A Cross-Layer Green Information-Centric Networking Design Toward the Energy Internet

Nguyễn, Quang Ngọc; Ullah, Rehmat; Kim, Byung-Seo; Hassan, Rosilah; Sato, Toru; Taleb, Tarik

doi:10.1109/tnse.2022.3148146

Cited by 8 publications

(2 citation statements)

References 59 publications

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“…Nguyen et al 16 implemented energy efficiency by utilizing Green Information‐Centric Networking Design (ICN). For the optimized utilization level proportionally, the power consumption of network nodes was modified by ICN.…”

Section: Related Workmentioning

confidence: 99%

Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation

Swamidoss,

Samiayya,

Gunasekar

2024

Concurrency and Computation

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SummaryIn green optical networking, designing an adaptive energy‐saving scheme plays a vital role, in optimizing energy consumption by dynamically adjusting resources based on network traffic and environmental conditions, to a more sustainable and efficient optical communication infrastructure. Traditional methods in optical networking face challenges such as static resource allocation, limited adaptability, inefficient power usage, environmental insensitivity, and scalability issues. Therefore this article proposed a novel method named Dynamic Quality of Service based Random update Genghis Khan (DQ‐RGK) algorithm, the proposed model can tackle the abovementioned complexities. In this study, cluster head dynamic placement is utilized to optimize the network's performance by adapting the placement of cluster heads to the current topology, load distribution, and energy levels in the network nodes. Additionally, Dynamic Quality of Service (QoS) is employed to respond dynamically to changes in network conditions, adapting to varying traffic patterns and resource availability. In this work, the Genghis Khan Shark optimization with a random update strategy is implemented for hyperparameter optimization to enhance the performance of the DQ‐RGK method. The DQ‐RGK adjusts the parameters of QoS in real‐time, and this ensures that network resources based on the requirements changed and priorities of applications, which ultimately optimizes performance and enhances user experience. By dynamically assigning and reallocating resources based on the current demand the algorithm enhances overall network efficiency and reduces energy consumption. Then, this work analyzes the experimental results, where some evaluation measures estimate the DQ‐RGK method's performance. Routing efficiency, latency, scalability, spectral efficiency, Packet Delivery Ratio, throughput, network lifetime, energy consumption, jitter, and energy consumption are the measures employed by the DQ‐RGK model. In The results, other routing models that do not provide efficiency are utilized, a comparison of these other routing models is represented in results. The overall DQ‐RGK model's effectiveness is represented in the experimental results and its effectiveness is greater among other methods.

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Section: Related Workmentioning

confidence: 99%

Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation

Swamidoss,

Samiayya,

Gunasekar

2024

Concurrency and Computation

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“…The hybrid transmission control protocol integrates both hop-by-hop and receiver control mechanisms, enabling both the receiving node and the intermediate routing node to manage the sending of interest packets. Some researchers have also focused on transmission control under multiple sources [20]. However, most of them establish independent congestion windows and RTT values for each source and use source prediction to execute the corresponding source's congestion algorithm and retransmission.…”

Section: Related Workmentioning

confidence: 99%

Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

Rashid Sangi,

Anamalamudi,

S. Alkatheiri

et al. 2023

Computers, Materials &Amp; Continua

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Vehicular Adhoc Networks (VANETs) enable vehicles to act as mobile nodes that can fetch, share, and disseminate information about vehicle safety, emergency events, warning messages, and passenger infotainment. However, the continuous dissemination of information from vehicles and their one-hop neighbor nodes, Road Side Units (RSUs), and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based network. Therefore, Information Centric Networks (ICN) are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile, dynamic, and errorprone domains. In ICN-based Vehicular-IoT networks, consumer mobility is implicitly supported, but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular nodes. This paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information dissemination. The proposed protocol, called Network Cording Multiple Solutions Scheduling (NCMSS), is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information sources. Experimental results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching, data retrieval delay, and end-to-end application throughput. The end-to-end throughput in proposed NCMSS is 22% higher (for 1024 byte data) than existing solutions whereas delay in NCMSS is reduced by 5% in comparison with existing solutions.

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Joint resource allocation and beamforming design for RIS-aided symbiotic radio networks: A DRL approach

Li,

Liu,

Xie

2024

Digital Communications and Networks

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A Cross-Layer Green Information-Centric Networking Design Toward the Energy Internet

Cited by 8 publications

References 59 publications

Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation

Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation

Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

Joint resource allocation and beamforming design for RIS-aided symbiotic radio networks: A DRL approach

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