Decentralized and Revised Content-Centric Networking-Based Service Deployment and Discovery Platform in Mobile Edge Computing for IoT Devices

@article{Nguyen2019DecentralizedAR,
  title={Decentralized and Revised Content-Centric Networking-Based Service Deployment and Discovery Platform in Mobile Edge Computing for IoT Devices},
  author={Tien-Dung Nguyen and Eui-nam Huh and Minho Jo},
  journal={IEEE Internet of Things Journal},
  year={2019},
  volume={6},
  pages={4162-4175}
}
Mobile edge computing (MEC) is used to offload services (tasks) from cloud computing in order to deliver those services to mobile Internet of Things (IoT) devices near mobile edge nodes. However, even though there are advantages to MEC, we face many significant problems, such as how a service provider (SP) deploys requested services efficiently on a destination MEC node, and how to discover existing services in neighboring MEC nodes to save edge resources. In this paper, we present a… 
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24 Citations
Background Citations
11
Methods Citations
5

24 Citations

Utility-Centric Service Provisioning in Multi-Access Edge Computing
TLDR
This paper forms the problem of service provisioning in MEC as an Integer Nonlinear Programming (INLP), aiming at maximizing the total utility of all users, and proposes a Nested-Genetic Algorithm (Nested-GA) consisting of two genetic algorithms, each of whom solves a sub-problem regarding service placement or request scheduling decisions.
Serving at the Edge: An Edge Computing Service Architecture Based on ICN
TLDR
An edge computing service architecture based on Information-Centric Networking is proposed, which contains the edge Computing service session model, service request forwarding strategies, and service dynamic deployment mechanism, which can avoid unnecessary network transmissions and reduce the service completion time.
Delay-aware concurrent data management method for IoT collaborative mobile edge computing environment
An Edge-Computing Based Task-Unloading Technique with Privacy Protection for Internet of Connected Vehicles
TLDR
The goal is to determine which of the algorithms has the best performance, based on their latency and offloading capacity, in edge computing by comparing their results, and to determine the best-performing method, the BES algorithm.
Green and Secure Computation Offloading for Cache-Enabled IoT Networks
TLDR
A resource-constrained optimization model is formulated, which minimizes the overall cost combining energy consumption and probable security-breach cost, and a two-stage heuristic algorithm is developed to find an acceptable solution in polynomial time.
Energy-Efficient Edge Offloading in Heterogeneous Industrial IoT Networks for Factory of Future
TLDR
This work jointly investigates the partial task offloading, computation, and communication (licensed and unlicensed) resource allocation problem in the trade-off between overall power consumption and quality of service (QoS) satisfaction, and proposed a mechanism call 5G and NR-U opportunity-cost-based offloading algorithm (5G/NR-U OCBOA) to optimize resource allocation.
Task Allocation Optimization Scheme Based on Queuing Theory for Mobile Edge Computing in 5G Heterogeneous Networks
TLDR
The task assignment algorithm based on quasi-Newton interior point (TA-QNIP) method is developed and it is demonstrated that the proposed algorithm can reduce the total consumption of MDs effectively and is closer to the objective function optimal solution.
Enhanced LISP Mapping System for Optimizing Service Path in Edge Computing Environment
TLDR
An enhanced control system using the locator/identifier separation protocol to provide service path optimization with mobility management and can provide an optimal service path to mobile users through fewer message exchanges and less delay than existing approaches are proposed.
An Efficient Service Function Chains Orchestration Algorithm for Mobile Edge Computing
TLDR
This work proposes a dynamic orchestration scheme based on an improved column generation algorithm that provides more than 30% reduction of run time and about 12% improvement in service deployment success ratio compared to the Viterbi algorithm based mechanism.
Computing Unloading Strategy of Massive Internet of Things Devices Based on Game Theory in Mobile Edge Computing
TLDR
The proposed computing offload scheme significantly improves the amount of data transmission and reduces the energy consumption of the task and has better convergence characteristics, compared with other schemes.
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