A Novel Framework of Three-Hierarchical Offloading Optimization for MEC in Industrial IoT Networks

Zhang, Zhao; Zhao, Rui; Xia, Junjuan; Lei, Xianfu; Li, Dong; Yuen, Chau; Fan, Lisheng

doi:10.1109/tii.2019.2949348

Cited by 121 publications

(61 citation statements)

References 28 publications

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“…where γ (1) th,n denotes the outage threshold for the received SNR at D n for the cache-aided network, and γ (1) th,n is defined as…”

Section: System Modelmentioning

confidence: 99%

“…where v * n can be replaced by v n since the selection of destination does not affect the relay-to-destination link. Using the probability density function (PDF) of random variable (RV) v n , f v n (x) = 1 β e − x β , we can obtain P (1) out,I as [32] P (1)…”

Section: Cache-aided Networkmentioning

confidence: 99%

“…Thus, the outage threshold for each transmissions varies. However, for criterion Cache-Aided-II, the probabilities of selecting D n are identical, i.e., Pr γ (1) th,n * = γ (1) th,n = 1/N. Therefore, the outage probability for criterion Cache-Aided-I is given by P (1) out,II = Pr Pv * n σ 2 < γ (1) th,n *…”

Section: Cache-aided Networkmentioning

confidence: 99%

“…Mobile edge computing (MEC) has been emerging as a powerful tool to support real-time and high-quality services, such as virtual reality and tactile internet applications [1]. Due to the limited computation capacity and storage, mobile devices have to offload the computation tasks to computing access points, e.g., cloud datacenters, which possess powerful computing capability and significantly reduce the computation latency.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is of vital importance to accommodate traditional wireless networks to support latency-sensitive computation task for the fifth-generation (5G) wireless networks. Accordingly, the authors in [2][3][4] have proposed the concept of mobile edge computing (MEC) wireless communications networks, where the computation tasks can be offloaded *Correspondence: xiajunjuan@gzhu.edu.cn 1 The School of Computer Science, Guangzhou University, Guangzhou, China 3 Guangdong New Generation Communication and Network Innovative Institute (GDCNi), Guangzhou, China 4 Guangzhou Panyu Polytechnic, Guangzhou, China Full list of author information is available at the end of the article to the edge nodes with computation capacity through the wireless links.…”

Section: Introductionmentioning

confidence: 99%

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Cache-aided mobile edge computing for B5G wireless communication networks

Xia

Lai

et al. 2020

J Wireless Com Network

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This paper investigates a cache-aided mobile edge computing (MEC) network, where the source offloads the computation task to multiple destinations with computation capacity, with the help of a cache-aided relay. For the proposed cache-aided MEC networks, two destination selection criteria have been proposed to maximize the computation capacity of the selected destination, the channel gain of relay link and the channel gain of direct link, respectively. Similarly, three destination selection criteria have been proposed for the cache-free MEC networks based on the computation capacities of destinations and the channel gains of transmission links, respectively. To evaluate the system performance regarding the latency constraint, we provide the outage probability for the proposed network which is defined based on the transmission-plus-computation time. Our analysis suggests that caching can significantly alleviate the impact of increasing the size of computation task, since only half of the transmission time of cache-free network is required. However, the cache-aided network can not fully exploit the signal from both direct and relay links, thus the improvement by caching is less significant in the high signal-to-noise ratio (SNR) region, compared with the cache-free network employing the destination with maximal channel gain of direct link. Numerical results are given to validate our analysis.

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“…where γ (1) th,n denotes the outage threshold for the received SNR at D n for the cache-aided network, and γ (1) th,n is defined as…”

Section: System Modelmentioning

confidence: 99%

Section: Cache-aided Networkmentioning

confidence: 99%

Section: Cache-aided Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cache-aided mobile edge computing for B5G wireless communication networks

Xia

Lai

et al. 2020

J Wireless Com Network

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Issues and Challenges Facing Low Latency in the Tactile Internet

ahiya

2021

The Tactile Internet

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Hybrid optimization‐based deep neuro‐fuzzy network for designing m‐user multiple‐input multiple‐output interference channel

Reddy,

Ravi Kumar

2023

Int J Communication

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SummaryMultiple‐input multiple‐outputs (MIMOs) have eminent quality in maximizing the throughput of wireless communication models. In MIMO, the antenna arrays can be utilized for fulfilling the needs of 5G by utilizing various spatial signatures of users. Even though 5G communication is imminent, there exist issues, like network interference that arise due to reused frequency spectrum resources. This delving presents an optimized deep model for suppressing interference occurring in the Rayleigh channel in the multiple‐user MIMO (MU‐MIMO) model. Here, an MU‐MIMO model is employed with correlated interference wherein there exist various users around the base station (BS) with several antennas at the transmitter and receiver. Here, a deep neuro‐fuzzy network (DNFN) is used to upgrade the performance of detectors underneath correlated interference. Here, the model comprises zero forcing‐maximum likelihood detection (ZF‐MLD) that assists to generate an initial estimate of broadcasted signals in a particular time slot. The DNFN is used to capture latent correlation among several symbols. Here, the DNFN training is performed using developed autoregressive Henry gas spider monkey optimization (RHGSMO), which is the combination of conditional autoregressive value at risk (CAViaR), Henry gas solubility optimization (HGSO), and spider monkey optimization (SMO). With the lowest symbol error rate (SER), bit error rate (BER), and signal to interference and noise ratio (SINR), the suggested RHGSMO‐based DNFN performed better than existing approaches.

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A Novel Framework of Three-Hierarchical Offloading Optimization for MEC in Industrial IoT Networks

Cited by 121 publications

References 28 publications

Cache-aided mobile edge computing for B5G wireless communication networks

Cache-aided mobile edge computing for B5G wireless communication networks

Issues and Challenges Facing Low Latency in the Tactile Internet

Hybrid optimization‐based deep neuro‐fuzzy network for designing m‐user multiple‐input multiple‐output interference channel

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