Joint wireless power transfer and task offloading in mobile edge computing: a survey

Mustafa, Ehzaz; Shuja, Junaid; Zaman, Sardar Khaliq uz; Jehangiri, Ali Imran; Din, Sadia; Rehman, Faisal; Mustafa, Saad; Maqsood, Tahir; Khan, Abdul Nasir

doi:10.1007/s10586-021-03376-3

Cited by 70 publications

(30 citation statements)

References 81 publications

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“…Some other offloading schemes can also achieve self-sustaining operations. In [15], for instance, the state-of-the-art of methodologies for task offloading in MEC and wireless power transfer to end nodes were recently described. The authors demonstrated the effective use of the Wireless Power Transfer (WPT) technique to charge end mobile phones which have gained more popularity in MEC.…”

Section: Related Workmentioning

confidence: 99%

“…This is referred to as joint energy harvesting and task offloading. Using this technology, users can extract energy, convert it into useful energy, store it in the appropriate energy-storage device, and use that energy to perform the corresponding local computing and offloading tasks [15,[89][90][91].…”

Section: Power Supply For Wearables With Task Offloading Capabilitiesmentioning

confidence: 99%

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Requirements for Energy-Harvesting-Driven Edge Devices Using Task-Offloading Approaches

et al. 2022

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Energy limitations remain a key concern in the development of Internet of Medical Things (IoMT) devices since most of them have limited energy sources, mainly from batteries. Therefore, providing a sustainable and autonomous power supply is essential as it allows continuous energy sensing, flexible positioning, less human intervention, and easy maintenance. In the last few years, extensive investigations have been conducted to develop energy-autonomous systems for the IoMT by implementing energy-harvesting (EH) technologies as a feasible and economically practical alternative to batteries. To this end, various EH-solutions have been developed for wearables to enhance power extraction efficiency, such as integrating resonant energy extraction circuits such as SSHI, S-SSHI, and P-SSHI connected to common energy-storage units to maintain a stable output for charge loads. These circuits enable an increase in the harvested power by 174% compared to the SEH circuit. Although IoMT devices are becoming increasingly powerful and more affordable, some tasks, such as machine-learning algorithms, still require intensive computational resources, leading to higher energy consumption. Offloading computing-intensive tasks from resource-limited user devices to resource-rich fog or cloud layers can effectively address these issues and manage energy consumption. Reinforcement learning, in particular, employs the Q-algorithm, which is an efficient technique for hardware implementation, as well as offloading tasks from wearables to edge devices. For example, the lowest reported power consumption using FPGA technology is 37 mW. Furthermore, the communication cost from wearables to fog devices should not offset the energy savings gained from task migration. This paper provides a comprehensive review of joint energy-harvesting technologies and computation-offloading strategies for the IoMT. Moreover, power supply strategies for wearables, energy-storage techniques, and hardware implementation of the task migration were provided.

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Section: Related Workmentioning

confidence: 99%

Section: Power Supply For Wearables With Task Offloading Capabilitiesmentioning

confidence: 99%

Requirements for Energy-Harvesting-Driven Edge Devices Using Task-Offloading Approaches

et al. 2022

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“…w in n =w in n +j w in n denotes the complex weight of the n th sub-carrier, with w in n and w in n being the corresponding real and imaginary part respectively. κ in is the integer set which indicates the frequency range that x in (t) involves 1,2 .…”

Section: Wpt System Modelmentioning

confidence: 99%

“…However, the explosion in the number of these lowpower devices is challenging traditional battery power supply or wired power supply which either needs periodic replacement or incurs deployment limits. Nevertheless, the dramatical reduction in the power consumption of these remote devices, attributed to higher efficient electronics and computation architectures, gives rise to developing far-field WPT as an alternative source of power [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Waveform Design for Wireless Power Transfer with Power Amplifier and Energy Harvester Non-Linearities

Zhang¹,

Clerckx²

2022

Preprint

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Waveform optimization has shown its great potential to boost the performance of far-field wireless power transfer (WPT). Current research has optimized transmit waveform, adaptive to channel state information (CSI), to maximize the harvested power in WPT while accounting for energy harvester (EH)'s non-linearity. However, the existing transmit waveform design disregards the non-linear high power amplifiers (HPA) at the transmitter. Driven by this, this paper optimizes the multi-carrier waveform at the input of HPA to maximize the harvested DC power considering both HPA's and EH's non-linearities. Two optimization models are formulated based on whether the frequencies of the multi-carrier waveform are concentrated within the transmit pass band or not. Analysis and simulations show that, while EH's non-linearity boosts the power harvesting performance, HPA's non-linearity degrades the harvested power. Hence, the optimal waveform shifts from multi-carrier that exploits EH's non-linearity to single-carrier that reduces HPA's detrimental non-linear distortion as the operational regime of WPT becomes more sensitive to HPA's non-linearity and less sensitive to EH's non-linearity (and inversely). Simultaneously, operating towards HPA's non-linear regime by increasing the input signal power benefits the harvested power since HPA's DC power supply is better exploited, whereas the end-to-end power transfer efficiency (PTE) might decrease because of the increasing non-linear degradation. Throughout the simulations, the proposed waveforms show significant gain over those not accounting for HPA's non-linearity, especially in frequency-flat channels. We also compare the two proposed waveforms and show that the severity of HPA's non-linearity dictates which of the two proposed waveforms is more beneficial.

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“…Ehzaz Mustafa et al studied task offloading in wireless power transmission scenarios. And they investigated the challenges of task offloading in conjunction with WPT [15]. However, the above research work is based on the background of generalization, and does not consider the topological structure of industrial ring network and the characteristics of wireless communication in mine tunnel.…”

Section: Related Workmentioning

confidence: 99%

Task Offloading and Resource Allocation Mechanism of Moving Edge Computing in Mining Environment

Meng¹,

2021

IEEE Access

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Joint wireless power transfer and task offloading in mobile edge computing: a survey

Cited by 70 publications

References 81 publications

Requirements for Energy-Harvesting-Driven Edge Devices Using Task-Offloading Approaches

Requirements for Energy-Harvesting-Driven Edge Devices Using Task-Offloading Approaches

Waveform Design for Wireless Power Transfer with Power Amplifier and Energy Harvester Non-Linearities

Task Offloading and Resource Allocation Mechanism of Moving Edge Computing in Mining Environment

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