Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.

This paper investigates the potential of a concept for the commercial utilization of surplus intermittent wind-generated electricity for municipal district heating based on the development of an electric-driven heat storage. The article is divided into three sections: (1) A review of energy storage systems; (2) Results and calculations after a market analysis based on electricity consumption statistics covering the years 2005–2013; and (3) Technology research and the development of an innovative thermal energy storage (TES) system. The review of energy storage systems introduces the basic principles and state-of-the-art technologies of TES. The market analysis describes the occurrence of excess wind power in Germany, particularly the emergence of failed work and negative electricity rates due to surplus wind power generation. Based on the review, an innovative concept for a prototype of a large-scale underwater sensible heat storage system, which is combined with a latent heat storage system, was developed. The trapezoidal prism-shaped storage system developed possesses a high efficiency factor of 0.98 due to its insulation, large volume, and high rate of energy conversion. Approximate calculations showed that the system would be capable of supplying about 40,000 people with hot water and energy for space heating, which is equivalent to the population of a medium-sized city. Alternatively, around 210,000 inhabitants could be supplied with hot water only. While the consumer´s costs for hot water generation and space heating would be lowered by approximately 20.0–73.4%, the thermal energy storage would generate an estimated annual profit of 3.9 million euros or more (excluding initial costs and maintenance costs).