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RPRTD: Routing protocol based on remaining time to encounter nodes with destination node in delay tolerant network using artificial neural network

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Abstract

Delay Tolerant Network (DTN) is a kind of network that there is no continuous network connectivity among nodes. There are no end-to-end and constant connection paths from source nodes to destination nodes due to the mobility nature of nodes. In such networks there exists an inevitable long delivery delay. DTN is designed to perform properly over intermittent connections among nodes. Because of mobile nature of nodes, routing in DTN is based on store-carry-forward patterns to forward messages. Store-carry-forward means when a node receives a message from one of its contacts, it stores the message in its buffer and carries the message until it encounters another Contact node with Better conditions to Take the message to the Destination node (CBTD). Recognizing one of visited nodes as CBTD is a challenging problem. After recognizing CBTD, forwarding messages to CBTDs is required and necessary for routing efficiency and performance, because the messages will be delivered to the destination faster through CBTDs. In this paper, routing is performed by recognizing CBTDs and forwarding messages to them using RPRTD algorithm. RPRTD algorithm is based on Remaining Time to encounter nodes with Destination node (RTD). A node with smaller RTD, encounters the destination node in less time than the others. RPRTD algorithm tries to take message somehow to the node with the smallest RTD value among all nodes. RTD is calculated with the help of predicting future coordinates of nodes based on artificial neural networks (ANN). Simulation results show that this algorithm makes the best decision and efficiently determines the most appropriate and the best route to deliver messages to the destination node, increases routing performance, improves number of delivered messages, and decreases delivery delay compared to the state of the art.

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References

  1. Singh AV, et al. (2017) Trust based intelligent routing algorithm for delay tolerant network using artificial neural network. Wireless Networks - Springer

    Book  Google Scholar 

  2. Sun X, et al. (2013) Performance of DTN protocols in space communications. Wireless Networks - Springer

  3. Evan P.C. Jones, et al. (2005) Practical routing in delay-tolerant networks. Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking

  4. Fabio Rafael Segundo, Eraldo Silveira e Silva, Jean-Marie Farines (2016) A DTN routing strategy based on neural networks for urban bus transportation system

  5. Pei-Chun Cheng et al. (2008) GeoDTN+Nav: A hybrid geographic and dtn routing with navigation assistance in urban vehicular networks

  6. Khadar F, Razafindralambo T (2007) Performance evolution of gradient routing strategy for wireless sensor. Network 5550:535–547

    Google Scholar 

  7. Etienne C. R. de Oliveira, (2009) NECTAR: A DTN Routing Protocol Based on Neighborhood Contact History Proceedings of the 2009 ACM symposium on Applied Computing

  8. Wei K, et al. (2013) Social-aware relay node selection in delay tolerant networks. International conference on computer communication and networks (ICCCN)

  9. Vahdat A and Becker D (2000) Epidemic routing for partially-connected ad hoc networks

  10. Spyropoulos T (2005) Spray and wait: an efficient routing scheme for intermittently connected Mobile networks. Proceedings of the 2005 ACM SIGCOMM workshop on delay-tolerant networking

  11. Lindgren A et al (2004) Probabilistic routing in intermittently connected networks. Springer, Berlin, Heidelberg

    Book  Google Scholar 

  12. Balasubramanian A et al. (2007) DTN routing as a resource allocation problem proceedings of the 2007 conference on applications, technologies, architectures, and protocols for computer communications

  13. Burgess J et al. (2006) MaxProp: routing for vehicle-based disruption-tolerant networks.

  14. Derakhshanfard N et al. (2015) CPTR: conditional probability tree based routing in opportunistic networks Springer Science Business Media New York 2015

  15. Meng X, Xu G, Guo T, Yang Y, Shen W, and Zhao K (2017) A novel routing method for social delay-tolerant networks

  16. S anjay K. Dhurandhera, Satya J. Boraha, I. Woungangb, Aman Bansala, Apoorv Guptaa (2017) A Location Prediction-based Routing Scheme for Opportunistic Networks in an IoT Scenario

  17. PeijunZou , Ming Zhao, JiaWu and Leilei Wang (2019) Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks

  18. Degan Zhang, Ting Zhang, Xiaohuan Liu (2018) Novel self-adaptive routing service algorithm for application in VANET

  19. Deepak Kumar Sharma, Sanjay Kumar Dhurandher, Divyansh Agarwal, Kunal Arora (2019) kROp: k-Means clustering based routing protocol for opportunistic networks

  20. Heni KAANICHE, Farouk KAMOUN (2010) Mobility prediction in wireless ad hoc networks using neural networks. Journal of Telecommunications 2(1):95–101

    Google Scholar 

  21. Lahouari Ghouti, (2016) Mobility prediction in mobile ad hoc networks using neural learning machines

  22. Kotilainen NP, Kurhinen J (2008) A genetic-neural approach for mobility assisted routing in a Mobile encounter. ICITA

  23. Partha Pratim Bhattacharya (2011) Artificial Neural Network Based Node Location Prediction for Applications in Mobile Communication

  24. Yujie Tang, Nan Cheng, Wen Wu, Miao Wang, Yanpeng Dai and Xuemin (Sherman) Shen (2019) Delay-Minimization Routing for Heterogeneous VANETs with Machine Learning based Mobility Prediction

  25. Wei Quan, Yana Liu, Hongke Zhang, and Shui Yu, Enhancing crowd collaborations for software defined vehicular networks (2017), IEEE Communications Magazine

  26. Wei Quan, Nan Cheng, Meng Qin, Hongke Zhang, H. Anthony Chan and Xuemin (Sherman) Shen (2018) Adaptive transmission control for software defined vehicular networks , IEEE WIRELESS COMMUNICATIONS LETTERS

  27. Richard P. Lippmann, An introduction to computing with neural nets

  28. Chen F-C (1989) Back-propagation neural networks for nonlinear self tuning adaptive control. Proceedings. IEEE International Symposium on Intelligent Control

    Book  Google Scholar 

  29. Bigus JP (1996) Data mining with neural networks: solving business problems from application development to decision support. McGraw-Hill, Inc.

    Google Scholar 

  30. Behnke S (2003) Hierarchical neural networks for image interpretation, Springer Science & Business Media

    Book  Google Scholar 

  31. Stanley KO , Miikkulainen R (2002) Efficient reinforcement learning through evolving neural network topologies, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), pp. 3–11

  32. Keränen A, et al. (2009) The ONE simulator for DTN protocol evaluation. Proceedings of the 2nd international conference on simulation tools and techniques

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Correspondence to Nahideh Derakhshanfard.

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Karami, A., Derakhshanfard, N. RPRTD: Routing protocol based on remaining time to encounter nodes with destination node in delay tolerant network using artificial neural network. Peer-to-Peer Netw. Appl. 13, 1406–1422 (2020). https://doi.org/10.1007/s12083-020-00873-x

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