Joint Optimization of Transmission Bandwidth Allocation and Data Compression for Mobile-Edge Computing Systems

Wang, Junbo; Zhang, Jinyuexue; Ding, Changfeng; Zhang, Hua; Lin, Min; Wang, Jiangzhou

doi:10.1109/lcomm.2020.2998474

Cited by 28 publications

(11 citation statements)

References 12 publications

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“…This design can not only simplify the communication volume of the wireless sensor application node but also make full use of the edge computing node to ideological and political. Due to the advantages of efficient transmission of teaching resources, edge computing nodes can optimize the real-time transmission of ideological and political teaching resources and reduce the transmission delay [17,18]. The ideological and political teaching resources based on edge computing are the ideological and political teaching resources transmitted through the edge computing nodes, which will be processed safely in the following sections.…”

Section: The Sharing Model Of Ideological and Political Teachingmentioning

confidence: 99%

Ideological and Political Teaching Resource Sharing Method Based on Edge Computing

2022

Wireless Communications and Mobile Computing

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In the process of ideological and political teaching resource sharing, the sharing time is long due to the interference of redundant data. Therefore, an ideological and political teaching resource sharing method based on edge computing is proposed. Based on the sharing model of ideological and political teaching resources of edge computing, the information entropy suppression method is used to eliminate the redundant data in the ideological and political teaching resources, calculate the sinking of the edge node of the resource after removing the redundancy, assign the task to the edge node, and finally build a location index for the ideological and political teaching resources to realize the sharing of ideological and political teaching resources. The results show that when using the research method to share ideological and political teaching resources, the completion time, average waiting delay, and resource download time of resource sharing are shorter.

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Section: The Sharing Model Of Ideological and Political Teachingmentioning

confidence: 99%

Ideological and Political Teaching Resource Sharing Method Based on Edge Computing

2022

Wireless Communications and Mobile Computing

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“…where the computing capability and bandwidth of AP m. AP m will not allocate resources to the user who are not within the service range. To allocate bandwidth and ensure that users can send signals to different APs in the AP cluster, the orthogonal frequency division multiple access (OFDMA) method is adopt in this paper [25]. When AP m provides bandwidth for user n to transmit task data, the uplink transmission rate of user n can be expressed by…”

Section: B Task Offloading Modelmentioning

confidence: 99%

When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization

Qin

2023

IEEE Commun. Mag.

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In the traditional mobile edge computing (MEC) system, the availability of MEC services is greatly limited for the edge users of the cell due to serious signal attenuation and inter-cell interference. User-centric MEC (UC-MEC) can be seen as a promising solution to address this issue. In UC-MEC, each user is served by a dedicated access point (AP) cluster enabled with MEC capability instead of a single MEC server, however, at the expense of more energy consumption and greater privacy risks. To achieve efficient and reliable resource utilization with user-centric services, we propose an energy efficient blockchainenabled UC-MEC system where blockchain operations and resource optimization are jointly performed. Firstly, we design a resource-aware, reliable, replicated, redundant, and faulttolerant (R-RAFT) consensus mechanism to implement secure and reliable resource trading. Then, an optimization framework based on alternating direction method of multipliers (ADMM) is proposed to minimize the total energy consumed by wireless transmission, consensus and task computing, where APs clustering, computing resource allocation and bandwidth allocation are jointly considered. Simulation results show superiority of the proposed UC-MEC system over reference schemes, at most 33.96% reduction in the total delay and 48.77% reduction in the total energy consumption.

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“…We assume that lossless compression is performed, so that the original, raw data can be reconstructed perfectly. 1 For lossless compression, the average number of CPU cycles required to compress one bit of raw data is given as [10], [15] E…”

Section: A Latency In Data Compressionmentioning

confidence: 99%

Analysis and Optimization of the Latency Budget in Wireless Systems with Mobile Edge Computing

Suman¹,

Stefanović²,

Došen³

et al. 2022

Preprint

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We present a framework to analyse the latency budget in wireless systems with Mobile Edge Computing (MEC). Our focus is on teleoperation and telerobotics, as use cases that are representative of mission-critical uplink-intensive IoT systems with requirements on low latency and high reliability. The study is motivated by a general question: What is the optimal compression strategy in reliability and latency constrained systems? We address this question by studying the latency of an uplink connection from a multi-sensor IoT device to the base station. This is a critical link tasked with a timely and reliable transfer of potentially significant amount of data from the multitude of sensors. We introduce a comprehensive model for the latency budget, incorporating data compression and data transmission. The uplink latency is a random variable whose distribution depends on the computational capabilities of the device and on the properties of the wireless link. We formulate two optimization problems corresponding to two transmission strategies: (1) Outage-constrained, and (2) Latency-constrained. We derive the optimal system parameters under a reliability criterion. We show that the obtained results are superior compared to the ones based on the optimization of the expected latency.

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Joint Optimization of Transmission Bandwidth Allocation and Data Compression for Mobile-Edge Computing Systems

Cited by 28 publications

References 12 publications

Ideological and Political Teaching Resource Sharing Method Based on Edge Computing

Ideological and Political Teaching Resource Sharing Method Based on Edge Computing

When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization

Analysis and Optimization of the Latency Budget in Wireless Systems with Mobile Edge Computing

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