Fog Computing, Cloud Computing and IoT Environment: Advanced Broker Management System

Masarweh, Mohammed Al; Alwada'n, Tariq; Afandi, Waleed

doi:10.3390/jsan11040084

Cited by 18 publications

(10 citation statements)

References 40 publications

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“…The amount of data determined in random packet loss fluctuates within the narrow range, and they are affected by backlog queue length with minimum and maximum range 16 that are expressed in Equations ( 1) and (2).…”

Section: Identifying Congestion and Traffic Degreementioning

confidence: 99%

An efficient routing protocol to reduce traffic and congestion control in cloud edge networks of wireless sensor networks

Alwarsamy,

Rethnaraj,

Gurumuni Nathan

et al. 2024

Int J Communication

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SummaryRecently, wireless sensor networks (WSNs) have been used for monitoring, sensing, processing, and communication purposes in real‐time applications. It is employed with a routing protocol that performs an effective data transmission process. However, while transmitting large data, there occurs an over fitting issue, which leads to determining a huge data leakage. Also, the delay is increased with heavy congestion in the network. Hence, a novel method is proposed to diminish the network congestion regarding distributed networks as well as cloud edge computing. Moreover, it diminished the data loss from an overloaded condition. However, the proposed technique controls congestion that resists the traffic in the network through lightweight, ultra‐dense label‐less federation and incorporates adaptive multi‐agent Markov reinforcement learning. Furthermore, a distributed energy‐efficient delay‐aware routing protocol is employed to analyze and regulate congestion control in the network. Also, it varies the network dynamically by adjusting the routing protocol that optimizes the congestion and implements the traffic mechanism. Moreover, the congestion in WSNs overwhelms the nodes and channels distributed in the packets. The evaluation of the proposed method is determined by various metrics such as queuing delay, network lifetime, energy efficiency, throughput, and packet delivery ratio. The experimental results revealed that the proposed method attained an enhanced performance by maximizing energy efficiency and packet delivery ratio by 94% as well as 89% and reducing the delay by 55%, respectively.

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Section: Identifying Congestion and Traffic Degreementioning

confidence: 99%

An efficient routing protocol to reduce traffic and congestion control in cloud edge networks of wireless sensor networks

Alwarsamy,

Rethnaraj,

Gurumuni Nathan

et al. 2024

Int J Communication

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“…Fog offers a promising implementation ground for envisioning Internet of Things at its fullest [33]. Applications are limitless.…”

Section: Future Directionsmentioning

confidence: 99%

FedFog - A federated learning based resource management framework in fog computing for zero touch networks

Khan

Soomro

Zheng

2023

Mehran Univ. res. j. eng. technol.

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Fog computing offers an optimal answer to the expansion challenge of today’s networks. It boasts scaling and reduced latency. Since the concept is still nascent, many research questions remain unanswered. One of these is the challenge of Resource Management. There is a pressing need for a reliable and scalable architecture that meets the Resource Management challenge without compromising the Quality of Service. Among the proposed solutions, Artificial Intelligence based path selection techniques and automated link detection methods can provide lasting and reliable answer. An optimal approach for introducing intelligence in the networks is the infusion of Machine learning methods. Such futuristic, intelligent networks form the backbone of the next generation of Internet. These self-learning and self-healing networks are termed as the Zero-Touch networks. This paper proposes FedFog, a Federated Learning based optimal, automated Resource Management framework in Fog Computing for Zero-touch Networks. The paper describes a series of experiments focusing on Quality of Service parameters such as Network latency, Resources processed, Energy consumption and Network usage. The simulation results from these experiments depict superiority of the proposed architecture over traditional, existing architecture.

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“…As a distributed computing paradigm, IoT-Fog aims to place computational and storage resources at the edge of the network for low-latency and high-reliability task execution. First, one of the common communication types is the Proxy-Broker approach [141], in which the proxy and broker work in tandem to handle communication and information exchange. The proxy is employed to communicate with end devices, collecting and forwarding data to the broker, while the broker is responsible for processing, managing and routing the data.…”

Section: Generated Dataset 2021mentioning

confidence: 99%

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Zhu

Yin

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Fog Computing, Cloud Computing and IoT Environment: Advanced Broker Management System

Cited by 18 publications

References 40 publications

An efficient routing protocol to reduce traffic and congestion control in cloud edge networks of wireless sensor networks

An efficient routing protocol to reduce traffic and congestion control in cloud edge networks of wireless sensor networks

FedFog - A federated learning based resource management framework in fog computing for zero touch networks

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

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