Uav-assisted data collection in wireless sensor networks: A comprehensive survey

Wireless sensor networks (WSNs) are usually deployed to different areas of interest to sense phenomena, process sensed data, and take actions accordingly. The networks are integrated with many advanced technologies to be able to fulfill their tasks that is becoming more and more complicated. These networks tend to connect to multimedia networks and to process huge data over long distances. Due to the limited resources of static sensor nodes, WSNs need to cooperate with mobile robots such as unmanned ground vehicles (UGVs), or unmanned aerial vehicles (UAVs) in their developments. The mobile devices show their maneuverability, computational and energystorage abilities to support WSNs in multimedia networks. This paper addresses a comprehensive survey of almost scenarios utilizing UAVs and UGVs with strogly emphasising on UAVs for data collection in WSNs. Either UGVs or UAVs can collect data from static sensor nodes in the monitoring fields. UAVs can either work alone to collect data or can cooperate with other UAVs to increase their coverage in their working fields. Different techniques to support the UAVs are addressed in this survey. Communication links, control algorithms, network structures and different mechanisms are provided and compared. Energy consumption or transportation cost for such scenarios are considered. Opening issues and challenges are provided and suggested for the future developments

Artificial Intelligence in Civil Infrastructure Health Monitoring—historical Perspectives, Current Trends, and Future Visions

Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the major technology progression in this research field is provided in a chronological order. Detailed applications, key contributions, and performance measures of each milestone publication are presented. Representative technologies are detailed to demonstrate current research trends. A road map for future research is outlined to address contemporary issues such as explainable and physics-informed AI. This paper will provide readers with a lucid memoir of the historical progress, a good sense of the current trends, and a clear vision for future research

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

UAV-Enabled Surface and Subsurface Characterization for Post-Earthquake Geotechnical Reconnaissance

Major earthquakes continue to cause significant damage to infrastructure systems and the loss of life (e.g. 2016 Kaikoura, New Zealand; 2016 Muisne, Ecuador; 2015 Gorkha, Nepal). Following an earthquake, costly human-led reconnaissance studies are conducted to document structural or geotechnical damage and to collect perishable field data. Such efforts are faced with many daunting challenges including safety, resource limitations, and inaccessibility of sites. Unmanned Aerial Vehicles (UAV) represent a transformative tool for mitigating the effects of these challenges and generating spatially distributed and overall higher quality data compared to current manual approaches. UAVs enable multi-sensor data collection and offer a computational decision-making platform that could significantly influence post-earthquake reconnaissance approaches. As demonstrated in this research, UAVs can be used to document earthquake-affected geosystems by creating 3D geometric models of target sites, generate 2D and 3D imagery outputs to perform geomechanical assessments of exposed rock masses, and characterize subsurface field conditions using techniques such as in situ seismic surface wave testing. UAV-camera systems were used to collect images of geotechnical sites to model their 3D geometry using Structure-from-Motion (SfM). Key examples of lessons learned from applying UAV-based SfM to reconnaissance of earthquake-affected sites are presented. The results of 3D modeling and the input imagery were used to assess the mechanical properties of landslides and rock masses. An automatic and semi-automatic 2D fracture detection method was developed and integrated with a 3D, SfM, imaging framework. A UAV was then integrated with seismic surface wave testing to estimate the shear wave velocity of the subsurface materials, which is a critical input parameter in seismic response of geosystems. The UAV was outfitted with a payload release system to autonomously deliver an impulsive seismic source to the ground surface for multichannel analysis of surface waves (MASW) tests. The UAV was found to offer a mobile but higher-energy source than conventional seismic surface wave techniques and is the foundational component for developing the framework for fully-autonomous in situ shear wave velocity profiling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145793/1/wwgreen_1.pd

The University Defence Research Collaboration In Signal Processing: 2013-2018

Signal processing is an enabling technology crucial to all areas of defence and security. It is called for whenever humans and autonomous systems are required to interpret data (i.e. the signal) output from sensors. This leads to the production of the intelligence on which military outcomes depend. Signal processing should be timely, accurate and suited to the decisions to be made. When performed well it is critical, battle-winning and probably the most important weapon which you’ve never heard of. With the plethora of sensors and data sources that are emerging in the future network-enabled battlespace, sensing is becoming ubiquitous. This makes signal processing more complicated but also brings great opportunities. The second phase of the University Defence Research Collaboration in Signal Processing was set up to meet these complex problems head-on while taking advantage of the opportunities. Its unique structure combines two multi-disciplinary academic consortia, in which many researchers can approach different aspects of a problem, with baked-in industrial collaboration enabling early commercial exploitation. This phase of the UDRC will have been running for 5 years by the time it completes in March 2018, with remarkable results. This book aims to present those accomplishments and advances in a style accessible to stakeholders, collaborators and exploiters

Air Force Institute of Technology Research Report 2017

This Research Report presents the FY18 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs)

Throughput Maximization in Unmanned Aerial Vehicle Networks

The use of Unmanned Aerial Vehicles (UAVs) swarms in civilian applications such as surveillance, agriculture, search and rescue, and border patrol is becoming popular. UAVs have also found use as mobile or portable base stations. In these applications, communication requirements for UAVs are generally stricter as compared to conventional aircrafts. Hence, there needs to be an efficient Medium Access Control (MAC) protocol that ensures UAVs experience low channel access delays and high throughput. Some challenges when designing UAVs MAC protocols include interference and rapidly changing channel states, which require a UAV to adapt its data rate to ensure data transmission success. Other challenges include Quality of Service (QoS) requirements and multiple contending UAVs that result in collisions and channel access delays. To this end, this thesis aims to utilize Multi-Packet Reception (MPR) technology. In particular, it considers nodes that are equipped with a Successive Interference Cancellation (SIC) radio, and thereby, allowing them to receive multiple transmissions simultaneously. A key problem is to identify a suitable a Time Division Multiple Access (TDMA) transmission schedule that allows UAVs to transmit successfully and frequently. Moreover, in order for SIC to operate, there must be a sufficient difference in received power. However, in practice, due to the location and orientation of nodes, the received power of simultaneously transmitting nodes may cause SIC decoding to fail at a receiver. Consequently, a key problem concerns the placement and orientation of UAVs to ensure there is diversity in received signal strength at a receiving node. Lastly, interference between UAVs serving as base station is a critical issue. In particular, their respective location may have excessive interference or cause interference to other UAVs; all of which have an impact on the schedule used by these UAVs to serve their respective users

Mechanics of axonal force generation in embryonic Drosophila neurons in vivo

Neurons are excitable cell types that process and convey information by producing electrochemical signals. Neurons are known to be the core functional components of the brain and spinal cords in all animals. They communicate with other neuron or muscle junctions at specific points by releasing neurotransmitter at their synapses. In recent years it has become increasingly evident that mechanical stimuli play an important role in the differentiation, growth, development, and motility of cells. Neurons in particular have been shown to be highly sensitive to a variety of mechanical inputs. For example, it has been shown that neurites undergo normal elongation when towed with an appropriately paced motor. Other evidences demonstrated that axonal elongation (up to several centimeters) can be induced by mechanical tension, and these axons retain their electrophysiological functions. More interestingly, recent experiments have provided new evidence of the role of mechanical forces in the functioning of neurons in vivo. These experiments have revealed that vesicle clustering in the presynaptic terminal of the neuromuscular junction in Drosophila embryos is dependent on mechanical tension in the axons. Vesicle clustering disappears with loss of mechanical tension and is regained upon restoration of tension. In addition, an increase in tension appears to increase the vesicle density at the synapse, suggesting that mechanical tension could be a signal to modulate synaptic plasticity in vivo. Based on these in vitro and in vivo observations, it is hypothesized that if mechanical tension modulates synaptic plasticity, neurons are expected to respond to stimuli that alter the tension in their axons. To verify whether this is the case, the mechanical behavior of axons in live Drosophila embryos was examined. In this dissertation I addressed these questions: 1) Do Drosophila axons have a rest tension, and, if so, what is its magnitude? 2) Do Drosophila neurons actively regulate their tension when subjected to mechanical perturbation? 3) How do axons respond upon sustained loss of tension? And finally 4) what is the origin/ mechanism of force generation in axons at cytoskeletal and molecular level? Our experiments showed that Drosophila neurons maintained a rest tension (1–13 nN) and behaved like viscoelastic solids in response to sustained stretching. More importantly, when the tension was suddenly diminished by a release of the externally applied force, the neurons contracted and actively generated force to restore tension, sometimes to a value close to their rest tension. In other set of experiments, mechanical tension in axonal shaft was removed by slackening the axons: bringing the neuro muscular junction (NMJ) towards the central nervous system (CNS) multiple times. It was observed that, in the absence of any pharmaceutical drug, axons always shortened and restored the straight configuration each time within 2-4 minutes. The total shortening was about 40% of the original length. This recovery however was significantly hampered with the depletion of ATP, inhibition of myosin motors, and disruption of actin filaments, but not with the disruption of microtubules. These results suggest that the actomyosin-machinery is the major active element in axonal contraction while microtubules contribute passively and minimally