Energy‐efficient node‐to‐node communication scheme for fog‐based cellular networks

Biswash, Sanjay Kumar; Jayakody, Dushantha Nalin K.

doi:10.1049/iet-com.2019.0799

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…In the selection of nodes, current methods can not completely and effectively remove interference nodes, so it is impossible to calculate the optimal communication nodes in cluttered nodes, and the effective selection of node spacing cannot be realized. Therefore, it is necessary to study selection method of communication node spacing in the Internet of Things [5,6].…”

Section: Introductionmentioning

confidence: 99%

Intelligent spacing selection model under energy-saving constraints for the selection of communication nodes in the Internet of Things

2021

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Current IoT communication node spacing selection process show may potential areas for improvements such as high delay ratio, high total energy consumption ratio, confusion of the optimal communication information band, intelligent spacing node design under the constraints of the energy-saving selection of IoT communication. Based on energy-saving constraints, the link status between nodes is evaluated through link stability and link quality. In order to prevent the generation of serious noisy nodes and frequency hopping data, the interference nodes under the intrusion of the Internet of Things are identified by determining transition amplitude of the noise nodes in the transmission data sequence. Finally, according to the calculation results of the optimal communication node selection, the design of the intelligent spacing selection model for the communication nodes of the Internet of Things is realized. The simulation results show that the established model not only reduces energy consumption of nodes, shortens the average transmission delay of nodes, but also improves anti-interference effect of node spacing selection.

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Section: Introductionmentioning

confidence: 99%

Intelligent spacing selection model under energy-saving constraints for the selection of communication nodes in the Internet of Things

2021

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“…The home subscriber server (HSS) used to process the user's and system-generated data, global MME-ID management is the part of this procedure [10]. It increases the processing time, communication delay, signaling overhead, and network registration cost [11,12]. These challenges motivate the researchers to produce a new network architecture such as fog networks.…”

Section: Introductionmentioning

confidence: 99%

A Fog Computing-Based Device-Driven Mobility Management Scheme for 5G Networks

Biswash

Jayakody

2020

Sensors

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The fog computing-based device-driven network is a promising solution for high data rates in modern cellular networks. It is a unique framework to reduce the generated-data, data management overheads, network scalability challenges, and help us to provide a pervasive computation environment for real-time network applications, where the mobile data is easily available and accessible to nearby fog servers. It explores a new dimension of the next generation network called fog networks. Fog networks is a complementary part of the cloud network environment. The proposed network architecture is a part of the newly emerged paradigm that extends the network computing infrastructure within the device-driven 5G communication system. This work explores a new design of the fog computing framework to support device-driven communication to achieve better Quality of Service (QoS) and Quality of Experience (QoE). In particular, we focus on, how potential is the fog computing orchestration framework? How it can be customized to the next generation of cellular communication systems? Next, we propose a mobility management procedure for fog networks, considering the static and dynamic mobile nodes. We compare our results with the legacy of cellular networks and observed that the proposed work has the least energy consumption, delay, latency, signaling cost as compared to LTE/LTE-A networks.

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EALSO: joint energy-aware and latency-sensitive task offloading for artificial Intelligence of Things in vehicular fog computing

Liang,

Zhao,

Gao

et al. 2024

Wireless Netw

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Energy‐efficient node‐to‐node communication scheme for fog‐based cellular networks

Cited by 6 publications

References 18 publications

Intelligent spacing selection model under energy-saving constraints for the selection of communication nodes in the Internet of Things

Intelligent spacing selection model under energy-saving constraints for the selection of communication nodes in the Internet of Things

A Fog Computing-Based Device-Driven Mobility Management Scheme for 5G Networks

EALSO: joint energy-aware and latency-sensitive task offloading for artificial Intelligence of Things in vehicular fog computing

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