Direct Perceptive Routing Protocol for Opportunistic Networks

Setting up a path from source node to destination node in an Opportunistic Network (OppNet) proves to be a very strenuous task because of two reasons, lack of infrastructure and constantly changing environment. In OppNet the message gets transferred in a store-carry-forward way. Security issues caused by malicious nodes such as Sybil Attack and Selective Forwarding Attack create an abrupt drop in packets tending to cause a lower delivery ratio and greater latency. Hence, we require a smart and secure store carry forward technique. In this paper a Deep Learning based routing protocol called Direct Perceptive Routing(DPR) has been proposed. It uses memory from individual nodes and gathers past experiences to make decisions. The protocol proposed gives an improved message delivery ratio, average hop count and overhead ratio when compared with other high performing protocols such as PRoPHET, Epidemic, HBPR and KNNR.

[1]  Isaac Woungang,et al.  Performance evaluation of various routing protocols in Opportunistic Networks , 2011, 2011 IEEE GLOBECOM Workshops (GC Wkshps).

[2]  Apoorv Gupta,et al.  CRPO: Cognitive Routing Protocol for Opportunistic Networks , 2017, HP3C-2017.

[3]  Anders Lindgren,et al.  Probabilistic routing in intermittently connected networks , 2003, MOCO.

[4]  Leszek Lilien,et al.  The Concept of Opportunistic Networks and their Research Challenges in Privacy and Security , 2007 .

[5]  Larry P. Heck,et al.  Contextual LSTM (CLSTM) models for Large scale NLP tasks , 2016, ArXiv.

[6]  Isaac Woungang,et al.  GAER: genetic algorithm-based energy-efficient routing protocol for infrastructure-less opportunistic networks , 2014, The Journal of Supercomputing.

[7]  Kun-Chan Lan,et al.  A Survey of Opportunistic Networks , 2008, 22nd International Conference on Advanced Information Networking and Applications - Workshops (aina workshops 2008).

[8]  Andrew W. Senior,et al.  Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition , 2014, ArXiv.

[9]  Isaac Woungang,et al.  HBPR: History Based Prediction for Routing in Infrastructure-less Opportunistic Networks , 2013, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA).

[10]  Marco Conti,et al.  HiBOp: a History Based Routing Protocol for Opportunistic Networks , 2007, 2007 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks.

[11]  Chai-Keong Toh,et al.  Ad Hoc Mobile Wireless Networks , 2002 .

[12]  Cauligi S. Raghavendra,et al.  Spray and wait: an efficient routing scheme for intermittently connected mobile networks , 2005, WDTN '05.

[13]  P J Webros.  BACKPROPAGATION THROUGH TIME: WHAT IT DOES AND HOW TO DO IT , 1990 .

[14]  Mohamed Morchid,et al.  Parallel Long Short-Term Memory for multi-stream classification , 2016, 2016 IEEE Spoken Language Technology Workshop (SLT).

[15]  Anders Lindgren,et al.  Probabilistic Routing in Intermittently Connected Networks , 2004, SAPIR.

[16]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[17]  Deepak Kumar Sharma,et al.  KNNR:K-nearest neighbour classification based routing protocol for opportunistic networks , 2017, 2017 Tenth International Conference on Contemporary Computing (IC3).

[18]  Amin Vahdat,et al.  Epidemic Routing for Partially-Connected Ad Hoc Networks , 2009 .

[19]  Isaac Woungang,et al.  Efficient routing based on past information to predict the future location for message passing in infrastructure-less opportunistic networks , 2014, The Journal of Supercomputing.