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The objective of this project is to investigate the use of unmanned air vehicles (UAVs) as mobile, adaptive communications backbones for ground-based sensor networks. In this type of network, the UAVs provide communication connectivity to sensors that cannot communicate with each other because of terrain, distance, or other geographical constraints. In these situations, UAVs provide a vertical communication path for the sensors, thereby mitigating geographic obstacles often imposed on networks. With the proper use of UAVs, connectivity to a widely disbursed sensor network in rugged terrain is readily achieved. Our investigation has focused on networks where multiple cooperating UAVs are used to form a network backbone. The advantage of using multiple UAVs to form the network backbone is parallelization of sensor connectivity. Many widely spaced or isolated sensors can be connected to the network at once using this approach. In these networks, the UAVs logically partition the sensor network into sub-networks (subnets), with one UAV assigned per subnet. Partitioning the network into subnets allows the UAVs to service sensors in parallel thereby decreasing the sensor-to-network connectivity. A UAV services sensors in its subnet by flying a route (path) through the subnet, uplinking data collected by the sensors, and forwarding the data to a ground station. An additional advantage of using multiple UAVs in the network is that they provide redundancy in the communications backbone, so that the failure of a single UAV does not necessarily imply the loss of the network.
During the last one and a half decades, wireless sensor networks have witnessed significant growth and tremendous development in both academia and industry. A large number of researchers, including computer scientists and engineers, have been interested in solving challenging problems that span all the layers of the protocol stack of sensor networking systems. Several venues, such as journals, conferences, and workshops, have been launched to cover innovative research and practice in this promising and rapidly advancing field. Because of these trends, I thought it would be beneficial to provide our sensor networks community with a comprehensive reference on as much of the findings as possible on a variety of topics in wireless sensor networks. As this area of research is in continuous progress, it does not seem to be a reasonable solution to keep delaying the publication of such reference any more. This book relates to the second volume and focuses on the advanced topics and applications of wireless sensor networks. Our rationale is that the second volume has all application-specific and non-conventional sensor networks, emerging techniques and advanced topics that are not as matured as what is covered in the first volume. Thus, the second volume deals with three-dimensional, underground, underwater, body-mounted, and societal networks. Following Donald E. Knuth’s above-quoted elegant strategy to focus on several important fields (The Art of Computer Programming: Fundamental Algorithms, 1997), all the book chapters in this volume include up-to-date research work spanning various topics, such as stochastic modeling, barrier and spatiotemporal coverage, tracking, estimation, counting, coverage and localization in three-dimensional sensor networks, topology control and routing in three-dimensional sensor networks, underground and underwater sensor networks, multimedia and body sensor networks, and social sensing. Most of these major topics can be covered in an advanced course on wireless sensor networks. This book will be an excellent source of information for graduate students majoring in computer science, computer engineering, electrical engineering, or any related discipline. Furthermore, computer scientists, researchers, and practitioners in both academia and industry will find this book useful and interesting.
Wireless Communication Networks Supported by Autonomous UAVs and Mobile Ground Robots covers wireless sensor networks and cellular networks. For wireless sensor networks, the book presents approaches using mobile robots or UAVs to collect sensory data from sensor nodes. For cellular networks, it discusses the approaches to using UAVs to work as aerial base stations to serve cellular users. In addition, the book covers the challenges involved in these two networks, existing approaches (e.g., how to use the public transportation vehicles to play the role of mobile sinks to collect sensory data from sensor nodes), and potential methods to address open questions. Gives a comprehensive understanding of the development of mobile robot-supported wireless communication approaches Provides the latest approaches of mobile robot-supported wireless communication, including scheduling approaches with multiple robots and the online and reactive navigation algorithm Covers interesting research scenarios that include the system model, problem statement, solution and results so that readers will be able to design their own system Presents unresolved research issues and future research directions
Relying on unmanned autonomous flight control programs, unmanned aerial vehicles (UAVs) equipped with radio communication devices have been actively developed around the world. Given their low cost, flexible maneuvering and unmanned operation, UAVs have been widely used in both civilian operations and military missions, including environmental monitoring, emergency communications, express distribution, even military surveillance and attacks, for example. Given that a range of standards and protocols used in terrestrial wireless networks are not applicable to UAV networks, and that some practical constraints such as battery power and no-fly zone hinder the maneuverability capability of a single UAV, we need to explore advanced communication and networking theories and methods for the sake of supporting future ultra-reliable and low-latency applications. Typically, the full potential of UAV network’s functionalities can be tapped with the aid of the cooperation of multiple drones relying on their ad hoc networking, in-network communications and coordinated control. Furthermore, some swarm intelligence models and algorithms conceived for dynamic negotiation, path programming, formation flight and task assignment of multiple cooperative drones are also beneficial in terms of extending UAV’s functionalities and coverage, as well as of increasing their efficiency. We call the networking and cooperation of multiple drones as the terminology ‘flying ad hoc network (FANET)’, and there indeed are numerous new challenges to be overcome before the idespread of so-called heterogeneous FANETs. In this book, we examine a range of technical issues in FANETs, from physical-layer channel modeling to MAC-layer resource allocation, while also introducing readers to UAV aided mobile edge computing techniques.
The first book to focus on the communications and networking aspects of UAVs, this unique resource provides the fundamental knowledge needed to pursue research in the field. The team of authors covers the foundational concepts of the topic, as well as offering a detailed insight into the state of the art in UAVs and UAV networks, discussing the regulations, policies, and procedures for deployment (including analysis of risks and rewards), along with demonstrations, test-beds, and practical real-world applications in areas such as wildlife detection and emergency communications. This is essential reading for graduate students, researchers, and professionals in communications and networking.
This book discusses how to plan the time-variant placements of the UAVs served as base station (BS)/relay, which is very challenging due to the complicated 3D propagation environments, as well as many other practical constraints such as power and flying speed. Spectrum sharing with existing cellular networks is also investigated in this book. The emerging unmanned aerial vehicles (UAVs) have been playing an increasing role in the military, public, and civil applications. To seamlessly integrate UAVs into future cellular networks, this book will cover two main scenarios of UAV applications as follows. The first type of applications can be referred to as UAV Assisted Cellular Communications. Second type of application is to exploit UAVs for sensing purposes, such as smart agriculture, security monitoring, and traffic surveillance. Due to the limited computation capability of UAVs, the real-time sensory data needs to be transmitted to the BS for real-time data processing. The cellular networks are necessarily committed to support the data transmission for UAVs, which the authors refer to as Cellular assisted UAV Sensing. To support real-time sensing streaming, the authors design joint sensing and communication protocols, develop novel beamforming and estimation algorithms, and study efficient distributed resource optimization methods. This book targets signal processing engineers, computer and information scientists, applied mathematicians and statisticians, as well as systems engineers to carve out the role that analytical and experimental engineering has to play in UAV research and development. Undergraduate students, industry managers, government research agency workers and general readers interested in the fields of communications and networks will also want to read this book.
This thesis extends previously developed self-tuning adaptive control algorithms to be applied to a scenario where multiple vehicles autonomously form a communication chain which maximizes the bandwidth of a wireless sensor network. In the simulated scenario, multiple unmanned aerial vehicles are guided to positions that optimize communication links between multiple ground antennas. Guidance is provided by a self-tuning extremum controller, which uses adaptive techniques to autonomously guide a vehicle to the optimal location with respect to a cost function in an uncertain and noisy environment. In the case of high-bandwidth communication, this optimal location is the point where signal-to-noise ratio is maximized between two antennas. Using UAVs as relay nodes, an optimized communication chain allows for greater communication range and bandwidth across a network. Control system models are developed and tested using computer and hardware-in-the-loop simulations, which will be validated with a flight test at a future date.
Active technological development has fuelled rapid growth in the number of Unmanned Aerial Vehicle (UAV) platforms being deployed around the globe. Novel UAV platforms, UAV-based sensors, robotic sensing and imaging techniques, the development of processing workflows, as well as the capacity of ultra-high temporal and spatial resolution data, provide both opportunities and challenges that will allow engineers and scientists to address novel and important scientific questions in UAV and sensor design, remote sensing and environmental monitoring.This work features papers on UAV sensor design, improvements in UAV sensor technology, obstacle detection, methods for measuring optical flow, target tracking, gimbal influence on the stability of UAV images, augmented reality tools, segmentation in digital surface models for 3D reconstruction, detection, location and grasping objects, multi-target localization, vision-based tracking in cooperative multi-UAV systems, noise suppression techniques , rectification for oblique images, two-UAV communication system, fuzzy-based hybrid control algorithms, pedestrian detection and tracking as well as a range of atmospheric, geological, , agricultural, ecological, reef, wildlife, building and construction, coastal area coverage, search and rescue (SAR), water plume temperature measurements, aeromagnetic and archaeological surveys applications.
Nanotechnology-Based Smart Remote Sensing Networks for Disaster Prevention outlines how nanotechnology and space technology could be applied for the detection of disaster risks in early stages, using cheap sensors, cheap constellations of low Earth orbit (LEO) satellites, and smart wireless networks with artificial intelligence (AI) tools. Nanomaterial-based sensors (nanosensors) can offer several advantages over their micro-counterparts, such as lower power or self-powered consumption, high sensitivity, lower concentration of analytes, and smaller interaction distances between the object and the sensor. Besides this, with the support of AI tools, such as fuzzy logic, genetic algorithms, neural networks, and ambient intelligence, sensor systems are becoming smarter when a large number of sensors are used. This book is an important reference source for materials scientists, engineers, and environmental scientists who are seeking to understand how nanotechnology-based solutions can help mitigate natural disasters. Shows how nanotechnology-based solutions can be combined with space technology to provide more effective disaster management solutions Explores the best materials for manufacturing different types of nanotechnology-based remote sensing devices Assesses the challenges of creating a nanotechnology-based disaster mitigation system in a cost-effective way