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Security and Privacy Preservation via Interference Tolerant Fast Convergence Zeroing Neural Network With Reptile Search Optimization Algorithm in fog-cloud computing

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2025年第4期36卷

作者： Kumar, Pakkarisamy Janakiraman Sathish Verma, Neha Gupta, Shivani Jothilakshmi, Rajendran Panimalar Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India GGSIPU Vivekananda Inst Profess Studies Dept Informat Technol Delhi India Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India RMD Engn Coll Dept Informat Technol Chennai Tamil Nadu India

More vertical service areas than only data processing, storing, and communication are promised by fog-cloud computing. Due to its great efficiency and scalability, distributed deep learning (DDL) across fog-cloud computing environments is a widely used application among them. With training limited to sharing parameters, DDL can offer more privacy protection than centralized deep learning. Nevertheless, DDL still faces two significant security obstacles when it comes to fog-cloud computing are How to ensure that users' identities are not stolen by outside enemies, and How to prevent users' privacy from being disclosed to other internal participants in the process of training. In this manuscript, Interference Tolerant Fast Convergence Zeroing Neural Network for Security and Privacy Preservation with Reptile Search Optimization Algorithm in fog-cloud computing environment (SPP-ITFCZNN-RSOA-FCC) is proposed. ITFCZNN is proposed for security and privacy preservation, Then Reptile Search Optimization Algorithm (RSOA) is proposed to optimize the ITFCZNN, and Effective Lightweight Homomorphic Cryptographic Algorithm (ELHCA) is used to encrypt and decrypt the local gradients. The proposed SPP-ITFCZNN-RSOA-FCC system attains a better security balance, efficiency, and functionality than existing efforts. The proposed SPP-ITFCZNN-RSOA-FCC is implemented using Python. The performance metrics like accuracy, resource overhead, computation overhead, and communication overhead are considered. The performance of the SPP-ITFCZNN-RSOA-FCC approach attains 29.16%, 20.14%, and 18.93% high accuracy, and 11.03%, 26.04%, and 23.51% lower Resource overhead compared with existing methods including FedSDM: Federated learning dependent smart decision making component for ECG data at internet of things incorporated Edge-fog-cloud computing (SPP-FSDM-FCC), A collaborative computation with offloading in dew-enabled vehicular fog computing to compute-intensive with latency-sensitive dependence-awar

关键词： effective lightweight homomorphic cryptographic algorithm fog-cloud computing interference-tolerant fast convergence zeroing neural network privacy preserving reptile search optimization algorithm

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Toward Secure and Privacy-Preserving Distributed Deep Learning in fog-cloud computing

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IEEE INTERNET OF THINGS JOURNAL 2020年第12期7卷 11460-11472页

作者： Li, Yiran Li, Hongwei Xu, Guowen Xiang, Tao Huang, Xiaoming Lu, Rongxing Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Peng Cheng Lab Cyberspace Secur Res Ctr Shenzhen 518000 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China CETC Cyberspace Secur Res Inst Co Ltd Technol Mkt Dept Chengdu 610041 Peoples R China Univ New Brunswick Fac Comp Sci Fredericton NB E3B 5A3 Canada

fog-cloud computing promises many new vertical service areas beyond simple data communication, storing, and processing. Among them, distributed deep learning (DDL) across fog-cloud computing environment is one of the most popular applications due to its high efficiency and scalability. Compared with the centralized deep learning, DDL can provide better privacy protection with training only on sharing parameters. Nevertheless, when DDL meets fog-cloud computing, it still faces two major security challenges: 1) how to protect users' privacy from being leaked to other internal participants in the training process and 2) how to guarantee users' identities from being forged by external adversaries. To combat them, several approaches have been proposed via various technologies. Nevertheless, those approaches suffer from drawbacks in terms of security, efficiency, and functionality, and cannot guarantee the legitimacy of participants' identities during training. In this article, we propose a secure and privacy-preserving DDL (SPDDL) for fog-cloud computing. Compared with the state-of-the-art works, our proposal achieves a better tradeoff between security, efficiency, and functionality. In addition, our SPDDL can guarantee the unforgeability of users' identities against external adversaries. Extensive experimental results indicate the practical feasibility and high efficiency of our SPDDL.

关键词： Training Servers Encryption cloud computing Privacy Machine learning Distributed deep learning (DDL) fog-cloud computing identity verification privacy preserving

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Optimizing deadline violation time and energy consumption of IoT jobs in fog-cloud computing

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NEURAL computing & APPLICATIONS 2022年第23期34卷 21157-21173页

作者： Dabiri, Samaneh Azizi, Sadoon Abdollahpouri, Alireza Univ Kurdistan Dept Comp Engn & Informat Technol Sanandaj Iran

Nowadays, Internet of Things (IoT) devices are ubiquitous and their number is growing rapidly. These devices produce massive amount of data which need to be efficiently processed. Since most of the IoT devices are resource constrained in terms of computational capability and power resources, they have to offload their computation jobs to more powerful computing devices. fog-cloud computing is a promising platform for processing IoT jobs. However, due to the heterogeneity of the computing devices, how to schedule IoT jobs in this environment is a challenging issue. To tackle this issue, in this paper, we first present a system model for the job scheduling problem in fog-cloud computing with the aim of optimizing the total deadline violation time of jobs and the energy consumption of the system. Then, we propose two nature-inspired optimization techniques, grey wolf optimization and grasshopper optimization algorithm to efficiently solve the job scheduling problem in the fog-cloud environment. The performance of the proposed algorithms is evaluated against the state-of-the-art algorithms using various simulation experiments. The results demonstrate that the proposed schedulers are capable of reducing the total deadline violation time about 68% and energy consumption about 22% compared to the second-best results.

关键词： Internet of Things (IoT) fog-cloud computing Job scheduling Deadline violation time Energy consumption Grey wolf optimization (GWO) Grasshopper optimization algorithm (GOA)

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MOTORS: multi-objective task offloading and resource scheduling algorithm for heterogeneous fog-cloud computing scenario

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JOURNAL OF SUPERcomputing 2024年第15期80卷 22315-22361页

作者： Shukla, Prashant Pandey, Sudhakar Natl Inst Technol Dept Informat Technol Raipur Chhattisgarh India

Along with the rising popularity of pay-as-you-go cloud services, many businesses and communities are deploying their business or scientific workflow applications on cloud-based computing platforms. The primary responsibility of cloud service providers is to reduce the monetary cost and execution time of Infrastructure as a Service (IaaS) cloud services. The majority of current solutions for cost and makespan reduction were developed for conventional cloud platforms and are incompatible with heterogeneous computing systems (HCS) having service-based resource management approaches and pricing models. fog-cloud infrastructures (FCI) have emerged as desirable target areas for workflow automation across several fields of application. In heterogeneous FCI, the execution of workflows involving tasks having different properties might influence the performance in terms of resource usage. The primary goal of this research is to efficiently offload the computational task and optimally schedule the workflow in such diverse computing environment. In this article, we present a novel strategy for building an environment that includes techniques for offloading and scheduling while balancing competing demands from the user and the resource providers. In order to address the issue of uncertainty, our approach incorporates a fuzzy dominance-based task clustering and offloading technique. To construct a suitable execution sequence of tasks that helps to limit the precedence relationship, by preserving dependency constraints among the tasks, a novel algorithm for tasks segmentation is employed. To simplify the problem of the complexity, a hybrid-heuristics based on Harmony Search Algorithm (HSA) and Genetic Algorithm (GA) for resource scheduling algorithm is used. The multi-objective optimization using three competing objectives is taken into consideration for investigation in heterogeneous FCI. The fitness function derived includes minimization of makespan and cost along with maximiza

关键词： fog-cloud computing Task offloading Resource scheduling MOTORS FDTCO HORSA

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A fuzzy approach for optimal placement of IoT applications in fog-cloud computing

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第1期25卷 303-320页

作者： Tavousi, Farhad Azizi, Sadoon Ghaderzadeh, Abdulbaghi Islamic Azad Univ Sanandaj Branch Dept Comp Engn Sanandaj Iran Univ Kurdistan Dept Comp Engn & IT Sanandaj Iran

fog-cloud computing is a promising distributed model for hosting ever-increasing Internet of Things (IoT) applications. IoT applications should meet different characteristics such as deadline, frequency rate, and input file size. fog nodes are heterogeneous, resource-limited devices and cannot accommodate all the IoT applications. Due to these difficulties, designing an efficient algorithm to deploy a set of IoT applications in a fog-cloud environment is very important. In this paper, a fuzzy approach is developed to classify applications based on their characteristics then an efficient heuristic algorithm is proposed to place applications on the virtualized computing resources. The proposed policy aims to provide a high quality of service for IoT users while the profit of fog service providers is maximized by minimizing resource wastage. Extensive simulation experiments are conducted to evaluate the performance of the proposed policy. Results show that the proposed policy outperforms other approaches by improving the average response time up to 13%, the percentage of deadline satisfied requests up to 12%, and the resource wastage up to 26%.

关键词： fog-cloud computing Internet of Things (IoT) Application placement Fuzzy approach Quality of service (QoS) Resource wastage

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A dynamic planning model for deploying service functions chain in fog-cloud computing

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第10期34卷 7948-7960页

作者： Zhang, Yongheng Zhang, Feng Tong, Si Rezaeipanah, Amin Yulin Univ Sch Informat Engn Yulin 719000 Shaanxi Peoples R China Liaocheng Univ Sch Media Technol Liaocheng 252059 Shandong Peoples R China Univ Rahjuyan Danesh Borazjan Dept Comp Engn Bushehr Iran

fog computing allows services to be deployed on computing resources at the edge of the network to address the limitations of centralized cloud systems. However, the adoption of fog computing concepts is in the early stages, and there are still many challenges to benefiting from infrastructure in fog -cloud computing-based Networks (FCCN). One of them is known as Service Function Chain (SFC), which can use network software instances instead of costly dedicated hardware to share resources. Network Function Virtualization (NFV) technology separates hardware middleboxes such as gateways, firewalls, and set-top boxes from hardware and treats them as Virtual Network Functions (VNFs), where they can execute as software instances on decentralized nodes in the FCCN. VNFs are chained together in specific sequences that form SFCs. Meanwhile, deploying VNFs on nodes in the FCCN to accomplish SFC is an NP-Hard problem that can lead to efficient utilization of resources and reduce latency and cost. Recent research has performed SFC placement through heuristic algorithms that often cannot cope with the dynamic behavior of the network. In addition, existing works explicitly ignores SFC placement with the reuse of VNF instances. Hence, in this paper, we address the SFC placement problem by reusing VNFs through Deep Reinforcement Learning (DRL) based approaches. The proposed algorithm as a dynamic planning model can reconcile service costs and Quality of Service (QoS) by considering resource con-straints and dynamic distribution analysis of VNFs required in the FCCN. Here, the Asynchronous Advantage Actor-Critic (A3C) algorithm is used as a DRL approach with the aim of maximizing long-term cumulative reward. The simulation results through real-world data traces show that the proposed algorithm effectively improves the system performance and outperform the best result of benchmark methods ranging from 14% to 28% by considering the resource cost.(c) 2022 Published by Elsevier B.V. on b

关键词： fog-cloud computing Service function chains SFC placement Reuse of VNFs Deep reinforcement learning

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A Privacy-Preserving Access Control Scheme with Verifiable and Outsourcing Capabilities in fog-cloud computing 19th

A Privacy-Preserving Access Control Scheme with Verifiable a...

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19th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Cheng, Zhen Zhang, Jiale Qian, Hongyan Xiang, Mingrong Wu, Di Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Deakin Univ Fac Sci Engn & Built Environm Melbourne Vic 3216 Australia Univ Technol Sydney Ctr Artif Intelligence Sch Comp Sci Sydney NSW 2007 Australia

ISBN: (纸本)9783030389918;9783030389901

fog computing is a distribution system architecture which uses edge devices to provide computation, storage, and sharing at the edge of the network as an extension of cloud computing architecture, where the potential network traffic jams can be resolved. Whereas, the untrustworthy edge devices which contribute the computing resources may lead to data security and privacy-preserving issues. To address security issues and achieve fine-grained access control to protect privacy of users, ciphertext-policy attribute-based encryption (CP-ABE) mechanism has been well-explored, where data owners obtain flexible access policy to share data between users. However, the major drawback of CP-ABE system is heavy computational cost due to the complicated cryptographic operations. To tackle this problem, we propose a privacy-preserving access control (PPAC) scheme and the contributions are tri-folded: (1) we introduce outsourcing capability in fog-cloud computing (FCC) environment;(2) the outsource verification mechanism has been considered to guarantee the third party execute the algorithm correctly;(3) we design a partiality hidden method to protect the privacy information embedded in the access structures. The experimental results show that our proposed PPAC is efficient, economical and suitable for mobile devices with limited resources.

关键词： fog-cloud computing Attribute-based encryption Privacy-preserving Access control

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PGA: A Priority-aware Genetic Algorithm for Task Scheduling in Heterogeneous fog-cloud computing

PGA: A Priority-aware Genetic Algorithm for Task Scheduling ...

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IEEE Conference on Computer Communications Workshops (IEEE INFOCOM)

作者： Hoseiny, Farooq Azizi, Sadoon Shojafar, Mohammad Ahmadiazar, Fardin Tafazolli, Rahim Univ Kurdistan Dept Comp Engn & IT Sanandaj Iran Univ Surrey 6GIC ICS Guildford Surrey England Univ Kurdistan Dept Ind Engn Sanandaj Iran

ISBN: (纸本)9781665404433

fog-cloud computing has become a promising platform for executing Internet of Things (IoT) tasks with different requirements. Although the fog environment provides low latency due to its proximity to IoT devices, it suffers from resource constraints. This is vice versa for the cloud environment. Therefore, efficiently utilizing the fog-cloud resources for executing tasks offloaded from IoT devices is a fundamental issue. To cope with this, in this paper, we propose a novel scheduling algorithm in fog-cloud computing named PGA to optimize the multi-objective function that is a weighted sum of overall computation time, energy consumption, and percentage of deadline satisfied tasks (PDST). We take the different requirements of the tasks and the heterogeneous nature of the fog and cloud nodes. We propose a hybrid approach based on prioritizing tasks and a genetic algorithm to find a preferable computing node for each task. The extensive simulations evaluate our proposed algorithm to demonstrate its superiority over the state-or-the-art strategies.

关键词： fog-cloud computing Internet of Things (IoT) task scheduling multi-objective optimization genetic algorithm

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A heuristic scheduling approach for fog-cloud computing environment with stationary IoT devices

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2021年 180卷 102994-102994页

作者： Aburukba, Raafat O. Landolsi, Taha Omer, Dalia Amer Univ Shrjah Dept Comp Sci & Engn Sharjah U Arab Emirates

The emergence of the Internet of Things (IoT) paradigm has led to the rise of a variety of applications with different characteristics and Quality of Service (QoS) requirements. Those applications require computational power and have time sensitive requirements. cloud computing paradigm provides an illusion to consumers with unlimited computation resource power. However, cloud computing fails to deliver on the time-sensitive requirements of applications. The main challenge in the cloud computing paradigm is the associated delays from the edge IoT device to the cloud data center and from the cloud data center back to the edge device. fog computing extends limited computational services closer to the edge device to achieve the time sensitive requirement of applications. This work proposes a scheduling solution which adopts the three-tier fog computing architecture in order to satisfy the maximum number of requests given their deadline requirements. In this work, an optimization model using mixed integer programming is introduced to minimize deadline misses. The model is validated with an exact solution technique. The scheduling problem is known to be an NP-hard, and hence, exact optimization solutions are inadequate for a typical size problem in fog computing. Given the complex nature of the problem, a heuristic approach using the genetic algorithm (GA) is presented. The performance of the proposed GA was evaluated and compared against round robin and priority scheduling. The results show that the deadline misses of the proposed approach is 20%?55% better than the other techniques.

关键词： fog-cloud computing Task scheduling Mixed integer programming Number of deadline misses Genetic algorithm

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Dynamic IoT service placement based on shared parallel architecture in fog-cloud computing

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INTERNET OF THINGS 2023年 23卷

作者： Qin, Maoyuan Li, Minghai Yahya, Rebaz Othman Xian Technol Univ Sch Comp Sci & Engn Xian 710021 Shaanxi Peoples R China Xian Univ Architecture & Technol Sch Informat & Control Engn Xian 710055 Shaanxi Peoples R China Cihan Univ Erbil Coll Sci Dept Comp Sci Erbil Iraq

fog-cloud computing is a promising computing paradigm for processing and storing massive data produced by Internet of Things (IoT) devices. Considering that the fog computing nodes are distributed, heterogeneous and have limited resources, it is not possible to deploy all the services belonging to IoT applications on the fog environment. Therefore, an efficient and autonomous mechanism is needed to map IoT services on the fog environment. The problem becomes more complicated when the various requirements of services including response time and cost are considered. With this motivation, we introduce a mixed-integer linear programming model to address the fog Service Placement (FSP) problem in fog-cloud computing environments. This model is formulated as a multi-objective model with the aim of minimizing deadline violation, resource loss and service cost, and maximizing resource usage. Meanwhile, we propose an Elitism-based Genetic Algorithm to solve the FSP problem developed with a Shared Parallel Architecture (EGASPE). Our algorithm considers the efficient distribution of resources under certain Quality of Service (QoS) requirements and seeks a balance between exploration and exploitation. Also, EGASPE can save more resources for future requests by considering the deadline of IoT services over time. Extensive experiments have been performed to evaluate the performance of EGASPE on a synthetic fog environment. The obtained results show the effectiveness of EGASPE up to 6.5% on average compared to state-of-the-art algorithms.

关键词： Internet of things fog-cloud computing Service placement Genetic algorithm Shared parallel architecture

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