Machine Learning for Unmanned Aerial System (UAS) Networking

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

Self-organization and management of wireless sensor networks

Wireless sensor networks (WSNs) are a newly deployed networking technology consisting of multifunctional sensor nodes that are small in size and communicate over short distances. These sensor nodes are mainly in large numbers and are densely deployed either inside the phenomenon or very close to it. They can be used for various application areas (e.g. health, military, home). WSNs provide several advantages over traditional networks, such as large-scale deployment, highresolution sensed data, and application adaptive mechanisms. However, due to their unique characteristics (having dynamic topology, ad-hoc and unattended deployment, huge amount of data generation and traffic flow, limited bandwidth and energy), WSNs pose considerable challenges for network management and make application development nontrivial. Management of wireless sensor networks is extremely important in order to keep the whole network and application work properly and continuously. Despite the importance of sensor network management, there is no generalize solution available for managing and controlling these resource constrained WSNs. In network management of WSNs, energy-efficient network selforganization is one of the main challenging issues. Self-organization is the property which the sensor nodes must have to organize themselves to form the network. Selforganization of WSNs is challenging because of the tight constraints on the bandwidth and energy resources available in these networks. A self organized sensor network can be clustered or grouped into an easily manageable network. However, existing clustering schemes offer various limitations. For example, existing clustering schemes consume too much energy in cluster formation and re-formation. This thesis presents a novel cellular self-organizing hierarchical architecture for wireless sensor networks. The cellular architecture extends the network life time by efficiently utilizing nodes energy and support the scalability of the system. We have analyzed the performance of the architecture analytically and by simulations. The results obtained from simulation have shown that our cellular architecture is more energy efficient and achieves better energy consumption distribution. The cellular architecture is then mapped into a management framework to support the network management system for resource constraints WSNs. The management framework is self-managing and robust to changes in the network. It is application-co-operative and optimizes itself to support the unique requirements of each application. The management framework consists of three core functional areas i.e., configuration management, fault management, and mobility management. For configuration management, we have developed a re-configuration algorithm to support sensor networks to energy-efficiently re-form the network topology due to network dynamics i.e. node dying, node power on and off, new node joining the network and cells merging. In the area of fault management we have developed a new fault management mechanism to detect failing nodes and recover the connectivity in WSNs. For mobility management, we have developed a two phase sensor relocation solution: redundant mobile sensors are first identified and then relocated to the target location to deal with coverage holes. All the three functional areas have been evaluated and compared against existing solutions. Evaluation results show a significant improvement in terms of re-configuration, failure detection and recovery, and sensors relocation

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science

Purposeful Deployment via Self-Organizing Flocking Coalition in Sensor Networks

This paper presents a deployment strategy via self organizing flocking coalitions for mobile sensor network coverage. The concepts of our approach are inspired by the flocking phenomenon which locally controls a group of mobile agents performed as follows: (i) global alignment of their velocity vectors, (ii) convergence of their velocities to a common one, and (iii) collision avoidance. Adding mobility of local control to sensor network significantly increase the capability of the sensor network by making it resilient to failures and responding to events. The controllable mobility in sensor network hence is used to help achieve the network’s missions. That is, mobility may be “purposeful ” instead of being treated as an uncontrollable external stimulus to which the ad-hoc networks must respond. To make use of the purposeful deployment, we implemented and evaluated a fire tracking application to determine how well sensor network deployment achieves its goal. Fire is modeled by agents that gradually spread throughout the specific environment. We then introduce a simulating model for the movement of autonomous fire tracker agents that exhibits flocking behavior and forms a mobile sensor network collectively. Fire tracker agents are used to form a perimeter around the fire agents to extinguish fire agents. Finally, we demonstrate that a group of fire tracker agents executing the leaderless flocking models with emulated sensor network can successfully flock (even in the presence of individual fire tracker agents failure) and that systematic characterization of sensor network flocking parameters is achievable

Strategic Latency Unleashed: The Role of Technology in a Revisionist Global Order and the Implications for Special Operations Forces

The article of record may be found at https://cgsr.llnl.govThis work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-59693This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-5969

Eye on Collaborative Creativity : Insights From Multiple-Person Mobile Gaze Tracking in the Context of Collaborative Design

Early Career WorkshopNon peer reviewe