A Survey and Design of a Scalable Mobile Edge Cloud Platform for the Smart IoT Devices and It’s Applications

Cho, Yeongpil; Paek, Yunheung; Ahmed, Ejaz; Ko, Kwangman

doi:10.1007/978-981-10-3023-9_106

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…A new video surveillance system service platform is built by edge computing to improve the intelligent processing ability of the front-end camera of the video surveillance system [17]. A large number of studies have shown that the use of edge computing can effectively reduce the amount of data transmission, computing load, and network traffic load [22,23]. However, there are limited studies on edge calculation of sensor data preprocessing.…”

Section: Edge Computing For the Iotmentioning

confidence: 99%

Sensor anomaly detection in the industrial internet of things based on edge computing

Kong¹,

Liu²,

Zhang³

et al. 2020

Turk J Elec Eng & Comp Sci

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In the industrial internet of things (IIoT), because thousands of pieces of hardware, instruments, and various controllers are involved, the core problem is the sensors. Detection using sensors is the bottom line of the IIoT, directly affecting the detection accuracy and control indicators of the IIoT system. However, when a large number of realtime data generated by IIoT devices are transferred to cloud computing centers, large-scale data will inevitably bring computing load, which will affect the computing speed of cloud computing centers and increase the computing load of cloud computing data centers. These factors directly lead to instability and delay in sensor data collected in real time in the IIoT. In this paper, a sensor outlier detection algorithm based on edge calculation is proposed. Firstly, focusing on the problem of the large amount of data in terminal equipment of the IIoT, the edge technology method of data compression is used to optimize the compression of sensor data, and different thresholds are set according to different industrial process requirements, so as to ensure the real-time aspect and authenticity of the data. Then, using the K-means clustering algorithm, the compressed test data sets are analyzed and the abnormal sensor detection values and labels are obtained. Finally, the effectiveness of such an approach is evaluated through a sample case study involving a temperature control system.

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Section: Edge Computing For the Iotmentioning

confidence: 99%

Sensor anomaly detection in the industrial internet of things based on edge computing

Kong¹,

Liu²,

Zhang³

et al. 2020

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Though the use of a cloud data center offers various benefits such as scalability and elasticity [8], its consolidation and centralization lead to a large separation between a mobile device and its associated data center. Offloading computation to the public cloud may involve long latency and low bandwidth for data exchange between the public clouds and edge device through the Internet [9,10]. Thus, MCC is not adequate for a wide-range of emerging mobile applications that are latency-critical.…”

Section: Introductionmentioning

confidence: 99%

A Novel Load Balancing Scheme for Mobile Edge Computing

Duan¹,

Tian²,

Zhang³

et al. 2021

Preprint

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To overcome long propagation delays for data exchange between the remote cloud data center and end devices in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC) is emerging to push mobile computing, network control and storage to the network edges. A cloudlet in MEC is a mobility-enhanced smallscale cloud, which contains several MEC servers located in close proximity to mobile devices. The main purpose of a cloudlet is to stably provide services to mobile devices with low latency. When a cloudlet offers hundreds kinds of services to millions of mobile devices, it is critical to balance the loads so as to improve performance.In this paper, we propose a three-layer mobile hybrid hierarchical P2P (MHP2P) model as a cloudlet. MHP2P performs satisfactory service lookup efficiency and system scalability as well as high stability. More importantly, a load balance scheme is provided so that the loads of MHP2P can be well balanced with the increasing of MEC servers and query load. A large number of simulation ex-

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“…At present, foreign research units have studied the delay optimization in task migration for mobile edge computing. For example, in [13], task migration scheduling is obtained for a single user based on task cache state, task processing state, and transmission unit state [14]. Applying energy harvesting technology to obtain minimum execution delay for a single user, a dynamic migration strategy is proposed.…”

Section: Introductionmentioning

confidence: 99%

Tolerable Data Transmission of Mobile Edge Computing Under Internet of Things

Liu

Wei²,

Jiang³

2019

IEEE Access

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With the emergence of in-depth research of emerging technologies or 5G mobile communication technology methods, the IoT applications have been further sublimated. In this paper, the new characteristics and new challenges appearing in the current mobile edge computing are sorted out, and the latest related models and work are summarized. The important optimization models and moving models and wireless block data in mobile edge computing are analyzed and discussed. On this basis, this paper mainly designs and verifies the following three aspects of mobile edge computing. A joint optimization model of task offloading and power allocation is established, and a centralized joint optimization algorithm for task unloading and power allocation is proposed. Based on the equalization delay and the impact of energy consumption on task unloading, the algorithm can use the idle resources that can be used to distribute and unload the computing tasks. The simulation experiments show that the algorithm can not only coordinate task offloading and power allocation effectively, but also improve the balance between system delay and energy consumption. Delay-tolerable data can be modeled as a partially observable Markov decision process in a software-defined transport and compute node selection process. Compared with the existing scheme, the proposed method can effectively reduce system overhead, shorten data calculation execution time, improve data calculation efficiency, and ensure that the delay can tolerate data transmission arrival rate under the condition of transmission delay. INDEX TERMS Internet of Things, latency tolerable data, mobile edge computing, task offloading, data transfer.

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A Survey and Design of a Scalable Mobile Edge Cloud Platform for the Smart IoT Devices and It’s Applications

Cited by 3 publications

References 15 publications

Sensor anomaly detection in the industrial internet of things based on edge computing

Sensor anomaly detection in the industrial internet of things based on edge computing

A Novel Load Balancing Scheme for Mobile Edge Computing

Tolerable Data Transmission of Mobile Edge Computing Under Internet of Things

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