Energy-Efficient Joint Task Assignment and Power Control in Energy-Harvesting D2D Offloading Communications

Hamdi, Monia; Hamed, Aws Ben; Yuan, Di; Zaied, Mourad

doi:10.1109/jiot.2021.3110319

Cited by 23 publications

(7 citation statements)

References 57 publications

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“…Chen et al [30] studied an energy-efficient task offloading and resource allocation scheme for Augmented Reality (AR) in a multi-MEC collaborative system. Monia et al [31] investigated the joint task assignment and power control problems for Device-to-Device (D2D) offloading communications with energy harvesting. A layered optimization method is proposed to solve this problem by decoupling the energy efficiency maximization problem into power allocation and offloading assignment.…”

Section: Related Workmentioning

confidence: 99%

Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network

Chen

Liu

2022

Sensors

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Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to the MEC server or the central cloud server for execution. However, the privacy disclosure issue may arise when the raw data is migrated to other MEC servers or the central cloud server. Since federated learning has the characteristics of protecting the privacy and improving training performance, it is introduced to solve the issue. In this article, we formulate the joint optimization problem of task offloading and resource allocation to minimize the energy consumption of all Internet of Things (IoT) devices subject to delay threshold and limited resources. A two-timescale federated deep reinforcement learning algorithm based on Deep Deterministic Policy Gradient (DDPG) framework (FL-DDPG) is proposed. Simulation results show that the proposed algorithm can greatly reduce the energy consumption of all IoT devices.

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Section: Related Workmentioning

confidence: 99%

Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network

Chen

Liu

2022

Sensors

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“…To solve these issues, authors proposed efficient traffic offloading schemes but haven't considered issues such as low power consumption, EE and SE of the users. To maximize the EE, authors in [13] assumed the Energy Harvesting model with the help of Dinkelbach, layered optimization method and matching technique by jointly solving the task assignment and power control problem.…”

Section: Related Workmentioning

confidence: 99%

“…As per survey done in [6]- [13] shows that authors proposed several traffic offloading schemes, but they focused only to solve limited issues. The requirements of high QoS, low power consumption, low delay, higher throughput, better utilization of spectral efficiency have not been jointly considered and were not explored in the existing proposals.…”

Section: Related Workmentioning

confidence: 99%

QoS-Aware Traffic Offloading for Energy Efficient D2D Communication in 5G Network for IoT application

Chandra

Prateek

Arya

2022

J. Phys.: Conf. Ser.

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In Device-to-Device (D2D) communication, where base station (BS) suffers from the issues of unbalanced loads of traffic due to interference which requires low power consumption. The energy efficiency of D2D communication in 5G Network for IoT application is a critical issue due to power consumption at BS is considerably high. In this paper, we propose D2D-based traffic offloading scheme that smartly allocate the traffic between D2D and base station while satisfying Quality of Service (QoS) requirements, which helps to minimize the load from the base station based on the information of users’ locations with low power consumption and high spectral efficiency which fulfils the QoS requirements. We make our proposed algorithm more efficient with less complexity which improves spectral efficiency, lowers the latency with minimum QoS performance of the network. With the help of simulation parameter, we obtained some results which determines that the proposed approach significantly outperforms the other existing approaches in terms of delay or latency, average energy consumption and average computing time up to 72%, 76% and 86% when the number of tasks/loads is 100 while satisfying the users’ demand. The work proposed in this article may be useful in energy efficient 5G-D2D Network for IoT application.

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“…Our study, however, focuses on scheduling algorithms for mobile devices that are energy-conscious. Santinelli et al [16] investigated ways to effectively lower real-time application power usage when resources are limited. Energy-Aware Scheduling by Minimizing Duplication [17], a novel approach put out by Mei and Li, takes into account both energy usage as well as makespan of programmes.…”

Section: Review Of Literaturementioning

confidence: 99%

Energy Harvesting based Mobile Cloud Network in Latency and QoS Improvement using 5G Systems by Energy Routing Optimization

Khatri

Sharma

2022

ijfrcsce

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D2D communication technology enables the User Equipment (UE) in 5G networks to instantly connect with other UEs, with or without partial infrastructure involvement. In a Cloud Assisted energy harvesting system, it has improved user numbers and data transmission between mobile nodes. This research propose energy harvesting for mobile cloud computing in enhancing the QoS and latency of the network. The main aim of this research is to enhance energy optimization using discrete energy efficient offloading algorithm. The routing has been optimized using fuzzy logic cognitive Bellman-Ford routing algorithm. To identify the failing node and find an alternative node to deliver the seamless services, an unique weight-based approach has been presented. The method relies on two working node parameters: execution time and failure rate. Threshold values are specified for the parameters of the chosen master node. By contrasting the values with the threshold values, the alternative node is chosen. The experimental results shows comparative analysis in terms of throughput of 96%, QoS of 96%, latency of 28%, energy consumption of 51%, end-end delay of 41%, average power consumption of 41% and PDR of 85%

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Energy-Efficient Joint Task Assignment and Power Control in Energy-Harvesting D2D Offloading Communications

Cited by 23 publications

References 57 publications

Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network

Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network

QoS-Aware Traffic Offloading for Energy Efficient D2D Communication in 5G Network for IoT application

Energy Harvesting based Mobile Cloud Network in Latency and QoS Improvement using 5G Systems by Energy Routing Optimization

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