• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4081-4098
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• 2022

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices (EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.211-232
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• 2024

In this paper, we consider the resource allocation and offloading decisions of device-to-device (D2D) cooperative UAV-assisted mobile edge computing (MEC) system, where the device with task request is served by unmanned aerial vehicle (UAV) equipped with MEC server and D2D device with idle resources. On the one hand, to ensure the fairness of time-delay sensitive devices, when UAV computing resources are relatively sufficient, an optimization model is established to minimize the maximum delay of device computing tasks. The original non-convex objective problem is decomposed into two subproblems, and the suboptimal solution of the optimization problem is obtained by alternate iteration of two subproblems. On the other hand, when the device only needs to complete the task within a tolerable delay, we consider the offloading priorities of task to minimize UAV computing resources. Then we build the model of joint offloading decision and power allocation optimization. Through theoretical analysis based on KKT conditions, we elicit the relationship between the amount of computing task data and the optimal resource allocation. The simulation results show that the D2D cooperation scheme proposed in this paper is effective in reducing the completion delay of computing tasks and saving UAV computing resources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4025-4041
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• 2020

Mobile edge computing (MEC) is capable of providing services to smart devices nearby through radio access networks and thus improving service experience of users. In this paper, an offloading strategy for the joint optimization of computing and communication resources in multi-user and multi-MEC overlapping scene was proposed. In addition, under the condition that wireless transmission resources and MEC computing resources were limited and task completion delay was within the maximum tolerance time, the optimization problem of minimizing energy consumption of all users was created, which was then further divided into two subproblems, i.e. offloading strategy and resource allocation. These two subproblems were then solved by the game theory and Lagrangian function to obtain the optimal task offloading strategy and resource allocation plan, and the Nash equilibrium of user offloading strategy games and convex optimization of resource allocation were proved. The simulation results showed that the proposed algorithm could effectively reduce the energy consumption of users.

• Journal of KIISE
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• v.42 no.6
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• pp.707-712
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• 2015

Mobile cloud computing is a technology that uses cloud services to overcome resource constrains of a mobile device, and it applies the computation offloading scheme to transfer a portion of a task which should be executed from a mobile device to the cloud. If the communication cost of the computation offloading is less than the computation cost of a mobile device, the mobile device commits a certain task to the cloud. The previous cost analysis models, which were used for separating functions running on a mobile device and functions transferring to the cloud, only considered the amount of data transfer and response time as the offloading cost. In this paper, we proposed a new task partitioning scheme that considers the frequency of function calls and data synchronization, during the cost estimation of the computation offloading. We also verified the energy efficiency of the proposed scheme by using experimental results.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.9
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• pp.263-272
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• 2023

Industrial Internet of Things (IIoT) is an important factor in increasing production efficiency in industrial sectors, along with data collection, exchange and analysis through large-scale connectivity. However, as traffic increases explosively due to the recent spread of IIoT, an allocation method that can efficiently process traffic is required. In this thesis, I propose a two-stage task offloading decision method to increase successful task throughput in an IIoT environment. In addition, I consider a hybrid offloading system that can offload compute-intensive tasks to a mobile edge computing server via a cellular link or to a nearby IIoT device via a Device to Device (D2D) link. The first stage is to design an incentive mechanism to prevent devices participating in task offloading from acting selfishly and giving difficulties in improving task throughput. Among the mechanism design, McAfee's mechanism is used to control the selfish behavior of the devices that process the task and to increase the overall system throughput. After that, in stage 2, I propose a multi-armed bandit (MAB)-based task offloading decision method in a non-stationary environment by considering the irregular movement of the IIoT device. Experimental results show that the proposed method can obtain better performance in terms of overall system throughput, communication failure rate and regret compared to other existing methods.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.226-238
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• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.5
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• pp.139-146
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• 2022

With the recent development of the Internet of Things (IoT) and the convergence of vehicles and IT technologies, high-performance applications such as autonomous driving are emerging, and multi-access edge computing (MEC) has attracted lots of attentions as next-generation technologies. In order to provide service to these computation-intensive tasks in low latency, many methods have been proposed to partition tasks so that they can be performed through cooperation of multiple MEC servers(MECSs). Conventional methods related to task partitioning have proposed methods for partitioning tasks on vehicles as mobile devices and offloading them to multiple MECSs, and methods for offloading them from vehicles to MECSs and then partitioning and migrating them to other MECSs. In this paper, the performance of task partitioning methods using offloading and migration is compared and analyzed in terms of service delay, blocking rate and energy consumption according to the method of selecting partitioning targets and the number of partitioning. As the number of partitioning increases, the performance of the service delay improves, but the performance of the blocking rate and energy consumption decreases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.383-403
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• 2021

With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.12
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• pp.363-370
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• 2023

Object detection technology that accurately recognizes the road and surrounding conditions is a key technology in the field of autonomous driving. In the field of autonomous driving, object detection technology requires real-time performance as well as accuracy of inference services. Task offloading technology should be utilized to apply object detection technology for accuracy and real-time on resource-constrained devices rather than high-performance machines. In this paper, experiments such as performance comparison of task offloading, performance comparison according to input image resolution, and performance comparison according to camera object resolution were conducted and the results were analyzed in relation to the application of task offloading for real-time object detection of autonomous driving in resource-constrained devices. In this experiment, the low-resolution image could derive performance improvement through the application of the task offloading structure, which met the real-time requirements of autonomous driving. The high-resolution image did not meet the real-time requirements for autonomous driving due to the increase in communication time, although there was an improvement in performance. Through these experiments, it was confirmed that object recognition in autonomous driving affects various conditions such as input images and communication environments along with the object recognition model used.

• KIISE Transactions on Computing Practices
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• v.23 no.6
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• pp.392-396
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• 2017

Although mobile device-related technologies have developed rapidly, many problems arising from resource constraints have not been solved. Computation offloading that uses resources of cloud servers over the Internet was proposed to overcome physical limitations, and many studies have been conducted in terms of energy saving. However, completing tasks within their deadlines is more important than saving energy in real-time applications. In this paper, we proposed an offloading decision scheme considering the scheduling latency in the cloud to support real-time applications. The proposed scheme can improve the reliability of real-time tasks by comparing the estimated laxity of offloading a task with the estimated laxity of executing a task in a mobile device and selecting a more effective way to satisfy the task's deadline.