Latency-energy optimization for joint WiFi and cellular offloading in mobile edge computing networks

Fan, Wenhao; Han, Junting; Yao, Le; Wu, Fan; Liu, Yuanan

doi:10.1016/j.comnet.2020.107570

Cited by 19 publications

(13 citation statements)

References 14 publications

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“…Under the time constraint, the offloading policy has been optimized based on the channel gains and the amount of energy used by local computing. (Fan, 2020), focused on the number of users who could be offloaded, but they didn't optimize both radio and computing resources at the same time. The authors looked into a problem where radio and computing resources have to be shared in a non-orthogonal MEC in a heterogeneous network.…”

Section: How To Optimally Allocate Resources To Applications In Mecs ...mentioning

confidence: 99%

Mobile Edge Computing Architecture Challenges, Applications, and Future Directions

Varma

Indukuri³

2023

International Journal of Grid and High Performance Computing

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In the current era of technology, the utilization of tablets and smart phones plays a major role in every situation. As the numbers of mobile users increase, the quality of service (QoS) and quality of experience (QoE) are facing the greater challenges. Thus, this can significantly reduce the latency and optimize the power consumed by the tasks executed locally. Most of the previous works are focused only on quality optimization in the dynamic service layouts. However, they ignored the significant impact of accurate access network selection and perfect service placement. This article performs the detailed survey of various MEC approaches with service provision and adoption. The survey also provides the analysis of various approaches for optimizing the QoS parameters and MEC resources. In this regarding, the survey classifies the approaches based on service placement, network selection, QoS, and QoE parameters, and resources such as latency, energy, bandwidth, memory, storage, and processing.

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Section: How To Optimally Allocate Resources To Applications In Mecs ...mentioning

confidence: 99%

Mobile Edge Computing Architecture Challenges, Applications, and Future Directions

Varma

Indukuri³

2023

International Journal of Grid and High Performance Computing

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“…To this end, they proposed a mix‐integer nonlinear programming method to optimize smart devices' response time and battery life. Fan et al 36 combined the linear programming with the alternating optimization technique to reduce latency and energy consumption in the WiFi and cellular network. To optimize the computing efficiency of EN, Peng et al 37 focused on computing offloading in end‐edge‐cloud collaborative environment.…”

Section: Related Workmentioning

confidence: 99%

Intelligent computation offloading for educational virtual reality applications in smart campus using MoCell

Liu

Peng

Peng³

2022

Computational Intelligence

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Nowadays, as the smart campus concept becomes a reality, virtual reality (VR) applications are being applied to connect students with the virtual teaching world via VR devices (VDs), enhancing learning efficiency. Nevertheless, VR applications are latency-sensitive while VDs are subjected to shortcomings to handle many VR applications simultaneously. Fortunately, mobile edge computing (MEC) has been recognized as a promising solution that can bring abundant resources to VDs to relieve hardware limits. However, the computing resources of edge nodes in MEC are limited, and thus how to allocate resources effectively is critical. Meanwhile, some sensitive information of students collected by VD needs to be protected. In view of this, we investigate computation offloading for educational VR applications in MEC-enabled smart campus. The aims of this issue are to optimize the motion-to-photon latency, energy consumption, and resource utilization while satisfying the privacy and security constraints. To this end, we propose a new multi-objective optimization method using MoCell. Eventually, the experimental evaluations are designed to illustrate the effectiveness and superiority our proposed method.

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“…The operator-domain offloading helps avoid selfish user behavior and ensures near-optimal profit and social utility. Wehnao et al [27] proposed a scheme for joint Wi-Fi and cellular offloading to optimally reduce the energy consumption and latency of mobile terminals in task processing.…”

Section: Wi-fi Offloading In Small Cellsmentioning

confidence: 99%

A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network

et al. 2021

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Small cells deliver cost-effective capacity and coverage enhancement in a cellular network. In this work, we present the interplay of two technologies, namely Wi-Fi offloading and small-cell cooperation that help in achieving this goal. Both these technologies are also being considered for 5G and B5G (Beyond 5G). We simultaneously consider Wi-Fi offloading and small-cell cooperation to maximize average user throughput in the small-cell network. We propose two heuristic methods, namely Sequential Cooperative Rate Enhancement (SCRE) and Sequential Offloading Rate Enhancement (SORE) to demonstrate cooperation and Wi-Fi offloading, respectively. SCRE is based on cooperative communication in which a user data rate requirement is satisfied through association with multiple small-cell base stations (SBSs). However, SORE is based on Wi-Fi offloading, in which users are offloaded to the nearest Wi-Fi Access Point and use its leftover capacity when they are unable to satisfy their rate constraint from a single SBS. Moreover, we propose an algorithm to switch between the two schemes (cooperation and Wi-Fi offloading) to ensure maximum average user throughput in the network. This is called the Switching between Cooperation and Offloading (SCO) algorithm and it switches depending upon the network conditions. We analyze these algorithms under varying requirements of rate threshold, number of resource blocks and user density in the network. The results indicate that SCRE is more beneficial for a sparse network where it also delivers relatively higher average data rates to cell-edge users. On the other hand, SORE is more advantageous in a dense network provided sufficient leftover Wi-Fi capacity is available and more users are present in the Wi-Fi coverage area.

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Latency-energy optimization for joint WiFi and cellular offloading in mobile edge computing networks

Cited by 19 publications

References 14 publications

Mobile Edge Computing Architecture Challenges, Applications, and Future Directions

Mobile Edge Computing Architecture Challenges, Applications, and Future Directions

Intelligent computation offloading for educational virtual reality applications in smart campus using MoCell

A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network

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