A new adaptive traffic engineering method for telesurgery using ACO algorithm over Software Defined Networks

Parsaei, Mohammad Reza; Mohammadi, Reza; Javidan, Reza

doi:10.1016/j.eurtel.2017.10.003

Cited by 25 publications

(19 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The proposed approach is a hybridization of a type‐2 fuzzy system and cuckoo optimization algorithm. The performance of the proposed T2FS‐COA approach is evaluated in comparison with the following algorithms: The proposed approach using the enhanced version of COA introduced in this paper and with type‐1 fuzzy system instead of the type‐2 fuzzy system. Ant Colony Optimization (ACO)‐based approach by Parsaie et al 7,48 which is introduced in the Related Works Section. The proposed approach using the basic COA instead of the enhanced version introduced in this paper and without fuzzy system. The OSPF which is a well‐known routing protocol for investigating the shortest possible path between source and destination nodes working based on the Dijkstra algorithm. The cost of each link is calculated based on the bandwidth of the link just like the one using Equation .

U_{uv} = \frac{G_{italicuv}}{{italicBW}_{italicuv}}

where G uv is the available bandwidth of link (u, v) , BW uv is its total bandwidth, and U uv is considered as the cost of (u, v).…”

Section: Performance Evaluationmentioning

confidence: 99%

“…In traditional networks like TCP/IP architecture, network operators have to implement policies and complicated management tasks with limited set of low level configuration instructions, and in a command line interface. On the other hand, in these types of networks, applying dynamic changes in network configuration is very hard and time‐consuming, which makes it insufficient for sensitive applications like telesurgery 7 …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Telesurgery QoS improvement over SDN based on a Type‐2 fuzzy system and enhanced cuckoo optimization algorithm

Parsaei

Boveiri

Javidan

et al. 2020

Int J Communication

View full text Add to dashboard Cite

Summary Telemedicine is a new area based on the information and communication technology for collecting, storing, organizing, retrieving and exchanging medical information. One of the most important applications of telemedicine is indeed telesurgery in which an efficient telecommunication infrastructure between the surgery room and remote surgeons need to be established. One of the most important issues to be tackled in telesurgery is to find favorable links for routing as well as providing high Quality of Service (QoS). In this paper, an efficient model based on the hybridization of Type‐2 Fuzzy System (T2FS) and Cuckoo Optimization Algorithm (COA) over the Software Defined Networks (SDN) is proposed in order to achieve optimal and reliable routes for telesurgery application. Using T2FS, the fitness of the links is determined; then, a COA is conducted over the Constraint Shortest Path (CSP) problem to find the best routes. Delay is considered as a CSP problem which is satisfied by trying to find the paths with minimum cost. Due to the NP‐completeness of the CSP problem, an Enhanced COA (so‐called E‐COA) is proposed and utilized as a metaheuristic solver. To the best of our knowledge, this paper is the first SDN‐based communication model that applies both T2FS and E‐COA for assigning proper costs to the network's links, and solves the consequence CSP problem according to the QoS requirement for telesurgery. The model also recognizes and preserves the second‐best routes in order to keep the reliability for such a critical application. In addition to the simulations, the performance evaluation is also conducted on a real experimental scenario. Many comparisons are carried out between the proposed model and other conventional methods, and the evaluation study shows the superiority of the proposed model on all the three QoS‐related metrics, i.e. average end‐to‐end delay, packet loss ratio and PSNR.

show abstract

U_{uv} = \frac{G_{italicuv}}{{italicBW}_{italicuv}}

where G uv is the available bandwidth of link (u, v) , BW uv is its total bandwidth, and U uv is considered as the cost of (u, v).…”

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Telesurgery QoS improvement over SDN based on a Type‐2 fuzzy system and enhanced cuckoo optimization algorithm

Parsaei

Boveiri

Javidan

et al. 2020

Int J Communication

View full text Add to dashboard Cite

show abstract

“…A large variety of meta-heuristic algorithms such as ACO [183][184][185][186][187][188][189][190][191], EAs [192][193][194], GAs [195][196][197][198][199][200][201][202][203], PSO [204][205][206][207][208][209], SA [210][211][212], bee colony optimisation-based [213,214], whale optimisation [215,216], FFO [217], BA [85], TLBO [87] and GWO [207] were used in SDN.…”

Section: Meta-heuristic Algorithms Used In Sdnmentioning

confidence: 99%

Artificial intelligence enabled software‐defined networking: a comprehensive overview

2019

View full text Add to dashboard Cite

Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, the authors provide a detailed overview of the recent efforts to include AI in SDN. The study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work, the authors investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

show abstract

“…Currently, there exist many optimization technologies, such as particle swarm algorithm [31], ant colony algorithm [32], artificial bee colony algorithm [33], etc. In our work, the artificial immune algorithm is adopted because of not only preserving the solution diversity and avoiding the local optimal, but also its fast convergence.…”

Section: Optimization Of Synchronous Evolution Process Based On Artifmentioning

confidence: 99%

Enhancing network cluster synchronization capability based on artificial immune algorithm

Chen

Shi

Yang

et al. 2019

Hum. Cent. Comput. Inf. Sci.

View full text Add to dashboard Cite

In recent years, social cluster events (mass events) take place frequently. The contradictions between social groups are gradually exposed, and their conflicts of interests and games are increasingly apparent. It comes with the fact that cluster behaviors are specifically manifested as cluster emergencies. According to statistics in the "Social Blue Book" in China over the past 10 years, mass incidents have occurred frequently. From 1993 to 2006, mass incidents were boosted from 8709 to 90,000, and even increased to more than 100,000 per year after 2010. When facing those events, people normally make decisions by their own opinions and the choices of surrounding people regarding their attitudes and behaviors. At the same time, with the influence of herd behavior, the individual decisions make these group events synchronized. For example, "candle-snatching

show abstract

A new adaptive traffic engineering method for telesurgery using ACO algorithm over Software Defined Networks

Cited by 25 publications

References 7 publications

Telesurgery QoS improvement over SDN based on a Type‐2 fuzzy system and enhanced cuckoo optimization algorithm

Telesurgery QoS improvement over SDN based on a Type‐2 fuzzy system and enhanced cuckoo optimization algorithm

Artificial intelligence enabled software‐defined networking: a comprehensive overview

Enhancing network cluster synchronization capability based on artificial immune algorithm

Contact Info

Product

Resources

About