Location and Mobility-Aware Routing for Improving Multimedia Streaming Performance in MANETs

Abstract Device mobility is an issue that affects both Mobile ad hoc networks (MANETs) and opportunistic networks. While the former employs conventional routing techniques with some element of mobility management, opportunistic networking protocols often use mobility as a means of delivering messages in intermittently connected networks. If nodes are able to determine the future locations of other nodes with reasonable accuracy then they could plan ahead and take into account and even benefit from such mobility. In an ad hoc network, devices form a network amongst themselves and forward packets for each other without infrastructure. Ad hoc networks could be deployed in a disaster scenario to enable communications between responders and base camp to provide telemedicine services. However, most ad hoc routing protocols cannot meet the necessary standards for streaming multimedia because they do not attempt to manage quality of service (QoS). Node mobility adds an additional layer of complexity leading to potentially detrimental effects on QoS. Geographic routing protocols use physical locations to make routing decisions and are typically lightweight, distributed, and require only local network knowledge. They are thus less susceptible to the effects of mobility, but are not impervious. Location-prediction can be used to enhance geographic routing, and counter the negative effects of mobility, but this has received relatively little attention. Location prediction in combination with geographic routing has been explored in previous literature. Most of these location prediction schemes have made simplistic assumptions about mobility. However more advanced location prediction schemes using machine learning techniques have been used for wireless infrastructure networks. These approaches rely on the use of infrastructure and are therefore unsuitable for use in opportunistic networks or MANETs. To solve the problem of accurately predicting future location in non-infrastructure networks, we investigate the prediction of continuous numerical coordinates using artificial neural networks. Simulation using three different mobility models representing human mobility has shown an average prediction error of <1 m in normal circumstances.

[1]  Tracy Camp,et al.  A survey of mobility models for ad hoc network research , 2002, Wirel. Commun. Mob. Comput..

[2]  Xiaoyan Hong,et al.  A group mobility model for ad hoc wireless networks , 1999, MSWiM '99.

[3]  Ahmed Helmy,et al.  The effect of mobility-induced location errors on geographic routing in mobile ad hoc sensor networks: analysis and improvement using mobility prediction , 2004, IEEE Transactions on Mobile Computing.

[4]  Pratap S. Prasad,et al.  Movement Prediction in Wireless Networks Using Mobility Traces , 2010, 2010 7th IEEE Consumer Communications and Networking Conference.

[5]  Leszek Lilien,et al.  Opportunistic Networks for Emergency Applications and Their Standard Implementation Framework , 2007, 2007 IEEE International Performance, Computing, and Communications Conference.

[6]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[7]  Raouf Boutaba,et al.  Mobility Prediction in Wireless Networks using Neural Networks , 2004, MMNS.

[8]  Klara Nahrstedt,et al.  Predictive location-based QoS routing in mobile ad hoc networks , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[9]  Kevin Curran,et al.  MANET Location Prediction Using Machine Learning Algorithms , 2012, WWIC.

[10]  Silvia Giordano,et al.  Spatiotemporal routing algorithm in opportunistic networks , 2008, 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks.

[11]  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.

[12]  Cecilia Mascolo,et al.  A community based mobility model for ad hoc network research , 2006, REALMAN '06.

[13]  Ivan Stojmenovic,et al.  Depth first search and location based localized routing and QoS routing in wireless networks , 2000, Proceedings 2000 International Conference on Parallel Processing.

[14]  Hewijin Christine Jiau,et al.  Dynamic route maintenance for geographic forwarding in mobile ad hoc networks , 2008, Comput. Networks.

[15]  Kun-Chan Lan,et al.  Adaptive Position Update in Geographic Routing , 2006, 2006 IEEE International Conference on Communications.

[16]  Margaret Martonosi,et al.  Erasure-coding based routing for opportunistic networks , 2005, WDTN '05.

[17]  Zygmunt J. Haas,et al.  Predictive distance-based mobility management for PCS networks , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

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

[19]  David A. Maltz,et al.  Dynamic Source Routing in Ad Hoc Wireless Networks , 1994, Mobidata.

[20]  Christian Igel,et al.  Improving the Rprop Learning Algorithm , 2000 .

[21]  Alessandro Puiatti,et al.  Probabilistic Routing Protocol for Intermittently Connected Mobile Ad hoc Network (PROPICMAN) , 2007, 2007 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks.

[22]  Elmar Gerhards-Padilla,et al.  BonnMotion: a mobility scenario generation and analysis tool , 2010, SimuTools.

[23]  Brad Karp,et al.  GPSR : Greedy Perimeter Stateless Routing for Wireless , 2000, MobiCom 2000.

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

[25]  Kevin Curran,et al.  An Analysis of the Effects of Intelligent Location Prediction Algorithms on Greedy Geographic Routing in Mobile Ad-Hoc Networks , 2011 .

[26]  Silvia Giordano,et al.  Routing in Opportunistic Networks , 2009, Int. J. Ambient Comput. Intell..

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

[28]  Federico Boccardi,et al.  Load & backhaul aware decoupled downlink/uplink access in 5G systems , 2014, 2015 IEEE International Conference on Communications (ICC).

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

[30]  S. Shatz,et al.  Toward Using Node Mobility to Enhance Greedy Forwarding in Geographic Routing for Mobile Ad Hoc Networks 1 , 2008 .