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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. The articles cited here were presented in 2015.

Y. Wang, T. Uehara and R. Sasaki, “Fog Computing: Issues and Challenges in Security and Forensics,” Computer Software and Applications Conference (COMPSAC), 2015 IEEE 39th Annual, Taichung, 2015, pp. 53-59. doi: 10.1109/COMPSAC.2015.173
Abstract: Although Fog Computing is defined as the extension of the Cloud Computing paradigm, its distinctive characteristics in the location sensitivity, wireless connectivity, and geographical accessibility create new security and forensics issues and challenges which have not been well studied in Cloud security and Cloud forensics. In this paper, through an extensive review of the motivation and advantages of the Fog Computing and its unique features as well as the comparison on various scenarios between the Fog Computing and Cloud Computing, the new issues and challenges in Fog security and Fog forensics are presented and discussed. The result of this study will encourage and promote more extensive research in this fascinating field, Fog security and Fog forensics.
Keywords: cloud computing; digital forensics; cloud computing paradigm; cloud forensics; cloud security; fog computing; fog forensics; fog security; geographical accessibility; location sensitivity; wireless connectivity; Cloud computing; Digital forensics; Mobile communication; Security; Wireless communication; Wireless sensor networks; Cloud Computing; Cloud Forensics; Cloud Security; Fog Computing; Fog Forensics; Fog Security (ID#: 16-10307)


K. Lee, D. Kim, D. Ha, U. Rajput and H. Oh, “On Security and Privacy Issues of Fog Computing Supported Internet of Things Environment,” Network of the Future (NOF), 2015 6th International Conference on the, Montreal, QC, 2015, pp. 1-3. doi: 10.1109/NOF.2015.7333287
Abstract: Recently, the concept of Internet of Things (IoT) is attracting much attention due to the huge potential. IoT uses the Internet as a key infrastructure to interconnect numerous geographically diversified IoT nodes which usually have scare resources, and therefore cloud is used as a key back-end supporting infrastructure. In the literature, the collection of the IoT nodes and the cloud is collectively called as an IoT cloud. Unfortunately, the IoT cloud suffers from various drawbacks such as huge network latency as the volume of data which is being processed within the system increases. To alleviate this issue, the concept of fog computing is introduced, in which foglike intermediate computing buffers are located between the IoT nodes and the cloud infrastructure to locally process a significant amount of regional data. Compared to the original IoT cloud, the communication latency as well as the overhead at the backend cloud infrastructure could be significantly reduced in the fog computing supported IoT cloud, which we will refer as IoT fog. Consequently, several valuable services, which were difficult to be delivered by the traditional IoT cloud, can be effectively offered by the IoT fog. In this paper, however, we argue that the adoption of IoT fog introduces several unique security threats. We first discuss the concept of the IoT fog as well as the existing security measures, which might be useful to secure IoT fog. Then, we explore potential threats to IoT fog.
Keywords: Internet of Things; cloud computing; data privacy; security of data; Internet of Things environment; IoT cloud; IoT fog; IoT nodes; back-end cloud infrastructure; back-end supporting infrastructure; cloud infrastructure; communication latency; fog computing; network latency; privacy issues; security issues; security threats; Cloud computing; Distributed databases; Internet of things; Privacy; Real-time systems; Security; Sensors (ID#: 16-10308)


M. Aazam and E. N. Huh, “Fog Computing Micro Datacenter Based Dynamic Resource Estimation and Pricing Model for IoT,” 2015 IEEE 29th International Conference on Advanced Information Networking and Applications, Gwangiu, 2015, pp. 687-694. 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: Internet of Things; Java; cloud computing; computer centres; pricing; resource allocation; wireless sensor networks; CloudSim toolkit; CoT; IoT; WSN; cloud of things; customer type based resource estimation; customer type based resource reservation; data filtration; fog computing microdata center based dynamic resource estimation; healthcare related services; latency sensitive services; media content; pervasive computing services; pricing model; real-time response; resource prediction issues; resource provisioning; service creation; service discovery; ubiquitous access; ubiquitous computing services; wireless sensor networks; Cloud computing; Logic gates; Mobile handsets; Performance evaluation; Pricing; Resource management; Wireless sensor networks; Cloud of Things; Edge computing; Fog computing; Micro Data Center; resource management (ID#: 16-10309)


M. A. Hassan, M. Xiao, Q. Wei and S. Chen, “Help Your Mobile Applications with Fog Computing,” Sensing, Communication, and Networking - Workshops (SECON Workshops), 2015 12th Annual IEEE International Conference on, Seattle, WA, 2015, pp. 1-6. doi: 10.1109/SECONW.2015.7328146
Abstract: Cloud computing has paved a way for resource-constrained mobile devices to speed up their computing tasks and to expand their storage capacity. However, cloud computing is not necessary a panacea for all mobile applications. The high network latency to cloud data centers may not be ideal for delay-sensitive applications while storing everything on public clouds risks users' security and privacy. In this paper, we discuss two preliminary ideas, one for mobile application offloading and the other for mobile storage expansion, by leveraging the edge intelligence offered by fog computing to help mobile applications. Preliminary experiments conducted based on implemented prototypes show that fog computing can provide an effective and sometimes better alternative to help mobile applications.
Keywords: cloud computing; mobile computing; cloud data centers; edge intelligence; fog computing; mobile applications; network latency; Androids; Bandwidth; Cloud computing; Mobile applications; Mobile handsets; Servers; Time factors (ID#: 16-10310)


M. Aazam and E. N. Huh, “Dynamic Resource Provisioning Through Fog Micro Datacenter,” Pervasive Computing and Communication Workshops (PerCom Workshops), 2015 IEEE International Conference on, St. Louis, MO, 2015, pp. 105-110. doi: 10.1109/PERCOMW.2015.7134002
Abstract: Lately, pervasive and ubiquitous computing services have been under focus of not only the research community, but developers as well. Different devices generate different types of data with different frequencies. 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, which we provide in this paper. Moreover, since Fog has to deal with mobile nodes and IoTs, which involves objects and devices of different types, having a fluctuating connectivity behavior. All such types of service customers have an unpredictable relinquish probability, since any object or device can quit resource utilization at any moment. In our proposed methodology for resource estimation and management, we have taken into account these factors and formulate resource management on the basis of fluctuating relinquish probability of the customer, service type, service price, and variance of the relinquish probability. Implementation of our system was done using Java, while evaluation was done on CloudSim toolkit. The discussion and results show that these factors can help service provider estimate the right amount of resources, according to each type of service customers.
Keywords: Internet of Things; cloud computing; computer centres; mobile computing; probability; resource allocation; CloudSim toolkit; Fog computing; Fog microdatacenter; IoT; Java; data filtration; data preprocessing; dynamic resource provisioning; mobile nodes; pervasive computing services; real-time response; research community; resource management framework; resource utilization; security measures; service price; service provider; service type; ubiquitous computing services; Cloud computing; Conferences; Estimation; Logic gates; Resource management; Sensors; Wireless sensor networks; Cloud of Things; Edge Computing; Fog-Smart Gateway (FSG); IoT; Micro Data Center (MDC); resource management (ID#: 16-10311)


C. Vallati, A. Virdis, E. Mingozzi and G. Stea, “Exploiting LTE D2D Communications in M2M Fog Platforms: Deployment and Practical Issues,” Internet of Things (WF-IoT), 2015 IEEE 2nd World Forum on, Milan, 2015, pp. 585-590.
doi: 10.1109/WF-IoT.2015.7389119
Abstract: Fog computing is envisaged as the evolution of the current centralized cloud to support the forthcoming Internet of Things revolution. Its distributed architecture aims at providing location awareness and low-latency interactions to Machine-to-Machine (M2M) applications. In this context, the LTE-Advanced technology and its evolutions are expected to play a major role as a communication infrastructure that guarantees low deployment costs, plug-and-play seamless configuration and embedded security. In this paper, we show how the LTE network can be configured to support future M2M Fog computing platforms. In particular it is shown how a network deployment that exploits Device-to-Device (D2D) communications, currently under definition within 3GPP, can be employed to support efficient communication between Fog nodes and smart objects, enabling low-latency interactions and locality-preserving multicast transmissions. The proposed deployment is presented highlighting the issues that its practical implementation raises. The advantages of the proposed approach against other alternatives are shown by means of simulation.
Keywords: Internet of Things; Long Term Evolution; cloud computing; mobile computing; D2D communication; LTE-Advanced technology; M2M fog platform; device-to-device communication; fog computing; machine-to-machine application; Actuators; Cloud computing; Computer architecture; Intelligent sensors; Long Term Evolution; D2D; Fog Computing; LTE; LTE-Advanced; M2M
(ID#: 16-10312)


Hongyu Xiang, Mugen Peng, Yuanyuan Cheng and H. H. Chen, “Joint Mode Selection and Resource Allocation for Downlink Fog Radio Access Networks Supported D2D,” Heterogeneous Networking for Quality, Reliability, Security and Robustness (QSHINE), 2015 11th International Conference on, Taipei, 2015, pp. 177-182. doi: (not provided)
Abstract: Presented as an innovative paradigm incorporating the cloud computing into radio access network, cloud radio access networks (C-RANs) have been shown advantageous in curtailing the capital and operating expenditures as well as providing better services to the customers. However, heavy burden on the non-ideal fronthaul limits performances of C-RANs. Here we focus on the alleviation of burden on the fronthaul via the edge devices' caches and propose a fog computing based RAN (F-RAN) architecture with three candidate transmission modes: device to device, local distributed coordination, and global C-RAN. Followed by the proposed simple mode selection scheme, the average energy efficiency (EE) of systems optimization problem considering congestion control is presented. Under the Lyapunov framework, the problem is reformulated as a joint mode selection and resource allocation problem, which can be solved by block coordinate descent method. The mathematical analysis and simulation results validate the benefits of F-RAN and an EE-delay tradeoff can be achieved by the proposed algorithm.
Keywords: mathematical analysis; optimisation; radio equipment; radio links; radio networks; C-RANs; F-RAN architecture; Lyapunov framework; capital expenditures; cloud computing; cloud radio access networks; congestion control; device to device; downlink fog radio access networks supported D2D; edge devices; joint mode selection; local distributed coordination; operating expenditures; optimization problem; resource allocation problem; Chlorine; Performance evaluation; Resource management (ID#: 16-10313)


M. Koschuch, M. Hombauer, S. Schefer-Wenzl, U. Haböck and S. Hrdlicka, “Fogging the Cloud — Implementing and Evaluating Searchable Encryption Schemes in Practice,” 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), Ottawa, ON, 2015, pp. 1365-1368. doi: 10.1109/INM.2015.7140497
Abstract: With the rise of cloud computing new ways to secure outsourced data have to be devised. Traditional approaches like simply encrypting all data before it is transferred only partially alleviate this problem. Searchable Encryption (SE) schemes enable the cloud provider to search for user supplied strings in the encrypted documents, while neither learning anything about the content of the documents nor about the search terms. Currently there are many different SE schemes defined in the literature, with their number steadily growing. But experimental results of real world performance, or direct comparisons between different schemes, are severely lacking. In this work we propose a simple Java client-server framework to efficiently implement different SE algorithms and compare their efficiency in practice. In addition, we demonstrate the possibilities of such a framework by implementing two different existing SE schemes from slightly different domains and compare their behavior in a real-world setting.
Keywords: Java; cloud computing; cryptography; document handling; Java client-server framework; SE schemes; encrypted documents; outsourced data security; searchable encryption schemes; user supplied strings; Arrays; Conferences; Encryption; Indexes; Servers (ID#: 16-10314)


R. Gupta and R. Garg, “Mobile Applications Modelling and Security Handling in Cloud-Centric Internet of Things,” Advances in Computing and Communication Engineering (ICACCE), 2015 Second International Conference on, Dehradun, 2015, pp. 285-290. doi: 10.1109/ICACCE.2015.119
Abstract: The Mobile Internet of Things (IoT) applications are already a part of technical world. The integration of these application with Cloud can increase the storage capacity and help users to collect and process their personal data in an organized manner. There are a number of techniques adopted for sensing, communicating and intelligently transmitting data from mobile devices onto the Cloud in IoT applications. Thus, security must be maintained while transmission. The paper outlines the need for Cloud-centric IoT applications using Mobile phones as the medium for communication. Overview of different techniques to use Mobile IoT applications with Cloud has been presented. Majorly four techniques namely Mobile Sensor Data Processing Engine (MOSDEN), Mobile Fog, Embedded Integrated Systems (EIS) and Dynamic Configuration using Mobile Sensor Hub (MosHub) are discussed and few of the similarities and comparisons between them is mentioned. There is a need to maintain confidentiality and security of the data being transmitted by these methodologies. Therefore, cryptographic mechanisms like Public Key Encryption (PKI)and Digital certificates are used for data mechanisms like Public Key Encryption (PKI) and Digital certificates are used for data management (TSCM) allows trustworthy sensing of data for public in IoT applications. The above technologies are used to implement an application called Smart Helmet by us to bring better understanding of the concept of Cloud IoT and support Assisted Living for the betterment of the society. Thus the Applications makes use of Nordic BLE board transmission and stores data onto the Cloud to be used by large number of people.
Keywords: Internet of Things; cloud computing; data acquisition; embedded systems; mobile computing; public key cryptography; trusted computing; EIS; MOSDEN; MosHub; Nordic BLE board transmission; PKI; Smart Helmet; TSCM; assisted living; cloud-centric Internet of Things; cloud-centric IoT applications; communication; cryptographic mechanisms; data confidentiality; data management; data mechanisms; data security; data transmission; digital certificates; dynamic configuration; embedded integrated systems; mobile Internet of Things; mobile IoT applications; mobile applications modelling; mobile devices; mobile fog; mobile phones; mobile sensor data processing engine; mobile sensor hub; personal data collection; personal data processing; public key encryption; security handling; sensing; storage capacity; trustworthy data; Bluetooth; Cloud computing; Mobile applications; Mobile communication; Mobile handsets; Security; Cloud IoT; Embedded Integrated Systems; Mobile Applications; Mobile Sensor Data Processing Engine; Mobile Sensor Hub; Nordic BLE board; Public Key Encryption; Smart Helmet (ID#: 16-10315)


M. Dong, K. Ota and A. Liu, “Preserving Source-Location Privacy Through Redundant Fog Loop for Wireless Sensor Networks,” Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on, Liverpool, 2015,
pp. 1835-1842. doi: 10.1109/CIT/IUCC/DASC/PICOM.2015.274
Abstract: A redundant fog loop-based scheme is proposed to preserve the source node-location privacy and achieve energy efficiency through two important mechanisms in wireless sensor networks (WSNs). The first mechanism is to create fogs with loop paths. The second mechanism creates fogs in the real source node region as well as many interference fogs in other regions of the network. In addition, the fogs are dynamically changing, and the communication among fogs also forms the loop path. The simulation results show that for medium-scale networks, our scheme can improve the privacy security by 8 fold compared to the phantom routing scheme, whereas the energy efficiency can be improved by 4 fold.
Keywords: data privacy; energy conservation; telecommunication power management; telecommunication security; wireless sensor networks; energy efficiency; medium-scale network; privacy security improvement; redundant fog loop-based scheme; source-location privacy preservation; wireless sensor network; Energy consumption; Phantoms; Position measurement; Privacy; Protocols; Routing; Wireless sensor networks; performance optimization; redundant fog loop; source-location privacy; wireless sensor networks
(ID#: 16-10316)


M. Zhanikeev, “A Cloud Visitation Platform to Facilitate Cloud Federation and Fog Computing,” in Computer, vol. 48, no. 5, pp. 80-83, May 2015. doi: 10.1109/MC.2015.122
Abstract: Evolving from hybrid clouds to true cloud federations and, ultimately, fog computing will require that cloud platforms allow for—and embrace—local hardware awareness.
Keywords: cloud computing; cloud federations; cloud visitation platform; fog computing; hybrid clouds; local hardware awareness; Cloud computing; Computer security; Software architecture; Streaming media; Cloud; cloud federations; hardware awareness; hardware virtualization (ID#: 16-10317)


Articles listed on these pages have been found on publicly available internet pages and are cited with links to those pages. Some of the information included herein has been reprinted with permission from the authors or data repositories. Direct any requests via Email to for removal of the links or modifications to specific citations. Please include the ID# of the specific citation in your correspondence.