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Wireless Sensor Network Based on Cloud Computing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 2658

Special Issue Editor


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Guest Editor
Next Generation Mobile Computing and Data Innovation Lab, University of Science and Technology of China, Hefei 230031, China
Interests: wireless network; group intelligence perception; passive sensing and networking; radio frequency imaging

Special Issue Information

Dear Colleagues,

In recent years, cloud computing has become more and more popular with the increasing demand for reducing local storage and computing costs. However, due to the huge energy consumption and difficult Internet access for many resource-constrained equipment types when outsourcing, cloud computing cannot be adapted smoothly. Edge computing can be a solution to the above challenges. Compared with cloud computing, edge computing provides users with various services, such as greater computing power, storage services, and communication bandwidth, in a location closer to the user side. Its advantages include lower response delay, smaller core network bandwidth pressure, and more effective privacy protection and data security. This Special Issue focuses on the topic of edge and cloud computing in wireless sensor networks and the Internet of Things. Topics of interest include:

  • Network/cloud/edge protocol in cloud and edge computing;
  • Network protocols in cloud and edge computing;
  • Multimedia contents analysis in cloud and edge computing;
  • Security, trust, and privacy in cloud and edge computing;
  • Intelligent data processing in cloud and edge computing;
  • Resource management and task scheduling in cloud and edge computing;
  • Scalability problems and solutions in cloud and edge computing;
  • Fog computing and Internet of Things technology for cloud and edge computing.

Prof. Dr. Panlong Yang
Guest Editor

Manuscript Submission Information

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Published Papers (1 paper)

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Research

26 pages, 5640 KiB  
Article
DRL-OS: A Deep Reinforcement Learning-Based Offloading Scheduler in Mobile Edge Computing
by Ducsun Lim, Wooyeob Lee, Won-Tae Kim and Inwhee Joe
Sensors 2022, 22(23), 9212; https://doi.org/10.3390/s22239212 - 26 Nov 2022
Cited by 7 | Viewed by 2287
Abstract
Hardware bottlenecks can throttle smart device (SD) performance when executing computation-intensive and delay-sensitive applications. Hence, task offloading can be used to transfer computation-intensive tasks to an external server or processor in Mobile Edge Computing. However, in this approach, the offloaded task can be [...] Read more.
Hardware bottlenecks can throttle smart device (SD) performance when executing computation-intensive and delay-sensitive applications. Hence, task offloading can be used to transfer computation-intensive tasks to an external server or processor in Mobile Edge Computing. However, in this approach, the offloaded task can be useless when a process is significantly delayed or a deadline has expired. Due to the uncertain task processing via offloading, it is challenging for each SD to determine its offloading decision (whether to local or remote and drop). This study proposes a deep-reinforcement-learning-based offloading scheduler (DRL-OS) that considers the energy balance in selecting the method for performing a task, such as local computing, offloading, or dropping. The proposed DRL-OS is based on the double dueling deep Q-network (D3QN) and selects an appropriate action by learning the task size, deadline, queue, and residual battery charge. The average battery level, drop rate, and average latency of the DRL-OS were measured in simulations to analyze the scheduler performance. The DRL-OS exhibits a lower average battery level (up to 54%) and lower drop rate (up to 42.5%) than existing schemes. The scheduler also achieves a lower average latency of 0.01 to >0.25 s, despite subtle case-wise differences in the average latency. Full article
(This article belongs to the Special Issue Wireless Sensor Network Based on Cloud Computing)
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