Narrow Results

The main idea of this framework is that it is capable of overcoming the drawbacks that are always linked with conventional cloud-based methods. Computation and storage resources in Internet of Things (IoT) networks are distributed closer to the network’s edge; therefore, the amount of data processed in real time is reduced. By decreasing the distance of the data transfers, less bandwidth is used. Structural problems, data safety, interoperability, and resource allocation-related matters denote challenges preventing the successful implementation of those ideas. The proposed work is the cloud-enabled fog computing framework (C-FCF) in data center systems based on the IoT platform. It brings cloud computing to a new level of scalability by compounding the following: Scalable architecture, uniform communication interfaces, the dynamic algorithms that allocate resources, and the data-centered approach, on the one hand, and strong security protocols, on the other. The wireless sensor network (WSN) approach to this technology represents a greater versatility of this system as it can be applied to perform different tasks in various industries like smart cities, healthcare, transportation, and industrial automation services. The application of the given services illustrates C-FCF’s capability of creating innovation, modeling effectiveness, and uncovering potential for integration within the IoT network. The virtual simulation analysis is necessary to validate C-FCF’s effectiveness in real-life scenarios. The simulations provide evidence of features, including low latency, efficient resource utilization, and overall system performance, which underlines the practical aspects of applying C-FCF in different IoT settings. Developing this advanced computing architecture, which can surpass the limitations of conventional technology and the ability to entail many different use cases, will potentially change the data processing and management paradigm in IoT-enabled settings.

The worsening air pollution is producing serious smog problems in the world. The high concentration of aerosol, that attenuates the scene radiance and adds undesired scattering illumination into the actual illumination values, has a serious effect on the visibility of the images. Therefore, images captured under smoggy environments suffer from poor visibility. Because, the smog particles attenuate the illumination reflected by the targets and add undesired scattering light. Therefore, imaging under smoggy environments affect the performance of many machine vision systems such as intelligent transportation system, remote sensing imaging, aerial imaging, etc. From the literature, it has been observed that majority of existing researchers have either focused on foggy or hazy images only.

Fog computing can provide an effective solution to the challenges presented by today’s ever-emerging Internet of Things (IoT) infrastructures. As the number of interconnected devices progressively increases, these infrastructures require better solutions to ensure high scalability and processing capacity, along with an efficient use of available resources. This is why this paper presents a distributed Fog architecture, specifically designed to address the challenges and difficulties presented by heterogeneous IoT environments. This Fog architecture is used as an intermediate layer between the IoT devices and the final layer, it has been designed after the previous analysis of the requirements to be met for the solution, then the modularization of the architecture has been carried out so that it can be easily distributed, and finally, an implementation has been generated on a real environment as a validation case of the proposal.

The Day/Night Band (DNB) low-light visible sensor, mounted on the Suomi National Polar-orbiting Partnership (S-NPP) satellite, can measure visible radiances from the earth and atmosphere (solar/lunar reflection, natural/anthropogenic nighttime light emissions) during both the day and night. In particular, it has achieved unprecedented nighttime lowlight-level imaging with its accurate radiometric calibration and splendid spatio-temporal resolution. Based on the superior characteristics of DNB, a multi-channel threshold algorithm combining DNB with other VIIRS channels was proposed to monitor nighttime fog/low stratus. Through a gradual separation of underlying surface (land, vegetation, water bodies), snow, and medium/high clouds, a fog/low stratus region could ultimately be extracted by the algorithm. Algorithmic feasibility then was verified by a typical case of heavy fog/low stratus in China, 2012. The experimental results demonstrate that the outcomes of the algorithm approximately coincide with the ground measured results.