Статті в журналах: "Fog Communications and Computing"

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Bhatt, Chintan, and C. K. Bhensdadia. "Fog Computing." International Journal of Grid and High Performance Computing 9, no. 4 (October 2017): 105–13. http://dx.doi.org/10.4018/ijghpc.2017100107.

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The Internet of Things could be a recent computing paradigm, defined by networks of extremely connected things – sensors, actuators and good objects – communication across networks of homes, buildings, vehicles, and even individuals whereas cloud computing could be ready to keep up with current processing and machine demands. Fog computing provides architectural resolution to deal with some of these issues by providing a layer of intermediate nodes what's referred to as an edge network [26]. These edge nodes provide interoperability, real-time interaction, and if necessary, computational to the Cloud. This paper tries to analyse different fog computing functionalities, tools and technologies and research issues.

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Chen, Songqing, Tao Zhang, and Weisong Shi. "Fog Computing." IEEE Internet Computing 21, no. 2 (March 2017): 4–6. http://dx.doi.org/10.1109/mic.2017.39.

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Wang, Shangguang, Ao Zhou, Michael M. Komarov, and Stephen S. Yau. "Services and communications in fog computing." China Communications 14, no. 11 (November 2017): iii—iv. http://dx.doi.org/10.1109/cc.2017.8233645.

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Mangla, Cherry, Shalli Rani, and Henry Kwame Atiglah. "Secure Data Transmission Using Quantum Cryptography in Fog Computing." Wireless Communications and Mobile Computing 2022 (January 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/3426811.

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Fog computing’s idea is to bring virtual existence into objects used on a daily basis. The “objects” layer of fog architecture is also known as the smart object layer (SOL). SOL has provided the fog network with a strong platform to outperform. Although the fog architecture decentralizes data, uses more data centers, and collects and transmits it to adjacent servers for faster processing in fog networks, it faces several security challenges. The security problems of fog computing need to be alleviated for the exploitation of all benefits of fog computing in classical networks. This article has addressed the security challenges in fog computing, potential solutions via quantum cryptography, a use case portraying the importance of quantum cryptography in fog computing along future scope, and research directions.

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Artem, Volkov, Kovalenko Vadim, Ibrahim A. Elgendy, Ammar Muthanna, and Andrey Koucheryavy. "DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework." Future Internet 14, no. 1 (December 27, 2021): 13. http://dx.doi.org/10.3390/fi14010013.

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Nowadays, 5G networks are emerged and designed to integrate all the achievements of mobile and fixed communication networks, in which it can provide ultra-high data speeds and enable a broad range of new services with new cloud computing structures such as fog and edge. In spite of this, the complex nature of the system, especially with the varying network conditions, variety of possible mechanisms, hardware, and protocols, makes communication between these technologies challenging. To this end, in this paper, we proposed a new distributed and fog (DD-fog) framework for software development, in which fog and mobile edge computing (MEC) technologies and microservices approach are jointly considered. More specifically, based on the computational and network capabilities, this framework provides a microservices migration between fog structures and elements, in which user query statistics in each of the fog structures are considered. In addition, a new modern solution was proposed for IoT-based application development and deployment, which provides new time constraint services like a tactile internet, autonomous vehicles, etc. Moreover, to maintain quality service delivery services, two different algorithms have been developed to pick load points in the search mechanism for congestion of users and find the fog migration node. Finally, simulation results proved that the proposed framework could reduce the execution time of the microservice function by up to 70% by deploying the rational allocation of resources reasonably.

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Al-khafajiy, Mohammed, Thar Baker, Hilal Al-Libawy, Zakaria Maamar, Moayad Aloqaily, and Yaser Jararweh. "Improving fog computing performance via Fog-2-Fog collaboration." Future Generation Computer Systems 100 (November 2019): 266–80. http://dx.doi.org/10.1016/j.future.2019.05.015.

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Neware, Rahul, and Urmila Shrawankar. "Fog Computing Architecture, Applications and Security Issues." International Journal of Fog Computing 3, no. 1 (January 2020): 75–105. http://dx.doi.org/10.4018/ijfc.2020010105.

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Fog computing spreads the cloud administrations and services to the edge of the system, and brings processing, communications and reserving, and storage capacity closer to edge gadgets and end-clients and, in the process, aims at enhancing versatility, low latency, transfer speed and safety and protection. This article takes an extensive and wide-ranging view of fog computing, covering several aspects. At the outset is the many-layered structural design of fog computing and its attributes. After that, chief advances like communication and inter-exchange, computing, etc. are delineated, while showing how these backup and facilitate the installations and various applications. Following that, it is shown that how, despite fog computing being a feature-rich platform, it is dogged by its susceptibility to several security, privacy, and safety concerns, which stem from the nature of its widely distributed and open architecture. Finally, some suggestions are advanced to address some of the safety challenges discussed so as to propel the further growth of fog computing.

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Pallas, Frank, Philip Raschke, and David Bermbach. "Fog Computing as Privacy Enabler." IEEE Internet Computing 24, no. 4 (July 1, 2020): 15–21. http://dx.doi.org/10.1109/mic.2020.2979161.

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An, Xingshuo, Fuhong Lin, Shenggang Xu, Li Miao, and Chao Gong. "A Novel Differential Game Model-Based Intrusion Response Strategy in Fog Computing." Security and Communication Networks 2018 (August 1, 2018): 1–9. http://dx.doi.org/10.1155/2018/1821804.

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Fog computing is an emerging network paradigm. Due to its characteristics (e.g., geo-location and constrained resource), fog computing is subject to a broad range of security threats. Intrusion detection system (IDS) is an essential security technology to deal with the security threats in fog computing. We have introduced a fog computing IDS (FC-IDS) framework in our previous work. In this paper, we study the optimal intrusion response strategy in fog computing based on the FC-IDS scheme proposed in our previous work. We postulate the intrusion process in fog computing and describe it with a mathematical model based on differential game theory. According to this model, the optimal response strategy is obtained corresponding to the optimal intrusion strategy. Theoretical analysis and simulation results demonstrate that our security model can effectively stabilize the intrusion frequency of the invaders in fog computing.

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Menon, Varun G., and Joe Prathap. "Vehicular Fog Computing." International Journal of Vehicular Telematics and Infotainment Systems 1, no. 2 (July 2017): 15–23. http://dx.doi.org/10.4018/ijvtis.2017070102.

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In recent years Vehicular Ad Hoc Networks (VANETs) have received increased attention due to its numerous applications in cooperative collision warning and traffic alert broadcasting. VANETs have been depending on cloud computing for networking, computing and data storage services. Emergence of advanced vehicular applications has led to the increased demand for powerful communication and computation facilities with low latency. With cloud computing unable to satisfy these demands, the focus has shifted to bring computation and communication facilities nearer to the vehicles, leading to the emergence of Vehicular Fog Computing (VFC). VFC installs highly virtualized computing and storage facilities at the proximity of these vehicles. The integration of fog computing into VANETs comes with a number of challenges that range from improved quality of service, security and privacy of data to efficient resource management. This paper presents an overview of this promising technology and discusses the issues and challenges in its implementation with future research directions.

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Lee, Jung-Hoon, Sang-Hwa Chung, and Won-Suk Kim. "Fog server deployment technique: An approach based on computing resource usage." International Journal of Distributed Sensor Networks 15, no. 1 (January 2019): 155014771882399. http://dx.doi.org/10.1177/1550147718823994.

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Cloud computing is a type of Internet-based computing that allows users to access computing resources that are connected to the Internet anytime and anywhere. Recently, as the Internet-of-Things market using the cloud has grown, a tremendous amount of data has been generated, and services requiring low latency are increasing. To solve these problems, a new architecture called fog computing has been proposed. Fog computing can process data on a network device close to the user, drastically reducing the bandwidth required from the network and providing near real-time response. However, not much research has been done on which network devices should be used to deploy the fog server. In this article, we propose a fog server deployment technique to minimize the data movement path in a fog computing environment and a technique to make full use of the computing resources of a fog device through a vector bin packing algorithm in a situation where many services are concentrated on one network device. Experimental results show that the proposed algorithm can reduce the data movement distance and maximize the utilization of the computing resources of the fog device.

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Phan, Linh-An, Duc-Thang Nguyen, Meonghun Lee, Dae-Heon Park, and Taehong Kim. "Dynamic fog-to-fog offloading in SDN-based fog computing systems." Future Generation Computer Systems 117 (April 2021): 486–97. http://dx.doi.org/10.1016/j.future.2020.12.021.

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Shrestha, Hewan, Puviyarai T., Sana Sodanapalli, and Chandramohan Dhasarathan. "Evolution of Fog Computing Applications, Opportunities, and Challenges." International Journal of Fog Computing 4, no. 1 (January 2021): 1–17. http://dx.doi.org/10.4018/ijfc.2021010101.

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The emerging trend of internet of things in recent times is a blessing for various industries in the world. With the increasing amount of data generated by these devices, it makes it difficult for proper data flow and computation over the regular cloud architecture. Fog computing is a great alternative for cloud computing as it supports computation in devices over a large distributed geographical area, which is a plus for fog computing. Having applications in various domains including healthcare, logistics, design, marketing, manufacturing, and many more, fog computing is a great boon for the future. Evolving fog computing in various domains with different methods and techniques has shaped a clear future for it. Applicability of fog computing in vehicular communications and storage-as-a-service has made the term more popular these days. It is a review of all the possible fog computing-enabled applications and their future scope. It also prepares a basis for further research into fog computing domain-enabled services with low latency and minimum costs.

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Ribeiro Junior, Franklin Magalhães, and Carlos Alberto Kamienski. "Data resilience system for fog computing." Computer Networks 195 (August 2021): 108218. http://dx.doi.org/10.1016/j.comnet.2021.108218.

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Ibrahim, Ahmed H., Zaki T. Fayed, and Hossam M. Faheem. "Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser." Future Internet 13, no. 12 (December 17, 2021): 320. http://dx.doi.org/10.3390/fi13120320.

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Cloud computing has been a dominant computing paradigm for many years. It provides applications with computing, storage, and networking capabilities. Furthermore, it enhances the scalability and quality of service (QoS) of applications and offers the better utilization of resources. Recently, these advantages of cloud computing have deteriorated in quality. Cloud services have been affected in terms of latency and QoS due to the high streams of data produced by many Internet of Things (IoT) devices, smart machines, and other computing devices joining the network, which in turn affects network capabilities. Content delivery networks (CDNs) previously provided a partial solution for content retrieval, availability, and resource download time. CDNs rely on the geographic distribution of cloud servers to provide better content reachability. CDNs are perceived as a network layer near cloud data centers. Recently, CDNs began to perceive the same degradations of QoS due to the same factors. Fog computing fills the gap between cloud services and consumers by bringing cloud capabilities close to end devices. Fog computing is perceived as another network layer near end devices. The adoption of the CDN model in fog computing is a promising approach to providing better QoS and latency for cloud services. Therefore, a fog-based CDN framework capable of reducing the load time of web services was proposed in this paper. To evaluate our proposed framework and provide a complete set of tools for its use, a fog-based browser was developed. We showed that our proposed fog-based CDN framework improved the load time of web pages compared to the results attained through the use of the traditional CDN. Different experiments were conducted with a simple network topology against six websites with different content sizes along with a different number of fog nodes at different network distances. The results of these experiments show that with a fog-based CDN framework offloading autonomy, latency can be reduced by 85% and enhance the user experience of websites.

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Ahuja, Sanjay P., and Niharika Deval. "From Cloud Computing to Fog Computing." International Journal of Fog Computing 1, no. 1 (January 2018): 1–14. http://dx.doi.org/10.4018/ijfc.2018010101.

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This article describes how in recent years, Cloud Computing has emerged as a fundamental computing paradigm that has significantly changed the approach of enterprises as well as end users towards implementation of Internet technology. The key characteristics such as on-demand resource provision, scalability, rapid elasticity, higher flexibility, and significant cost savings have influenced enterprises of all sizes in the wide and successful adoption of Cloud Computing. Despite numerous advantages, Cloud Computing has its fair share of downsides as well. One of those major concerns is latency issues which has relevance to the Internet of Things (IoT). A new computing paradigm has been proposed by Cisco in early 2014 and termed 'Fog Computing'. Fog Computing otherwise known as Edge Computing is the integration of Cloud Computing and IoT. Being located in close proximity to the IoT devices, the Fog assists with latency requirements of IoT related applications. It also meets the data processing needs of IoT devices which are resource constrained by bringing computation, communication, control and storage closer to the end users. Clouds continue to offer support for data analytics. One can think of the IoT-Fog-Cloud as being part of a continuum. This article surveys the current literature on Fog Computing and provides a discussion on the background, details and architecture of Fog Computing, as well as the application areas of Fog Computing. The article concludes with some recommendations in the areas of future research.

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Whaiduzzaman, Md, Nishat Farjana, Alistair Barros, Md Julkar Nayeen Mahi, Md Shahriare Satu, Shanto Roy, and Colin Fidge. "HIBAF: A data security scheme for fog computing." Journal of High Speed Networks 27, no. 4 (November 10, 2021): 381–402. http://dx.doi.org/10.3233/jhs-210673.

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Fog computing complemented cloud computing integration services in the Internet of Things (IoT) and the web of real-time interactivity. Fog offers faster computing and other services facilities sitting close to user applications. However, secure data transfer in the fog is still a challenging issue requiring attention and efficient deployment of a secure data security scheme. We present an Identity Based Encryption (IBE) scheme to secure data security and transmission in fog clouds and IoT ecosystems. We devise and develop a four-level Hierarchical Identity Based Architecture for Fog Computing (HIBAF) data security scheme to enhance data security. We also analyze the system’s performance regarding response time, CPU utilization, run-time encryption-decryption, and key generation time in the fog computing paradigm to an increasing number of users data-loads. Moreover, we evaluate our scheme and compare the outcomes with different cryptography structures to discern our scheme’s effectiveness. Furthermore, we also evaluate secret key updating time, re-encrypted key updating time, and file revoking time by launching DDoS attacks both in the cloud and fog computing environment to compare improvements of HIBAF in the fog computing paradigm. Finally, through this overall evaluation, we have found that the developed HIBAF scheme provides a 33% performance enhancement in a fog environment in terms of data processing, provision, and management compared to the cloud environment.

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Soo, Sander, Chii Chang, Seng W. Loke, and Satish Narayana Srirama. "Proactive Mobile Fog Computing using Work Stealing." International Journal of Mobile Computing and Multimedia Communications 8, no. 4 (October 2017): 1–19. http://dx.doi.org/10.4018/ijmcmc.2017100101.

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A common design of the Internet of Things (IoT) system relies on distant Cloud for management and processing, which faces the challenge of latency, especially when the application requires rapid response in the edge network. Therefore, researchers have proposed the Fog computing architecture, which distributes the computational data processing tasks to the edge network nodes located in the vicinity of data sources and end-users to reduce the latency. Although the Fog computing architecture is promising, it still faces a challenge in mobility when the tasks come from ubiquitous mobile applications in which the data sources are moving objects. In order to address the challenge, this article proposes a proactive Fog service provisioning framework, which hastens the task distribution process in Mobile Fog use cases. Further, the proposed framework provides an optimization scheme in task allocation based on runtime context information. A proof-of-concept prototype has been implemented and tested on real devices.

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Liu, Ming, Yuming Mao, and Supeng Leng. "Cooperative Fog-Cloud Computing Enhanced by Full-Duplex Communications." IEEE Communications Letters 22, no. 10 (October 2018): 2044–47. http://dx.doi.org/10.1109/lcomm.2018.2866145.

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Xu, Xiaolong, Shucun Fu, Qing Cai, Wei Tian, Wenjie Liu, Wanchun Dou, Xingming Sun, and Alex X. Liu. "Dynamic Resource Allocation for Load Balancing in Fog Environment." Wireless Communications and Mobile Computing 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/6421607.

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Fog computing is emerging as a powerful and popular computing paradigm to perform IoT (Internet of Things) applications, which is an extension to the cloud computing paradigm to make it possible to execute the IoT applications in the network of edge. The IoT applications could choose fog or cloud computing nodes for responding to the resource requirements, and load balancing is one of the key factors to achieve resource efficiency and avoid bottlenecks, overload, and low load. However, it is still a challenge to realize the load balance for the computing nodes in the fog environment during the execution of IoT applications. In view of this challenge, a dynamic resource allocation method, named DRAM, for load balancing in fog environment is proposed in this paper. Technically, a system framework for fog computing and the load-balance analysis for various types of computing nodes are presented first. Then, a corresponding resource allocation method in the fog environment is designed through static resource allocation and dynamic service migration to achieve the load balance for the fog computing systems. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of DRAM.

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Jiang, Jielin, Zheng Li, Yuan Tian, and Najla Al-Nabhan. "A Review of Techniques and Methods for IoT Applications in Collaborative Cloud-Fog Environment." Security and Communication Networks 2020 (September 7, 2020): 1–15. http://dx.doi.org/10.1155/2020/8849181.

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Cloud computing is widely used for its powerful and accessible computing and storage capacity. However, with the development trend of Internet of Things (IoTs), the distance between cloud and terminal devices can no longer meet the new requirements of low latency and real-time interaction of IoTs. Fog has been proposed as a complement to the cloud which moves servers to the edge of the network, making it possible to process service requests of terminal devices locally. Despite the fact that fog computing solves many obstacles for the development of IoT, there are still many problems to be solved for its immature technology. In this paper, the concepts and characteristics of cloud and fog computing are introduced, followed by the comparison and collaboration between them. We summarize main challenges IoT faces in new application requirements (e.g., low latency, network bandwidth constraints, resource constraints of devices, stability of service, and security) and analyze fog-based solutions. The remaining challenges and research directions of fog after integrating into IoT system are discussed. In addition, the key role that fog computing based on 5G may play in the field of intelligent driving and tactile robots is prospected.

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Alshehri, Mohammed, Brajendra Panda, Sultan Almakdi, Abdulwahab Alazeb, Hanan Halawani, Naif Al Mudawi, and Riaz U. Khan. "A Novel Blockchain-Based Encryption Model to Protect Fog Nodes from Behaviors of Malicious Nodes." Electronics 10, no. 24 (December 16, 2021): 3135. http://dx.doi.org/10.3390/electronics10243135.

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The world has experienced a huge advancement in computing technology. People prefer outsourcing their confidential data for storage and processing in cloud computing because of the auspicious services provided by cloud service providers. As promising as this paradigm is, it creates issues, including everything from data security to time latency with data computation and delivery to end-users. In response to these challenges, the fog computing paradigm was proposed as an extension of cloud computing to overcome the time latency and communication overhead and to bring computing and storage resources close to both the ground and the end-users. However, fog computing inherits the same security and privacy challenges encountered by traditional cloud computing. This paper proposed a fine-grained data access control approach by integrating the ciphertext policy attribute-based encryption (CP-ABE) algorithm and blockchain technology to secure end-users’ data security against rogue fog nodes in case a compromised fog node is ousted. In this approach, we proposed federations of fog nodes that share the same attributes, such as services and locations. The fog federation concept minimizes the time latency and communication overhead between fog nodes and cloud servers. Furthermore, the blockchain idea and the CP-ABE algorithm integration allow for fog nodes within the same fog federation to conduct a distributed authorization process. Besides that, to address time latency and communication overhead issues, we equip each fog node with an off-chain database to store the most frequently accessed data files for a particular time, as well as an on-chain access control policies table (on-chain files tracking table) that must be protected from tampering by rogue fog nodes. As a result, the blockchain plays a critical role here because it is tamper-proof by nature. We assess our approach’s efficiency and feasibility by conducting a simulation and analyzing its security and performance.

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., Shruti, Himanshi Babbar, and Shalli Rani. "Security Architecture and Its Methodology for Fog Computing." ECS Transactions 107, no. 1 (April 24, 2022): 4549–62. http://dx.doi.org/10.1149/10701.4549ecst.

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With the rise of Internet of Things (IoT) based applications, cloud computing faced lots of challenges like bandwidth limitation and latency in real time applications. Therefore, fog computing came into existence. It is not a replacement of cloud computing but complements it. The services of fog computing are extended to the edge of the network making communication fast and secure. On the other hand, Software Defined Network (SDN) is also discussed, which is helpful in providing network virtualization. In this paper, study about fog computing and SDN, their characteristics, architecture, applications, key technologies of fog computing are over-viewed, and how fog computing in collaboration with these technologies will be deployed in different applications and areas. Along with it, security of fog computing and a methodology to secure a fog computing environment is proposed.

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Pham-Nguyen, Hoang-Nam, and Quang Tran-Minh. "Dynamic Resource Provisioning on Fog Landscapes." Security and Communication Networks 2019 (May 2, 2019): 1–15. http://dx.doi.org/10.1155/2019/1798391.

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A huge amount of smart devices which have capacity of computing, storage, and communication to each other brings forth fog computing paradigm. Fog computing is a model in which the system tries to push data processing from cloud servers to “near” IoT devices in order to reduce latency time. The execution orderings and the deployed places of services make significant effect on the overall response time of an application. Beside new research directions in fog computing, e.g., fog-cloud collaboration, service scalability, fog scalability, mobile fog computing, fog federation, trade-off between energy consumption and communication efficiency, duration of storing data locally, storage security and communication security, and semantic-aware fog computing, the service deployment problem is one of the attractive research fields of fog computing. The service deployment is a multiobjective optimization problem; there are so many proposed solutions for various targets, such as response time, communication cost, and energy consumption. In this paper, we focus on the optimization problem which minimizes the overall response time of an application with awareness of network usage and server usage. Then, we have conducted experiments on two service deployment strategies, called cloudy and foggy strategies. We analyze numerically the overall response time, network usage, and server usage of those two strategies in order to prove the effectiveness of our proposed foggy service deployment strategy.

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Hui-Juan, Wang, and Jiang Yong. "A Fog Computing Security: 2-Adic Complexity of Balanced Sequences." Wireless Communications and Mobile Computing 2018 (September 9, 2018): 1–9. http://dx.doi.org/10.1155/2018/7209475.

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In the fog computing environment, the periodic sequence can provide sufficient authentication code and also reduce the power consumption in the verification. But the periodic sequence faces a known full-cycle attack threat in fog computing. This paper studies the 2-adic complexity attack ability of the periodic balance sequence in the fog computing environment. It uses the exponential function as a new approach to study the 2-adic properties of periodic balance sequence and presents that the 2-adic complexity of the periodic balanced sequence is not an attacking threat when used in fog computing.

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Pg. Ali Kumar, Dk Siti Nur Khadhijah, S. H. Shah Newaz, Fatin Hamadah Rahman, Gyu Myoung Lee, Gour Karmakar, and Thien-Wan Au. "Green Demand Aware Fog Computing: A Prediction-Based Dynamic Resource Provisioning Approach." Electronics 11, no. 4 (February 16, 2022): 608. http://dx.doi.org/10.3390/electronics11040608.

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Анотація:

Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance.

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Mai, Trung Dong. "Research on Internet of Things security architecture based on fog computing." International Journal of Distributed Sensor Networks 15, no. 11 (November 2019): 155014771988816. http://dx.doi.org/10.1177/1550147719888166.

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Анотація:

The traditional data processing of the Internet of Things is concentrated in cloud computing, and its huge number of devices and massive real-time data transmission are extremely stressful on network bandwidth and cloud computing data centers. Fog computing is the infrastructure that can use processing power anywhere in the cloud. Virtual computing extends the power of cloud computing to the edge of the network, enabling any computing device to host and process software services, analyzing and storing data closer to where data are generated. The architecture of the fog computing brings enormous processing power. Since its processing power is often located near the required equipment, the distance of data transmission is reduced and the delay is reduced. This article explores how to use the fog computing layer between the cloud data center and the end node layer to store and process large amounts of local data in a timely manner, speeding decision making and enabling Internet of Things manufacturers and software developers to limit their ability to send data. They reduced cloud computing costs and built a reasonable security architecture.

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Tian, Jun-Feng, and Hao-Ning Wang. "An efficient and secure data auditing scheme based on fog-to-cloud computing for Internet of things scenarios." International Journal of Distributed Sensor Networks 16, no. 5 (May 2020): 155014772091662. http://dx.doi.org/10.1177/1550147720916623.

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With the widespread use of fog-to-cloud computing–based Internet of things devices, how to ensure the integrity of the data uploaded to the cloud has become one of the most important security issues. This article proposes an efficient and secure data auditing scheme based on fog-to-cloud computing for Internet of things scenarios, which can better meet performance and security requirements. The proposed scheme realizes data sharing under the condition of protecting privacy by encrypting sensitive information. Using the private key separation method, the private key is divided into two parts using identity information generation and random selection which are, respectively, held by the user and the fog center. Then, using the two-time signature method, the Internet of things and fog computing center use two parts of the private key to generate the original signature and final signature in two separate times. Since the fog computing center only has a part of the private key generated using the identity information, the security of the system will not be damaged due to the leakage of part of the private key held by the fog center, and the fog center significantly participates in the signature generation process, which significantly reduces the computation and communication overhead of the Internet of things device. Security analysis and performance evaluation show that the proposed scheme is safe and efficient.

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Guevara, Judy C., and Nelson L. S. da Fonseca. "Task scheduling in cloud-fog computing systems." Peer-to-Peer Networking and Applications 14, no. 2 (January 18, 2021): 962–77. http://dx.doi.org/10.1007/s12083-020-01051-9.

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30

Alraddady, Sara, Ben Soh, Mohammed A. AlZain, and Alice S. Li. "Fog Computing: Strategies for Optimal Performance and Cost Effectiveness." Electronics 11, no. 21 (November 3, 2022): 3597. http://dx.doi.org/10.3390/electronics11213597.

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Анотація:

The proliferation of IoT devices has amplified the challenges for cloud computing, causing bottleneck congestion which affects the delivery of the required quality of service. For some services that are delay sensitive, response time is extremely critical to avoid fatalities. Therefore, Cisco presented fog computing in 2012 to overcome such limitations. In fog computing, data processing happens geographically close to the data origin to reduce response time and decrease network and energy consumption. In this paper, a new fog computing model is presented, in which a management layer is placed between the fog nodes and the cloud data centre to manage and control resources and communication. This layer addresses the heterogeneity nature of fog computing and complex connectivity that are considered challenges for fog computing. Sensitivity analysis using simulation is conducted to determine the efficiency of the proposed model. Different cluster configurations are implemented and evaluated in order to reach the optimal clustering method. The results show that the management layer improves QoS, with less bandwidth consumption and execution time.

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Chang, Victor, Lewis Golightly, Paolo Modesti, Qianwen Ariel Xu, Le Minh Thao Doan, Karl Hall, Sreeja Boddu, and Anna Kobusińska. "A Survey on Intrusion Detection Systems for Fog and Cloud Computing." Future Internet 14, no. 3 (March 13, 2022): 89. http://dx.doi.org/10.3390/fi14030089.

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The rapid advancement of internet technologies has dramatically increased the number of connected devices. This has created a huge attack surface that requires the deployment of effective and practical countermeasures to protect network infrastructures from the harm that cyber-attacks can cause. Hence, there is an absolute need to differentiate boundaries in personal information and cloud and fog computing globally and the adoption of specific information security policies and regulations. The goal of the security policy and framework for cloud and fog computing is to protect the end-users and their information, reduce task-based operations, aid in compliance, and create standards for expected user actions, all of which are based on the use of established rules for cloud computing. Moreover, intrusion detection systems are widely adopted solutions to monitor and analyze network traffic and detect anomalies that can help identify ongoing adversarial activities, trigger alerts, and automatically block traffic from hostile sources. This survey paper analyzes factors, including the application of technologies and techniques, which can enable the deployment of security policy on fog and cloud computing successfully. The paper focuses on a Software-as-a-Service (SaaS) and intrusion detection, which provides an effective and resilient system structure for users and organizations. Our survey aims to provide a framework for a cloud and fog computing security policy, while addressing the required security tools, policies, and services, particularly for cloud and fog environments for organizational adoption. While developing the essential linkage between requirements, legal aspects, analyzing techniques and systems to reduce intrusion detection, we recommend the strategies for cloud and fog computing security policies. The paper develops structured guidelines for ways in which organizations can adopt and audit the security of their systems as security is an essential component of their systems and presents an agile current state-of-the-art review of intrusion detection systems and their principles. Functionalities and techniques for developing these defense mechanisms are considered, along with concrete products utilized in operational systems. Finally, we discuss evaluation criteria and open-ended challenges in this area.

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32

Liu, Zhou-zhou, and Shi-ning Li. "Sensor-cloud data acquisition based on fog computation and adaptive block compressed sensing." International Journal of Distributed Sensor Networks 14, no. 9 (September 2018): 155014771880225. http://dx.doi.org/10.1177/1550147718802259.

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The emergence of sensor-cloud system has completely changed the one-to-one service mode of traditional wireless sensor networks, and it greatly expands the application field of wireless sensor networks. As the high delay of large-scale data processing tasks in sensor-cloud, a sensor-cloud data acquisition scheme based on fog computing and adaptive block compressive sensing is proposed. First, the sensor-cloud framework based on fog computing is constructed, and the fog computing layer includes many wireless mobile nodes, which helps to realize the implementation of information transfer management between lower wireless sensor networks layer and upper cloud computing layer. Second, in order to further reduce network traffic and improve data processing efficiency, an adaptive block compressed sensing data acquisition strategy is proposed in the lower wireless sensor networks layer. By dynamically adjusting the size of the network block and building block measurement matrix, the implementation of sensor compressed sensing data acquisition is achieved; in order to further balance the lower wireless sensor networks’ node energy consumption, reduce the time delay of data processing task in fog computing layer, the mobile node data acquisition path planning strategy and multi-mobile nodes collaborative computing system are proposed. Through the introduction of the fitness value constraint transformation processing technique and parallel discrete elastic collision optimization algorithm, the efficient processing of the fog computing layer data is realized. Finally, the simulation results show that the sensor-cloud data acquisition scheme can effectively achieve large-scale sensor data efficient processing. Moreover, compared with cloud computing, the network traffic is reduced by 20% and network task delay is reduced by 12.8%–20.1%.

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33

QingQingChang, Iftikhar Ahmad, Xiaoqun Liao, and Shah Nazir. "Evaluation and Quality Assurance of Fog Computing-Based IoT for Health Monitoring System." Wireless Communications and Mobile Computing 2021 (April 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/5599907.

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Computation and data sensitivity are the metrics of the current Internet of Things (IoT). In cloud data centers, current analytics are often hosted and reported on suffering from high congestion, limited bandwidth, and security mechanisms. Various platforms are developed in the area of fog computing and thus implemented and assessed to run analytics on multiple devices, including IoT devices, in a distributed way. Fog computing advances the paradigm of cloud computing on the network edge, introducing a number of options and facilities. Fog computing enhances the processing, verdicts, and interventions to occur through IoT devices and spreads only the necessary details. The ideas of fog computing based on IoT in healthcare frameworks are exploited by shaping the disseminated delegate layer of insight between sensor hubs and the cloud. The cloud proposed a system adapted to overcome various challenges in omnipresent medical services frameworks, such as portability, energy efficiency, adaptability, and unwavering quality issues, by accepting the right to take care of certain weights of the sensor network and a distant medical service group. An overview of e-health monitoring system in the context of testing and quality assurance of fog computing is presented in this paper. Relevant papers were analyzed in a comprehensive way for the identification of relevant information. The study has compiled contributions of the existing methodologies, methods, and approaches in fog computing e-healthcare.

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34

Zhou, Yiqing, Lin Tian, Ling Liu, and Yanli Qi. "Fog Computing Enabled Future Mobile Communication Networks: A Convergence of Communication and Computing." IEEE Communications Magazine 57, no. 5 (May 2019): 20–27. http://dx.doi.org/10.1109/mcom.2019.1800235.

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35

Li, Yang. "Construction of U2S communications system based on edge fog computing." Computer Communications 153 (March 2020): 569–79. http://dx.doi.org/10.1016/j.comcom.2020.02.038.

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Al Masarweh, Mohammed, Tariq Alwada’n, and Waleed Afandi. "Fog Computing, Cloud Computing and IoT Environment: Advanced Broker Management System." Journal of Sensor and Actuator Networks 11, no. 4 (December 9, 2022): 84. http://dx.doi.org/10.3390/jsan11040084.

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Cloud computing is a massive amount of dynamic ad distributed resources that are delivered on request to clients over the Internet. Typical centralized cloud computing models may have difficulty dealing with challenges caused by IoT applications, such as network failure, latency, and capacity constraints. One of the introduced methods to solve these challenges is fog computing which makes the cloud closer to IoT devices. A system for dynamic congestion management brokerage is presented in this paper. With this proposed system, the IoT quality of service (QoS) requirements as defined by the service-level agreement (SLA) can be met as the massive amount of cloud requests come from the fog broker layer. In addition, a forwarding policy is introduced which helps the cloud service broker to select and forward the high-priority requests to the appropriate cloud resources from fog brokers and cloud users. This proposed idea is influenced by the weighted fair queuing (WFQ) Cisco queuing mechanism to simplify the management and control of the congestion that may possibly take place at the cloud service broker side. The system proposed in this paper is evaluated using iFogSim and CloudSim tools, and the results demonstrate that it improves IoT (QoS) compliance, while also avoiding cloud SLA violations.

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37

Al-Rubaie, Noor Razaq Obaied, Rafal Nader Neamah Kamel, and Raghda M. Alshemari. "Simulating fog computing in OMNeT++." Bulletin of Electrical Engineering and Informatics 12, no. 2 (April 1, 2023): 979–86. http://dx.doi.org/10.11591/eei.v12i2.4201.

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Fog computing is a technology architecture in which data from IoT devices is received in real time by a number of nodes. These nodes process the data they receive in real time, with millisecond reaction times. The nodes communicate analytical summary data to the cloud on a regular basis. Fog computing scenario demands higher output, reduced latency, and greater performance as demand and requirements for improving performance in IoT applications grow. The resources allocation in effective manner in the fog environment is also a major problem in IoT-fog computing. Fog computing has been considered as a necessity within several IoT resources domains. In this paper the proposed fog simulation environment is focused on IoT sensors, fog node, and cloud as the used network architecture. However, the network features are properly explored in the proposed system and they are evaluated based on the throughput, latency, and channel allocation.

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Sobecki, Andrzej, Julian Szymański, David Gil, and Higinio Mora. "Deep learning in the fog." International Journal of Distributed Sensor Networks 15, no. 8 (August 2019): 155014771986707. http://dx.doi.org/10.1177/1550147719867072.

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In the era of a ubiquitous Internet of Things and fast artificial intelligence advance, especially thanks to deep learning networks and hardware acceleration, we face rapid growth of highly decentralized and intelligent solutions that offer functionality of data processing closer to the end user. Internet of Things usually produces a huge amount of data that to be effectively analyzed, especially with neural networks, demands high computing capabilities. Processing all the data in the cloud may not be sufficient in cases when we need privacy and low latency, and when we have limited Internet bandwidth, or it is simply too expensive. It poses a challenge for creating a new generation of fog computing that supports artificial intelligence and selects the architecture appropriate for an intelligent solution. In this article, we show from four perspectives, namely, hardware, software libraries, platforms, and current applications, the landscape of components used for developing intelligent Internet of Things solutions located near where the data are generated. This way, we pinpoint the odds and risks of artificial intelligence fog computing and help in the process of selecting suitable architecture and components that will satisfy all requirements defined by the complex Internet of Things systems.

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39

Rahman, Gohar, and Chuah Chai Wen. "Fog Computing, Applications, Security and Challenges, Review." International Journal of Engineering & Technology 7, no. 3 (July 27, 2018): 1615. http://dx.doi.org/10.14419/ijet.v7i3.12612.

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The internet of things originates a world where on daily basis objects can join the internet and interchange information and in addition process, store, gather them from the nearby environment, and effectively mediate on it. A remarkable number of services might be imagined by abusing the internet of things. Fog computing which is otherwise called edge computing was introduced in 2012 as a considered is a prioritized choice for the internet of things applications. As fog computing extend services of cloud near to the edge of the network and make possible computations, communications, and storage services in proximity to the end user. Fog computing cannot only provide low latency, location awareness but also enhance real-time applications, quality of services, mobility, security and privacy in the internet of things applications scenarios. In this paper, we will summarize and overview fog computing model architecture, characteristic, similar paradigm and various applications in real-time scenarios such as smart grid, traffic control system and augmented reality. Finally, security challenges are presented.

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40

Hegarty, R., and M. Taylor. "Digital evidence in fog computing systems." Computer Law & Security Review 41 (July 2021): 105576. http://dx.doi.org/10.1016/j.clsr.2021.105576.

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41

Wei, Xuejiang, and Libing Wu. "Assessment of Fog Enabled Sensor Cloud Platform for Smart Logistics Park." Mobile Information Systems 2022 (October 7, 2022): 1–12. http://dx.doi.org/10.1155/2022/7875137.

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This paper is a comparative study on the performance of the fog-enabled sensor cloud (FSC) and traditional cloud computing and fog computing modes in a smart logistics park. Based on our previous work, we describe the physical sensor virtualization scheme and framework of the proposed FSC, construct the network model, and mathematically describe the parameters of the FSC. To assess the performance of the proposed platform, we take a large logistics enterprise in China as an example and illustrate the network setup of the proposed platform in a real logistics scenario. The experiment proves that the FSC for smart logistics parks has a practical advantage over the traditional cloud computing and fog computing modes in terms of bandwidth consumption and service latency.

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42

Ray, Niranjan Kumar, Deepak Puthal, and Ashish Virendra Chandak. "QSFN: QoS-aware fog node provisioning in fog computing." International Journal of Computer Applications in Technology 69, no. 1 (2022): 36. http://dx.doi.org/10.1504/ijcat.2022.10051190.

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43

Chandak, Ashish Virendra, Niranjan Kumar Ray, and Deepak Puthal. "QSFN: QoS-aware fog node provisioning in fog computing." International Journal of Computer Applications in Technology 69, no. 1 (2022): 36. http://dx.doi.org/10.1504/ijcat.2022.126092.

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44

Anawar, Muhammad Rizwan, Shangguang Wang, Muhammad Azam Zia, Ahmer Khan Jadoon, Umair Akram, and Salman Raza. "Fog Computing: An Overview of Big IoT Data Analytics." Wireless Communications and Mobile Computing 2018 (2018): 1–22. http://dx.doi.org/10.1155/2018/7157192.

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A huge amount of data, generated by Internet of Things (IoT), is growing up exponentially based on nonstop operational states. Those IoT devices are generating an avalanche of information that is disruptive for predictable data processing and analytics functionality, which is perfectly handled by the cloud before explosion growth of IoT. Fog computing structure confronts those disruptions, with powerful complement functionality of cloud framework, based on deployment of micro clouds (fog nodes) at proximity edge of data sources. Particularly big IoT data analytics by fog computing structure is on emerging phase and requires extensive research to produce more proficient knowledge and smart decisions. This survey summarizes the fog challenges and opportunities in the context of big IoT data analytics on fog networking. In addition, it emphasizes that the key characteristics in some proposed research works make the fog computing a suitable platform for new proliferating IoT devices, services, and applications. Most significant fog applications (e.g., health care monitoring, smart cities, connected vehicles, and smart grid) will be discussed here to create a well-organized green computing paradigm to support the next generation of IoT applications.

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45

Zeghib, Nour El Imane, Ali A. Alwan, Abedallah Zaid Abualkishik, and Yonis Gulzar. "Multi-Route Plan for Reliable Services in Fog-Based Healthcare Monitoring Systems." International Journal of Grid and High Performance Computing 14, no. 1 (January 1, 2022): 1–20. http://dx.doi.org/10.4018/ijghpc.304908.

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The main concern of fog computing is reducing data transmission on the cloud. Moreover, due to the short distance between end-user and fog nodes, fog computing considered more reliable to handle time-sensitive situations like the critical data provided by the Internet of Things (IoT). This may include sensory healthcare data which needs rapid processing to make decisions. However, in healthcare monitoring systems it is necessary to ensure the services’ availability when fog node failure occurred. The issue of monitoring service interruption during fog node failure has not received much attention. This paper proposes a multi-route plan that aims to identify an alternative route to ensure the availability of time-critical medical services. Various scenarios have been designed to evaluate the performance of the proposed strategy. The experimental results illustrate the superiority of our approach in terms of latency, energy consumption, and network usage in comparison with most recent related work.

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46

Al Muhtadi, Jalal, Rawan A. Alamri, Farrukh Aslam Khan, and Kashif Saleem. "Subjective logic-based trust model for fog computing." Computer Communications 178 (October 2021): 221–33. http://dx.doi.org/10.1016/j.comcom.2021.05.016.

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47

Prieto González, Lisardo, Corvin Jaedicke, Johannes Schubert, and Vladimir Stantchev. "Fog computing architectures for healthcare." Journal of Information, Communication and Ethics in Society 14, no. 4 (November 14, 2016): 334–49. http://dx.doi.org/10.1108/jices-05-2016-0014.

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Purpose The purpose of this study is to analyze how embedding of self-powered wireless sensors into cloud computing further enables such a system to become a sustainable part of work environment. Design/methodology/approach This is exemplified by an application scenario in healthcare that was developed in the context of the OpSIT project in Germany. A clearly outlined three-layer architecture, in the sense of Internet of Things, is presented. It provides the basis for integrating a broad range of sensors into smart healthcare infrastructure. More specifically, by making use of short-range communication sensors (sensing layer), gateways which implement data transmission and low-level computation (fog layer) and cloud computing for processing the data (application layer). Findings A technical in-depth analysis of the first two layers of the infrastructure is given to prove reliability and to determine the communication quality and availability in real-world scenarios. Furthermore, two example use-cases that directly apply to a healthcare environment are examined, concluding with the feasibility of the presented approach. Practical implications Finally, the next research steps, oriented towards the semantic tagging and classification of data received from sensors, and the usage of advanced artificial intelligence-based algorithms on this information to produce useful knowledge, are described together with the derived social benefits. Originality/value The work presents an innovative, extensible and scalable system, proven to be useful in healthcare environments.

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48

Samann, Fady Esmat Fathel, Adnan Mohsin Abdulazeez, and Shavan Askar. "Fog Computing Based on Machine Learning: A Review." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 12 (June 18, 2021): 21. http://dx.doi.org/10.3991/ijim.v15i12.21313.

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<p>Internet of Things (IoT) systems usually produce massive amounts of data, while the number of devices connected to the internet might reach billions by now. Sending all this data over the internet will overhead the cloud and consume bandwidth. Fog computing's (FC) promising technology can solve the issue of computing and networking bottlenecks in large-scale IoT applications. This technology complements the cloud computing by providing processing power and storage to the edge of the network. However, it still suffers from performance and security issues. Thus, machine learning (ML) attracts attention for enabling FC to settle its issues. Lately, there has been a growing trend in utilizing ML to improve FC applications, like resource management, security, lessen latency and power usage. Also, intelligent FC was studied to address issues in industry 4.0, bioinformatics, blockchain and vehicular communication system. Due to the ML vital role in the FC paradigm, this work will shed light on recent studies utilized ML in a FC environment. Background knowledge about ML and FC also presented. This paper categorized the surveyed studies into three groups according to the aim of ML implementation. These studies were thoroughly reviewed and compared using sum-up tables. The results showed that not all studies used the same performance metric except those worked on security issues. In conclusion, the simulations of proposed ML models are not sufficient due to the heterogeneous nature of the FC paradigm.</p>

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Wang, Cheng, Haiyang Huang, Jianwei Chen, Wei Wei, and Tian Wang. "An online and real-time adaptive operational modal parameter identification method based on fog computing in Internet of Things." International Journal of Distributed Sensor Networks 16, no. 2 (February 2020): 155014772090361. http://dx.doi.org/10.1177/1550147720903610.

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A large number of smart devices make the Internet of Things world smarter. However, currently cloud computing cannot satisfy real-time requirements and fog computing is a promising technique for real-time processing. Operational modal analysis obtains modal parameters that reflect the dynamic properties of the structure from the vibration response signals. In Internet of Things, the operational modal analysis method can be embedded in the smart devices to achieve structural health monitoring and fault detection. In this article, a four-layer framework for combining fog computing and operational modal analysis in Internet of Things is designed. This four-layer framework introduces fog computing to solve tasks that cloud computing cannot handle in real time. Moreover, to reduce the time and space complexity of the operational modal analysis algorithm and support the real-time performance of fog computing, a limited memory eigenvector recursive principal component analysis–based operational modal analysis approach is proposed. In addition, by examining the cumulative percent variance of principal component analysis, this article explains the reasons behind the identified modal order exchange. Finally, the time-varying operational modal identification results from non-stationary random response signals of a cantilever beam whose density changes slowly indicate that the limited memory eigenvector recursive principal component analysis–based operational modal analysis method requires less memory and runtime and has higher stability and identification effect.

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Akram, Waseem, Zahoor Najar, Abid Sarwar, and Iraq Ahmad Reshi. "Fog Computing for Delay Minimization and Load Balancing." International Journal of Cloud Applications and Computing 12, no. 1 (January 1, 2022): 1–16. http://dx.doi.org/10.4018/ijcac.312563.

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Cloud is used to store and process data at a very high rate. Moreover, nearly everyone in this world is using the cloud. However, the problem arises that the data centers are not positioned well. The data reach the cloud by passing through the various links, due to which more delays occur. So this world is now moving into fog. Fog computing provides us the capability to process data nearer to the IoT devices. During the past decade, IoT devices have been growing rapidly, resulting in the production of a tremendous amount of data every day. For the processing of this ever-growing data, efficient algorithms are required to reduce the load on the cloud and give the results in a faster and more precise manner. The processing should be done on the fog node to handle this issue. In this paper, the authors study load balancing on fog nodes with a novel technique so that they distribute the load among different fog nodes so that none of the fog nodes remains idle while other takes time for processing the data.

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