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Fog Computing Security

Fog computing is a concept that extends the Cloud concept to the end user. As with most new technologies, a survey of the scope and types of security problems is necessary.  Much of the research presented relates to the Internet of Things.


Aazam, Mohammad; Huh, Eui-Nam, "Fog Computing Micro Datacenter Based Dynamic Resource Estimation and Pricing Model for IoT," Advanced Information Networking and Applications (AINA), 2015 IEEE 29th International Conference on, pp. 687, 694, 24-27 March 2015. doi: 10.1109/AINA.2015.254
Abstract: Pervasive and ubiquitous computing services have recently been under focus of not only the research community, but developers as well. Prevailing wireless sensor networks (WSNs), Internet of Things (IoT), and healthcare related services have made it difficult to handle all the data in an efficient and effective way and create more useful services. Different devices generate different types of data with different frequencies. Therefore, amalgamation of cloud computing with IoTs, termed as Cloud of Things (CoT) has recently been under discussion in research arena. CoT provides ease of management for the growing media content and other data. Besides this, features like: ubiquitous access, service creation, service discovery, and resource provisioning play a significant role, which comes with CoT. Emergency, healthcare, and latency sensitive services require real-time response. Also, it is necessary to decide what type of data is to be uploaded in the cloud, without burdening the core network and the cloud. For this purpose, Fog computing plays an important role. Fog resides between underlying IoTs and the cloud. Its purpose is to manage resources, perform data filtration, preprocessing, and security measures. For this purpose, Fog requires an effective and efficient resource management framework for IoTs, which we provide in this paper. Our model covers the issues of resource prediction, customer type based resource estimation and reservation, advance reservation, and pricing for new and existing IoT customers, on the basis of their characteristics. The implementation was done using Java, while the model was evaluated using CloudSim toolkit. The results and discussion show the validity and performance of our system.
Keywords: Cloud computing; Logic gates; Mobile handsets; Performance evaluation; Pricing; Resource management; Wireless sensor networks; Cloud of Things; Edge computing; Fog computing; IoT; Micro Data Center; resource management (ID#: 15-5318)


Stojmenovic, I.; Sheng Wen, "The Fog Computing Paradigm: Scenarios And Security Issues," Computer Science and Information Systems (FedCSIS), 2014 Federated Conference on, vol., no., pp.1,8, 7-10 Sept. 2014. doi: 10.15439/2014F503
Abstract: Fog computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. In this article, we elaborate the motivation and advantages of Fog computing, and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state-of-the-art of Fog computing and similar work under the same umbrella. Security and privacy issues are further disclosed according to current Fog computing paradigm. As an example, we study a typical attack, man-in-the-middle attack, for the discussion of security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device.
Keywords: cloud computing; data privacy; trusted computing; CPU consumption; Fog device; cloud computing; cloud services; fog computing paradigm; man-in-the-middle attack; memory consumption; privacy issue; security issue; smart grid; smart traffic lights; software defined networks; vehicular networks; Cloud computing; Companies; Intelligent sensors; Logic gates; Security; Wireless sensor networks; Cloud Computing; Fog Computing; Internet of Things; Software Defined Networks (ID#: 15-5319)


Dsouza, C.; Ahn, G.-J.; Taguinod, M., "Policy-Driven Security Management for Fog Computing: Preliminary Framework and A Case Study," Information Reuse and Integration (IRI), 2014 IEEE 15th International Conference on, pp. 16, 23, 13-15 Aug. 2014. doi: 10.1109/IRI.2014.7051866
Abstract: With the increasing user demand for elastic provisioning of resources coupled with ubiquitous and on-demand access to data, cloud computing has been recognized as an emerging technology to meet such dynamic user demands. In addition, with the introduction and rising use of mobile devices, the Internet of Things (IoT) has recently received considerable attention since the IoT has brought physical devices and connected them to the Internet, enabling each device to share data with surrounding devices and virtualized technologies in real-time. Consequently, the exploding data usage requires a new, innovative computing platform that can provide robust real-time data analytics and resource provisioning to clients. As a result, fog computing has recently been introduced to provide computation, storage and networking services between the end-users and traditional cloud computing data centers. This paper proposes a policy-based management of resources in fog computing, expanding the current fog computing platform to support secure collaboration and interoperability between different user-requested resources in fog computing.
Keywords: Internet of Things; cloud computing; computer centres; open systems; resource allocation; security of data; Internet of things; IoT; cloud computing data centers; dynamic user demands; elastic resources provisioning; exploding data usage; fog computing; interoperability; networking services; on-demand data access; policy-driven security management; real-time data analytics; secure collaboration; storage services; ubiquitous data access; user-requested resources; virtualized technologies; Cloud computing; Collaboration; Computer architecture; Educational institutions; Global Positioning System; Security; Vehicles (ID#: 15-5320)


Stojmenovic, I., "Fog Computing: A Cloud To The Ground Support For Smart Things And Machine-To-Machine Networks," Telecommunication Networks and Applications Conference (ATNAC), 2014 Australasian , vol., no., pp.117,122, 26-28 Nov. 2014. doi: 10.1109/ATNAC.2014.7020884
Abstract: Cloud services to smart things face latency and intermittent connectivity issues. Fog devices are positioned between cloud and smart devices. Their high speed Internet connection to the cloud, and physical proximity to users, enable real time applications and location based services, and mobility support. Cisco promoted fog computing concept in the areas of smart grid, connected vehicles and wireless sensor and actuator networks. This survey article expands this concept to the decentralized smart building control, recognizes cloudlets as special case of fog computing, and relates it to the software defined networks (SDN) scenarios. Our literature review identifies a handful number of articles. Cooperative data scheduling and adaptive traffic light problems in SDN based vehicular networks, and demand response management in macro station and micro-grid based smart grids are discussed. Security, privacy and trust issues, control information overhead and network control policies do not seem to be studied so far within the fog computing concept.
Keywords: cloud computing; computer network security; data privacy; software defined networking; trusted computing; Cisco; SDN; adaptive traffic light problems; cloud devices; cloud services; cloudlets; connected vehicles; control information overhead; cooperative data scheduling; decentralized smart building control; demand response management; fog computing; high speed Internet connection; location based services; machine-to-machine networks; macro station; microgrid based smart grids; mobility support; network control policy; privacy issue; security issue; smart devices; smart grid; smart things; software defined networks; trust issue; wireless sensor and actuator networks; Actuators; Cloud computing; Mobile communication; Optimal scheduling; Smart grids; Vehicles; Wireless communication; Fog computing; Machine-to-machine networks (ID#: 15-5321)


Yannuzzi, M.; Milito, R.; Serral-Gracia, R.; Montero, D.; Nemirovsky, M., "Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing," Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2014 IEEE 19th International Workshop on, pp. 325, 329, 1-3 Dec. 2014. doi: 10.1109/CAMAD.2014.7033259
Abstract: This paper examines some of the most promising and challenging scenarios in IoT, and shows why current compute and storage models confined to data centers will not be able to meet the requirements of many of the applications foreseen for those scenarios. Our analysis is particularly centered on three interrelated requirements: 1) mobility; 2) reliable control and actuation; and 3) scalability, especially, in IoT scenarios that span large geographical areas and require real-time decisions based on data analytics. Based on our analysis, we expose the reasons why Fog Computing is the natural platform for IoT, and discuss the unavoidable interplay of the Fog and the Cloud in the coming years. In the process, we review some of the technologies that will require considerable advances in order to support the applications that the IoT market will demand.
Keywords: Internet of Things; cloud computing; computer centres; data analysis; mobile computing; storage management ;IoT recipe; actuation reliability; cloud computing; control reliability; data analytics; data centers; fog computing; mobility requirement; storage models; Cloud computing; Handover; Mobile nodes; Reliability; Cloud Computing; Fog Computing; IoT; actuation; data analytics; mobility; real-time control; security (ID#: 15-5322)


Popov, S.; Kurochkin, M.; Kurochkin, L.; Glazunov, V., "Network Synchronization Of Vehicle Multiprotocol Unit System Clock," Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2014 6th International Congress on, pp. 105, 110, 6-8 Oct. 2014. doi: 10.1109/ICUMT.2014.7002087
Abstract: Recent achievements of automotive telematics in the area of communication channels integration for providing a persistent bidirectional link between vehicle and cloud infrastructure have intensified research in the field of mobile multiprotocol networks of intelligent vehicles oriented on cloud and fog environmental services. Synchronization of multiprotocol unit system clock is crucial for intelligent vehicle networks in terms of security system functioning, navigation, driver and passenger services. In spite of the fact that clock accuracy requirements are comparable with those for stationary systems, limited lifetime route to server in the cloud or fog and substantial restrictions on wireless network traffic complicates the achievement of this objective. Method of mobile multiprotocol unit synchronization in dynamic wireless networks of different technologies with virtual cloud servers is presented drawing on Network Time Protocol (NTP). This method provides the required quality of multiprotocol unit system clock accuracy while minimizing network traffic. Synchronization path selection algorithm is described, as based on probabilistic approach and synchronization quality retrospectives in the chosen technology network. The way of local network traffic reduction while maintaining the required accuracy of multiprotocol unit system clock is shown. The method involved can be used for multiprotocol unit synchronization in intelligent transportation networks.
Keywords: cloud computing; mobile radio; probability; protocols; synchronisation; telecommunication traffic; wireless channels; NTP; automotive telematics; bidirectional link; cloud infrastructure; communication channel integration; driver services; environmental services; intelligent transportation networks; intelligent vehicle networks; local network traffic reduction; mobile multiprotocol networks; mobile multiprotocol unit synchronization; navigation; network synchronization; network time protocol; network traffic minimization; passenger services; probabilistic approach; security system functioning; synchronization path selection algorithm; synchronization quality; vehicle multiprotocol unit system clock; virtual cloud servers; wireless network traffic; Accuracy; Mobile communication; Routing protocols; Servers; Synchronization; Vehicles (ID#: 15-5323)




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