原标题：边缘计算 | SCI期刊JoCCASA诚邀专刊稿件
期刊基本信息
期刊名称
Journal of Cloud Computing: Advances, Systems and Applications
影响因子
2.14
期刊难度
★ ★
领域
计算机体系结构,并行与分布式计算
JCR分区
大类 : 工程技术 - 3区
小类 : 计算机：信息系统 - 3区
专刊名称
Call for papers: Edge-cloud computing cooperation for task offloading in internet-of-things
专刊简介
With the fast development trend of Internet of Things (IoTs), the demand for User Terminals (UTs) such as smartphones, unmanned aerial vehicles, and wearable devices is increasing dramatically. However, UTs are constrained by limited resources, such as CPU computing power, storage space, energy capacities, environmental awareness and complex computing tasks. To solve the above contradictions, one effective way is to offload complex computing tasks from UTs either to remote cloud servers or nearby edge servers. Compared to cloud servers, edge servers are closer to UTs and thus achieve lower latency; however, edge servers have low computing capacity while cloud servers have relatively sufficient computing power. Therefore, edge computing and cloud computing can cooperate and complement with each other in terms of computing, storage, and communication facilities. The combination of edge and cloud computing will make task execution faster, cheaper, and more stable.
This thematic series is devoted to state-of-the-art research covering concepts of task offloading technologies for IoT applications. It is of great significance to the rapid promotion of collaboration between Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC). With the continuous development of theory and methods of decision-making and thorough perception of the hybrid task offloading, and further meets the application requirements on UTs, compensates for the lack of computing capacity and limited battery power for IoT systems.
关键日期
Submission Deadline
-09-01
征稿主题
Topics of interest include but are not limited to:返回搜狐，查看更多
Computing paradigm frontiers: edge, fog, mist and cloud computing cooperation
Optimization algorithms for edge-cloud computing cooperation
Delay and energy minimization for edge-cloud computing cooperation
Novel techniques and future perspectives for edge-cloud computing cooperation
Energy-efficient task offloading in edge-cloud computing environments
5G-enabled services for task offloading in edge-cloud computing environments
Security and privacy issues for task offloading with hybrid clouds in IoTs
Model and architecture design for computation offloading, resource management and task scheduling in IoTs
Computation and communication integration for task offloading in IoTs
High-performance low-cost communication task offloading in IoTs
Sustainable and green computing for task offloading in IoTs
Deep learning-driven algorithms for task offloading in IoTs
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