Resource Allocation for Enhancing Offloading Security in NOMA-Enabled MEC Networks

Wu, Wei; Wang, Xinxin; Zhou, Fuhui; Wong, Kai-Kit; Li, Chunguo; Wang, Baoyun

doi:10.1109/jsyst.2020.3009723

Cited by 48 publications

(24 citation statements)

References 11 publications

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“…ree classical resource allocation and task for service m ∈ service cache order co m , do (10) if rs ≥ m , then (11) amn � 1 (12) rs � rs − m (13) end if (14) end for (15) scheduling algorithms in recent years are selected for comparison, which are MEC-based resource management and task scheduling (MEC-RMTS) [28], coalitional gamebased cooperative offloading (CGCO) [29], and multiservice task computing offload algorithm (MTCOA) [30]. e MEC-RMTS framework is used for efficient task offloading in the internet of things, CGCO is a cooperative offloading algorithm based on the coalitional game, and MTCOA solves the multiservice task offloading problem.…”

Section: Results Analysismentioning

confidence: 99%

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Intelligent Scheduling Method Supporting Stadium Sharing

Fang

2021

Discrete Dynamics in Nature and Society

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At present, the fast-paced work and life make people under great pressure, and people’s enthusiasm for fitness is getting higher and higher, which is in contradiction with the shortage of existing stadiums. So it is considerably significant to open shared stadiums near where citizens live for booking. Therefore, how to allow citizens to book a suitable stadium according to their own needs through mobile phones or computers is an urgent problem to be solved. The booking of the shared stadium can be regarded as a mobile edge computing (MEC) scenario, and the problem can be transformed into task scheduling research under MEC through intelligent scheduling method. When using edge computing (EC) technology for service calculation, the mobile terminal needs to offload the service to the edge computing server. After the server completes the calculation, the calculation results will be sent back to the mobile terminal. Therefore, the calculation time and system energy consumption in the calculation process can be further reduced through task scheduling to improve user satisfaction. In this study, joint scheduling of service caching and task algorithm is proposed to reduce the latency of booking shared stadium request and improve user experience. The simulation results show that the proposed algorithm with edge cooperation idea can achieve lower average system latency at lower load level and can significantly reduce the cloud offloading ratio under low and middle pressure. In addition, the proposed algorithm uses the secondary transfer of more tasks to reduce the pressure of local task running. Finally, the quality of experience (QoE) satisfaction rate under low pressure is guaranteed.

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Section: Results Analysismentioning

confidence: 99%

“…In addition, a secure mechanism was proposed to lower the economic returns. ere were most important two aspects in MEC, which were the security of task offloading and the connectivity of users, and in [15], the constraint of offloading rate and latency was studied to express the optimal solution of MEC.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Scheduling Method Supporting Stadium Sharing

Fang

2021

Discrete Dynamics in Nature and Society

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“…Studies on NOMA-MEC: A proper joint management of NOMA and MEC has been explored to improve the performance of computation and communication. There have been many studies on investigating the integration of MEC and NOMA [13]- [16]. For example, Qian et al [13] proposed an optimal algorithm for the NOMA-MEC-assisted IoT network to obtain the optimal SIC ordering and computation resource allocation by the convex computation resource allocation optimization followed by the combinatorial SIC ordering optimization.…”

Section: Related Workmentioning

confidence: 99%

“…Li et al [15] investigated how to exploit the cooperative mechanism between NOMA user pairs to enhance the security of the NOMA-MEC system. Wu et al [16] maximized the minimum anti-eavesdropping ability for uplink NOMA users in the context of the worstcase secrecy rate requirements and limited transmit power budgets for the uplink NOMA-MEC network. Fang et al [17] proposed a low complexity algorithm to minimize the total energy consumption by the task assignment, power allocation and user association for the multi-user NOMA-MEC network.…”

Section: Related Workmentioning

confidence: 99%

Joint Multi-domain Resource Allocation and Trajectory Optimization in UAV-assisted M-IoT Networks

Qian¹

2022

Preprint

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<div>The integration of Maritime Internet of Things (M-IoT) technology and unmanned aerial/surface vehicles (UAVs/USVs) has been emerging as a promising navigational information technique in intelligent ocean systems. With the unprecedented increase of computation-intensive yet latency sensitive marine mobile Internet services, mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) have been envisioned as promising approaches to providing with the low-latency as well as reliable computing services and ultra-dense connectivity. In this paper, we investigate the energy consumption minimization based energy-efficient MEC via cooperative NOMA for the UAV-assisted M-IoT networks. We consider that USVs offload their computation-workload to the UAV equipped with the edge-computing server subject to the UAV mobility. To improve the energy efficiency of offloading transmission and workload computation, we focus on minimizing the total energy consumption by jointly optimizing the USVs’ offloaded workload, transmit power, computation resource allocation as well as the UAV trajectory subject to the USVs’ latency requirements. Despite the nature of mixed discrete and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a two-layered algorithm for solving it efficiently. Specifically, the top-layered algorithm is proposed to solve the problem of optimizing the UAV trajectory based on the idea of Deep Reinforcement Learning (DRL), and the underlying algorithm is proposed to optimize the underlying multi-domain resource allocation problem based on the idea of the Lagrangian multiplier method. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of NOMA-enabled computation offloading in terms of overall energy consumption.</div>

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“…Wireless Communications and Mobile Computing researched the security behaviors of NOMA for MECaware networks, considered passive eavesdropping schemes, measured the security performance of computing offload by using security interruption probability, and deduced the semiclosed form expression of the optimal solution. In order to further evaluate the eavesdropping ability of users without knowing the CSI of the Eve, the authors in [23] proposed an effective iterative algorithm to maximize the minimum antieavesdropping ability of uplink NOMA users by jointly formulating the security rate, locally calculating bits, and allocating power. To solve the delay problem of NOMA in the presence of malicious Eves, an algorithm based on binary search was proposed, which can ensure the security rate and make the maximum task delay of uplink NOMA users minimal [24].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of MEC Based on NOMA under Imperfect CSI with Eavesdropper

Pei

Jiang

et al. 2021

Wireless Communications and Mobile Computing

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Mobile edge computing (MEC) is becoming more and more popular because of improving computing power in virtual reality, augmented reality, unmanned driving, and other fields. This paper investigates a nonorthogonal multiple access- (NOMA-) based MEC system, which is under imperfect channel state information (ipCSI). In this system model, a pair of users offloads their tasks to the MEC server with the existence of an eavesdropper (Eve). To evaluate the impact of Eve on the performance of the NOMA-MEC system, the secrecy outage probability (SOP) expressions for two users with the conditions of imperfect CSI and perfect channel state information (pCSI) are derived. In addition, both throughput and energy efficiency are discussed in the delay-limited transmission mode. Simulation results reveal that (1) due to the influence of channel estimation errors, the secrecy outage behaviors of two users under ipCSI conditions are worse than those of users with pCSI; (2) the secrecy performance of NOMA-MEC is superior to orthogonal multiple access- (OMA-) aided MEC systems; and (3) the NOMA-MEC systems have the ability to attain better system throughput and energy efficiency compared with OMA-MEC.

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Resource Allocation for Enhancing Offloading Security in NOMA-Enabled MEC Networks

Cited by 48 publications

References 11 publications

Intelligent Scheduling Method Supporting Stadium Sharing

Intelligent Scheduling Method Supporting Stadium Sharing

Joint Multi-domain Resource Allocation and Trajectory Optimization in UAV-assisted M-IoT Networks

Performance Analysis of MEC Based on NOMA under Imperfect CSI with Eavesdropper

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