Retention based energy harvesting technique for efficient internet of things aided edge devices

Xie, Zhiqiang; Poovendran, Parthasarathy; Premalatha, R.

doi:10.1016/j.seta.2021.101424

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Xie et al [21] introduced a minimal retention energy harvesting (MREH) method for edge devices. The proposed method is mainly used for Internet of Things (IoT)-based devices and applications.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization

Kaliappan¹,

Ranganathan

Selvaraju³

et al. 2022

Energies

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Edge devices and their associated computing techniques require energy efficiency to improve sustainability over time. The operating edge devices are timed to swap between different states to achieve stabilized energy efficiency. This article introduces a Cognitive Energy Management Scheme (CEMS) by considering the offloading and computational states for energy efficacy. The proposed scheme employs state learning for swapping the computing intervals for scheduling or offloading depending on the load. The edge devices are distributed at the time of scheduling and organized for first come, first serve for offloading features. In state learning, the reward is allocated for successful scheduling over offloading to prevent device exhaustion. The computation is therefore swapped for energy-reserved scheduling or offloading based on the previous computed reward. This cognitive management induces device allocation based on energy availability and computing time to prevent energy convergence. Cognitive management is limited in recent works due to non-linear swapping and missing features. The proposed CEMS addresses this issue through precise scheduling and earlier device exhaustion identification. The convergence issue is addressed using rewards assigned to post the state transitions. In the transition process, multiple device energy levels are considered. This consideration prevents early detection of exhaustive devices, unlike conventional wireless networks. The proposed scheme’s performance is compared using the metrics computing rate and time, energy efficacy, offloading ratio, and scheduling failures. The experimental results show that this scheme improves the computing rate and energy efficacy by 7.2% and 9.32%, respectively, for the varying edge devices. It reduces the offloading ratio, scheduling failures, and computing time by 14.97%, 7.27%, and 14.48%, respectively.

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Section: Related Workmentioning

confidence: 99%

Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization

Kaliappan¹,

Ranganathan

Selvaraju³

et al. 2022

Energies

View full text Add to dashboard Cite

show abstract

“…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. In addition to the energy constraint, we also take performance and time constraints into account in our study.…”

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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Retention based energy harvesting technique for efficient internet of things aided edge devices

Cited by 2 publications

References 25 publications

Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization

Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization

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

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