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基于物联网的移动云计算中任务调度的多层次中央信任管理方法。

Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing.

机构信息

Department of Computer Science, The University of Engineering and Technology, Taxila 47080, Pakistan.

Department of Computer Science, Govt Akhtar Nawaz Khan (Shaheed) Degree College KTS, Haripur 22620, Pakistan.

出版信息

Sensors (Basel). 2021 Dec 24;22(1):108. doi: 10.3390/s22010108.

DOI:10.3390/s22010108
PMID:35009649
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8747413/
Abstract

With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the trustable tasks needed to offload towards the mobile cloud computing. Then, an efficient and dynamic scheduler is added to enhance the task scheduling after trust computation using social and environmental trust computation techniques. To improve the time and energy efficiency of IoT and mobile devices using the proposed technique, the energy computation and time request computation are compared with the existing methods from literature, which identified improvements in the results. Our proposed approach is centralized to tackle constant SyncUPs of incoming devices' trust values with mobile cloud computing. With the benefits of mobile cloud computing, the centralized data distribution method is a positive approach.

摘要

随着移动设备和物联网设备在广泛的现实应用中数量的增加，我们的移动云计算设备将很快无法应对这不断增长的用户数量，这意味着需要转向雾计算。任务调度是可信节点内信任计算之后最具挑战性的范围之一。移动设备和物联网设备将资源密集型任务转移到移动云计算中。有些任务资源密集且不可信，无法分配给移动云计算资源。这就需要对加入和离开网络的设备进行信任评估和数据同步。云计算和移动云计算的资源更加密集。由于不可信节点，时间、能源和资源被浪费了。本研究文章提出了一种多级信任增强方法，用于移动云计算环境中的高效任务调度。我们首先计算需要卸载到移动云计算的可信任务。然后，使用社交和环境信任计算技术，在信任计算之后添加一个高效和动态的调度程序来增强任务调度。为了提高物联网和移动设备的时间和能源效率，使用所提出的技术对能量计算和时间请求计算与文献中的现有方法进行了比较，结果表明有所改进。我们提出的方法是集中式的，以解决移动云计算中传入设备信任值的恒定 SyncUP 问题。利用移动云计算的优势，集中式数据分发方法是一种积极的方法。

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3
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4
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6
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