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![References [1] L. Lilien, Z. H. Kamal, V. Bhuse, and A. Gupta, “Opportunistic networks: The concept and research challenges in privacy and security,” in Proc. NSF WSPWN, Miami, FL, USA, Mar. 2006, pp. 134–1 [2] C. Boldrini, M. Conti, I. Iacopini, and A. Passarella, “HIBOP: A history based routing protocol for opportunistic networks,” in Proc. IEEE WOWMOM, Espoo, Finland, Jun. 2007, pp. 1–12. [3] A. Vahdat and D. Becker, “Epidemic routing for partially connected ad hoc networks,” Dept. Comput. Sci., Duke Univ., Durham, NC, USA, Tech. Rep. CS-2000-06, 2000. [4] T. K. Huang, C. K Lee, and L.-J. Chen, “PROPHET+: An adaptive prophet-based routing protocol for opportunistic network,” in Proc. 24th IEEE Int. Conf. Adv. Inf. Netw. Appl., Perth, Australia, Apr. 2010, pp. 112–119. [5] S. K. Dhurandher, D. K. Sharma, I. Woungang, and S. Bhati, “HBPR: History based prediction for routing in infrastructure-less opportunistic networks,” in Proc. 27th IEEE Int. Conf. Adv. Inf. Netw. Appl., Barcelona, Spain, Mar. 2013, pp. 931–936. [6] A. Lindgren, A. Doria, and D. Schelen, “Probabilistic routing in intermittently connected networks,” ACM SIGMOBILE Mobile Comp. Commun. Rev., vol. 7, no. 3, pp. 19–20, 2003. [7] S. K. Dhurandher, S. J. Borah, D. K. Sharma, I. Woungang, K. Arora, and D. Agarwal, “EDR: An encounter and distance based routing protocol for opportunistic networks,” in Proc. 30th IEEE Int. Conf. Adv. Inf. Netw. Appl., 2016, pp. 297–302.](https://image.slidesharecdn.com/amachinelearning-basedprotocolforefficientroutinginopportunisticnetworks-180509201430/75/A-machine-learning-based-protocol-for-efficient-routing-in-opportunistic-networks-15-2048.jpg)
![References [8] Y. LeCun, L. Bottou, G. B. Orr, K-R. Muller, “Efficient BackProp,” Neural Networks: Tricks of the Trade, vol. 1524, Lecture Notes Comput. Sci., pp. 9–50, Mar. 2002. [9] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. Hoboken, NJ, USA: Wiley, Oct. 2012. [10] A. Keranen, “Opportunistic network environment simulator,” Dept. Commun. Netw., Helsinki Univ. Technol., Espoo, Finland, Special Assignment Rep. ONE v. 1.4.1, May 2008. [11] S. Drazin and M. Montag, “Decision tree analysis using Weka,” Machine Learning-Project II, Univ. Miami, Miami, FL, USA, 2012. [Online]. Available: http://www.samdrazin.com/classes/een548/](https://image.slidesharecdn.com/amachinelearning-basedprotocolforefficientroutinginopportunisticnetworks-180509201430/75/A-machine-learning-based-protocol-for-efficient-routing-in-opportunistic-networks-16-2048.jpg)
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A machine learning based protocol for efficient routing in opportunistic networks
This document presents MLProph, a machine learning-based routing protocol for opportunistic networks. It uses decision trees and neural networks to select the next hop for packet forwarding. Simulation results show that MLProph achieves higher delivery probability and lower packet dropping than the PROPHET+ routing protocol. Future work will involve simulating MLProph using real mobility traces and exploring other machine learning classifiers.
A machine learning based protocol for efficient routing in opportunistic networks
- 1. A Machine Learning-BasedProtocol for Efficient Routing in Opportunistic Networks Presenter: Aydin Ayanzadeh Student ID: 504161503 Email: ayanzadeh17@itu.edu.tr Deepak K. Sharma Sanjay K. Dhurandher,
- 2. Agenda ● INTRODUCTION ● Relatedwork ● Neural Network ● Decision Tree ● Experimental Results ● Conclusion & Further works
- 3. Prerequisite concepts ● OPPORTUNISTICnetworks: type of challenged networks where link performance is highly variable and the network contacts are intermittent. ● Routing protocols in OppNets: 1) infrastructure-based routing protocols form of infrastructure that augments when forwarding the packets to their destinations is assumed 2) infrastructure-less-based routing protocol: assumption prevails and only the contact opportunities are used to route the packets within the network. ● MLProph
- 4. Decision Tree entropy impurity P(w j ) is the fraction of the patterns at a particular node that are in category w j .
- 5. Neural Networks ❏ InputImage ❏ Hidden Layers ❏ Output
- 6.
- 7. MLProph Algorithm The MLProphalgorithm for next hop selection is divided into the following parts: ● Training The data is generated for the next hop by running the simulation on the training scenario ● Real simulation
- 8. Simulation setup ❖ MovementModel: This defines the motion of nodes inside a community with respect to a particular scenario. ❖ Transmit Speed: For an interface, this represents the speed at which the messages will move in the network. ❖ Transmit Range: This is the maximum distance at which a sender node can send the data to a receiver. ❖ Buffer Size: This is the space available in the node to store the packets before eventually forwarding or dropping them. ❖ Speed: This defines the range of speeds that the nodes in the movement model can take. ❖ Number of Hosts: This is the number of hosts in the group. ❖ Message TTL: This defines the lifespan of the message in the network. ❖ Wait Time: This defines the time a group has to wait since the simulation has started before initiating any movement.
- 9. performance metrics ❏ buffertime ❏ Delivery probability ❏ number of packets dropped ❏ overhead ratio ❏ latency ❏ hop count
- 10. Simulation Results 1) Varyingthe Buffer Size:
- 11. Simulation Results 2) Varyingthe Message TTL: Delivery probability
- 12. Simulation Results 3) Varyingthe Number of Nodes: higher values of the delivery probability compared to that obtainted by the PROPHET+ router, and MLProphNN outperforms
- 13. Simulation Results 4) ConfidenceInterval of Results ● Lower values of the number of packets dropped an overhead ratio compared to PROPHET+
- 14. Conclusion This paper hasproposed MLProph a novel ML-based routing protocol for OppNets. Simulation results have shown that: performance of MLProph is superior to that of PROPHET+ in terms of delivery probability, overhead ratio, with a payoff in the average latency, and buffer size due to the constraints imposed on the next hop selection process by MLProph. It has also been shown that in terms of delivery probabilities, packet loss, and overhead ratio, the MLProph N N model is preferable for use compared to the MLProph D T model. Future Works Simulate MLProph using real mobility traces. We will also investigate the use of other ML classifiers such as support vector machines.
- 15. References [1] L. Lilien,Z. H. Kamal, V. Bhuse, and A. Gupta, “Opportunistic networks: The concept and research challenges in privacy and security,” in Proc. NSF WSPWN, Miami, FL, USA, Mar. 2006, pp. 134–1 [2] C. Boldrini, M. Conti, I. Iacopini, and A. Passarella, “HIBOP: A history based routing protocol for opportunistic networks,” in Proc. IEEE WOWMOM, Espoo, Finland, Jun. 2007, pp. 1–12. [3] A. Vahdat and D. Becker, “Epidemic routing for partially connected ad hoc networks,” Dept. Comput. Sci., Duke Univ., Durham, NC, USA, Tech. Rep. CS-2000-06, 2000. [4] T. K. Huang, C. K Lee, and L.-J. Chen, “PROPHET+: An adaptive prophet-based routing protocol for opportunistic network,” in Proc. 24th IEEE Int. Conf. Adv. Inf. Netw. Appl., Perth, Australia, Apr. 2010, pp. 112–119. [5] S. K. Dhurandher, D. K. Sharma, I. Woungang, and S. Bhati, “HBPR: History based prediction for routing in infrastructure-less opportunistic networks,” in Proc. 27th IEEE Int. Conf. Adv. Inf. Netw. Appl., Barcelona, Spain, Mar. 2013, pp. 931–936. [6] A. Lindgren, A. Doria, and D. Schelen, “Probabilistic routing in intermittently connected networks,” ACM SIGMOBILE Mobile Comp. Commun. Rev., vol. 7, no. 3, pp. 19–20, 2003. [7] S. K. Dhurandher, S. J. Borah, D. K. Sharma, I. Woungang, K. Arora, and D. Agarwal, “EDR: An encounter and distance based routing protocol for opportunistic networks,” in Proc. 30th IEEE Int. Conf. Adv. Inf. Netw. Appl., 2016, pp. 297–302.
- 16. References [8] Y. LeCun,L. Bottou, G. B. Orr, K-R. Muller, “Efficient BackProp,” Neural Networks: Tricks of the Trade, vol. 1524, Lecture Notes Comput. Sci., pp. 9–50, Mar. 2002. [9] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. Hoboken, NJ, USA: Wiley, Oct. 2012. [10] A. Keranen, “Opportunistic network environment simulator,” Dept. Commun. Netw., Helsinki Univ. Technol., Espoo, Finland, Special Assignment Rep. ONE v. 1.4.1, May 2008. [11] S. Drazin and M. Montag, “Decision tree analysis using Weka,” Machine Learning-Project II, Univ. Miami, Miami, FL, USA, 2012. [Online]. Available: http://www.samdrazin.com/classes/een548/
Editor's Notes
- #4 for which the presence of some form of infrastructure that augments when forwarding the packets to their destinations is assumed; to train itself based on various factors such as buffer capacity, hop count, node energy, node movement speed, popu- larity parameter, and number of successful deliveries.
- #5 the query that decreases the impurity as much as possible. The drop in impurity is defined by
- #6 oo
- #8 The MLProph algorithm for next hop selection is divided into the following parts. Training: The data is generated for the next hop by running the simulation on the training scenario. Each entry in the data depicts whether the final delivery has occurred for the hop selec- tion in question or not, by sending the message from the sender to the receiver given the parameters described in Table I at that time. In order to ensure that a message is transmitted in all pos- sible cases, this simulation is run by using the Epidemic routing protocol [3].
- #11 Varying the Buffer Size: The buffer size for all groups in the network is varied and the aforementioned performance met- rics are evaluated. The results are captured in Figs. 2–4. It can be observed that the average buffer time increases as the buffer size is increased. This is understandable since a large buffer means that the messages are held by nodes for a larger duration of time.
- #12 Varying the Message TTL: The message TTL is varied and the aforementioned performance metrics are evaluated. The results are captured in Figs. 5 and 6. It can be observed that the delivery probabilities do not vary much when the TTL is varied. The median delivery probabilities across varying TTL values are 0.47, 0.39, and 0.34 for MLProph N N , MLProph D T , and PROPHET+, respectively, validating the hypothesis that ML- Proph delivers more messages due to significantly lower packet loss. In terms of overhead ratio, we found that the overhead ratios obtained from the MLProph algorithms are much lesser than that obtained using PROPHET+. Additionally, in Fig. 6, it can be observed that the average latency is higher for ML- Proph compared to PROPHET+. This is attributed to the fact that the criteria for forwarding a packet is more constrained in MLProph than in PROPHET+, causing the messages to remain in the buffer of nodes for a longer period time, thereby, the messages will eventually take more time to get delivered, albeit with a better delivery probability.