176 research outputs found
Cellular Automata Based Data Security Scheme in Computer Network using Single Electron Device
The internet conceptualized new ways of social interaction, activities globally. Internet serves billions of users worldwide. By the end of 2011it is expected that 22% of the world’s population will regularly surf internet. Beside this, internet incorporated high risks for e-users by enabling intruders to gain access via security holes. Network security is a course of action for assuring data from illicit accessing, exploitation, exposure, damage, alteration, or disorders related to the impulsive growth of popularity of e-users. Cellular Automata (CA) has been recommended in favor of the potential usage of data security. Single Electron devices (SED) have unanimously contributed in significant reduction of size of electronic devices and are now weighed up as the best substitute of future device family. Here we address a novel adaptive method to assimilate CA using SED in data security
New Classes of Binary Random Sequences for Cryptography
In the vision for the 5G wireless communications advancement that yield new security prerequisites and challenges we propose a catalog of three new classes of pseudorandom random sequence generators. This dissertation starts with a review on the requirements of 5G wireless networking systems and the most recent development of the wireless security services applied to 5G, such as private-keys generation, key protection, and flexible authentication. This dissertation proposes new complexity theory-based, number-theoretic approaches to generate lightweight pseudorandom sequences, which protect the private information using spread spectrum techniques. For the class of new pseudorandom sequences, we obtain the generalization. Authentication issues of communicating parties in the basic model of Piggy Bank cryptography is considered and a flexible authentication using a certified authority is proposed
Internet of Vehicles and Real-Time Optimization Algorithms: Concepts for Vehicle Networking in Smart Cities
Achieving sustainable freight transport and citizens’ mobility operations in modern cities are becoming critical issues for many governments. By analyzing big data streams generated through IoT devices, city planners now have the possibility to optimize traffic and mobility patterns. IoT combined with innovative transport concepts as well as emerging mobility modes (e.g., ridesharing and carsharing) constitute a new paradigm in sustainable and optimized traffic operations in smart cities. Still, these are highly dynamic scenarios, which are also subject to a high uncertainty degree. Hence, factors such as real-time optimization and re-optimization of routes, stochastic travel times, and evolving customers’ requirements and traffic status also have to be considered. This paper discusses the main challenges associated with Internet of Vehicles (IoV) and vehicle networking scenarios, identifies the underlying optimization problems that need to be solved in real time, and proposes an approach to combine the use of IoV with parallelization approaches. To this aim, agile optimization and distributed machine learning are envisaged as the best candidate algorithms to develop efficient transport and mobility systems
Motion Planning of UAV Swarm: Recent Challenges and Approaches
The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work
A Survey on UAV-enabled Edge Computing: Resource Management Perspective
Edge computing facilitates low-latency services at the network's edge by
distributing computation, communication, and storage resources within the
geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent
advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new
opportunities for edge computing in military operations, disaster response, or
remote areas where traditional terrestrial networks are limited or unavailable.
In such environments, UAVs can be deployed as aerial edge servers or relays to
facilitate edge computing services. This form of computing is also known as
UAV-enabled Edge Computing (UEC), which offers several unique benefits such as
mobility, line-of-sight, flexibility, computational capability, and
cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices
are typically very limited in the context of UEC. Efficient resource management
is, therefore, a critical research challenge in UEC. In this article, we
present a survey on the existing research in UEC from the resource management
perspective. We identify a conceptual architecture, different types of
collaborations, wireless communication models, research directions, key
techniques and performance indicators for resource management in UEC. We also
present a taxonomy of resource management in UEC. Finally, we identify and
discuss some open research challenges that can stimulate future research
directions for resource management in UEC.Comment: 36 pages, Accepted to ACM CSU
Mesoscale fluid simulation with the Lattice Boltzmann method
PhDThis thesis describes investigations of several complex fluid effects., including
hydrodynamic spinodal decomposition, viscous instability. and self-assembly of a
cubic surfactant phase, by simulating them with a lattice Boltzmann computational
model.
The introduction describes what is meant by the term "complex fluid", and why
such fluids are both important and difficult to understand. A key feature of complex
fluids is that their behaviour spans length and time scales. The lattice Boltzmann
method is presented as a modelling technique which sits at a "mesoscale" level
intermediate between coarse-grained and fine-grained detail, and which is therefore
ideal for modelling certain classes of complex fluids.
The following chapters describe simulations which have been performed using
this technique, in two and three dimensions. Chapter 2 presents an investigation
into the separation of a mixture of two fluids. This process is found to involve several
physical mechanisms at different stages. The simulated behaviour is found to be in
good agreement with existing theory, and a curious effect, due to multiple competing
mechanisms, is observed, in agreement with experiments and other simulations.
Chapter 3 describes an improvement to lattice Boltzmann models of Hele-Shaw
flow, along with simulations which quantitatively demonstrate improvements in both
accuracy and numerical stability. The Saffman-Taylor hydrodynamic instability is
demonstrated using this model.
Chapter 4 contains the details and results of the TeraGyroid experiment, which
involved extremely large-scale simulations to investigate the dynamical behaviour
of a self-assembling structure. The first finite- size-effect- free dynamical simulations
of such a system are presented. It is found that several different mechanisms are
responsible for the assembly; the existence of chiral domains is demonstrated, along
with an examination of domain growth during self-assembly.
Appendix A describes some aspects of the implementation of the lattice Boltzmann
codes used in this thesis; appendix B describes some of the Grid computing
techniques which were necessary for the simulations of chapter 4.
Chapter 5 summarises the work, and makes suggestions for further research and
improvement.Huntsman Corporation Queen Mary University Schlumberger Cambridge Researc
2018-2019 Undergraduate Catalog
https://digitalcommons.sacredheart.edu/g_cat/1059/thumbnail.jp
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