Processing IoT applications directly in the cloud may not be the most efficient solution for each IoT scenario, especially for time-sensitive applications. A promising alternative is to use fog and edge computing, which address the issue of managing the large data bandwidth needed by end devices. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud-based IoT environments is resource management, which typically revolves around resource allocation, workload balance, resource provisioning, task scheduling, and QoS to achieve performance improvements. In this paper, we review resource management techniques that can be applied for cloud, fog, and edge computing. The goal of this review is to provide an evaluation framework of metrics for resource management algorithms aiming at the cloud/fog and edge environments. To this end, we first address research challenges on resource management techniques in that domain. Consequently, we classify current research contributions to support in conducting an evaluation framework. One of the main contributions is an overview and analysis of research papers addressing resource management techniques. Concluding, this review highlights opportunities of using resource management techniques within the cloud/fog/edge paradigm. This practice is still at early development and barriers need to be overcome.
Many researchers try to make a comparison between various IoT platforms based on specific requirements. However, none of the reviewed studies proposed a thorough analysis of the variety of comparative methods. Since there is a lack of comparison frameworks for IoT platforms, individuals or companies have difficulties when selecting a suitable IoT platform matching their associated business requirements. In order to support this selection process, a set of functional and non-functional requirements is identified. A framework containing methods in selecting an IoT platform is presented. The methodology is based on statistical and visualization techniques to recommend a suitable IoT platform. Five IoT platforms: Azure, AWS, SaS, ThingWorx, and Kaa IoT are studied to evaluate the performance of the framework. Different comparison methods are proposed, and multi-criteria decision analysis method was applied by using an Analytical Hierarchical Process (AHP). One of the methods clusters the functional requirements and compares the IoT platforms based on their ability in supporting a specific requirement or not. K-means clustering was applied to determine the clusters of functional requirements. The comparison was made based on hierarchical level of requirements per main requirement. The other methods use the following statistical tests: Error Bar test, One-way Anova Test, and Tukey's Honest Significant Difference Test. Based on the selected requirements, an approach is suggested which IoT platform can be used.
Followed by the introduction of IoT and new sustainable technologies, energy management, Quality of Service and decrease of communication costs become important and complex for enterprise systems at airports. The aviation authorities' reports reveal that the airport ICT investments are mainly focused on travel safety, mobile commerce, and new technologies. The main idea behind a smart airport is to deploy IoT network managed through a Cloud-Fog-Edge paradigm for a smart platform and optimize the airport's efficiency. An IoT cloud-based platform solution supports multiple types of data, advanced analytics, artificial intelligence, and machine learning techniques. However, cloud computing has certain limitations such as increased delay in data reporting, increased latency in accessing user network, limited customization, increased reliance on external network and data privacy. Fog-Cloud and Edge-Cloud paradigms can overcome the weaknesses of cloud computing architectures. Therefore, to understand the organizational impact of combining the usage of cloud, fog, and edge computing, we created an enterprise architecture that can be applied in a smart airport demonstration study. The enterprise architecture modelling was done by using ArchiMate and validated by means of an expert assessment and prototype implementation.
There are many factors influencing the user awareness level of privacy and security concerns when storing data on the cloud. One such factor is the users' cultural background, which has been an inspiration to many studies comparing various cultures. Along those lines, this paper compares the user awareness level between Dutch and Macedonian users, which has not been investigated before. An online study was conducted to measure users' attitude towards privacy and security of data in the cloud-based systems. The research process was conducted by delivering an online survey to Computer Science students and employees working in different software companies in the Netherlands and Macedonia. The comparative analysis indicates that there are differences in user's attitude towards storing private data in the cloud. The results of this paper demonstrate that Dutch compared to Macedonian users in general have higher level of awareness regarding the privacy and security of cloud storage.
There are many factors influencing the user awareness level of privacy and security concerns when storing data on the cloud. One such factor is the users' cultural background, which has been an inspiration to many studies comparing various cultures. Along those lines, this paper compares the user awareness level between Dutch and Macedonian users, which has not been investigated before. An online study was conducted to measure users' attitude towards privacy and security of data in the cloud-based systems. The research process was conducted by delivering an online survey to Computer Science students and employees working in different software companies in the Netherlands and Macedonia. The comparative analysis indicates that there are differences in user's attitude towards storing private data in the cloud. The results of this paper demonstrate that Dutch compared to Macedonian users in general have higher level of awareness regarding the privacy and security of cloud storage.
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