Wireless Network Evolution Towards Service Continuity in 5G enabled Mobile Edge Computing

Barzegar, Hamid Reza; Ioini, Nabil El; Le, Van Thanh; Pahl, Claus

doi:10.1109/fmec49853.2020.9144852

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…However, we aim to explore this in more concrete application settings. We consider here traffic management and coordinated cars as a concrete IoT and edge setting, where traffic and car movement is captured and processed, maybe combined by infotainment information with image and video data [6,16,20,25]. This mobile setting would need to be supported by local clusters that act autonomously, but require some higher-level coordination [7,14,27].…”

Section: Discussionmentioning

confidence: 99%

Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

Azimi

Pahl

Shirvani

2021

Communications in Computer and Information Science

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Recently distributed computing capacities are brought to the edge of the Internet, permitting Internet-of-Things applications to process calculation all the more locally and subsequently more productively and this has brought a totally different scope of apparatuses and usefulness. This instrument can The most significant characterizing highlights of edge processing are low latency, location awareness, wide geographic distribution, versatility, support for countless nodes, etc. We want to likely limit the latency and delay in edge-based structures. We center around a progressed compositional setting that considers communication and processing delays and the management effort notwithstanding a real request execution time in an operational efficiency situation. Our design is based on multi-cluster edge layer with nearby autonomous edge node clusters. We will contend that particle swarm optimization as a bio-motivated optimization approach is a perfect candidate for distributed IoT load handling in self-managed edge clusters. By designing a controller and utilizing a particle swarm optimization algorithm, we show that delay and end-to-end latency can be reduced.

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Section: Discussionmentioning

confidence: 99%

Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

Azimi

Pahl

Shirvani

2021

Communications in Computer and Information Science

Self Cite

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“…Some applications of MANET include waste management systems [6], the use o MANET and VANET in smart cities [7], healthcare [8], etc. Smart applications in 5G New Radio (NR) are classified into three major categories, including enhanced Mobile Broad band (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Ma chine Type Communication (mMTC), as mentioned in Figure 1 [9]. The targeted technol ogy of massive NB-IoT or IEEE 802.15.4, though, falls under mMTC but can also be used in URLLC as the communication governed by the technology tends to provide reliabl communication and low latency between two nodes connected in a complex network.…”

Section: Introductionmentioning

confidence: 99%

Queue-Buffer Optimization Based on Aggressive Random Early Detection in Massive NB-IoT MANET for 5G Applications

2022

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Elements in massive narrowband Internet of Things (NB-IoT) for 5G networks suffer severely from packet drops due to queue overflow. Active Queue Management (AQM) techniques help in maintaining queue length by dropping packets early, based on certain defined parameters. In this paper, we have proposed an AQM technique, called Aggressive Random Early Detection (AgRED) which, in comparison to previously used Random Early Detection (RED) and exponential RED technique, improves the overall end-to-end delay, throughput, and packet delivery ratio of the massive NB-IoT 5G network while using UDP. This improvement has been achieved due to a sigmoid function used by the AgRED technique, which aggressively and randomly drops the incoming packets preventing them from filling the queue. Because of the incorporation of the AgRED technique, the queue at different nodes will remain available throughout the operation of the network and the probability of delivering the packets will increase. We have analyzed and compared the performance of our proposed AgRED technique and have found that the performance gain for the proposed technique is higher than other techniques (RED and exponential RED) and passive queue management techniques (drop-tail and drop-head). The improvement in results is most significant in congested network deployment scenarios and provides improvements in massive Machine Type Communication, while also supporting ultra-low latency and reliable communication for 5G applications.

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Cross Border Service Continuity with 5G Mobile Edge

Barzegar¹,

Ioini²,

Le³

et al. 2021

Mobile Edge Computing

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Wireless Network Evolution Towards Service Continuity in 5G enabled Mobile Edge Computing

Cited by 10 publications

References 26 publications

Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

Queue-Buffer Optimization Based on Aggressive Random Early Detection in Massive NB-IoT MANET for 5G Applications

Cross Border Service Continuity with 5G Mobile Edge

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