Titlebook: Energy Efficient Computation Offloading in Mobile Edge Computing; Ying Chen,Ning Zhang,Sherman Shen Book 2022 The Editor(s) (if applicable

Relinquish

Humble

Conspiracy

Conclusion,In this chapter, we provide a summary of the book and suggest future research directions.

觀點(diǎn)

Soziale Identit?ten Jugendlichery-deployed and gained more and more attention. Although the development of mobile devices and mobile applications have brought great convenience to people’s production and life, it has also lead to some new issues. Due to the resource limitations of mobile devices, such as limited battery capacity a

APEX

Degener Theresia,Mogge-Grotjahn Hildegard rapidly. However, the computing capacity of IoT devices is limited and the devices can not process so much data by themselves, which increases the delay and lead to the decline of service quality. Mobile edge computing is a promising computing paradigm, which deploys servers near IoT devices to pro

巧思

,Das europ?ische Mehrebenensystem,, thus improve users’ service experience. Mobile devices can offload computation-intensive tasks to MEC for computing. MEC can greatly reduce the energy consumption of mobile devices while also extending their battery life. However, task assignment based on MEC becomes more difficult due to the unce

encyclopedia

https://doi.org/10.1007/978-3-658-33908-1e offloaded to the edge servers for processing, rather than sending them to the remote cloud servers. As a result, the service latency can be greatly improved and the network congestion can be mitigated. In this chapter, we investigate computation offloading in a dynamic MEC system with multiple coo

heirloom

投票

https://doi.org/10.1007/978-3-031-16822-2Mobile Edge Computing; Internet Of Things; computation offloading; task scheduling; energy efficiency; dy

懸掛

2366-1186 end-to-end deep reinforcement learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. Finally978-3-031-16824-6978-3-031-16822-2Series ISSN 2366-1186 Series E-ISSN 2366-1445