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With the rapid growth of Internet of Things (IoT) applications, the classic centralized cloud computing paradigm faces several challenges such as high latency, low capacity and network failure. To address these challenges, fog computing brings the cloud closer to IoT devices. The fog provides IoT data processing and storage locally at IoT devices instead of sending them to the cloud. In contrast to the cloud, the fog provides services with faster response and greater quality. Therefore, fog computing may be considered the best choice to enable the IoT to provide efficient and secure services for many IoT users. This paper presents the state-of-the-art of fog computing and its integration with the IoT by highlighting the benefits and implementation challenges. This review will also focus on the architecture of the fog and emerging IoT applications that will be improved by using the fog model. Finally, open issues and future research directions regarding fog computing and the IoT are discussed.

The integration of cloud-fog-edge computing in Software-Defined Vehicular Ad hoc Networks (SDN-VANETs) brings a new paradigm that provides the needed resources for supporting a myriad of emerging applications. While an abundance of resources may offer many benefits, it also causes management problems. In this work, we propose an intelligent approach to flexibly and efficiently manage resources in these networks. The proposed approach makes use of an integrated fuzzy logic system that determines the most appropriate resources that vehicles should use when set under various circumstances. These circumstances cover the quality of the network created between the vehicles, its size and longevity, the number of available resources, and the requirements of applications. We evaluated the proposed approach by computer simulations. The results demonstrate the feasibility of the proposed approach in coordinating and managing the available SDN-VANETs resources.

Several factors are motivating the development of preventive, personalized, connected, virtual, and ubiquitous healthcare services. These factors include declining public health, increase in chronic diseases, an ageing population, rising healthcare costs, the need to bring intelligence near the user for privacy, security, performance, and costs reasons, as well as COVID-19. Motivated by these drivers, this paper proposes, implements, and evaluates a reference architecture called Imtidad that provides Distributed Artificial Intelligence (AI) as a Service (DAIaaS) over cloud, fog, and edge using a service catalog case study containing 22 AI skin disease diagnosis services. These services belong to four service classes that are distinguished based on software platforms (containerized gRPC, gRPC, Android, and Android Nearby) and are executed on a range of hardware platforms (Google Cloud, HP Pavilion Laptop, NVIDIA Jetson nano, Raspberry Pi Model B, Samsung Galaxy S9, and Samsung Galaxy Note 4) and four network types (Fiber, Cellular, Wi-Fi, and Bluetooth). The AI models for the diagnosis include two standard Deep Neural Networks and two Tiny AI deep models to enable their execution at the edge, trained and tested using 10,015 real-life dermatoscopic images. The services are evaluated using several benchmarks including model service value, response time, energy consumption, and network transfer time. A DL service on a local smartphone provides the best service in terms of both energy and speed, followed by a Raspberry Pi edge device and a laptop in fog. The services are designed to enable different use cases, such as patient diagnosis at home or sending diagnosis requests to travelling medical professionals through a fog device or cloud. This is the pioneering work that provides a reference architecture and such a detailed implementation and treatment of DAIaaS services, and is also expected to have an extensive impact on developing smart distributed service infrastructures for healthcare and other sectors.

Vehicular fog computing enabled by the Fifth Generation (5G) has been on the rise recently, providing real-time services among automobiles in the field of smart transportation by improving road traffic safety and enhancing driver comfort. Due to the public nature of wireless communication channels, in which communications are conveyed in plain text, protecting the privacy and security of 5G-enabled vehicular fog computing is of the utmost importance. Several existing works have proposed an anonymous authentication technique to address this issue. However, these techniques have massive performance efficiency issues with authenticating and validating the exchanged messages. To face this problem, we propose a novel anonymous authentication scheme named ANAA-Fog for 5G-enabled vehicular fog computing. Each participating vehicle’s temporary secret key for verifying digital signatures is generated by a fog server under the proposed ANAA-Fog scheme. The signing step of the ANAA-Fog scheme is analyzed and proven secure with the use of the ProfVerif simulator. This research also satisfies privacy and security criteria, such as conditional privacy preservation, unlinkability, traceability, revocability, and resistance to security threats, as well as others (e.g., modify attacks, forgery attacks, replay attacks, and man-in-the-middle attacks). Finally, the result of the proposed ANAA-Fog scheme in terms of communication cost and single signature verification is 108 bytes and 2.0185 ms, respectively. Hence, the assessment metrics section demonstrates that our work incurs a little more cost in terms of communication and computing performance when compared to similar studies.

Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. However, the current fog-based systems cannot handle the ever-increasing IoMT’s big data. Moreover, they are not lightweight and are designed for network attack detection only. In this work, a hybrid (for host and network) lightweight system is proposed for early attack detection in the IoMT fog. In an adaptive online setting, six different incremental classifiers were implemented, namely a novel Weighted Hoeffding Tree Ensemble (WHTE), Incremental K-Nearest Neighbors (IKNN), Incremental Naïve Bayes (INB), Hoeffding Tree Majority Class (HTMC), Hoeffding Tree Naïve Bayes (HTNB), and Hoeffding Tree Naïve Bayes Adaptive (HTNBA). The system was benchmarked with seven heterogeneous sensors and a NetFlow data infected with nine types of recent attack. The results showed that the proposed system worked well on the lightweight fog devices with ~100% accuracy, a low detection time, and a low memory usage of less than 6 MiB. The single-criteria comparative analysis showed that the WHTE ensemble was more accurate and was less sensitive to the concept drift.

In response to the rise of sophisticated vehicular applications, there has been a surge in demand for strong communication and processing capabilities low in latency. Since cloud computing cannot meet these requirements, the focus becomes turned to make communication and computation capabilities closer to the vehicles, resulting in the creation of vehicular fog computing (VFC). The VFC has been proposed as an option promising for relieving the strain on base stations and reducing processing delays during peak periods. Because of the underutilized processing resources of surrounding vehicles, calculation activities may be transferred from the control center via vehicular fog nodes, increasing the system’s efficiency. On the other side, most smart vehicles, which have increased processing, store, and power capacities, waste higher than 90% of their time in parking areas. Using the underused processing capacity of parked vehicles or vehicle parked lots as fog nodes. With the combination of vehicular networks and fog computing, numerous difficulties and challenges arise, such as increasing service quality while also ensuring that resources are utilized and managed efficiently. This paper provides an overview of a prospective VFC technology, along with a set of architectures for investigating the fog paradigm in a vehicular environment. Finally, we will investigate the challenges that will arise during the implementation of vehicular fog computing, and the most important current trends in vehicular fog computing.

The explosive growth of mobile devices and the rapid development of wireless networks and mobile computing technologies have stimulated the emergence of many new computing paradigms, such as Fog Computing, Mobile Cloud Computing (MCC) etc. These newly emerged computation paradigms try to promote the mobile applications’ Quality of Service (QoS) through allowing the mobile devices to offload their computation tasks to the edge cloud and provide their idle computation capabilities for executing other devices’ offloaded tasks. Therefore, it is very critical to efficiently schedule the offloaded tasks especially when the available computation, storage, communication resources and energy supply are limited. In this paper, we investigate the MCC-assisted execution of multi-tasks scheduling problem in hybrid MCC architecture. Firstly, this problem is formulated as an optimization problem. Secondly, a Cooperative Multi-tasks Scheduling based on Ant Colony Optimization algorithm (CMSACO) is put forward to tackle this problem, which considers task profit, task deadline, task dependence, node heterogeneity and load balancing. Finally, a series of simulation experiments are conducted to evaluate the performance of the proposed scheduling algorithm. Experimental results have shown that our proposal is more efficient than a few typical existing algorithms.

Mobility management for supporting the location tracking and location-based service (LBS) is an important issue of smart city by providing the means for the smooth transportation of people and goods. The mobility is useful to contribute the innovation in both public and private transportation infrastructures for smart cities. With the assistance of edge/fog computing, this paper presents a fully new mobility management using the proposed follow-me cloud-cloudlet (FMCL) approach in fog-computing-based radio access networks (Fog-RANs) for smart cities. The proposed follow-me cloud-cloudlet approach is an integration strategy of follow-me cloud (FMC) and follow-me edge (FME) (or called cloudlet). A user equipment (UE) receives the data, transmitted from original cloud, into the original edge cloud before the handover operation. After the handover operation, an UE searches for a new cloud, called as a migrated cloud, and a new edge cloud, called as a migrated edge cloud near to UE, where the remaining data is migrated from the original cloud to the migrated cloud and all the remaining data are received in the new edge cloud. Existing FMC results do not have the property of the VM migration between cloudlets for the purpose of reducing the transmission latency, and existing FME results do not keep the property of the service migration between data centers for reducing the transmission latency. Our proposed FMCL approach can simultaneously keep the VM migration between cloudlets and service migration between data centers to significantly reduce the transmission latency. The new proposed mobility management using FMCL approach aims to reduce the total transmission time if some data packets are pre-scheduled and pre-stored into the cache of cloudlet if UE is switching from the previous Fog-RAN to the serving Fog-RAN. To illustrate the performance achievement, the mathematical analysis and simulation results are examined in terms of the total transmission time, the throughput, the probability of packet loss, and the number of control messages.

With the goal of enhancing traffic flow and decreasing road accidents, fifth-generation (5G)-assisted vehicular fog computing was developed through innovative studies in wireless network connection technologies. But, with such high speeds and open wireless networks built into the system, privacy and security are major issues. To ensure the safety of vehicular fog computing with 5G assistance, it is essential to verify vehicle-to-vehicle traffic communication. Numerous conditional privacy-preserving authentications (CPPA) solutions have been created to safeguard communications connected to traffic in systems. Nevertheless, utilising these CPPA approaches to validate signatures is computationally costly. Elliptic curve cryptography provides authentication and conditional privacy in this certificateless authentication method for 5G-assisted vehicular fog computing, which streamlines the process of verifying vehicle signatures. In contrast, the certificateless CPPA method rapidly authenticates a signature using blockchain technology, eliminating the need for any prior identification or validation of its legitimacy. According to our experiment carried out the AVISPA tool, there are no vulnerabilities in the system that could be exploited by a Doley-Yao threat. In comparison to older approaches, the proposed solution significantly reduces the computational, communication, and energy consumption expenses.

Internet of things (IoT) services have been accepted and accredited globally for the past couple of years and have had increasing interest from researchers. Fog architecture has been brought up in IoT for enhancing its competence in a variety of applications. Fog computing is an emerging concept that transforms centralized Cloud to distributed Fog by bringing storage and computation closer to the user end. The aim of this paper is to highlight the fundamental Fog three-tier model and emphasize its advantages, challenges and possible attacks. This paper will also focus on Fog computing models pertaining to IoT scenario that have been developed over the period to conquer the challenges of existing Fog computing architecture. This paper also highlights Fog’s real importance which will include a review of scenario-based examples. Moreover, open issues have also been discussed to be worked upon in future.