Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach

Abbas, Khizar; Shaiba, Hadil; Muthanna, Ammar; Abuarqoub, Abdelrahman; Khayyat, Mashael

doi:10.3390/electronics9061010

Cited by 41 publications

(18 citation statements)

References 48 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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“…Nowadays, many machine learning algorithms are used to measure and predict body motion for wearable devices based on IMU Data. Several machine learning systems are working in diverse domain from marketing to medical services [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. In Reference [ 35 ], a fingerprinting based indoor positioning uses a deep neural network to reduce the error in positioning.…”

Section: Related Workmentioning

confidence: 99%

Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation

Jamil

Iqbal

Ahmad

et al. 2020

Sensors

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Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding emerging area is an Indoor Localization due to the increased fascination towards location-based services. Numerous inertial assessments unit-based indoor localization mechanisms have been suggested in this regard. However, these methods have many shortcomings pertaining to accuracy and consistency. In this study, we propose a novel position estimation system based on learning to the prediction model to address the above challenges. The designed system consists of two modules; learning to prediction module and position estimation using sensor fusion in an indoor environment. The prediction algorithm is attached to the learning module. Moreover, the learning module continuously controls, observes, and enhances the efficiency of the prediction algorithm by evaluating the output and taking into account the exogenous factors that may have an impact on its outcome. On top of that, we reckon a situation where the prediction algorithm can be applied to anticipate the accurate gyroscope and accelerometer reading from the noisy sensor readings. In the designed system, we consider a scenario where the learning module, based on Artificial Neural Network, and Kalman filter are used as a prediction algorithm to predict the actual accelerometer and gyroscope reading from the noisy sensor reading. Moreover, to acquire data, we use the next-generation inertial measurement unit, which contains a 3-axis accelerometer and gyroscope data. Finally, for the performance and accuracy of the proposed system, we carried out numbers of experiments, and we observed that the proposed Kalman filter with learning module performed better than the traditional Kalman filter algorithm in terms of root mean square error metric.

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“…Moreover, radio access network slicing still fails to provide different isolation levels and sharing to permit the slice owner to customize separated user plane (UP), control plane (CP), and control logic (CL) for specific services. Providing separate resources can also increase the RAN network utilization in terms of spectrum and resource blocks [13][14][15][16][17].…”

Section: End-to-end Network Slicingmentioning

confidence: 99%

Slicing the Core Network and Radio Access Network Domains through Intent-Based Networking for 5G Networks

et al. 2020

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The fifth-generation mobile network presents a wide range of services which have different requirements in terms of performance, bandwidth, reliability, and latency. The legacy networks are not capable to handle these diverse services with the same physical infrastructure. In this way, network virtualization presents a reliable solution named network slicing that supports service heterogeneity and provides differentiated resources to each service. Network slicing enables network operators to create multiple logical networks over a common physical infrastructure. In this research article, we have designed and implemented an intent-based network slicing system that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just need to provide higher-level network configurations in the form of intents/contracts for a network slice, and in return, our system deploys and configures the requested resources accordingly. Further, our system grants the automation of the network configurations process and reduces the manual effort. It has an intent-based networking (IBN) tool which can control, manage, and monitor the network slice resources properly. Moreover, a deep learning model, the generative adversarial neural network (GAN), has been used for the management of network resources. Several tests have been carried out with our system by creating three slices, which shows better performance in terms of bandwidth and latency.

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Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach

Cited by 41 publications

References 48 publications

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

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

Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation

Slicing the Core Network and Radio Access Network Domains through Intent-Based Networking for 5G Networks

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