Fog computing based radio access network in 5G wireless communications

Lavanya, S.; Kumar, N. M. Saravana; Thilagam, S.; Sinduja, S.

doi:10.1109/wispnet.2017.8299819

Cited by 6 publications

(8 citation statements)

References 13 publications

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“…This resource information may be collected at centralized servers or can be disseminated among fog nodes. In this regard, several authors 26,28,[30][31][32][33][34][35][36][37] have made attempts for resource discovery and dissemination of fog nodes. The authors of articles 26,28,31,37 used centralized resource discovery and allocation mechanism to deploy virtual images and microservices in a network substrate.…”

Section: Related Workmentioning

confidence: 99%

“…In this regard, several authors 26,28,[30][31][32][33][34][35][36][37] have made attempts for resource discovery and dissemination of fog nodes. The authors of articles 26,28,31,37 used centralized resource discovery and allocation mechanism to deploy virtual images and microservices in a network substrate. However, authors 30,[32][33][34][35][36] used DHT-based overlay of fog nodes for discovery and dissemination of computing information among fog nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Several efforts 2,17,18,[26][27][28][31][32][33]37,[42][43][44][45][46] have been made to realize the frameworks and tools for deploying microservices in wireless networks. Many efforts have been proposed to enable fog environment in the wired network, which are not covered here.…”

Section: Related Workmentioning

confidence: 99%

“…The licensed wireless networks such as 3G, 5G, and RAN are more popular among researchers to enable fog environments. 2,27,31,33,37,46 In these attempts, they have considered gateway nodes as fog devices to deploy microservices. However, only few attempts 26,32,44,45 have been made for the unlicensed wireless network such as WiFi, mobile ad hoc network (MANET), and WMN.…”

Section: Related Workmentioning

confidence: 99%

“…The attempts that considered wireless network for establishing fog environments are broadly classified in two kinds of wireless networks: unlicensed and licensed wireless network. The licensed wireless networks such as 3G, 5G, and RAN are more popular among researchers to enable fog environments 2,27,31,33,37,46 . In these attempts, they have considered gateway nodes as fog devices to deploy microservices.…”

Section: Related Workmentioning

confidence: 99%

See 4 more Smart Citations

Wireless‐Fog Mesh: A framework for in‐network computing of microservices in semipermanent smart environments

2020

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Large gathering events such as open air rock concerts and religious gatherings require a semipermanent communication and computation infrastructure for providing IoT-based smart solutions. In these places, the installation of wired network is not a cost-effective solution to provide connectivity. Wireless mesh network (WMN) presents itself as a quickly deployable and dismantable solution to provide wireless connectivity to IoT devices. In this paper, we have proposed a "wireless-fog mesh" computing framework for installation of microservices in network devices of WMN. This requires continuous monitoring of residual resources of devices and associated network characteristics. Our proposed fog controller exploits underutilized resources of mesh devices and network metrics for mapping a microservice to a fog node. This controller uses a DHT-based overlay of fog nodes (also acting as distributed MQTT broker) for disseminating resource profiles of fog nodes. The network metrics like path cost, routing loads, number of stations, and delay are used to implement a policy-based FNS algorithm for electing a fog node to run microservice. We have designed and implemented a testbed that consists of 10 OpenWrt-based Raspberry Pi3 node to form a WMN. We have deployed various microservices such as MQTT broker, air quality, web servers, annd video surveillance in fog nodes (mesh routers and access points) to test the FNS algorithm. The results establish that the FNS algorithm significantly outperforms the random node selection (RNS) algorithm and advocates the applicability of our wireless-fog mesh framework for semipermanent use cases.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Wireless‐Fog Mesh: A framework for in‐network computing of microservices in semipermanent smart environments

2020

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Reinforcement Learning-Based Resource Management Model for Fog Radio Access Network Architectures in 5G

2021

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The need to cope with the continuously growing number of connected users and the increased demand for mobile broadband services in the Internet of Things has led to the notion of introducing the fog computing paradigm in fifth generation (5G) mobile networks in the form of fog radio access network (F-RAN). The F-RAN approach emphasises bringing the computation capability to the edge of the network so as to reduce network bottlenecks and improve latency. However, despite the potential, the management of computational resources remains a challenge in F-RAN architectures. Thus, this paper aims to overcome the shortcomings of conventional approaches to computational resource allocation in F-RANs. Reinforcement learning (RL) is presented as a method for dynamic and autonomous resource allocation, and an algorithm is proposed based on Q-learning. RL has several benefits in resource allocation problems and simulations carried out show that it outperforms reactive methods. Furthermore, the results show that the proposed algorithm improves latency and thus has the potential to have a major impact in 5G applications, particularly the Internet of Things.

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Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective

Rodrigues

Suto

Nishiyama

et al. 2020

IEEE Commun. Surv. Tutorials

216

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Fog computing based radio access network in 5G wireless communications

Cited by 6 publications

References 13 publications

Wireless‐Fog Mesh: A framework for in‐network computing of microservices in semipermanent smart environments

Wireless‐Fog Mesh: A framework for in‐network computing of microservices in semipermanent smart environments

Reinforcement Learning-Based Resource Management Model for Fog Radio Access Network Architectures in 5G

Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective

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