Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make use of machine learning algorithms to learn from vehicle interactions with fog nodes. Our approach uses a three-layer feed-forward neural network to predict the correct fog node at a given location and time with 99.2 % accuracy on a test set. We also implement a dual stacked recurrent neural network (RNN) with long shortterm memory (LSTM) cells capable of learning the latency, or cost, associated with these service requests. We create a simulation in JAMScript using a dataset of real-world vehicle movements to create a dataset to train these networks. We further propose the use of this predictive system in a smarter request routing mechanism to minimize the service interruption during handovers between fog nodes and to anticipate areas of low coverage through a series of experiments and test the models' performance on a test set.
Autonomous driving is expected to provide a range of far-reaching economic, environmental and safety benefits. In this study, we propose a fog computing based framework to assist autonomous driving. Our framework relies on overhead views from cameras and data streams from vehicle sensors to create a network of distributed digital twins, called an edge twin, on fog machines. The edge twin will be continuously updated with the locations of both autonomous and human-piloted vehicles on the road segments. The vehicle locations will be harvested from overhead cameras as well as location feeds from the vehicles themselves. Although the edge twin can make fair road space allocations from a global viewpoint, there is a communication cost (delay) in reaching it from the cameras and vehicular sensors. To address this, we introduce a machine learning forecaster as a part of the edge twin which is responsible for predicting the future location of vehicles. Lastly, we introduce a box algorithm that will use the forecasted values to create a hazard map for the road segment which would be used by the framework to suggest safe manoeuvres for the autonomous vehicles such as lane changes and accelerations. We present the complete fog computing framework for autonomous driving assist and evaluate key portions of the proposed framework using simulations based on a real-world dataset of vehicle position traces on a highway.
Maslow's initial five levelled theory of ‘hierarchy of needs is one of the most popular theories on motivation albeit its many criticisms particularly related to the lack of scientific rigour to narrow cultural perspectives. However, it’s propensity towards the self has called for a review of the theory with Maslow himself finally proposing a sixth level (self-transcendence) at the new apex of the pyramid above ‘self-actualization’ and the other four levels of needs. However, the original five level of needs is still preferred by most researchers globally. Human motivation and needs have long been discussed in ‘Islamic scholarship’ for centuries, but it is rarely represented in ‘Western academic’ discussions of ‘Islamic motivation’. Unfortunately, some studies tend to explore the integration of Islamic theories and Western ideologies by incorporating the Maqasid Sharī‘ah with ‘Maslow’s hierarchy of needs’. Such approach tends to ignore the distinct influence of Islam on behaviour and self-perception as Islam delivers a unique ‘spiritual perspective’ on the relationship between motivators and the self that most ‘Western models’ do not provide. The authors believe that the Maqasid Sharī‘ah is constantly revolving in a dynamic state that changes continuously and should not be portrayed as static in nature. Thus, a framework with the Maqasid Sharī‘ah as overarching contextual factor that constantly influences Maslow's five levels of needs is proposed accordingly. Keywords: contextual factors, integration, Islamic motivation, Maslow’s-hierarchy-of-needs-theory, Maqasid Sharī‘ah
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