Abstract
Edge computing applications leverage advances in edge computing along with the latest trends of convolutional neural networks in order to achieve ultra-low latency, high-speed processing, low-power consumptions scenarios, which are necessary for deploying real-time Internet of Things deployments efficiently. As the importance of such scenarios is growing by the day, we propose to undertake two different kind of models, such as an algebraic models, with a process algebra called ACP and a coding model with a modeling language called Promela. Both approaches have been used to build models considering an edge infrastructure with a cloud backup, which has been further extended with the addition of extra fog nodes, and after having applied the proper verification techniques, they have all been duly verified. Specifically, a generic edge computing design has been specified in an algebraic manner with ACP, being followed by its corresponding algebraic verification, whereas it has also been specified by means of Promela code, which has been verified by means of the model checker Spin.
Highlights
Edge computing applications leverage advances in edge computing along with the latest trends of convolutional neural networks in order to achieve ultra-low latency, high-speed processing, low-power consumptions scenarios, which are necessary for deploying real-time Internet of Things deployments efficiently
May be further classified as shallow if there is only one hidden layer, or deep if there are more than one, which are referred to as deep neural networks (DNN). This three-layer distribution leads to artificial neural networks (ANN) being known as feed-forward neural networks because of the direction of processing, which provokes challenges in some scenarios, such as capturing sequential information or solving image classification [25]. The former is fixed by recurrent neural networks (RNN), even though spatial relationships are better handled by convolutional neural networks (CNN)
Regarding Algebra of Communicating Processes (ACP) [144], the models proposed will be exhibited by means of algebraic expressions to portray the behavior of the concurrent communicating processes involved, containing the specifications and verifications, whilst respecting Spin [145], the models presented will be exposed by means of Promela code [146], including the verification by means of the Spin model checker, along with some message sequence charts (MSCs) describing the message exchanges performed by communicating concurrent processes involved in a visual way
Summary
Regarding AI, it may be considered as machine intelligence, as opposed to human intelligence [20]. ML functionality is two-fold, such as training for a task, and in turn, running that task, where the former is defined by the quick application of knowledge and training through huge data sets, whilst the latter is done by executing pattern recognition and predicting future patterns In this sense, deep learning (DL) may be deemed as a subset of ML where the techniques being used are organized into neural networks so as to simulate the process of decision-making in humans, requiring a massive number of parameters [22]. This three-layer distribution leads to ANN being known as feed-forward neural networks because of the direction of processing, which provokes challenges in some scenarios, such as capturing sequential information or solving image classification [25] The former is fixed by recurrent neural networks (RNN), even though spatial relationships are better handled by convolutional neural networks (CNN). Some enhancements of CNN have been proposed for specific duties in recent times, to obtain greater accuracy in predicting visual recognition in data science, such as subpixel displacement measures [36], defect identification in high-speed trains [37], correlating image-like data out of quantum systems [38], modeling wind field downscaling [39], designing a zero knowledge proof scheme [40], classifying satellite image time series [41], working with ensembles [42], dealing with osteoporosis diagnoses [43], screening and staging diabetic retinopathy [44], analyzing cloud particles [45], inspecting diffraction data [46], or examining x-ray images [47]
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