Service-differentiated QoS routing based on ant colony optimisation for named data networking

Hou, Rui; Zhang, Lang; Zheng, Yongjun; Chang, Yuzhou; Li, Bing; Huang, Tao; Luo, Jiangtao

doi:10.1007/s12083-018-0669-6

Cited by 5 publications

(6 citation statements)

References 23 publications

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“…We heuristically found this weight in preliminary numerical simulations. In Eq (19), p ij (L), defined in Eq (18), is calculated when the iteration t is L. Further, we defined an In these simulations, we randomly selected four typical edges from different nodes in the networks. In this figure, "high-high," "high-low," "low-high," and "low-low" are transmitting probabilities that connect high-degree nodes, high and low-degree nodes, low and high-degree nodes, and low-degree nodes, respectively.…”

Section: Routing Methods That Adaptively Adjusts the Weight Of Memory...mentioning

confidence: 99%

“…evaluation function for this routing method as follows: 8), (10), (18), (19) and (20) compose the memory method with fixed transmitting probabilities (hereafter, we call this method memory-pfix. The memory-pfix method transmits the packets using the static paths determined by the distance and modified memory information.…”

Section: Plos Onementioning

confidence: 99%

“…Recently, Hou et al . proposed QoS routing algorithms based on the particle swarm optimization [ 17 ] and the ant colony optimization [ 18 ] for different types of services in named data networking, which are the next generation of network architectures. The above-mentioned routing methods [ 2 , 9 – 11 , 14 – 16 ] require real-time information such as the number of stored packets at adjacent (or connected) nodes to determine the next transmitting node.…”

Section: Introductionmentioning

confidence: 99%

“…Horiguchi et al [14] proposed a routing strategy by using the Hopfield and Tank typed mutually connected neural networks, and this method was further improved by reinforcement of a learning strategy [15] and incorporation of the stochastic effect [16]. Recently, Hou et al proposed QoS routing algorithms based on the particle swarm optimization [17] and the ant colony optimization [18] for different types of services in named data networking, which are the next generation of network architectures. The above-mentioned routing methods [2,[9][10][11][14][15][16]] require real-time information such as the number of stored packets at adjacent (or connected) nodes to determine the next transmitting node.…”

Section: Introductionmentioning

confidence: 99%

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A routing method with adaptively adjusting memory information based on local routing history

Kimura

Shimada

2023

PLoS ONE

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Finding the shortest paths for packets from sources to destinations in packet-switched communication networks is an inevitable problem in building a future high-speed information society. A routing method with memory information has already been proposed to alleviate the congestion of large volumes of packet flow. This routing method shows a high transmission completion rate even for large volumes of packet flows in communication networks with scale-free properties. However, the method exhibits poor performance for networks with local triangular connections and long distances between nodes. To overcome these problems, in this study, we first enhanced the routing performance of the conventional communication network models by using the betweenness centrality of nodes, which is one of the network centralities that measures the number of shortest paths that pass through each node in the networks. Subsequently, we adaptively changed the transmitting paths of packets by using only local information. Numerical simulations indicated that our routing method performs successfully for various topologies of communication networks by avoiding the congested node, and effectively using the memory information.

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Section: Routing Methods That Adaptively Adjusts the Weight Of Memory...mentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A routing method with adaptively adjusting memory information based on local routing history

Kimura

Shimada

2023

PLoS ONE

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“…These works inspired several researchers to design more network protocols using adaptive natures [56], [57]. Most recent works are now aiming to improve the quality service on routing protocols [23], [58], [59]. Rathee et al (2019) [60] proposed an Ant Colony Optimization based WSN system for energy balancing in secure routing.…”

Section: Protocol and Network Securitymentioning

confidence: 99%

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

et al. 2020

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Cloud computing gained much popularity in the recent past due to its many internet-based services related to data, application, operating system, and eliminating the need for central hardware access. Many of the challenges associated with cloud computing can be specified as network load, security intrusion, authentication, biometric identification, and information leakage. Numerous algorithms have been proposed and evaluated to solve those challenges. Among those, bio-inspired algorithms such as Evolutionary, Swarm, Immune, and Neural algorithms are the most prominent ones which are developed based on nature's ecosystems. Bio-inspired algorithms' adaptability allows many researchers and practitioners to utilize them to solve many security-related cloud computing issues. This paper aims to explore previous research, recent studies, challenges, and scope for further analysis of cloud security. Therefore, this study provides an overview of bio-inspired algorithms application and evaluations, taking into account cloud security challenges,

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Heuristic Initialization Based Modified ACO (HIMACO) Mimicking Ant Safety Features for Multicast Routing and its Parameter Tuning

Kumari

Sahana

2022

Microprocessors and Microsystems

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Service-differentiated QoS routing based on ant colony optimisation for named data networking

Cited by 5 publications

References 23 publications

A routing method with adaptively adjusting memory information based on local routing history

A routing method with adaptively adjusting memory information based on local routing history

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

Heuristic Initialization Based Modified ACO (HIMACO) Mimicking Ant Safety Features for Multicast Routing and its Parameter Tuning

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