Secrecy-Based Delay-Aware Computation Offloading via Mobile Edge Computing for Internet of Things

Wu, Yuan; Shi, Jiajun; Ni, Kejie; Qian, Liping; Zhu, Wei; Shi, Zhiguo; Meng, Limin

doi:10.1109/jiot.2018.2875241

Cited by 34 publications

(35 citation statements)

References 40 publications

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“…In [30], the authors formulate the energy management problem as a stochastic optimization programming by using the Lyapunov optimization-based online algorithm. Wu et al [31] propose a delay-aware computation offloading algorithm with joint optimization of secrecy-provisioning, computation workload, and radio resource allocation to minimize the overall execution delay. Authors in [32] propose a binary-search water-filling algorithm to optimize the MEC server's available resources by investigating resource allocation policy in a multi-user scenario.…”

Section: B Related Workmentioning

confidence: 99%

“…That is, if λ m T ≤ µ e T , then φ e = 1 (from (11)). Substituting this in (25), the optimization problem reduces to the analytical expression (31). Next, we consider the case of (λ m T > µ e T ), hence, φ e = µ e T /λ m T (from (14)).…”

Section: Problem Formulation and Proposed Algorithmmentioning

confidence: 99%

“…(26) to (30) the optimization problem reduces to (32). Objective functions L 1 and L 2 , given in (31) and (32) have similar constraints from (26) to (30). Constraint (26) is derived from (1), which ensures that the locally executed service request rate is less than the processing rate of the SDs.…”

Section: Problem Formulation and Proposed Algorithmmentioning

confidence: 99%

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Computation Offloading for Distributed Mobile Edge Computing Network: A Multiobjective Approach

Sufyan

Banerjee

2020

IEEE Access

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Mobile edge computing (MEC) is emerging as a cornerstone technology to address the conflict between resource-constrained smart devices (SDs) and the ever-increasing computational demands of the mobile applications. MEC enables the SDs to offload computational-intensive tasks to the nearby edge nodes for providing better quality-of-services (QoS). The recently proposed offloading strategies, mainly consider a centralized approach for a limited number of SDs. However, with the growing popularity of the SDs, these offloading models may have the scalability issue and can be susceptible to single point failure. Although there are few distributed offloading models in the literature, they ignore the vast computational resources of the cloud, load sharing between the MEC servers, and other optimization parameters. Toward this end, we propose an efficient computation offloading scheme for a distributed load sharing MEC network in cooperation with cloud computing to enhance the capabilities of the SDs. We formulate a nonlinear multiobjective optimization problem by applying queuing theory to model the execution delay, energy consumption, and payment cost for using edge and cloud services. To solve the formulated problem, we propose a stochastic gradient descent (SGD) algorithm based solution approach to jointly optimize the offloading probability and transmission power of the SDs for finding an optimal trade-off between energy consumption, execution delay, and cost of the SDs. Finally, we perform extensive simulations to demonstrate the effectiveness of the proposed offloading scheme. Moreover, compared to the other solutions, the proposed scheme is scalable and outperforms the existing schemes.

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Section: B Related Workmentioning

confidence: 99%

Section: Problem Formulation and Proposed Algorithmmentioning

confidence: 99%

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Computation Offloading for Distributed Mobile Edge Computing Network: A Multiobjective Approach

Sufyan

Banerjee

2020

IEEE Access

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“…However, this paper mainly focuses on the detection of security threats. In [33], a joint optimization problem which includes the secrecy provisioning, computation offloading, and radio resource allocation is formulated, and an efficient algorithm is proposed to solve this joint optimization problem. However, this paper failed to optimize the long-term computation task offloading cost.…”

Section: Related Workmentioning

confidence: 99%

Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

Huang

et al. 2019

Wireless Communications and Mobile Computing

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With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping and alteration). Aiming at this problem, we propose a security and cost-aware computation offloading (SCACO) strategy for mobile users in mobile edge computing environment, the goal of which is to minimize the overall cost (including mobile device’s energy consumption, processing delay, and task loss probability) under the risk probability constraints. Specifically, we first formulate the computation offloading problem as a Markov decision process (MDP). Then, based on the popular deep reinforcement learning approach, deep Q-network (DQN), the optimal offloading policy for the proposed problem is derived. Finally, extensive experimental results demonstrate that SCACO can achieve the security and cost efficiency for the mobile user in the mobile edge computing environment.

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“…The scenario of multi-user MEC is close to our study here. Focusing on minimizing the delay in completing the computation tasks, there have been many studies investigating the proper multi-user computational resource allocation [ 33 , 34 ]. In [ 35 ], a multi-user computational resource allocation (i.e., the CPU cycle) scheme has been proposed to save the users’ energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain

Chen

Shi

et al. 2018

Sensors

Self Cite

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Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. The mining process of Blockchain, however, incurs a heavy computational workload for miners to solve the proof-of-work puzzle (i.e., a series of the hashing computation), which is prohibitive from the perspective of the mobile terminals (MTs). The advanced multi-access mobile edge computing (MEC), which enables the MTs to offload part of the computational workloads (for solving the proof-of-work) to the nearby edge-servers (ESs), provides a promising approach to address this issue. By offloading the computational workloads via multi-access MEC, the MTs can effectively increase their successful probabilities when participating in the mining game and gain the consequent reward (i.e., winning the bitcoin). However, as a compensation to the ESs which provide the computational resources to the MTs, the MTs need to pay the ESs for the corresponding resource-acquisition costs. Thus, to investigate the trade-off between obtaining the computational resources from the ESs (for solving the proof-of-work) and paying for the consequent cost, we formulate an optimization problem in which the MTs determine their acquired computational resources from different ESs, with the objective of maximizing the MTs’ social net-reward in the mining process while keeping the fairness among the MTs. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose efficient distributed algorithms for the MTs to individually determine their optimal computational resources acquired from different ESs. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our proposed multi-access MEC for Blockchain.

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Secrecy-Based Delay-Aware Computation Offloading via Mobile Edge Computing for Internet of Things

Cited by 34 publications

References 40 publications

Computation Offloading for Distributed Mobile Edge Computing Network: A Multiobjective Approach

Computation Offloading for Distributed Mobile Edge Computing Network: A Multiobjective Approach

Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain

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