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Key Parameters in 5G for Optimized Performance
Published in Mangesh M. Ghonge, Ramchandra Sharad Mangrulkar, Pradip M. Jawandhiya, Nitin Goje, Future Trends in 5G and 6G, 2021
Dhanashree A. Kulkarni, Anju V. Kulkarni
Mobile Edge Computing (MEC) is also a technique which is used to reduce latency in the required processing time [33]. MEC has gained a significant interest by all the researchers since it provides a platform to load the applications at the very edge of the mobile network [34]. This also helps in reducing the energy consumption of the mobile devices and also reduces congestion. In [35–37] and [38] single-users and multi-user MEC systems are studied. In [35] and [36] the resource utilization schemes are derived with single-users to minimize the latency. Aforementioned papers have worked on the computational offloading either at the mobile device or at the edge cloud. The authors of [39], [40] have focused on the algorithm for partial offloading but no such system has been formed with all the results. In [41] partial offloading with piecewise convex problem is focused, which is further solved by sub-gradient method to reduce the scheduling time and significantly the weighted-sum latency. In [42] the author has focused on partial offloading and has also contributed in three types of compression: Local compression, edge compression, cloud compression, and partial compression. In local compression data is compressed at mobile devices for which a convex optimization problem is designed to minimize the weighted sum delay. At the edge cloud compression joint resource allocation is formulated. In partial compression offload model piecewise optimization problem has worked on reducing latency at all the parts on MEC.
Mobile Edge Computing for the 5G Internet of Things
Published in Yulei Wu, Haojun Huang, Cheng-Xiang Wang, Yi Pan, 5G-Enabled Internet of Things, 2019
Haojun Huang, Wang Miao, Geyong Min, Chunbo Luo
It is worth noting that the limitations of mobile terminal resources are related to the mobility feature. Compared with static devices such as laptops, mobile devices are designed with lower computation, less storage and narrower broadband to meet the requirements of portability and mobility. In the design of mobile terminals, in order to satisfy the requirements of portability and mobility, the processing capability of the terminal device, the network connection and the like are transferred [17]. In this case, computing offloading is an effective method to extend the resources for mobile devices. During computation offloading, the intensive computing tasks of the IoT application are sent to a remote high-performance data center or to nearby mobile devices to be executed, and the execution results are downloaded back to the mobile devices for further processing, significantly enhancing the computation capability of mobile devices.
Energy-Efficient Technologies in Device-to-Device Based Proximity Service
Published in Yufeng Wang, Athanasios V. Vasilakos, Qun Jin, Hongbo Zhu, Device-to-Device based Proximity Service, 2017
Yufeng Wang, Athanasios V. Vasilakos, Qun Jin, Hongbo Zhu
In most ProSe applications, on one hand, the sensors embedded in mobile devices have to be sampled often in order to capture users’ behaviors, which, however, may lead to faster depletion of the battery; on the other hand, if the sensors are sampled at a slower rate, then it may not be possible to accurately capture the user’s behaviors. To meet the challenges posed by phone sensing, three adaptive schemes were designed by Rachuri [32]. First, an adaptive sampling framework was designed in which sensors sample data by intentionally considering the user’s context to conserve energy, while providing the required accuracy to the applications. Second, to further reduce the energy consumption of capturing data, a specific framework is proposed, which exploits the sensors in buildings and dynamically distributes the sensing tasks between the local phone and the sensors in buildings. Third, to efficiently process the data, a computation offloading scheme is provided, which determines whether to locally compute the classification tasks on the mobile device or remotely in the cloud by considering various dimensions such as latency, energy and data traffic, and so on.
Computation Offloading for Smart Devices in Fog-Cloud Queuing System
Published in IETE Journal of Research, 2023
To realize the benefits of computation offloading, an appropriate offloading decision needs to consider various offloading aspects, including why, what, when, where, and how to offload [18]. Why emphasize the application requirement, which may be the redaction of the execution time of a task, or reducing the energy consumption of the device, or achieving a combination of both. Secondly, what to offload, generally focuses on finding the computation-intensive tasks of an application that should be offloaded for remote execution to achieve performance enhancement. The motif of when is to find the right time for offloading data to the cloud. The fourth issue in offloading deals with where to offload a task to achieve the best result, which can be a local server, fog nodes, or to the cloud [17]. Lastly, How concentrates on selecting a communication medium like the cellular network or wireless network to schedule offloading operation [19].
A novel variable neighborhood search for the offloading and resource allocation in Mobile-Edge Computing
Published in International Journal of Computers and Applications, 2022
Mohamed Younes Kaci, Malika Bessedik, Amina Lammari
In recent years, MEC has attracted the interest of several researchers and research centers, thus several architectures have been proposed as well as different algorithms for resource or computation offloading. This involves transferring resource-intensive computing tasks to a separate processor, such as a hardware accelerator, or an external platform. An offloading algorithm is then designed to determine the optimal offloading decision for all mobile users in the MEC system [3]. Many studies have applied the concept of computation offloading on the mobile edge computing paradigm to minimize energy consumption, satisfy delay requirements, allocate radio resources efficiently, maximize total revenue, maximize system utility, and/or reduce total cost of mobile users (or devices, equipment).
‘Un’-blocking the industry 4.0 value chain with cyber-physical social thinking
Published in Enterprise Information Systems, 2023
Subodh Mendhurwar, Rajhans Mishra
While many of these technologies are still evolutionary (Wang et al. 2019), researchers have been experimenting with some of these technologies or their select combinations. e.g., Dai et al. (2019) explored the integration of blockchain and Artificial Intelligence (AI) techniques for next-generation wireless networks (spanning Cloud, Edge, and User planes), featuring resource management use cases like (i) spectrum sharing, (ii) Deep Reinforcement Learning (DRL) based Device-to-Device (D2D) caching, (iii) energy trading and (iv) computational offloading.