Fog Computing. Группа авторов

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      1.5.5.3 Security Monitoring and Management

      The system must be capable of observing security state in the network and reacting to new threats via monitoring and management mechanisms. Security management should allow definition and updating of security policies and propagation of the policies over the network in real-time.

      In addition to policy management, identity and credential management is a requirement. In addition to the necessary registration and credential storage functions, an MFC system must handle the challenge of authentication and access control in situations with intermittent connectivity, e.g. negotiating session keys when crossing different trust domains while ensuring data integrity and privacy [67]. Security monitoring, on the other hand, should collect log traces while ensuring their integrity.

      1.5.5.4 Trust Management and Multitenancy Security

       Managing trust. The information flows in MFC raise the issue of how to determine which nodes in the network are trustworthy for a particular client and request? Trust management frameworks need to manage trust assessment of both devices and applications and be able to adjust to the real-time updates of trust-related data, such as social relationships and execution results [68]. Some scenarios may also require decentralized trust management achievable with the help of blockchain technology; however, this aspect has issues with latency [69].

       Multitenancy. As the fog architecture dictates that fog platforms can host services for multiple parties, this poses the issue of how to ensure isolation in the runtime environment and ensure that only data that was intended to be shared is available across the instances [61, 70]. Secure isolation of tenant and user space continues to be a challenging requirement, as vulnerabilities allowing adversaries to access memory of other tenants VMs without permission [71] have surfaced, even in mass-produced CPUs.

      Fog computing has been introduced to overcome many challenges that cloud computing was incapable of handling, such as latency-sensitivity, connectivity between the large set of cloud-based applications, etc. Therefore, many researchers investigated and proposed different architectures for introducing fog computing into the traditional cloud computing to create an extension of the existing design. This action resolved into solving some of the old obstacles and generate new perspectives and ambitions that led to new challenges.

      1.6.1 Challenges in Land Vehicular Fog Computing

      1.6.2 Challenges in Marine Fog Computing

      Existing works [4, 28] have proposed the frameworks for improving the efficiency of the communication and for application management in the hybrid MFC environment that consist of both iFog and mFog nodes. Essentially, comparing to the UAV-Fog nodes and the UE-fog nodes, the Marine Fog nodes do not have the resource-constraint issue in terms of computation and data storage. However, because the operating marine environment lacks infrastructure, the communication between the Marine Fog nodes and the distant central cloud faces the latency issue derived from the limitation of the underline network topology and technology. For example, the bandwidth of the common VHF radio used in maritime communication can reach only 28.8 kbps and the range of the infrastructure-less 4G/5G LTE device-to-device communication is unable to fulfill the need of a marine environment. In order to overcome the fundamental communication issue, developers may consider integrating UAV-fog nodes [31] in which the system can deploy the UAV-fog nodes between vessels to form a mesh network toward dynamically supporting better bandwidth and more stable connection. However, UAVs have a limited available operation time slot because they are battery-powered. The system needs to introduce an adaptive scheduling scheme, physical location placement scheme. Further, the system needs to dynamically adjust the movement of the UAV-fog nodes based on the interconnected vessels in order to seamlessly maintain the mesh network.

      1.6.3 Challenges in Unmanned Aerial Vehicular Fog Computing

      1.6.4 Challenges in User Equipment-based Fog Computing

      There exist a large number of frameworks designed for supporting mobile UE from iFog. However, existing works rarely address the challenges in UE-fog nodes. The use cases described in the previous section indicate that systems which integrate UE-fog nodes require a dynamic program deployment mechanism. For example, in the advanced crowd sensing use case, which utilize UE-fog nodes to provide the interpreted context information derived from the collected sensory data, the UE-fog nodes need to provide the corresponding service that allows the clients to deploy the program code of the context reasoning algorithm on the UE-fog nodes. Explicitly, considering the UE-fog nodes have constraint resources, they are unable to support the common VM or containers engine-based service for the dynamic program deployment. Instead, developers generally would develop the standalone solutions which leave the interoperability as an unsolved problem in UE-fog. In order to address such an issue, developers may consider integrating an open standard–based service interface or to develop a specific mobile fog node description language based on the extension of existing cloud service-based standard, such as OASIS Topology and Orchestration Specification for Cloud Applications (TOSCA).

      1.6.5 General Challenges

      1.6.5.1 Testbed Tool


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