A survey on network path identification using bio inspired algorithms

Arun, Kumar S.; Rejimoan, R.

doi:10.1109/aeeicb.2016.7538314

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…Improve P2P structured overlay with bio-inspired algorithms. [190,191,192] Mobile Cloud, Fog and edge computing in p2p overlays Low bandwidth, latency, and realtime response.…”

Section: Blockchain P2p Overlaysmentioning

confidence: 99%

Peer-to-peer overlay techniques for vehicular ad hoc networks: Survey and challenges

Ameur

Lakas

Bachir

et al. 2022

Vehicular Communications

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“…Improve P2P structured overlay with bio-inspired algorithms. [190,191,192] Mobile Cloud, Fog and edge computing in p2p overlays Low bandwidth, latency, and realtime response.…”

Section: Blockchain P2p Overlaysmentioning

confidence: 99%

Peer-to-peer overlay techniques for vehicular ad hoc networks: Survey and challenges

Ameur

Lakas

Bachir

et al. 2022

Vehicular Communications

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“…A bio-inspired approach [17,18]is a combination between the biological and the artificial life, in a way to enhance the artificial life through inspired from biological life characteristics. Because they success in solving many artificial problems, there is an increasing demand for these approaches such as neural networks and genetic algorithms, and the improvements it made in hardware sections [19,20,21].…”

Section: Introductionmentioning

confidence: 99%

Requirements Variability Specification for Data Intensive Software

Muslah¹,

Ghoul²

2019

IJSEA

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Nowadays, the use of feature modeling technique, in software requirements specification, increased the variation support in Data Intensive Software Product Lines (DISPLs) requirements modeling. It is considered the easiest and the most efficient way to express commonalities and variability among different products requirements. Several recent works, in DISPLs requirements, handled data variability by different models which are far from real world concepts. This,leaded to difficulties in analyzing, designing, implementing, and maintaining this variability. However, this work proposes a software requirements specification methodology based on concepts more close to the nature and which are inspired from genetics. This bio-inspiration has carried out important results in DISPLs requirements variability specification with feature modeling, which were not approached by the conventional approaches.The feature model was enriched with features and relations, facilitating the requirements variation management, not yet considered in the current relevant works.The use of genetics-based methodology seems to be promising in data intensive software requirements variability specification.

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“…Several structure and topological properties of networks have been widely studied in the recent years ( [12,14,5,9]). One of the most basic concepts in network science is the definition of network path ( [3,2]), i.e., a sequence of edges that joins a sequence of edges. In the case of a finite path, it is possible to identify the origin and the destination of a path as the starting and ending node of the sequence.…”

Section: Introductionmentioning

confidence: 99%

“…Several structure and topological properties of networks have been widely studied in the recent years (Bondy, 1976; Diestel, 2005; Ferozuddin Riaz, 2011; Gross and Yellen, 2005). One of the most basic concepts in network science is the definition of network path (Ahuja, 2017; Arun and Rejimoan, 2016), i.e. a sequence of edges that joins a sequence of edges.…”

Section: Introductionmentioning

confidence: 99%

Graph input representations for machine learning applications in urban network analysis

Pagani

Mehrotra

Musolesi

2019

Environment and Planning B: Urban Analytics and City Science

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Understanding and learning the characteristics of network paths has been of particular interest for decades and has led to several successful applications. Such analysis becomes challenging for urban networks as their size and complexity are significantly higher compared to other networks. The stateof-the-art machine learning (ML) techniques allow us to detect hidden patterns and, thus, infer the features associated with them. However, very little is known about the impact on the performance of such predictive models by the use of different input representations. In this paper, we design and evaluate six different graph input representations (i.e., representations of the network paths), by considering the network's topological and temporal characteristics, for being used as inputs for machine learning models to learn the behavior of urban networks paths. The representations are validated and then tested with a real-world taxi journeys dataset predicting the tips using a road network of New York. Our results demonstrate that the input representations that use temporal information help the model to achieve the highest accuracy (RMSE of 1.42$).Recently, machine learning (ML) techniques ([28, 27, 23]) have been applied to the analysis of urban networks ([11, 17, 25]). However, due to memory and computational limitations, performing analysis on the networks remains challenging as their dimension increases (such as in the case of the road or transport networks of large cities). This is also due to the fact that, unlike other types of data that are easily transformed for example into time series or grids (e.g., an image can be represented as a matrix of pixels), there is no standard way of representing the network paths for using them as input to ML models. When the training dataset is large in size, ML algorithms such as Random Decision Forests or Deep Neural Networks, require a significative amount of memory and computational power to train their models ([21, 19]). For this reason, the traditional path representations (such as adjacency matrix) could be used only when the networks are small (their size increase exponentially with the size of the networks).

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A survey on network path identification using bio inspired algorithms

Cited by 4 publications

References 4 publications

Peer-to-peer overlay techniques for vehicular ad hoc networks: Survey and challenges

Peer-to-peer overlay techniques for vehicular ad hoc networks: Survey and challenges

Requirements Variability Specification for Data Intensive Software

Graph input representations for machine learning applications in urban network analysis

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