Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks

Zhao, Junhui; Li, Qiuping; Gong, Yi; Zhang, Ke

doi:10.1109/tvt.2019.2917890

Cited by 656 publications

(336 citation statements)

References 37 publications

Supporting

Mentioning

299

Contrasting

Order By: Relevance

“…This is is because the destination with highest computation capacity is selected, which leads to the loss of diversity on transmission channel. Remarks 2 From (16)- (18) and (23)- (30), we see that the increase of the number of destination N may results in performance loss of network for criteria Cache-Aided-II, Cache-Free-II and Cache-Free-III. This is due to the fact that the overall outage probability is obtained by averaging the outage probabilities of each destinations with different computation capacities.…”

Section: Cache-free Networkmentioning

confidence: 97%

“…where B is the dedicated bandwidth for the transmission of computation task, v n ∼ E(β) is the channel gain of the R-to-D n link [27][28][29], P is the transmit power at the source and relay, and σ 2 is the variance of the additive Gaussian noise n ∼ CN 0, σ 2 [30,31]. Note that we consider a latency-constraint scenario, in which the maximal transmission plus computation time is fixed to T, where T > KL min n∈ [1,N] (δ n ) is assumed to ensure the implementation of task computing for arbitrary D n .…”

Section: System Modelmentioning

confidence: 99%

See 1 more Smart Citation

Cache-aided mobile edge computing for B5G wireless communication networks

Xia

Lai

et al. 2020

J Wireless Com Network

View full text Add to dashboard Cite

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.

show abstract

Section: Cache-free Networkmentioning

confidence: 97%

Section: System Modelmentioning

confidence: 99%

Cache-aided mobile edge computing for B5G wireless communication networks

Xia

Lai

et al. 2020

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…In highlighting the impact of the new emerged concept of mobile edge computing in the field of VANET, see the authors of [24,25]. In this paper, vehicular edge computing (VEC) was investigated as an important application of mobile-edge computing in vehicular networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Handoff Technique for Internet of Vehicles Communication using Dynamic Edge-Backup Node

et al. 2020

View full text Add to dashboard Cite

A vehicular adhoc network (VANET) recently emerged in the the Internet of Vehicles (IoV); it involves the computational processing of moving vehicles. Nowadays, IoV has turned into an interesting field of research as vehicles can be equipped with processors, sensors, and communication devices. IoV gives rise to handoff, which involves changing the connection points during the online communication session. This presents a major challenge for which many standardized solutions are recommended. Although there are various proposed techniques and methods to support seamless handover procedure in IoV, there are still some open research issues, such as unavoidable packet loss rate and latency. On the other hand, the emerged concept of edge mobile computing has gained crucial attention by researchers that could help in reducing computational complexities and decreasing communication delay. Hence, this paper specifically studies the handoff challenges in cluster based handoff using new concept of dynamic edge-backup node. The outcomes are evaluated and contrasted with the network mobility method, our proposed technique, and other cluster-based technologies. The results show that coherence in communication during the handoff method can be upgraded, enhanced, and improved utilizing the proposed technique.

show abstract

“…They defined the optimization as a mixed integer problem and applied the heuristic algorithm to optimize the cost required for cloud operation. Zhao et al [24] employed a vehicle network comprising edge and cloud, and tried to optimize cloud utilization using convex optimization. Zhang et al [25,26] used whole-sale and buy-back models between edge and cloud to share computing resources, and optimized the profit from the edge's perspective.…”

Section: Previous Workmentioning

confidence: 99%

Comparative Analysis of 5G Mobile Communication Network Architectures

et al. 2020

View full text Add to dashboard Cite

Mobile communication technology is evolving from 4G to 5G. Compared to previous generations, 5G has the capability to implement latency-critical services, such as autonomous driving, real-time AI on handheld devices and remote drone control. Multi-access Edge Computing is one of the key technologies of 5G in guaranteeing ultra-low latency aimed to support latency critical services by distributing centralized computing resources to networks edges closer to users. However, due to its high granularity of computing resources, Multi-access Edge Computing has an architectural vulnerability in that it can lead to the overloading of regional computing resources, a phenomenon called regional traffic explosion. This paper proposes an improved communication architecture called Hybrid Cloud Computing, which combines the advantages of both Centralized Cloud Computing and Multi-access Edge Computing. The performance of the proposed network architecture is evaluated by utilizing a discrete-event simulation model. Finally, the results, advantages, and disadvantages of various network architectures are discussed.

show abstract

Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks

Cited by 656 publications

References 37 publications

Cache-aided mobile edge computing for B5G wireless communication networks

Cache-aided mobile edge computing for B5G wireless communication networks

Smart Handoff Technique for Internet of Vehicles Communication using Dynamic Edge-Backup Node

Comparative Analysis of 5G Mobile Communication Network Architectures

Contact Info

Product

Resources

About