Computation Offloading for Mobile-Edge Computing with Multi-user

Dong, Luobing; Satpute, Meghana N.; Shan, Junyuan; Liu, Baoqi; Yang, Yu; Yan, Tihua

doi:10.1109/icdcs.2019.00088

Cited by 38 publications

(24 citation statements)

References 14 publications

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“…The deployment strategy is determined by the ED specific local parameters such as residual energy, workload etc. There are various offloading decision mechanisms proposed in the literature such as [29], however, this discussion falls outside the scope of the work presented in this paper.…”

Section: ) Splitpred3mentioning

confidence: 99%

Privacy-Aware Resource Sharing in Cross-Device Federated Model Training for Collaborative Predictive Maintenance

Bharti¹,

McGibney

2021

IEEE Access

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The proliferation of Industry 4.0 has made modern industrial assets a rich source of data that can be leveraged to optimise operations, ensure efficiency, and minimise maintenance costs. The availability of data is advantageous for asset management, however, attempts to maximise the value of this data often fall short due to additional constraints, such as privacy concerns and data stored in distributed silos that is difficult to access and share. Federated Learning (FL) has been explored to address these challenges and has been demonstrated to provide a mechanism that allows highly distributed data to be mined in a privacy-preserving manner and offering new opportunities for a collaborative approach to asset management. Despite the benefits, FL has some challenges that need to be overcome to make it fully compatible for asset management or more specifically predictive maintenance applications. FL requires a set of clients that participate in the model training process, however, orchestration, device heterogeneity and scalability can hinder the speed and accuracy in the context of collaborative predictive maintenance. To address this challenge, this work proposes a split-learning-based framework (SplitPred) that enables FL clients to maximise available resources within their local network without compromising the benefits of a FL approach (i.e., privacy and shared learning). Experiments performed on the benchmark C-MAPSS dataset demonstrate the advantage of applying SplitPred in the FL process in terms of efficient use of resources, i.e., model convergence time, accuracy, and network load.

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Section: ) Splitpred3mentioning

confidence: 99%

Privacy-Aware Resource Sharing in Cross-Device Federated Model Training for Collaborative Predictive Maintenance

Bharti¹,

McGibney

2021

IEEE Access

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“…Sun et al [31] optimizes the computational energy efficiency and latency on offloading to the mobile edge. Jiao et al [20] and Dong et al [19] discuss multi-user task offloading, which involves multiple users offloading tasks to an edge server. Partial offloading which involves both local processing and edge processing to reduce latency and energy have been discussed by Ren et al [32] and Kuang et al [33] respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Several works have tried to address these challenges (QoS, energy efficiency, security, and privacy) in distributed environments [16][17][18][19][20][21]. Despite these efforts, a number of gaps exist (see Section 2 for a detailed literature review).…”

Section: Introductionmentioning

confidence: 99%

UbiPriSEQ—Deep Reinforcement Learning to Manage Privacy, Security, Energy, and QoS in 5G IoT HetNets

et al. 2020

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5G networks and Internet of Things (IoT) offer a powerful platform for ubiquitous environments with their ubiquitous sensing, high speeds and other benefits. The data, analytics, and other computations need to be optimally moved and placed in these environments, dynamically, such that energy-efficiency and QoS demands are best satisfied. A particular challenge in this context is to preserve privacy and security while delivering quality of service (QoS) and energy-efficiency. Many works have tried to address these challenges but without a focus on optimizing all of them and assuming fixed models of environments and security threats. This paper proposes the UbiPriSEQ framework that uses Deep Reinforcement Learning (DRL) to adaptively, dynamically, and holistically optimize QoS, energy-efficiency, security, and privacy. UbiPriSEQ is built on a three-layered model and comprises two modules. UbiPriSEQ devises policies and makes decisions related to important parameters including local processing and offloading rates for data and computations, radio channel states, transmit power, task priority, and selection of fog nodes for offloading, data migration, and so forth. UbiPriSEQ is implemented in Python over the TensorFlow platform and is evaluated using a real-life application in terms of SINR, privacy metric, latency, and utility function, manifesting great promise.

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“…Specifically, they try to jointly optimize the computation and communication resources by a low-complexity algorithm. To minimize the transmission energy, the computation offloading problem is modeled as a graph cut problem in [15]. Some operators such as graph compressing and combing are conducted based on label propagation theory.…”

Section: A Computing Resource Related Optimizationmentioning

confidence: 99%

Intelligent Resource Allocation for Utility Optimization in RSU-Empowered Vehicular Network

et al. 2020

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Intelligent transportation system (ITS) has attracted extensive attention in both academia and industry for its potential benefits. For example, ITS is dedicated to convenient, economical and environmentally friendly service provisioning for the drivers and passengers in vehicles via advanced technologies including artificial intelligence (AI), knowledge mining, depth fusion, etc. Besides, several newly emerging computing paradigms revolved around ITS such as vehicular cloud and vehicular fog computing are proposed to fully exploit idle computing and communication resources within connected vehicles. As the number of vehicular applications is explosively increasing, it has posed great challenges to the limited capabilities of vehicle loaded computer systems and communication facility. Accordingly, more intelligent resource allocation strategies are needed for computationally intensive and time sensitive vehicular applications. In this paper we propose a road side unit (RSU) empowered vehicular network that consists of three hierarchical layers-vehicular cloud, RSU-enabled cloudlet, and central cloud, respectively. RSU is enhanced with edge servers such that it can intelligently respond to the resource requests in a real time fashion. To this end, an approximate but efficient resource allocation strategy is proposed that can intelligently optimize the utility value from the perspective of RSU-enabled cloudlet. Extensive experiments are carried out to evaluate the performance of the strategy. The results reveal that the proposed algorithm DbHA shows great advantages over other approaches such as the genetic algorithm (GA) and particle swarm optimization (PSO) in both respects (i.e., performance and response latency).

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Computation Offloading for Mobile-Edge Computing with Multi-user

Cited by 38 publications

References 14 publications

Privacy-Aware Resource Sharing in Cross-Device Federated Model Training for Collaborative Predictive Maintenance

Privacy-Aware Resource Sharing in Cross-Device Federated Model Training for Collaborative Predictive Maintenance

UbiPriSEQ—Deep Reinforcement Learning to Manage Privacy, Security, Energy, and QoS in 5G IoT HetNets

Intelligent Resource Allocation for Utility Optimization in RSU-Empowered Vehicular Network

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