for radiological accidents

In critical radiological situations, the real time information that we could get from the disaster area becomes of great importance. However, communication systems could be affected after a radiological accident. The proposed network in this research consists of distributed sensors in charge of collecting radiological data and ground vehicles that are sent to the nuclear plant at the moment of the accident to sense environmental and radiological information. Afterwards, data would be analyzed in the control center. Collected data by sensors and ground vehicles would be delivered to a control center using Remotely Piloted Aircraft Systems (RPAS) as a message carrier. We analyze the pairwise contacts, as well as visiting times, data collection, capacity of the links, size of the transmission window of the sensors, and so forth. All this calculus was made analytically and compared via network simulations.Peer ReviewedPostprint (published version

Porting Computer Vision Models to the Edge for Smart City Applications: Enabling Autonomous Vision-Based Power Line Inspection at the Smart Grid Edge for Unmanned Aerial Vehicles (UAVs)

Smart grid infrastructure must be monitored and inspected - especially when subject to harsh operating conditions in extreme, remote environments such as the highlands of Iceland. Current methods for monitoring such critical infrastructure includes manual inspection, static video analysis (where connectivity is available) and unmanned aerial vehicle (UAV) inspection. UAVs offer certain inspection efficiencies; however, challenges persist given the time and UAV operator skill required. Collaborating with Landsnet, the Icelandic smart grid operator, we apply convolutional neural networks for image processing to detect smart grid transmission infrastructure and modify the resulting computer vision (CV) model to function on the edge of a UAV. In doing so, we overcome significant edge processing barriers. Our real-time CV model delivers decision insight on the UAV edge and enables autonomous flight path planning for use in smart grid inspection. Our approach is transferable to other smart city applications that could benefit from edge-based monitoring and inspection

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements

Evaluation of the utility and performance of an autonomous surface vehicle for mobile monitoring of waterborne biochemical agents

Real-time water quality monitoring is crucial due to land utilization increases which can negatively impact aquatic ecosystems from surface water runoff. Conventional monitoring methodologies are laborious, expensive, and spatio-temporally limited. Autonomous surface vehicles (ASVs), equipped with sensors/instrumentation, serve as mobile sampling stations that reduce labor and enhance data resolution. However, ASV autopilot navigational accuracy is affected by environmental forces (wind, current, and waves) that can alter trajectories of planned paths and negatively affect spatio-temporal resolution of water quality data. This study demonstrated a commercially available solar powered ASV equipped with a multi-sensor payload ability to operate autonomously to accurately and repeatedly maintain established A-B line transects under varying environmental conditions, where lateral deviation from a planned linear route was measured and expressed as cross-track error (XTE). This work provides a framework for development of spatial/temporal resolution limitations of ASVs for real-time monitoring campaigns and future development of in-situ sampling technologies

Harnessing single board computers for military data analytics

Executive summary: This chapter covers the use of Single Board Computers (SBCs) to expedite onsite data analytics for a variety of military applications. Onsite data summarization and analytics is increasingly critical for command, control, and intelligence (C2I) operations, as excessive power consumption and communication latency can restrict the efficacy of down-range operations. SBCs offer power-efficient, inexpensive data-processing capabilities while maintaining a small form factor. We discuss the use of SBCs in a variety of domains, including wireless sensor networks, unmanned vehicles, and cluster computing. We conclude with a discussion of existing challenges and opportunities for future use.https://digitalcommons.usmalibrary.org/books/1010/thumbnail.jp