Fog computing is an intermediate computing layer that has emerged to address the latency issues of cloud-based Internet of things (IoT) environments. As a result, new forms of security and privacy threats are emerging. These threats are mainly due to the huge number of sensors, as well as the enormous amount of data generated in IoT environments that needs to be processed in real time. These sensors send data to the cloud through the fog computing layer, creating an additional layer of vulnerabilities. In addition, the cloud by nature is vulnerable because cloud services can be located in different geographical locations and provided by multiple service providers. Moreover, cloud services can be hybrid and public, which exposes them to risks due to their infinite number of anonymous users. Access control (AC) is one of the essential prevention measures to protect data and services in computing environments. Many AC models have been implemented by researchers from academia and industry to address the problems associated with data breaches in pervasive computing environments. However, the question of which AC model(s) should be used to prevent unauthorized access to data remains. The selection of AC models for cloud-based IoT environments is highly dependent on the application requirements and how the AC models can impact the computation overhead. In this paper, we survey the features and challenges of AC models in the fog computing environment. We also discuss the diversity of different AC models. This survey provides the reader with state-of-the-art practices in the field of fog computing AC and helps to identify the existing gaps within the field.
With the rise of the Internet of Things (IoT), there is a demand for computation at network edges because of the limited processing capacity of IoT devices. Fog computing is a middle layer that has appeared to address the latency issues between the Internet of things (IoT) and the cloud. Fog computing is becoming more important as companies face increasing challenges in collecting and sending data from IoT devices to the cloud. However, this has led to new security and privacy issues as a result of the large number of sensors in IoT environments as well as the massive amount of data that must be analyzed in real time. To overcome the security challenges between the IoT layer and fog layer and, thus, meet the security requirements, this paper proposes a fine-grained data access control model based on the attribute-based encryption of the IoT–Fog–Cloud architecture to limit the access to sensor data and meet the authorization requirements. In addition, this paper proposes a blockchain-based certificate model for the IoT–Fog–Cloud architecture to authenticate IoT devices to fog devices and meet the authentication requirements. We evaluated the performance of the two proposed security models to determine their efficiency in real-life experiments of the IoT–Fog–Cloud architecture. The results demonstrate that the performance of the IoT–Fog–Cloud architecture with and without the blockchain-based certificate model was the same when using one, two, or three IoT devices. However, the performance of the IoT–Fog–Cloud architecture without the access control model was slightly better than that of the architecture with the model when using one, two, or three IoT devices.
An operating system provides numerous functions, such as I/O management, memory management, process management, and file management. Since the operating system is a set of the programs that interacts with computer hardware during executing time, process management is the most important function provided by an operating system. CPU scheduling is extremely necessary, as it makes a multi-tasking environment that keeps the CPU and I/O devices busy at all times which results in increased CPU utilization [1]. However, numerous scheduling algorithms have already been designed to regulate the access of threads and processes to the CPU, such as FCFS-SJF-SRT-RR. We simulated these scheduling algorithms and evaluated their performance (throughput, latency, utilization, turnaround time, and waiting time) in a multi-processor environment.
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