Energy efficient offloading scheme for MEC-based augmented reality system

Ateya, Abdelhamied A.; Muthanna, Ammar; Koucheryavy, Andrey; Maleh, Yassine; El‐Latif, Ahmed A. Abd

doi:10.1007/s10586-022-03914-7

Cited by 12 publications

(6 citation statements)

References 22 publications

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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%

“…166 Python, 40,62,63,65,81,109,110,144,151,161 C++, 99 and Java, 71,94 programming languages were considered by 15.00% of researchers. A 13.75% of the researchers have used various simulators and emulators as CloudSim, 34,74,158 EdgeCloudSim, 166 iFogSim, 148 Contiki Cooja environment, 108 Open AI Gym Toolkit, 144 SDN-BC emulated environment including Minine, 144 COOJA, 66 SimuLTE, 157 and SUMO. 103 ProVerif, 160 Hyperledger caliper, 67,170 and Fabric BFT SMarT, 167 Ethereum, 67,144 were the blockchain and distributed ledger platforms considered by 7.50% of researchers for evaluating the authenticity while offloading tasks either on edge or cloud environment.…”

Section: Toolsmentioning

confidence: 99%

“…Further, the EEDOS scheme classified the offloading requests based on energy constraints and the deadline of a particular requesting device, and a minimum cost graph algorithm was utilized to find the offloading destination. Ateya et al 158 provided a modified multi‐level MEC system for system and web‐based augmented reality applications. Authors proposed latency and energy‐aware algorithms for managing the data that was offloaded from MDs to EdSr.…”

Section: Energy‐based Co Techniques In Ecmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Toolsmentioning

confidence: 99%

Section: Toolsmentioning

confidence: 99%

Section: Energy‐based Co Techniques In Ecmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Baixa Latência [Sadiki et al 2023] x x [Liu et al 2022] x [Chen et al 2021] x [Dutta et al 2023] x [Wang et al 2023] x x [Maier et al 2020] x x [Yu et al 2023] x x [Chu et al 2023] x x [Trinh and Muntean 2023] x x [Liubogoshchev et al 2021] x [Lagen et al 2023] x x [Sehad et al 2023] x x [Qian et al 2022] x x [Binucci et al 2022] x x [Li et al 2022a] x x [Hsu et al 2022] x x ] [Bojović et al 2023 x [Perez et al 2022] x [Ahmad et al 2020] x x [Zhang et al 2023] x x [Du et al 2022] x x [Chukhno et al 2023] x x [Picano and Fantacci 2023] x x [Passas et al 2022] [Ateya et al 2023 x [Li et al 2022b] x x [Wang et al 2021] x x [Nguyen et al 2022]…”

Section: Consumo De Energiamentioning

confidence: 99%

Revisão Sistemática das Aplicações Imersivas com base nas Tecnologias Habilitadoras B5G/6G, MEC e IA

Gonçalves,

Oliveira-Jr,

Freitas

2023

Anais Da XI Escola Regional De Informática De Goiás (ERI-GO 2023)

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Esta revisão sistemática tem como objetivo principal identificar estudos que abordem questões relacionadas às aplicações imersivas com base nas tecnologias habilitadoras B5G/6G, MEC e IA. A motivação é compreender os avanços realizados nesse campo e identificar soluções emergentes e lacunas de conhecimento. Os resultados obtidos indicam a predominância do uso da técnica de Aprendizado por Reforço Profundo para abordar soluções de rede baseadas na computação de borda de múltiplo acesso, a fim de viabilizar as aplicações imersivas. Espera-se que esta revisão sistemática contribua para uma melhor compreensão do estado atual das aplicações imersivas e para pesquisas futuras relacionadas ao tema.

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“…Through virtually eliminating latency and accommodating an unprecedented number of connected devices, 6G is expected to push the boundaries of data speeds to terabits per second [4]. These advancements will unlock the potential for highly immersive IoT applications, such as massive-scale augmented reality experiences, remote robotic control with minimal delay, and the real-time monitoring of vast and complex systems [5]. The convergence of IoT with 5G and 6G technologies will propel the growth of smart cities, precision agriculture, industrial automation, and personalized healthcare, further blurring the lines between the physical and digital worlds [6].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Offloading in Flying Fog Computing: Optimizing IoT Network Performance with Mobile Drones

Min,

Khakimov,

Ateya

et al. 2023

Drones

Self Cite

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The rapid growth of Internet of Things (IoT) devices and the increasing need for low-latency and high-throughput applications have led to the introduction of distributed edge computing. Flying fog computing is a promising solution that can be used to assist IoT networks. It leverages drones with computing capabilities (e.g., fog nodes), enabling data processing and storage closer to the network edge. This introduces various benefits to IoT networks compared to deploying traditional static edge computing paradigms, including coverage improvement, enabling dense deployment, and increasing availability and reliability. However, drones’ dynamic and mobile nature poses significant challenges in task offloading decisions to optimize resource utilization and overall network performance. This work presents a novel offloading model based on dynamic programming explicitly tailored for flying fog-based IoT networks. The proposed algorithm aims to intelligently determine the optimal task assignment strategy by considering the mobility patterns of drones, the computational capacity of fog nodes, the communication constraints of the IoT devices, and the latency requirements. Extensive simulations and experiments were conducted to test the proposed approach. Our results revealed significant improvements in latency, availability, and the cost of resources.

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Energy efficient offloading scheme for MEC-based augmented reality system

Cited by 12 publications

References 22 publications

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

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

Revisão Sistemática das Aplicações Imersivas com base nas Tecnologias Habilitadoras B5G/6G, MEC e IA

Dynamic Offloading in Flying Fog Computing: Optimizing IoT Network Performance with Mobile Drones

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