Fast and Secure Computational Offloading With Lagrange Coded Mobile Edge Computing

  title={Fast and Secure Computational Offloading With Lagrange Coded Mobile Edge Computing},
  author={Alia Asheralieva and Tao Dusit Niyato},
  journal={IEEE Transactions on Vehicular Technology},
This paper proposes a novel framework based on Lagrange coded computing (LCC) for fast and secure offloading of computing tasks in the mobile edge computing (MEC) network. The network is formed by multiple base stations (BSs) acting as “masters” which offload their computations to edge devices acting as “workers”. The framework aims to ensure efficient allocation of computing loads and bandwidths to workers, and providing them with proper incentives to finish their tasks by the specified… 

An Efficient Three-Party Authentication and Key Agreement Protocol for Privacy-Preserving of IoT Devices in Mobile Edge Computing

An efficient three-party authentication and key agreement protocol without using bilinear pairings is designed that realized authentication among users, edge devices and cloud server, and at the same time, three parties conduct key agreement to obtain a common session key.

Secure Federated Learning Based on Coded Distributed Computing

This paper develops the first integrated FL-CDC model that represents a low-complexity approach for enhancing security of FL systems and implements the model for predicting the traffic slowness in vehicular applications and proves that the model can effectively secure the system even if the number of malicious devices is large.



Optimal Computational Offloading and Content Caching in Wireless Heterogeneous Mobile Edge Computing Systems With Hopfield Neural Networks

  • Alia Asheralieva
  • Computer Science
    IEEE Transactions on Emerging Topics in Computational Intelligence
  • 2021
This paper explores the problem of joint computational offloading and content caching (OCP) in the wireless heterogeneous mobile edge computing (MEC) system, where each small-cell base station (BS)

Double Auction-Based Resource Allocation for Mobile Edge Computing in Industrial Internet of Things

Two double auction schemes with dynamic pricing in MEC are proposed, namely a breakeven-based double auction (BDA) and a more efficient dynamic pricing based double Auction (DPDA), to determine the matched pairs between IIoT MDs and edge servers, as well as the pricing mechanisms for high system efficiency, under the locality constraints.

Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

This paper designs a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics.

Timely-Throughput Optimal Coded Computing over Cloud Networks

This work considers a two-state Markov model for variability of computing speed in cloud networks, and develops a dynamic computation strategy called Lagrange Estimate-and-Allocate (LEA) strategy, which achieves the optimal timely computation throughput.

Price-Based Resource Allocation for Edge Computing: A Market Equilibrium Approach

A new market-based framework for efficiently allocating resources of heterogeneous capacity-limited edge nodes to multiple competing services at the network edge is proposed and it is shown that the equilibrium allocation is Pareto-optimal and satisfies desired fairness properties including sharing incentive, proportionality, and envy-freeness.

Learning-Based Mobile Edge Computing Resource Management to Support Public Blockchain Networks

A hierarchical learning framework is developed based on fully- and partially-observable Markov decision models of the decision processes of the SP and miners for a public blockchain realized in the mobile edge computing (MEC) network, where the blockchain miners compete against each other to solve the proof-of-work puzzle and win a mining reward.

Coded Computing in Unknown Environment via Online Learning

An online learning policy is proposed called online coded computing policy, which provably achieves asymptotically-optimal performance in terms of regret loss compared with the optimal offline policy.

Distributed Multi-Dimensional Pricing for Efficient Application Offloading in Mobile Cloud Computing

This is the first paper that applies economic theories and pricing mechanisms to manage application offloading in mobile cloud systems and it is proved that the proposed pricing mechanism can significantly improve the system performance.

On Batch-Processing Based Coded Computing for Heterogeneous Distributed Computing Systems

This paper focuses on practical computing systems with heterogeneous computing resources, and proposes a novel CDC approach, called batch-processing based coded computing (BPCC), which exploits the fact that every computing node can obtain some coded results before it completes the whole task.

Limited-Sharing Multi-Party Computation for Massive Matrix Operations

It is shown that for basic operation such as addition and multiplication, the proposed scheme offers order wise gain, in terms of number of servers needed, compared to the approaches formed by concatenation of job splitting and conventional MPC approaches.