Efficient and Secure Multi-User Multi-Task Computation Offloading for Mobile-Edge Computing in Mobile IoT Networks

Elgendy, Ibrahim A.; Zhang, Weizhe; Zeng, Yiming; He, Hui; Tian, Yu-Chu; Yang, Yuanyuan

doi:10.1109/tnsm.2020.3020249

Cited by 123 publications

(43 citation statements)

References 42 publications

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“…Considering the characteristics of small cellular networks and mobile edge computing, Guo [7] used a distributed three-stage iterative method based on the energy harvesting SCN combined with the MEC environment to realize the network's green load management and computing resource allocation. Elgendy et al [8] conducted joint processing of wireless resources and computing resources, adopted JPEG and MPEG4 compression algorithms to reduce transmission overhead, and introduced a security layer to protect transmitted data from network attacks. On this basis, an 2…”

Section: Related Workmentioning

confidence: 99%

Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

Liang

Wang

2021

Mobile Information Systems

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As computing-intensive mobile applications become increasingly diversified, mobile devices’ computing power is hard to keep up with demand. Mobile devices migrate tasks to the Mobile Edge Computing (MEC) platform and improve the performance of task processing through reasonable allocation and caching of resources on the platform. Small cellular networks (SCN) have excellent short-distance communication capabilities, and the combination of MEC and SCN is a promising research direction. This paper focuses on minimizing energy consumption for task migration in small cellular networks and proposes a task migration energy optimization strategy with resource caching by combining optimal stopping theory with migration decision-making. Firstly, the process of device finding the MEC platform with the required task processing resources is formulated as the optimal stopping problem. Secondly, we prove an optimal stopping rule’s existence, obtain the optimal processing energy consumption threshold, and compare it with the device energy consumption. Finally, the platform with the best energy consumption is selected to process the task. In the simulation experiment, the optimization strategy has lower average migration energy consumption and higher average data execution energy efficiency and average distance execution energy efficiency, which improves task migration performance by 10% ∼ 60%.

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Section: Related Workmentioning

confidence: 99%

Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

Liang

Wang

2021

Mobile Information Systems

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“…In addition, it provides the opportunity to serve better streaming services, which are both latencysensitive and bandwidth-intensive such as Google Stadia and Netflix. Moreover, edge computing architecture avoids uploading/downloading of massive files and prevents pre-processing of offloading tasks, which contributes to minimize the overall service time [12,13]. However, managing edge-cloud computing resources efficiently and handling computation tasks for latencysensitive applications is a critical issue [14] which could require proposing an efficient task scheduling technique to enhance the overall service performance and minimize the delay of offloaded tasks.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for IoT tasks offloading in edge-cloud environments

Almutairi

Aldossary

2021

J Cloud Comp

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Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

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“…For the game-theoretic methodology, using game-theoretic approaches to determine pure Nash equilibria is an efficient non-deterministic approach, and game-theoretic-based techniques are widely utilized in this field. Chen et al [ 13 ] jointly formulated the computation offloading problem and solved it using a game-theoretic approach by showing the existence of a Nash equilibrium; Wang et al [ 14 ] designed a partial computation offloading method that optimized both communication and resources; Ma et al [ 15 ] proposed an energy-aware computation offloading algorithm; Dong et al [ 16 ] proposed an evolutionary game approach to optimize the task offloading in edge computing; Elgendy et al [ 17 ] proposed an efficient offloading algorithm achieving the computation offloading decision for computation tasks, which use the method of finding the near-optimal computation offloading and compression decision; Zhou and Jadoon [ 18 ] proposed a partial computation offloading strategy based on game theory for multi-user edge computing; Wang et al [ 19 ] proposed a decentralized computation offloading algorithm with multi-agent imitation learning.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Huang

Zhai

Chen

et al. 2021

Sensors

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Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

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Efficient and Secure Multi-User Multi-Task Computation Offloading for Mobile-Edge Computing in Mobile IoT Networks

Cited by 123 publications

References 42 publications

Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

A novel approach for IoT tasks offloading in edge-cloud environments

Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

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