Edge QoE: Computation Offloading With Deep Reinforcement Learning for Internet of Things

Lu, Haodong; He, Xiaoming; Du, Miao; Ruan, Xiukai; Sun, Yuqing; Wang, Kun

doi:10.1109/jiot.2020.2981557

Cited by 137 publications

(56 citation statements)

References 39 publications

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“…The objective is to jointly Chen et al in [32], propose a gradient-based method for energy minimization while meeting certain values of delay and energy consumption. Lu et al [33] address an edge-enabled IoT scenario and study computation offloading by considering task latency, energy consumption, and task success rate through the use of a deep reinforcement algorithm. The work in [34] studies a fog-based mobile cloud computing, where mobile devices are modeled with queues, fog nodes act as access points, and a central cloud is available for computations.…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments

Bozorgchenani

Mashhadi

Tarchi

et al. 2021

IEEE Trans. on Mobile Comput.

102

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In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.

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Section: Related Workmentioning

confidence: 99%

Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments

Bozorgchenani

Mashhadi

Tarchi

et al. 2021

IEEE Trans. on Mobile Comput.

102

View full text Add to dashboard Cite

show abstract

“…In addition, for rapid algorithm convergence, an order-preserving quantization method and an adaptive procedure are designed. Meanwhile, a multi-user with a multi-task offloading model for IoT was proposed in Lu et al, 31 in which the latency of service, energy consumption, and success rate of the task are jointly formulated to enhance the QoE-oriented computation offloading. A Double-Dueling-Deterministic Policy Gradients algorithm is developed for solving this problem and deriving the optimal computation offloading.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Advanced Deep Learning for Resource Allocation and Security Aware Data Offloading in Industrial Mobile Edge Computing

et al. 2021

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The Internet of Things (IoT) is permeating our daily lives through continuous environmental monitoring and data collection. The promise of low latency communication, enhanced security, and efficient bandwidth utilization lead to the shift from mobile cloud computing to mobile edge computing. In this study, we propose an advanced deep reinforcement resource allocation and security-aware data offloading model that considers the constrained computation and radio resources of industrial IoT devices to guarantee efficient sharing of resources between multiple users. This model is formulated as an optimization problem with the goal of decreasing energy consumption and computation delay. This type of problem is non-deterministic polynomial time-hard due to the curse-ofdimensionality challenge, thus, a deep learning optimization approach is presented to find an optimal solution. In addition, a 128-bit Advanced Encryption Standard-based cryptographic approach is proposed to satisfy the data security requirements. Experimental evaluation results show that the proposed model can reduce offloading overhead in terms of energy and time by up to 64.7% in comparison with the local execution approach. It also outperforms the full offloading scenario by up to 13.2%, where it can select some computation tasks to be offloaded while optimally rejecting others. Finally, it is adaptable and scalable for a large number of mobile devices.

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“…In addition, for rapid algorithm convergence, an order-preserving quantization method and an adaptive procedure are designed. Meanwhile, a multi-user with a multi-task offloading model for IoT was proposed in [33], in which the latency of service, energy consumption and success rate of task are jointly formulated to enhance the QoE-oriented computation offloading. However, the common drawback of [32], [33] is the absence of security mechanisms to protect application's data from attacks during the transmission.…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

Security-Aware Data Offloading and Resource Allocation For MEC Systems: A Deep Reinforcement Learning

Elgendy¹,

Muthanna²,

Hammoudeh³

et al. 2021

Preprint

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The Internet of Things (IoT) is permeating our daily lives where it can provide data collection tools and important measurement to inform our decisions. In addition, they are continually generating massive amounts of data and exchanging essential messages over networks for further analysis. The promise of low communication latency, security enhancement and the efficient utilization of bandwidth leads to the new shift change from Mobile Cloud Computing (MCC) towards Mobile Edge Computing (MEC). In this study, we propose an advanced deep reinforcement resource allocation and securityaware data offloading model that considers the computation and radio resources of industrial IoT devices to guarantee that shared resources between multiple users are utilized in an efficient way. This model is formulated as an optimization problem with the goal of decreasing the consumption of energy and computation delay. This type of problem is NP-hard, due to the curseof-dimensionality challenge, thus, a deep learning optimization approach is presented to find an optimal solution. Additionally, an AES-based cryptographic approach is implemented as a security layer to satisfy data security requirements. Experimental evaluation results show that the proposed model can reduce offloading overhead by up to 13.2% and 64.7% in comparison with full offloading and local execution while scaling well for large-scale devices.

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Edge QoE: Computation Offloading With Deep Reinforcement Learning for Internet of Things

Cited by 137 publications

References 39 publications

Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments

Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments

Advanced Deep Learning for Resource Allocation and Security Aware Data Offloading in Industrial Mobile Edge Computing

Security-Aware Data Offloading and Resource Allocation For MEC Systems: A Deep Reinforcement Learning

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