Quasi-opportunistic contact prediction in delay/disruption tolerant network

Segundo, Fabio Rafael; Farines, Jean-Marie; Silva, Eraldo Silveira e

doi:10.1109/giis.2013.6684382

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…Segundo et al [15] proposed an ANN model to predict the next node and the moment of contact. The predictor based on an ANN trained with historical data extracted from synthetic traces provides a high hit rate for next node and next moment of contact.…”

Section: Related Workmentioning

confidence: 99%

“…), but Prophet does not take the vehicle movement patterns into consideration very well, and Community-based Bus System (CBS) is only for public buses [11]. With the popularity of machine learning in recent years, many machine learning algorithms have been applied to DTNs such as decision tree [12], reinforcement learning [13], [14], Artificial Neural Network (ANN) [15] and Naive Bayesian (NB) classification model [16], [17], etc. However, decision tree and ANN are prone to the over-fit problem, and reinforcement learning may result in a large number of copies in DTNs.…”

Section: Introductionmentioning

confidence: 99%

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Vehicular Delay Tolerant Network Routing Algorithm Based on Bayesian Network

Guo

Zhou

et al. 2020

IEEE Access

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Delay Tolerant Networks (DTNs) are novel wireless mobile networks, which suffer from frequent disruption, high latency, and the lack of a complete path from source to destination. Vehicular Delay Tolerant Network (VDTN) is a special type of DTNs with vehicles as nodes. In VDTN, most nodes have specific movement patterns, however, traditional routing algorithms in DTNs do not take this characteristic into considerations very well. In this paper, a new routing algorithm based on Bayesian Network (BN) is proposed to construct the prediction model, which intends to predict the movement patterns of nodes in the real VDTN scenarios. Firstly, a comprehensive BN model is established, where more attributes of nodes are selected to improve the accuracy of the model prediction. Then, considering the complexity of the structure learning problem of BN, a novel structure learning algorithm, K2 algorithm based on Genetic Algorithm (K2-GA), is proposed to search the optimal BN structure efficiently. At last, Junction Tree Algorithm (JTA) is adopted in the inference of BN, which can accelerate the inference process through variable elimination and calculation sharing for large scale BN. The simulation results show that the proposed VDTN routing algorithm based on the BN model can improve the delivery ratio with a minor forwarding overhead. INDEX TERMS Vehicular delay tolerant network, routing algorithm, Bayesian network, genetic algorithm, optimization.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicular Delay Tolerant Network Routing Algorithm Based on Bayesian Network

Guo

Zhou

et al. 2020

IEEE Access

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“…The next node and contact time is predicted, based on Artificial Neural Networks(ANN) [15], [16]. This neural network has 5 input parameters(current node id, contact begin time, contact end time, previous node id, previous contact begin time), one hidden layer and outputs, namely, Next contact node id, and next contact begin time.…”

Section: A Supervised Learningmentioning

confidence: 99%

“…In TBIR [21], the routing decision is based on the calculated trust value. If one of the intermediate nodes have [2] Neural Networks [4], [15], [16], [21], [24] Decision Trees [4], [23] [6]…”

Section: A Routingmentioning

confidence: 99%

Machine Learning in Delay Tolerant Networks: Algorithms, Strategies, and Applications

2019

IJITEE

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Delay Tolerant Networks (DTNs) has intermittent connectivity, nodes in the network experience a long delay in the delivery of packets, and the nodes are sparsely distributed. DTN is deployed in the applications where human intervention is least like underwater communication, interplanetary communication, disaster management, tracking wildlife, etc. Any changes in the environment affect the deployed sensor nodes, so it is required that the sensor nodes adapt to these environmental changes. Machine-Learning (ML) techniques can be deployed to overcome such difficulty. ML improves the network lifetime. ML in DTN facilitates routing by adapting to the network changes, mitigates congestion, reduces overhead. This paper provides a survey of ML techniques used in DTN. To the best of our knowledge, this work is the first of its kind to survey ML techniques in DTN

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