Optimizing Energy consumption and Latency based on computation offloading and cell association in MEC enabled Industrial IoT environment

Rafiq, Ahsan; Wang, Ping; Wei, Min; Hong, Seung Ho; Josbert, Nteziriza Nkerabahizi

doi:10.1109/icsp51882.2021.9408693

Cited by 11 publications

(4 citation statements)

References 15 publications

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“…For mobile edge computing (MEC), in order to greatly reduce the time and energy consumption of processing tasks in edge computing, literature [4] designed a minimum system cost unloading strategy based on Hungarian algorithm and game algorithm. Reference [5] designed a scheme that can guarantee and improve the unloading performance of edge computing, and solved and improved it through Karush Kuhn Tucker condition.…”

Section: Related Workmentioning

confidence: 99%

An Energy-efficient Task Offloading Model based on Trust Mechanism and Multi-agent Reinforcement Learning

Fengjun,

Guo

2024

Preprint

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A task offloading model based on deep reinforcement learning and user experience degree is proposed. Firstly, after users generate blockchain tasks, Proof of Work (PoW) consensus mechanism is introduced to pack the transaction information into blocks to ensure the real reliability of the interaction information in the system. Then, the user experience degree is defined by introducing the total system consumption delay and user gain, and the goal of optimal user experience degree is constructed. Furthermore, the deep reinforcement learning algorithm is used to optimize the offloading model, and the deep reinforcement learning model is constructed by taking the size of the transaction data and the difficulty of the PoW consensus process as the network state, the task offloading and resource allocation relationship between the user and the edge server as the network action, and the user experience as the network reward. Finally, the gradient descent method and back propagation algorithm are used to train the depth network until convergence, and the optimal offloading and resource allocation decision is obtained, and the superiority of the offloading model and algorithm proposed in this thesis is verified by simulation.

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Section: Related Workmentioning

confidence: 99%

An Energy-efficient Task Offloading Model based on Trust Mechanism and Multi-agent Reinforcement Learning

Fengjun,

Guo

2024

Preprint

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“…The rising demand for higher-quality wireless services drives upcoming wireless transmission systems to be responsible for widespread coverage and connectivity over any mobile device [ 1 , 2 ]. Also, the variety of network applications greatly demands energy consumption, network capability, and service latency for masses of mobile devices.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Slime Mould Optimization with Deep-Learning-Based Resource Allocation in UAV-Enabled Wireless Networks

Alkanhel

Rafiq

Mokrov

et al. 2023

Sensors

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Unmanned aerial vehicle (UAV) networks offer a wide range of applications in an overload situation, broadcasting and advertising, public safety, disaster management, etc. Providing robust communication services to mobile users (MUs) is a challenging task because of the dynamic characteristics of MUs. Resource allocation, including subchannels, transmit power, and serving users, is a critical transmission problem; further, it is also crucial to improve the coverage and energy efficacy of UAV-assisted transmission networks. This paper presents an Enhanced Slime Mould Optimization with Deep-Learning-based Resource Allocation Approach (ESMOML-RAA) in UAV-enabled wireless networks. The presented ESMOML-RAA technique aims to efficiently accomplish computationally and energy-effective decisions. In addition, the ESMOML-RAA technique considers a UAV as a learning agent with the formation of a resource assignment decision as an action and designs a reward function with the intention of the minimization of the weighted resource consumption. For resource allocation, the presented ESMOML-RAA technique employs a highly parallelized long short-term memory (HP-LSTM) model with an ESMO algorithm as a hyperparameter optimizer. Using the ESMO algorithm helps properly tune the hyperparameters related to the HP-LSTM model. The performance validation of the ESMOML-RAA technique is tested using a series of simulations. This comparison study reports the enhanced performance of the ESMOML-RAA technique over other ML models.

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“…Evolved from cloud computing, MEC has been widely considered as a key application in 5G communication. This promising technology extends the capabilities of cloud computing at the network edge [28] and performs excellently in smart manufacturing, industrial Internet of things (IoT) as well as many other areas [29][30][31]. In recent years, many efforts have been put into MEC.…”

Section: Introduction 1| Literature Reviewmentioning

confidence: 99%

“…in(30),(29) Set the tolerance ε = 10−3 and the maximum number of iterations I max = 20. 2: Repeat 3: Solve problem (29) with given S ðiÞ 2 to obtain the optimal solution of (p*, f*, r*) and update (S ðiþ1Þ ¼ p ) ; f ) ; r ) Set i :¼ i + 1; Until Convergence or i > I max .…”

mentioning

confidence: 99%

Unmanned aerial vehicle‐aided edge networks with ultra‐reliable low‐latency communications: A digital twin approach

Huynh

Do‐Duy

et al. 2022

IET Signal Processing

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A digital twin (DT) framework for Internet‐of‐thing (IoT) networks is proposed where unmanned aerial vehicles (UAVs) acting as flying mobile edge computing (MEC) servers support the task offloading on the fly. The considered DT model is very well suitable for industrial automation with the strict constraints of mission‐critical services' ultra‐reliable low‐latency communication (URLLC) links. To support low‐latency IoT devices, we formulate the end‐to‐end (e2e) latency minimisation problem of digital twin‐aided offloading UAV‐URLLC. Specifically, the minimised latency is obtained by jointly optimising both communication and computation parameters, namely power, offloading factors, and the processing rate of IoT devices and MEC‐UAV servers. Due to the highly non‐convex optimisation problem, we first consider the K‐means clustering algorithm to optimally deploy the on‐demand UAVs. Then, an alternative optimisation approach combined with appropriate inner approximations is effectively exploited to tackle this challenge. We demonstrate the effectiveness of the proposed DT framework through representative numerical results.

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Optimizing Energy consumption and Latency based on computation offloading and cell association in MEC enabled Industrial IoT environment

Cited by 11 publications

References 15 publications

An Energy-efficient Task Offloading Model based on Trust Mechanism and Multi-agent Reinforcement Learning

An Energy-efficient Task Offloading Model based on Trust Mechanism and Multi-agent Reinforcement Learning

Enhanced Slime Mould Optimization with Deep-Learning-Based Resource Allocation in UAV-Enabled Wireless Networks

Unmanned aerial vehicle‐aided edge networks with ultra‐reliable low‐latency communications: A digital twin approach

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