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Authentication and Key Agreement Protocol in Hybrid Edge-fog-cloud computing Enhanced by 5G Networks

FUTURE INTERNET 2024年第6期16卷 209页

作者： Zhang, Jiayi Ouda, Abdelkader Abu-Rukba, Raafat Western Univ Dept Elect & Comp Engn London ON N6A 5B9 Canada American Univ Sharjah Dept Comp Sci & Engn POB 26666 Sharjah U Arab Emirates

The Internet of Things (IoT) has revolutionized connected devices, with applications in healthcare, data analytics, and smart cities. For time-sensitive applications, 5G wireless networks provide ultra-reliable low-latency communication (URLLC) and fog computing offloads IoT processing. Integrating 5G and fog computing can address cloud computing's deficiencies, but security challenges remain, especially in Authentication and Key Agreement aspects due to the distributed and dynamic nature of fog computing. This study presents an innovative mutual Authentication and Key Agreement protocol that is specifically tailored to meet the security needs of fog computing in the context of the edge-fog-cloud three-tier architecture, enhanced by the incorporation of the 5G network. This study improves security in the edge-fog-cloud context by introducing a stateless authentication mechanism and conducting a comparative analysis of the proposed protocol with well-known alternatives, such as TLS 1.3, 5G-AKA, and various handover protocols. The suggested approach has a total transmission cost of only 1280 bits in the authentication phase, which is approximately 30% lower than other protocols. In addition, the suggested handover protocol only involves two signaling expenses. The computational cost for handover authentication for the edge user is significantly low, measuring 0.243 ms, which is under 10% of the computing costs of other authentication protocols.

关键词： fog-cloud computing fog security authentication protocols key agreement protocols

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Star-Quake: A New Operator in Multi-Objective Gravitational Search Algorithm for Task Scheduling in IoT-Based cloud-fog computing System

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 907-915页

作者： Ahmadabadi, Jamal Zarepour Mood, Sepehr Ebrahimi Souri, Alireza Yazd Univ Dept Comp Sci Yazd *** Iran Halic Univ Fac Engn Dept Software Engn TR-34060 Istanbul Turkiye

The past decade has witnessed the advancement of Internet of Things (IoT). Task scheduling is the most important challenge in this system for managing makespan, energy, and cost. In this paper, a new multi-objective function is proposed to jointly minimize makespan, energy and monetary cost for task scheduling in fog-cloud system. Moreover, star-quake, a new operator, is defined and added to multi-objective version of the Gravitational Search Algorithm (MOGSA). This operator can balance the abilities of the algorithm such as selection pressure, exploitation and exploration, so this algorithm has the ability to avoid becoming trapped in local optima. and converge toward better solutions in complex problems. In this paper, the performance of the proposed algorithm is evaluated in two scenarios. First, the performance of the proposed method is compared with some popular multi-objective optimization methods on some standard test functions. Results show the proposed algorithm has better performance compared to other methods. Then, this algorithm is used to find good solution for the task scheduling problem. The proposed approach has improved makespan by 18%, energy consumption by 22% and processing cost by 40%. Statistical analysis illustrates that the algorithm has rank one among other approaches for task scheduling.

关键词： Optimization Task analysis Linear programming Scheduling Internet of Things cloud computing Costs Internet of Things (IoT) connected consumer devices fog-cloud computing artificial intelligence multiobjective optimization

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Security-SLA-guaranteed service function chain deployment in cloud-fog computing networks

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2021年第3期24卷 2479-2494页

作者： Zhao, Dongcheng Luo, Long Yu, Hongfang Chang, Victor Buyya, Rajkumar Sun, Gang Univ Elect Sci & Technol China Key Lab Opt Fiber Sensing & Commun Minist Educ Chengdu Peoples R China Peng Cheng Lab Shenzhen Peoples R China Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough Cleveland England Univ Melbourne Sch Comp & Informat Syst Cloud Comp & Distributed Syst CLOUDS Lab Parkville Vic Australia Agile & Intelligent Comp Key Lab Sichuan Prov Chengdu Peoples R China

Network function virtualization (NFV) has gained prominence in next-generation cloud computing, such as the fog-based radio access network, due to their ability to support better QoS in network service provision. However, most of the current service function chain (SFC) deployment researches do not consider the Security-Service-Level-Agreement (SSLA) in the deployment solution. Therefore, in this work, we introduce the SSLA into SFC deployment to defend attacks. Firstly, we formulate the SSLA guaranteed SFC deployment problem by using linear programming. Then, we propose the Maximal-security SFC deployment algorithm (MS) to maximize the security of the SFC deployment. However, the MS algorithm results in a high deployment cost. To reduce the deployment cost, we propose the Minimal-cost and SSLA-guaranteed SFC deployment algorithm (MCSG) to minimize the deployment while satisfying the SSLA. In order to reduce the blocking ratio caused by MCSG, the Minimal-cost and SSLA-guaranteed SFC deployment algorithm with feedback adjustment (MCSG-FA) is proposed. Finally, we evaluate our proposed algorithms through simulations. The simulation results show that the blocking ratio and the deployment cost of our algorithms are better than that of the existing algorithm when meeting the SSLAs.

关键词： Service function chain Network function virtualization Deployment Security fog-cloud computing

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Scheduling bag-of-task jobs with security requirements and dual-criteria partial computations in a fog - cloud system

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SIMULATION MODELLING PRACTICE AND THEORY 2025年 140卷

作者： Karatza, Helen D. Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece

Internet of Things (IoT) applications have become integral to daily life, but they often impose stringent security and timing constraints. fog computing, a modern computing paradigm, extends the capabilities of cloud resources to the edge of the network, offering potential solutions to these challenges. In these collaborative computing environments, workloads are composed of tasks with varying security requirements. Consequently, implementing security-aware scheduling schemes is crucial to ensure the proper execution of applications and to achieve the desired Quality of Service (QoS). This study explores security-conscious scheduling methodologies specifically designed for Bag-of-Tasks (BoT) applications within a fog-cloud computing framework. fog resources are suitable for processing BoT tasks with specific security requirements, referred to as "fog jobs". Conversely, BoT applications without specific security requirements, referred to as "cloud jobs", can be processed on either cloud or fog resources. To expedite the execution of fog jobs, a method that utilizes partial computations for cloud job tasks is employed. This study provides valuable insights into the effects of approximations on system performance through simulation-based evaluations. It considers partial computations based on delay levels in relation to service demands and the size of cloud jobs. This approach enhances system performance with only a minor trade-off in results accuracy.

关键词： fog-cloud computing Bag-of-tasks Scheduling Dual-criteria partial computations

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An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy

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SCIENTIFIC REPORTS 2025年第1期15卷 1-31页

作者： Rateb, Roqia Hadi, Ahmed Adnan Tamanampudi, Venkata Mohit Abualigah, Laith Ezugwu, Absalom E. Alzahrani, Ahmed Ibrahim Alblehai, Fahad Jia, Heming Al Ahliyya Amman Univ Coll Informat Technol Dept Comp Sci Amman Jordan Al Mustaqbal Univ Coll Sci Artificial Intelligence Sci Dept Babil 51001 Iraq JPMorgan Chase New York NY USA Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan North West Univ Unit Data Sci & Comp 11 Hofman St ZA-2520 Potchefstroom South Africa King Saud Univ Community Coll Comp Sci Dept Riyadh 11437 Saudi Arabia Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India

Today, with the increasing use of the Internet of Things (IoT) in the world, various workflows that need to be stored and processed on the computing platforms. But this issue, causes an increase in costs for computing resources providers, and as a result, system Energy Consumption (EC) is also reduced. Therefore, this paper examines the workflow scheduling problem of IoT devices in the fog-cloud environment, where reducing the EC of the computing system and reducing the MakeSpan Time (MST) of workflows as main objectives, under the constraints of priority, deadline and reliability. Therefore, in order to achieve these objectives, the combination of Aquila and Salp Swarm Algorithms (ASSA) is used to select the best Virtual Machines (VMs) for the execution of workflows. So, in each iteration of ASSA execution, a number of VMs are selected by the ASSA. Then by using the Reducing MakeSpan Time (RMST) technique, the MST of the workflow on selected VMs is reduced, while maintaining reliability and deadline. Then, using VM merging and Dynamic Voltage Frequency Scaling (DVFS) technique on the output from RMST, the static and dynamic EC is reduced, respectively. Experimental results show the effectiveness of the proposed method compared to previous methods.

关键词： fog-cloud computing Workflow scheduling Dynamic Voltage Frequency Scaling Aquila-Salp Swarm Algorithm

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Modified grey wolf optimization for energy-efficient internet of things task scheduling in fog computing

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Scientific Reports 2025年第1期15卷 1-16页

作者： Alsadie, Deafallah Alsulami, Musleh Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah 21961 Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah 21961 Saudi Arabia

fog-cloud computing has emerged as a transformative paradigm for managing the growing demands of Internet of Things (IoT) applications, where efficient task scheduling is crucial for optimizing system performance. However, existing task scheduling methods often struggle to balance makespan minimization and energy efficiency in dynamic and resource-constrained fog-cloud environments. Addressing this gap, this paper introduces a novel Task Scheduling algorithm based on a modified Grey Wolf Optimization approach (TS-GWO), tailored specifically for IoT requests in fog-cloud systems. The proposed TS-GWO incorporates innovative operators to enhance exploration and exploitation capabilities, enabling the identification of optimal scheduling solutions. Extensive evaluations using both synthetic and real-world datasets, such as NASA Ames iPSC and HPC2N workloads, demonstrate the superior performance of TS-GWO over established metaheuristic methods. Notably, TS-GWO achieves improvements in makespan by up to 46.15% and reductions in energy consumption by up to 28.57%. These results highlight the potential of TS-GWO to effectively address task scheduling challenges in fog-cloud environments, paving the way for its application in broader optimization tasks. © The Author(s) 2025.

关键词： fog-cloud computing Grey wolf optimizer (GWO) Internet of things (IoT) Metaheuristic algorithm Task scheduling

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fog computing for Distributed Family Learning in Cyber-Manufacturing Modeling

Fog Computing for Distributed Family Learning in Cyber-Manuf...

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IEEE International Conference on Industrial Cyber Physical Systems (ICPS)

作者： Zhang, Yutong Wang, Lening Chen, Xiaoyu Jin, Ran Virginia Tech Grado Dept Ind & Syst Engn Blacksburg VA 24061 USA

ISBN: (纸本)9781538685006

Cyber-manufacturing systems (CMS) interconnect manufacturing facilities via sensing and actuation networks to provide reliable computation and communication services in smart manufacturing. In CMS, various advanced data analytics have been proposed to support effective decision-making. However, most of them were formulated in a centralized manner to be executed on single workstations, or on cloud computation units as the data size dramatically increases. Therefore, the computation or communication service may not be responsive to support online decision-making in CMS. In this research, a method to decompose a group of existing advanced data analytics models (i.e., family learning for CMS modeling) into their distributed variants is proposed via alternative direction method of multipliers (ADMM). It improves the computation services in a fog-cloud computation network. A simulation study is conducted to validate the advantages of the proposed distributed method on fog-cloud computation network over cloud computation system. Besides, six performance evaluation metrics are adopted from the literature to access the performance of computation and communication. The evaluation results also indicate the relationship between fog-cloud architectures and computation performances, which can contribute to the efficient design of fog-cloud architectures in the future.

关键词： Cyber-manufacturing distributed computing family learning fog-cloud computing

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Multi-Layer fog-cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

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Computers, Materials & Continua 2023年第4期75卷 633-649页

作者： Mohammad Aldossary Department of Computer Science College of Arts and SciencePrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia

In the smart city paradigm, the deployment of Internet of Things(IoT) services and solutions requires extensive communication and computingresources to place and process IoT applications in real time, which consumesa lot of energy and increases operational costs. Usually, IoT applications areplaced in the cloud to provide high-quality services and scalable ***, the existing cloud-based approach should consider the above constraintsto efficiently place and process IoT applications. In this paper, anefficient optimization approach for placing IoT applications in a multi-layerfog-cloud environment is proposed using a mathematical model (Mixed-Integer Linear Programming (MILP)). This approach takes into accountIoT application requirements, available resource capacities, and geographicallocations of servers, which would help optimize IoT application placementdecisions, considering multiple objectives such as data transmission, powerconsumption, and cost. Simulation experiments were conducted with variousIoT applications (e.g., augmented reality, infotainment, healthcare, andcompute-intensive) to simulate realistic scenarios. The results showed thatthe proposed approach outperformed the existing cloud-based approach interms of reducing data transmission by 64% and the associated processingand networking power consumption costs by up to 78%. Finally, a heuristicapproach was developed to validate and imitate the presented approach. Itshowed comparable outcomes to the proposed model, with the gap betweenthem reach to a maximum of 5.4% of the total power consumption.

关键词： IoT application placement fog-cloud computing power consumption data transmission optimization smart cities

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Multi-agent system-based fuzzy constraints offer negotiation of workflow scheduling in fog-cloud environment

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computing 2023年第7期105卷 1361-1393页

作者： Marwa, Mokni Hajlaoui, Jalel Eddine Sonia, Yassa Omri, Mohamed Nazih Rachid, Chelouah Univ Sousse MARS Lab Sousse Tunisia CY Cergy Paris Univ ETIS Lab CNRS UMR8051 Cergy France

This paper presents the multi-agent system fuzzy-constraints offer negotiation of Workflow Scheduling in fog-cloud environment, called (Fuzzy-Cone) approach, to solve the workflow scheduling problem with conflicting constraints in fog-cloud IT infrastructures. A client agent and a supplier agent are created to represent the client and supplier sides respectively, with a win-win strategy based on negotiation. The novelty of this approach is the design of a multi-agent system with agents supervised by a strategy based on a fuzzy inference system modeling all possible cases, thus facilitating decision-making. The workflow scheduling problem is treated as a set of fuzzy constraint satisfaction problems (FCSP). Each agent has an FCSP modeling a set of fuzzy constraints based on the negotiation with other agents by proposing offers or counter-offers. The proposed negotiation approach is implemented to respect all the imposed restrictions and represent the imprecise preferences of the approach entities by pre-defining the fuzzy constraints and optimizing the workflow scheduling solution in terms of time and cost. of compiling. The proposed approach has been tested with different experiments and compared with state-of-the-art algorithms. The experimental results show that the negotiation between the solutions, of mutually satisfactory scheduling, considerably improved the values of time and cost of compilation, while respecting the set of the imposed constraints. The proposed approach achieves a workflow scheduling scheme that reduces compilation time by 37% and increases cost by 6% compared to state-of-the-art algorithms. The different solutions we have proposed respect the constraints of time and budget by executing workflows of different sizes in reasonable time and cost.

关键词： Workflow Scheduling fog-cloud computing Conflicting constraints Negotiation Optimization MAS Fuzzy-constraints

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Computation Offloading for Smart Devices in fog-cloud Queuing System

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IETE JOURNAL OF RESEARCH 2023年第3期69卷 1509-1521页

作者： Sufyan, Farhan Banerjee, Amit South Asian Univ Dept Comp Sci New Delhi 110021 India

Advancements in sensor and hardware technology have surged the growth of smart devices (SDs), including smartphones, and wearable devices. The data generated by the built-in sensors are utilized by different applications such as health-care, smart-city, and connected-vehicles. However, due to the computation and energy limitations of the SDs, they often need to offload the computation-intensive tasks for processing to the remote server. The cloud-based offloading can meet various applications' demands, but due to high network latency, it is inefficient for real-time applications. fog computing provides an alternative for the same, as it aggregates the fog nodes' resources at the edge of the network to meet the computational requirements of the real-time applications. In this paper, we consider a fog-cloud architecture consisting of multiple SDs, fog nodes, and the cloud. We use appropriate queuing models to simulate the traffic delay at different network elements and formulate a non-linear multi-objective optimization problem to minimize the energy consumption, execution delay, and cost of remote execution. Finally, the Stochastic Gradient descent (SGD) algorithm based solution approach is proposed that jointly optimizes offloading probability and transmission power to find the optimal trade-off between the offloading objectives. Simulation results show the effectiveness and the efficiency of the proposed system validated by the results.

关键词： Computation offloading fog-cloud computing queuing theory smart devices (SDs)

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