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Artificial Intelligence Based data offloading Technique for Secure MEC Systems

Computers, Materials & Continua 2022年第8期72卷 2783-2795页

作者： Fadwa Alrowais Ahmed S.Almasoud Radwa Marzouk Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammed Rizwanullah Abdelwahed Motwakel Ishfaq Yaseen Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRafha StreetRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems Faculty of Computer and Information TechnologySana’a University61101Yemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(QoS)of end users by offloading the high computation ***,the introduction of deep learning(DL)and hardware technologies paves amethod in detecting the current traffic status,data offloading,and cyberattacks in *** study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC(AIMDO-SMEC)*** proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks(SNN)to determine the traffic status in the MEC ***,an adaptive sampling cross entropy(ASCE)technique is utilized for data offloading in MEC ***,the modified salp swarm algorithm(MSSA)with extreme gradient boosting(XGBoost)technique was implemented to identification and classification of cyberattack that exist in the MEC *** examining the enhanced outcomes of the AIMDO-SMEC technique,a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDOSMEC technique with the minimal completion time of tasks(CTT)of 0.680.

关键词： data offloading mobile edge computing security machine learning artificial intelligence XGBoost salp swarm algorithm

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Hierarchical data Aggregation with data offloading Scheme for Fog Enabled IoT Environment

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Computer Systems Science & Engineering 2023年第3期44卷 2033-2047页

作者： P.Nalayini R.Arun Prakash Department of Computer Science and Engineering Kings College of EngineeringPunalkulam613303TamilnaduIndia Department of Computer Science and Engineering University College of EngineeringAriyalur621704TamilnaduIndia

Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things(IoT)*** the emergence of IoT-based services,the industry of internet-based devices has *** number of these devices has raised from millions to billions,and it is expected to increase further in the near ***,additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in real-time without affecting the user *** data aggregation models for Fog enabled IoT environ-ments possess high computational complexity and communication ***-fore,in order to resolve the issues and improve the lifetime of the network,this study develops an effective hierarchical data aggregation with chaotic barnacles mating optimizer(HDAG-CBMO)*** HDAG-CBMO technique derives afitness function from many relational matrices,like residual energy,average distance to neighbors,and centroid degree of target ***,a chaotic theory based population initialization technique is derived for the optimal initial position of ***,a learning based data offloading method has been developed for reducing the response time to IoT user requests.A wide range of simulation analyses demonstrated that the HDAG-CBMO technique has resulted in balanced energy utilization and prolonged lifetime of the Fog assisted IoT networks.

关键词： Internet of things fog computing barnacles mating optimizer data offloading data aggregation

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UAV-Assisted data offloading for Smart Container in Offshore Maritime Communications

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China Communications 2022年第1期19卷 153-165页

作者： Yanpeng Dai Bin Lin Yudi Che Ling Lyu School of Information Science and Technology Dalian Maritime UniversityDalian 116026China Network Communication Research Centre Peng Cheng LaboratoryShenzhen 518052China State Key Laboratory of Integrated Services Networks Xidian UniversityShaanxi 710071China

Smart containers have been extensively applied in the maritime industry by embracing the Internet of Things to realize container status monitoring and data offloading without human ***, the offloading rate and delay in the offshore region are limited by the coverage of the onshore base station(BS). In this paper, we investigate the unmanned aerial vehicle(UAV)-assisted data offloading for smart containers in offshore maritime communications where the UAV is as a relay node between smart containers and onshore BS. We first consider the mobility of container vessel in the offshore region and establish a UAV-assisted data offloading model. Based on this model, a data offloading algorithm is proposed to reduce the average offloading delay under data-size requirements and available energy constraints of smart containers. Specifically, the convex-concave procedure is used to update time-slot assignment,offloading approach selection, and power allocation in an iterative manner. Simulation results show that the proposed algorithm can efficiently reduce average offloading delay and increase offloading success ***, it is shown that the UAV relay cannot always bring the performance gain on offloading delay especially in the close-to-shore area, which could give an insight on the deployment of UAV relay in offshore communications.

关键词： data offloading smart container unmanned aerial vehicle maritime Internet of Things

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Joint College Admissions Game and Auction Theory for data offloading in Heterogeneous Networks

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Chinese Journal of Electronics 2018年第1期27卷 168-174页

作者： WANG Gang LIU Peizhen YANG Zhao XUE Rui School of Electrical and Information Engineering Beihang University

Mobile data offloading through third-party Femtocell access points(FAPs) is an emerging technology which is used to alleviate congestion in cellular *** propose a distributed algorithm that combines college admissions game with auction theory to achieve the maximum social welfare of offloading and guarantee the Quality of service(Qo S) of Mobile users(MUs). As the optimal problem is NP-hard, the college admissions game is developed to solve the sub-problem of matching between FAPs and MUs. The Second revenue sealed-bid auction with a reserve price(SRSARP) algorithm is developed to solve another sub-problem of matching between Wireless operators(WOs) and FAPs. The simulation results reveal that the distributed algorithm we proposed achieves the performance very close to that of the centralized method and significantly better than those of the random and Maxprice algorithms.

关键词： data offloading College admissions game Auction theory

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An optimization model for fragmentation-based routing in delay tolerant networks

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Science China(Information Sciences) 2016年第2期59卷 185-200页

作者： Xuyan BAO Yong ZHANG Da GUO Mei SONG School of Electronic Engineering Beijing University of Posts and Telecommunications

The explosive growth of mobile data traffic has raised big penetration to cellular network. Fortunately, offloading part of traffic through Delay Tolerant Network(DTN) would be a promising option to relieve load pressure. However, the existing routing strategies designed for DTNs are inappropriate in offloading scenario, where i) large data item would take on the dominance of mobile traffic, ii) centralized control can be exploited with merge between DTN and cellular network. To improve the routing performance of large data transmission, we propose a novel optimization model by creating two-tier solution space based on fragmentation,maximizing the probability that the requested data item is successfully delivered before expiration, taking limited buffer space as constraint metric. Moreover, a specific routing scheme is instantiated from our model, which is supported by uniform fragmentation and fine-grained path selection. Extensive trace-driven simulations show that our scheme is more appropriate for offloading case with outstanding performance in terms of replication overhead and acceptable routing capability.

关键词： multi-path routing delay tolerant networks optimization model data offloading stochastic orderings

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