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Fog computing is an emerging computing paradigm that extends cloud services to the edge of the network by moving computation tasks from cloud to network edges to reduce response latency in a wireless network. Fog computing inherits the principle of peerto-peer networking, decentralization, and geographical distribution from clouds. Hence, fog computing becomes an ideal platform for readily supporting vehicular applications due to its dynamic support for mobility of clientdevices and low latent heterogeneous communication capabilities. Despite many advantages, a multitude of security and privacy issues affects the platforms and renders it as a target for unknown adversaries. This has significant implication in the development of safety critical applications, such as vehicular cloud and intelligent transportation system. This paper presents, an overview of existing security and privacy vulnerabilities in fog computing, particularly in vehicular networks. Moreover, state-of-the-art security and privacy solutions for fog based vehicular networks are analyzed. In conclusion, open challenges and future research directions are discussed.

Cloud computing provides computing and storage resources over the Internet to provide services for different industries. However, delay-sensitive applications like smart health and city applications now require computation over large amounts of data transferred to centralized cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide new solutions by bringing resources closer to the user and provide low latency and energy efficiency compared to cloud services. It is important to find optimal placement of services and resources in the three-tier IoT to achieve improved cost and resource efficiency, higher QoS, and higher level of security and privacy. In this paper, we propose a cost-aware genetic-based (CAG) task scheduling algorithm for fog-cloud environments, which improves the cost efficiency in real-time applications with hard deadlines. iFogSim simulator, which is an extended version of CloudSim is used to deploy and test the performance of the proposed method in terms of latency, network congestion, and cost. The performance results show that the proposed algorithm provides better efficiency in terms of the cost and throughput compared to Round-Robin and Minimum Response Time algorithms.

Fog computing has been recently introduced to bridge the gap between cloud resources and the network edge. Fog enables low latency and location awareness, which is considered instrumental for the realization of IoT, but also faces reliability and dependability issues due to node mobility and resource constraints. This paper focuses on the latter, and surveys the state of the art concerning dependability and fog computing, by means of a systematic literature review. Our findings show the growing interest in the topic but the relative immaturity of the technology, without any leading research group. Two problems have attracted special interest: guaranteeing reliable data storage/collection in systems with unreliable and untrusted nodes, and guaranteeing efficient task allocation in the presence of varying computing load. Redundancy-based techniques, both static and dynamic, dominate the architectures of such systems. Reliability, availability and QoS are the most important dependability requirements for fog, whereas aspects such as safety and security, and their important interplay, have not been investigated in depth.

Fog computing aims to support novel real-time applications by extending cloud resources to the network edge. This technology is highly heterogeneous and comprises a wide variety of devices interconnected through the so-called fog layer. Compared to traditional cloud infrastructure, fog presents more varied reliability challenges, due to its constrained resources and mobility of nodes. This paper summarizes current research efforts on fault tolerance and dependability in fog computing and identifies less investigated open problems, which constitute interesting research directions to make fogs more dependable.