Automated Evaluation of Smart City Data from Cloud-System

Smart city data processing is an important taskfor the promotion and development of smart cities. The articledescribes and presents the types of smart city data, discusses theexisting modern methods and approaches to the processing ofsmart city data, such as pre-processing, assessment and analysis,and their tasks. This article contains architectural solutions andmethods used in the developed automated smart city dataevaluation system. There is also a detailed description of theintegration of the developed system with the DriveCloud cloudserver for receiving and storing smart city data

Improved QoS with Fog computing based on Adaptive Load Balancing Algorithm

As the number of sensing devices rises, traffic on the cloud servers is boosting day by day. When a device connected to the IoTwants access to data, cloud computing encourages the pairing of fog & cloud nodes to provide that information. One of the key needs in a fog-based cloud system, is efficient job scheduling to decrease the data delay and improve the QoS (Quality of Service). The researchers have used a variety of strategies to maintain the QoS criteria. However, because of the increased service delay caused by the busty traffic, job scheduling is impacted which leads to the unbalanced load on the fog environment. The proposed work uses a novel model which curates the features and working style of Genetic algorithm and the optimization algorithm with the load balancing scheduling on the fog nodes. The performance of the proposed hybrid model is contrasted with the other well-known algorithms in contrast to the fundamental benchmark optimization test functions. The proposed work displays better results in sustaining the task scheduling process when compared to the existing algorithms, which include Round Robin (RR) method, Hybrid RR, Hybrid Threshold based and Hybrid Predictive Based models, which ensures the efficacy of the proposed load balancing model to improve the quality of service in fog environment