A Resource Allocation Scheme for Real-Time Energy-Aware Offloading in Vehicular Networks with MEC

Zhang, Haibo; Liu, Xiangyu; Bian, Xia; Cheng, Yan; Xiang, Shengting

doi:10.1155/2022/8138079

Cited by 6 publications

(7 citation statements)

References 38 publications

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“…Existing work on offloading optimization of the assignment of tasks to edge resources can be categorised according to the optimization objective as follows: (i) minimizing the response time (delay) in task execution [12,[53][54][55][56][57] and (ii) maximising the energy savings of user equipment [46,[58][59][60][61][62][63]. Some studies also considered both energy consumption and delay, opting to strike a balance [64][65][66][67][68].…”

Section: Metaheuristic Optimizationmentioning

confidence: 99%

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Maray

Shuja

2022

Mobile Information Systems

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Cloud and mobile edge computing (MEC) provides a wide range of computing services for mobile applications. In particular, mobile edge computing enables a computing and storage infrastructure provisioned closely to the end-users at the edge of a cellular network. The small base stations are deployed to establish a mobile edge network that can be coined with cloud infrastructure. A large number of enterprises and individuals rely on services offered by mobile edge and clouds to meet their computational and storage demands. Based on user behavior and demand, the computational tasks are first offloaded from mobile users to the mobile edge network and then executed at one or several specific base stations in the mobile edge network. The MEC architecture has the capability to handle a large number of devices that in turn generate high volumes of traffic. In this work, we first provide a holistic overview of MCC/MEC technology that includes the background and evolution of remote computation technologies. Then, the main part of this paper surveys up-to-date research on the concepts of offloading mechanisms, offloading granularities, and computational offloading techniques. Furthermore, we discuss the offloading mechanism in the static and dynamic environment along with optimization techniques. We further discuss the challenges and potential future directions for MEC research.

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Section: Metaheuristic Optimizationmentioning

confidence: 99%

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Maray

Shuja

2022

Mobile Information Systems

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“…Researchers have used various ways to evaluate results using tools, as shown in Figure 13. A 31.25% of researchers have used the development environments such as Microsoft Visual Studio, 82,89 UBUNTU, 67,111 Anaconda(spyder), 81 MATLAB, 37,54,56,59,68,69,72,73,77,103,105,116,126,138,141,142,146,154,158,162 IBM Ilog Cplex optimization Studio. 71 A 20.00% of researchers used DL and ML based libraries such as TensorFlow, 46,47,62,63,65,66,110,144,151,161 Keras, 47 scipy, 45 sckit tool, 113 Pytorch, 111,123 TVM deep learning compiler, 120 LIBSVM.…”

Section: Toolsmentioning

confidence: 99%

“…CO in MEC offers potential in‐vehicle networks; however, poor offloading algorithms might lead to substantial ECp and delay. Zhang et al 126 developed a real‐time energy‐aware offloading strategy to improve resource allocation and balance ECp and latency trade‐offs using a bi‐level optimization approach in multicell networks and DRL approach was used for helping users to make optimal decisions while offloading tasks. Efficient optimization techniques based on binary offloading have acquired substantial attention for a real‐time MEC system, but efficient algorithms for partial offloading under time‐varying channels have received less attention.…”

Section: Energy‐based Co Techniques In Ecmentioning

confidence: 99%

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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SummaryIn today's era, Internet of Things (IoT) devices generate a vast amount of data, which is typically stored in the cloud environment and can be accessed by edge and IoT devices. The data generated by these devices are offloaded through computation offloading (CO) techniques in an edge/cloud computing environment. This paper conducts a systematic literature review (SLR) to review the state‐of‐the‐art CO techniques in edge computing (EC) in the context of energy, Quality of Service (QoS), authentication and traceability. In this SLR, the evolution of offloading techniques is analyzed in detail. A total of 138 articles, spanning from 2016 to 2023 (till date), have been classified into QoS, energy, and authentication and traceability‐based CO techniques. The optimization‐based techniques are the most preferred choices to improve the QoS and reduce energy in the research field of CO in EC. In addition, this paper explores the significant issues and challenges that require further investigation. For future research, energy, QoS, and data provenance for dependent or dynamic task offloading in mobility scenarios can be explored further.

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“…The authors of [114] suggest a real-time strategy to balance energy consumption and task latency for vehicles. They use a MINLP problem to optimize computation offloading and resource allocation, employing bi-level optimization to break down the problem into two subproblems.…”

Section: Resource Allocationmentioning

confidence: 99%

A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks

Pashazadeh,

Nardini,

Stea

2023

Future Internet

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In recent years, the need for computation-intensive applications in mobile networks requiring more storage, powerful processors, and real-time responses has risen substantially. Vehicular networks play an important role in this ecosystem, as they must support multiple services, such as traffic monitoring or sharing of data involving different aspects of the vehicular traffic. Moreover, new resource-hungry applications have been envisaged, such as autonomous driving or in-cruise entertainment, hence making the demand for computation and storage resources one of the most important challenges in vehicular networks. In this context, Mobile Edge Computing (MEC) has become the key technology to handle these problems by providing cloud-like capabilities at the edge of mobile networks to support delay-sensitive and computation-intensive tasks. In the meantime, researchers have envisaged use of onboard vehicle resources to extend the computing capabilities of MEC systems. This paper presents a comprehensive review of the most recent works related to MEC-assisted vehicular networks, as well as vehicle-assisted MEC systems. We illustrate the MEC system architecture and discuss its deployment in vehicular environments, as well as the key technologies to realize this integration. After that, we review the recent literature by identifying three different areas, i.e.: (i) MEC providing additional resources to vehicles (e.g., for task offloading); (ii) MEC enabling innovative vehicular applications (e.g., platooning), and (iii) vehicular networks providing additional resources to MEC systems. Finally, we discuss open challenges and future research directions, addressing the possible interplays between MEC systems and vehicular networks.

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A Resource Allocation Scheme for Real-Time Energy-Aware Offloading in Vehicular Networks with MEC

Cited by 6 publications

References 38 publications

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks

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