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Secure Edge Computing Management based on Independent Microservices Providers for Gateway-Centric IoT Networks

Wenquan Jin, Rongxu Xu, Taewan You, Yong-Geun Hong, Dohyeun Kim
2020 IEEE Access  
Nevertheless, computing and networking resources are constrained for devices in the network edge. Providing secure services from edge computing is a challenge based on constrained resources.  ...  Edge computing is an emerging computing paradigm that distributes the computational capability to the edge of networks for enabling the computation near to the environment where the sensors and actuators  ...  The edge computing enables the computation to be performed at the edge of the network based on the interactions with other network elements including cloud servers and IoT devices.  ... 
doi:10.1109/access.2020.3030297 fatcat:yjd5ejlpxfbj3ljc5sxonrz7pi

Resource Prediction-Based Edge Collaboration Scheme for Improving QoE

Jinho Park, Kwangsue Chung
2021 Sensors  
In this paper, we propose a resource prediction-based edge collaboration scheme for improving QoE. We estimate computing resource usage based on the tasks received from the devices.  ...  According to the predicted computing resources, the edge server probabilistically collaborates with other edge servers. The proposed scheme is based on the delay model, and uses the greedy algorithm.  ...  The device is connected to the base station via a wireless network, and a large number of devices can be connected to the edge server.  ... 
doi:10.3390/s21248500 pmid:34960593 pmcid:PMC8708411 fatcat:fhsswn3mrjctpeahdlpfhuukyi

Enhanced Service Framework based on Microservice Management and Client Support Provider for Efficient User Experiment in Edge Computing Environment

Rongxu Xu, Wenquan Jin, DoHyeun Kim
2021 IEEE Access  
For providing the edge computing service and management in the network edge, this paper presents an edge-computing architecture that provides various functions through microservice modules on the edge  ...  Leveraging the edge computing paradigm, computing resources are deployed in the network edge to provide heterogeneous services.  ...  EdgeX foundry proposed a standard IoT edge computing framework to services based on the microservices in the edge of networks using constrained devices such as Raspberry Pi [30] .  ... 
doi:10.1109/access.2021.3102595 fatcat:jd4yai3klven5m5ge5fgjb5ygy

A Hybrid Artificial Neural Network for Task Offloading in Mobile Edge Computing [article]

Raby Hamadi
2022 arXiv   pre-print
Afterwards, the tasks generated by devices will be fed to a hybrid Artificial Neural Network (ANN) model that predicts, based on these tasks, the profiles, i.e., features, of the edge computers with enough  ...  Finally, we choose for each task the edge computer that is expected to provide the fastest response time.  ...  PROPOSED HYBRID ANN MODEL FOR EDGE COMPUTER OFFLOADING In this study, we propose an offloading method for tasks that are submitted by IoT devices to be processed by an edge device within the same network  ... 
arXiv:2206.06301v1 fatcat:5yugkrgyq5cotb6fdf33xi7w6a

Energy Efficient Consensus Approach of Blockchain for IoT Networks with Edge Computing

Shivani Wadhwa, Shalli Rani, Kavita Kavita, Sahil Verma, Jana Shafi, Marcin Wozniak
2022 Sensors  
This paper proposes the consensus approach on the basis of PoW, where a single miner is selected for mining the task. The mining task is offloaded to the edge networking.  ...  The miner is selected on the basis of the digitization of the specifications of the respective machines.  ...  Acknowledgments: Jana Shafi would like to thank the Deanship of Scientific Research, Prince Sattam bin Abdul Aziz University, for supporting this work.  ... 
doi:10.3390/s22103733 pmid:35632142 pmcid:PMC9147960 fatcat:pb3mfkjqjfabtlzrxokrjfe3zi

An Analysis of Fog Computing Data Placement Algorithms

Daniel Maniglia Amancio da Silva, Godwin Asamooning, Hector Orrillo, Rute C. Sofia, Paulo M. Mendes
2020 arXiv   pre-print
The paper describes the three algorithms(Cloud-only, Mapping, Edge-ward) in the context of an Internetof Things scenario, which has been based on an e-Health systemwith variations in applications and network  ...  Resultsachieved show that edge placement strategies are beneficial toassist cloud computing in lowering latency and cloud energyexpenditure.  ...  Fog Computing, also known as Edge Computing [2] , is a set of paradigms that assist computation, networking, and storage between the edges of the network and the Cloud.  ... 
arXiv:2005.11847v1 fatcat:s7pi2z62nvg5zbrljs6wmvubbu

A Discussion on Context-awareness to Better Support the IoT Cloud/Edge Continuum

Daniel Maniglia Amancio Da Silva, Rute C. Sofia
2020 IEEE Access  
This paper debates on notions of context-awareness as a relevant asset of networking and computing architectures for an Internet of Things (IoT), in particular in regards to a smoother support of the the  ...  networking operation between Cloud and Edge.  ...  Sumit et al., propose an Edge selection algorithm for AR applications (2)  ... 
doi:10.1109/access.2020.3032388 fatcat:w75pvoj5hrbyvocrlorgyfuzsa

A Discussion on Context-awareness to BetterSupport the IoT Cloud/Edge Continuum [article]

Daniel M. A. Silva, Rute C. Sofia
2020 arXiv   pre-print
This paper debates on notions of context-awareness as a relevant asset of networking and computing architectures for an Internet of Things (IoT), in particular in regards to a smoother support of the the  ...  networking operation between Cloud and Edge.  ...  based pol- icy [100] Network policies Edge/Cloud selection Delay threshold 9 ThriftyEdge [19] Task resource usage Resource-efficient computation Node selection Latency, minimum node usage  ... 
arXiv:2010.04563v1 fatcat:aeez7pvnarez3gaudvl4rolxii

SoftSystem: Smart Edge Computing Device Selection Method for IoT Based on Soft Set Technique

Muhammad Shafiq, Zhihong Tian, Ali Kashif Bashir, Korhan Cengiz, Adnan Tahir, Rahul Yadav
2020 Wireless Communications and Mobile Computing  
It is evident that our proposed system is effective for the selection of edge computing devices in the IoT network.  ...  The critical issue in these networks is the effective edge computing IoT device selection whenever there are several edge nodes to carry information.  ...  Based on our study knowledge, it is the first time to integrate a soft set technique for the selection of IoT devices in the IoT network (4) Afterward, we put forward the results and selected edge computing  ... 
doi:10.1155/2020/8864301 fatcat:xow5u4vspvdxlhd3peijt6ohpy

Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G [article]

Shashank Jere, Qiang Fan, Bodong Shang, Lianjun Li, Lingjia Liu
2020 arXiv   pre-print
By provisioning computing resources at the network edge, Mobile Edge Computing (MEC) has become a promising technology capable of collaborating with distributed IoT devices to facilitate federated learning  ...  On the other hand, as various IoT devices have different training datasets which have varying influence on the accuracy of the global model derived at the edge server, an IoT device selection scheme is  ...  • We present the problem of selecting IoT devices for federated learning based on their different weights in enhancing the training accuracy while the computing and storage resources of the edge server  ... 
arXiv:2007.08030v1 fatcat:zpsonkcmzjb6fb2stvrtzczcxq

Distributed Slice Selection-based Computation Offloading for Intelligent Vehicular Networks

Jianhang Tang, Yiqun Duan, Yi Zhou, Jiangming Jin
2021 IEEE Open Journal of Vehicular Technology  
In this work, a slice selection-based online offloading (SSOO) algorithm is proposed for distributed intelligence in future vehicular networks.  ...  Index Terms-Resource slice, slice selection, computation offloading, distributed intelligence J. Tang is with the  ...  Computation Offloading DRL-based technologies are promising solutions for computation offloading in vehicular networks [21] - [25] . Ke et al.  ... 
doi:10.1109/ojvt.2021.3087355 fatcat:otijw2shd5cijb32casdf35ksm

Dynamic Inference Approach Based on Rules Engine in Intelligent Edge Computing for Building Environment Control

Wenquan Jin, Rongxu Xu, Sunhwan Lim, Dong-Hwan Park, Chanwon Park, Dohyeun Kim
2021 Sensors  
Moreover, for bridging the Internet of Things (IoT) device network to the Internet, the gateway provides device management and proxy to enable device access to web clients.  ...  The dynamic inference approach is provided based on the rules engine that is deployed on the edge gateway to select an inference function by the triggered rule.  ...  Intelligent edge computing is enabled based on the edge gateway that is deployed in the entry of the network edge to provide services for device management, device proxy, client service, intelligent service  ... 
doi:10.3390/s21020630 pmid:33477481 fatcat:ufzdrejx4jcdljbu5emtosk3nu

Mobile Edge Cloud: Opportunities and Challenges [article]

Sayed Chhattan Shah
2018 arXiv   pre-print
Compared to traditional mobile distributed computing systems, mobile edge cloud introduces several complex challenges due to the heterogeneous computing environment, heterogeneous and dynamic network environment  ...  In mobile edge cloud, multiple mobiles and stationary devices interconnected through wireless local area networks are combined to create a small cloud infrastructure at a local physical area such as a  ...  Whereas in mobile infrastructure-based cloud, mobile devices are integrated with a cloud computing system through an infrastructure-based communication network such as cellular network.  ... 
arXiv:1811.01929v1 fatcat:moa7c7l335hoffll5allfselu4

Moving to the Edge-Cloud-of-Things: Recent Advances and Future Research Directions

Hind Bangui, Said Rakrak, Said Raghay, Barbora Buhnova
2018 Electronics  
However, cloud services are still far from IoT devices. Notably, the transmission of IoT data experiences network issues, such as high latency.  ...  Recently, edge cloud computing has been proposed to bring cloud services closer to the IoT end-users, becoming a promising paradigm whose pitfalls and challenges are not yet well understood.  ...  networks (e.g., base stations), while running cloud computing capabilities at the edge of the network.  ... 
doi:10.3390/electronics7110309 fatcat:p7ia27q7znbvfgctuzlcog4jzu

Communication-Computation Efficient Device-Edge Co-Inference via AutoML [article]

Xinjie Zhang, Jiawei Shao, Yuyi Mao, Jun Zhang
2021 arXiv   pre-print
Device-edge co-inference, which partitions a deep neural network between a resource-constrained mobile device and an edge server, recently emerges as a promising paradigm to support intelligent mobile  ...  By selecting a suitable model split point and a pair of encoder/decoder for the intermediate feature vector, this problem is casted as a sequential decision problem, for which, a novel automated machine  ...  With the aid of MEC, DNN models can be deployed at an edge server with relatively abundant computational resources, and thus mobile devices can offload their raw data for server-based inference [2] .  ... 
arXiv:2108.13009v2 fatcat:v3aze4p525cafdmjrylfobedja
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