Energy-Efficient Task Offloading Based on Differential Evolution in Edge Computing System With Energy Harvesting

Sun, Yingying; Song, Chunhe; Yu, Shimao; Liu, Yiyang; Pan, Hao; Zeng, Peng

doi:10.1109/access.2021.3052901

Cited by 40 publications

(20 citation statements)

References 19 publications

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“…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%

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: 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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“…However, in literature [13], intercustomer interference of e-commerce is not taken into account when the unloading decision of mobile users is determined. Studies show that effective power control can effectively suppress intercustomer interference of e-commerce in a small e-commerce customer network [5,14]. erefore, some MEC algorithms based on transmission power control are proposed.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficiency Analysis of e-Commerce Customer Management System Based on Mobile Edge Computing

Chen

Yang

2022

Scientific Programming

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Energy efficiency optimization of mobile edge computing e-commerce clients and reasonable management of server computing resources are worth further study. The participant of the algorithm game model proposed in this paper is mobile e-commerce customer management. The decision space is a two-dimensional space composed of unloading decision and power control, and the benefit function is the energy efficiency function and delay function. The existence and uniqueness of the multidimensional game model are proved theoretically. The simulation results show that the proposed multidimensional game based energy efficiency optimization algorithm of mobile edge computing can reduce the energy consumption and delay of mobile terminals and improve the energy efficiency of unloading calculation under the same task compared with the game scheme without considering power consumption control when the number of e-commerce customer management is larger. This paper deduces the optimal load migration decision of mobile e-commerce customer management and the optimal pricing strategy of mobile edge cloud service providers and proves that the optimal decision and optimal pricing constitute the Starkberg equilibrium. The semidistributed and decentralized task transfer decision-making mechanisms are designed, respectively, and the management decision-making behaviors of mobile e-commerce customers in the mobile edge cloud energy trading market are studied by numerical analysis, as well as the time efficiency of the two mechanisms.

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“…Wireless communication technology has also been rapidly developed, data acquisition system relying on wireless communication technology, and began to develop to wireless sensor network. The main working mode of wireless sensor network is to collect information through nodes and carry out communication and data transmission among nodes through wireless communication [12]. With the continuous research on wireless sensor network, its application is no longer limited to the military field and gradually extends from military weapons to antiterrorism and disaster relief, large-scale structural health monitoring, environmental monitoring, medical care and transportation support, and other fields.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Mobile Edge Computing Offloading Model for Distributed Wireless Sensor Devices

Han

Kang

et al. 2022

Journal of Sensors

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The development and popularization of mobile Internet and wireless communication technology have spawned a large number of computation-intensive and delay-intensive applications. Limited computing resources and existing technologies cannot meet the performance requirements of new applications. Mobile edge computing technology can use wireless communication technology to offload data to be stored and computing tasks to the nearby assistant or edge server with idle resources. Based on the data offloading of distributed wireless sensor device to device communication, the architecture is designed and the basic framework of distributed mobile edge computing is constructed. To solve the problem of high mobile cloud computing technology, the offloading model of optimized mobile edge computing was proposed, and the stability and convergence of the proposed algorithm were proved. Finally, the system performance of the proposed algorithm is verified by simulation. The results show that the proposed algorithm can converge within a finite number of steps. Compared with other benchmark schemes, the proposed algorithm has better performance in reducing system energy consumption, reducing moving edge response delay and system total delay.

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Energy-Efficient Task Offloading Based on Differential Evolution in Edge Computing System With Energy Harvesting

Cited by 40 publications

References 19 publications

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

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

Energy Efficiency Analysis of e-Commerce Customer Management System Based on Mobile Edge Computing

Optimization of Mobile Edge Computing Offloading Model for Distributed Wireless Sensor Devices

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