A Resource Allocation Scheme for Joint Optimizing Energy Consumption and Delay in Collaborative Edge Computing-Based Industrial IoT

Jin, Zilong; Zhang, Chengbo; Jin, Yuanfeng; Zhang, Lejun; Su, Jian

doi:10.1109/tii.2021.3125376

Cited by 26 publications

(14 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance comparison with different schemes is shown in Fig. 4, where we consider our proposal with the case without robust design, i.e., using the deterministic constraints as in ( 2) and ( 9), rather than (18) and (19), to solve the problem, while also evaluating the cases to offload all the data or locally process all the data. In particular, Figs.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In [17], the authors consider the computation offloading from IIoT to fog servers, while minimizing the energy consumption subject to delay constraints. In [18], the authors investigate the association and offloading strategy among multiple edge servers and IIoT devices for energy minimization. In [19], the authors propose an energy-efficient dispersed computing paradigm to jointly optimize the computing tasks and network resources.…”

Section: Related Workmentioning

confidence: 99%

“…In this respect, we exploit the Bernstein-type inequality to convert the constraints into deterministic forms to facilitate the discussion [34]. Specifically, for the UAV flying constraint (18), by substituting the waypoint uncertainties in ( 16) into the speed condition, i.e., the term within the probability in (18), we have that…”

Section: Robust Optimization Against Uav Jittering a Problem Formulat...mentioning

confidence: 99%

See 2 more Smart Citations

Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things

Tang¹,

Zhang²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

Efficient data processing and computation are essential for the industrial Internet of things (IIoT) to empower various applications, which yet can be significantly bottlenecked by the limited energy capacity and computation capability of the IIoT nodes. In this paper, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering. Specifically, we propose a joint design on trajectory and offloading strategies to minimize energy consumption due to local and edge computation, as well as data transmission. We particularly address the UAV jittering that induces Gaussian-distributed uncertainties associated with flying waypoints, resulting in probabilistic-form flying speed and data offloading constraints. We exploit the Bernstein-type inequality to reformulate the constraints in deterministic forms and decompose the energy minimization to solve for trajectory and offloading separately within an alternating optimization framework. The subproblems are then tackled with the successive convex approximation technique. Simulation results show that our proposal strictly guarantees robustness under uncertainties and effectively reduces energy consumption as compared with the baselines.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Robust Optimization Against Uav Jittering a Problem Formulat...mentioning

confidence: 99%

See 1 more Smart Citation

Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things

Tang¹,

Zhang²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The emergence of edge computing technology alleviates the pressure of core network. Storage, computing and other services can be distributed to the edge network [4], [5]. The core network can be divided into several domains.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Based Edge Computing Network Aided Resource Allocation Algorithm for Smart Grid

et al. 2023

View full text Add to dashboard Cite

The dramatic increase in the volume of users and services makes scheduling network resources for smart grids a key challenge. Network slicing is an important technology to solve this problem. We introduce edge computing networks into the smart grid to intelligently allocate resources based on users' quality of service (QoS) and available resources. However, existing heuristic resource scheduling algorithms often lead to resource fragmentation and thus fall into local optima. To this end, we propose a deep reinforcement learning (DRL)-based virtual network embedding algorithm to optimize the resource allocation strategy of smart grids from a network virtualization perspective. We extract the network properties of the smart grid to construct a policy network as a training environment for DRL agents. Finally, DRL derives the probability of each node being embedded based on the extracted attributes of edge computing nodes and completes user request (UR) embedding based on this probability. The experimental results show that the algorithm proposed in this paper has excellent performance with guaranteed low latency, 21% improvement in long-term revenue and 5.6% improvement in UR success rate compared with the other two algorithms.

show abstract

“…Based on the above description, how to make a reasonable offloading decision and resource allocation scheme subject to limited resources has become a key problem. The joint optimization of task offloading and resource allocation is a mixed-integer nonlinear programming problem [5,6]. Currently, scholars have some research works on task offloading and resource allocation.…”

Section: Introductionmentioning

confidence: 99%

Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network

Chen

Liu

2022

Sensors

View full text Add to dashboard Cite

Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to the MEC server or the central cloud server for execution. However, the privacy disclosure issue may arise when the raw data is migrated to other MEC servers or the central cloud server. Since federated learning has the characteristics of protecting the privacy and improving training performance, it is introduced to solve the issue. In this article, we formulate the joint optimization problem of task offloading and resource allocation to minimize the energy consumption of all Internet of Things (IoT) devices subject to delay threshold and limited resources. A two-timescale federated deep reinforcement learning algorithm based on Deep Deterministic Policy Gradient (DDPG) framework (FL-DDPG) is proposed. Simulation results show that the proposed algorithm can greatly reduce the energy consumption of all IoT devices.

show abstract

A Resource Allocation Scheme for Joint Optimizing Energy Consumption and Delay in Collaborative Edge Computing-Based Industrial IoT

Cited by 26 publications

References 21 publications

Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things

Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things

Deep Reinforcement Learning Based Edge Computing Network Aided Resource Allocation Algorithm for Smart Grid

Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network

Contact Info

Product

Resources

About