Mobile Cloud Computing or Fog computing refers to offloading computationally intensive algorithms from a mobile device to the cloud or an intermediate cloud in order to save resources e.g. time and energy in the mobile device. This paper proposes new solutions for situations when the cloud or fog is not available. First, the sensor network is modelled using a network of queues, then a linear programming technique is used to make scheduling decisions. Various centralised and distributed algorithms are then proposed, which improves overall system performance. Extensive simulations show slightly higher energy usage in comparision to the baseline non-offloading case, however, job completion rate is significantly improved, the efficiency score metric show the extra energy usage is justified. The algorithms have been simulated in various environments including high and low bandwidth, partial connectivity, and different rate of information exchanges to study the pros and cons of the proposed algorithms.
Advances in edge computing are powering the development and deployment of Internet of Things (IoT) systems to provide advanced services and resource efficiency. However, large‐scale IoT‐based load‐altering attacks (LAAs) can seriously impact power grid operations, such as destabilising the grid's control loops. Timely detection and identification of any compromised nodes are essential to minimise the adverse effects of these attacks on power grid operations. In this work, two data‐driven algorithms are proposed to detect and identify compromised nodes and the attack parameters of the LAAs. The first method, based on the Sparse Identification of Nonlinear Dynamics approach, adopts a sparse regression framework to identify attack parameters that best describe the observed dynamics. The second method, based on physics‐informed neural networks, employs neural networks to infer the attack parameters from the measurements. Both algorithms are presented utilising edge computing for deployment over decentralised architectures. Extensive simulations are performed on IEEE 6‐, 14‐, and 39‐bus systems to verify the effectiveness of the proposed methods. Numerical results confirm that the proposed algorithms outperform existing approaches, such as those based on unscented Kalman filter, support vector machines, and neural networks (NN), and effectively detect and identify locations of attack in a timely manner.
John Thompson for their excellent supervision throughout this project. I would like to say a big thank you for all the supervision and guidance. The meetings I had with you always had a calming effect during the years, when I am lost and panicking. To say I couldn't have completed this work without your exemplary supervision would be an understatement. Equally, special thanks go to Prof. Neil Robertson for lending his expertise in computer vision which is a substantial part of this thesis. Next, I would like to thank my friends and colleagues in both Digital Communication lab at the University of Edinburgh and the Vision lab in the Heriot-Watt University. Even though most of us work in different domains, I always enjoyed the company and all the fun we had during these years. I gratefully acknowledge the funding received towards my PhD studies from the School of Engineering and University Defence Research Consortium (UDRC).I would like to thank my family: my parents, and my brother and sister in law for supporting me spiritually throughout this project and my life in general. I feel fortunate to have such wonderful parents. Last but not least, I am very grateful to my wife Anita, for encouraging me to do a PhD in the first place and then continuously supporting me for the entire length of the PhD. I am genuinely thankful for having you by my side every day.
Power grid parameter estimation involves the estimation of unknown parameters, such as the inertia and damping coefficients, from the observed dynamics. In this work, we present physics-informed machine learning algorithms for the power system parameter estimation problem. First, we propose a novel algorithm to solve the parameter estimation based on the Sparse Identification of Nonlinear Dynamics (SINDy) approach, which uses sparse regression to infer the parameters that best describe the observed data. We then compare its performance against another benchmark algorithm, namely, the physics-informed neural networks (PINN) approach applied to parameter estimation. We perform extensive simulations on IEEE bus systems to examine the performance of the aforementioned algorithms. Our results show that the SINDy algorithm outperforms the PINN algorithm in estimating the power grid parameters over a wide range of system parameters (including high and low inertia systems) and power grid architectures. Particularly, in case of the slow dynamics system, the proposed SINDy algorithms outperforms the PINN algorithm, which struggles to accurately determine the parameters. Moreover, it is extremely efficient computationally and so takes significantly less time than the PINN algorithm, thus making it suitable for real-time parameter estimation. Furthermore, we present an extension of the SINDy algorithm to a scenario where the operator does not have the exact knowledge of the underlying system model. We also present a decentralised implementation of the SINDy algorithm which only requires limited information exchange between the neighbouring nodes of a power grid.
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