Geographic information and node selfish-based routing algorithm for delay tolerant networks

Lu, Fang; Li, Jianbo; Shan, Jiayuan; Song, Youmei; Wang, Fushu

doi:10.23919/tst.2017.7914197

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…The next works could be focused on the two points: on the one hand, studying the more advanced algorithms is a possible research, the expected algorithms should show better performance than the current algorithms on solution quality and solving speed; on the other hand, the methods and strategies [16–25] for solving other problems can also be studied for solving this problem.…”

Section: Discussionmentioning

confidence: 99%

Artificial bee colony algorithm with generating neighbourhood solution for large scale coloured traveling salesman problem

Dong

Lin

Xu³

et al. 2019

IET Intelligent Transport Systems

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Section: Discussionmentioning

confidence: 99%

Artificial bee colony algorithm with generating neighbourhood solution for large scale coloured traveling salesman problem

Dong

Lin

Xu³

et al. 2019

IET Intelligent Transport Systems

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“…A large number of simulations have been carried out, and the results show that, compared with GRONE, GINS can achieve a higher transmission rate and a lower number of hops. In addition, its management expense ratio is 25% lower than GRONE [5]. Wang et al described the structure and function expectations of the energy router from the perspective of the network and improved the existing energy router design.…”

Section: Related Workmentioning

confidence: 99%

Load Balancing Routing Algorithm of Low‐Orbit Communication Satellite Network Traffic Based on Machine Learning

Liu

Sun

Zhang

2021

Wireless Communications and Mobile Computing

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Satellite communication has become an important research trend in the field of communication technology. Low-orbit satellites have always been the focus of extensive attention by scholars due to their wide coverage, strong flexibility, and freedom from geographical constraints. This article introduces some technologies about low-orbit satellites and introduces a routing algorithm DDPG based on machine learning for simulation experiments. The performance of this algorithm is compared with the performance of three commonly used low-orbit satellite routing algorithms, and a conclusion is drawn. The routing algorithm based on machine learning has the smallest average delay, and the average value is 126 ms under different weights. Its packet loss rate is the smallest, with an average of 2.9%. Its throughput is the largest, with an average of 201.7 Mbps; its load distribution index is the smallest, with an average of 0.54. In summary, the performance of routing algorithms based on machine learning is better than general algorithms.

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“…Lu et al 29 proposed a geographic information and node selfish‐based scheme to tackle the problem of selfishness. To choose a decent next‐hop relay node, the readiness of the relay node is merged with geographic information.…”

Section: Related Workmentioning

confidence: 99%

SOS: Socially omitting selfishness in IoT for smart and connected communities

Rehman

Ghani

Zubair³

et al. 2020

Int J Communication

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Smart and connected communities (SCC) is an emerging field of internet of things (IoT), and it has potential applications to improve human life. The improvement may be in terms of preservation, revitalization, livability, and sustainability of a community. The resources of the nodes and devices in the SCC have certain constraints that may not allow the devices and nodes to cooperate to save their resources such as memory, energy, and buffer, or simply maximize their performance. Thus, to stimulate the nodes to avoid selfish behavior, SSC needs a novel and well-organized solution to motivate nodes for cooperation. This article aims to resolve the issue of selfish behaviors in SCC and to encourage the nodes for cooperation. A novel mechanism, socially omitting selfishness (SOS), has been proposed to manage/eradicate selfishness using a socially oriented election process. The election process elects different heads based on weight and cooperation (using Vickrey, Clarke, and Groves model). The election of heads and incentive mechanism encourages the nodes to show participation and behave as highly cooperative members of the community. Furthermore, an extended version of the Dempster Shafer model has been used to discourage the selfish behavior of the participating nodes in the SOS scheme. It uses different monitoring and gateway nodes to efficiently employ the proposed scheme. A mathematical model has been developed for the aforementioned aspects and simulated through NS2 simulation environment to analyze the performance of SOS. The results of the proposed scheme outperform the contemporary schemes in terms of average delivery delay, packet delivery ratio, throughput, and average energy.

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Geographic information and node selfish-based routing algorithm for delay tolerant networks

Cited by 11 publications

References 16 publications

Artificial bee colony algorithm with generating neighbourhood solution for large scale coloured traveling salesman problem

Artificial bee colony algorithm with generating neighbourhood solution for large scale coloured traveling salesman problem

Load Balancing Routing Algorithm of Low‐Orbit Communication Satellite Network Traffic Based on Machine Learning

SOS: Socially omitting selfishness in IoT for smart and connected communities

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