Price based routing for event driven prioritized traffic in wireless sensor networks

We present a dynamic price based routing protocol in which packets from different applications dynamically choose their paths by evaluating the price to be paid for taking each path and their ability to pay. We propose a mechanism in which the prices reflect congestion on routers and thus the waiting time for packet to pass through the router. These prices increase as usage of the usually preferred shorter routes increases. The packet's ability to pay price on a router is defined by the product of application's priority and the delay experienced at the router. As a result, the low priority applications intelligently avoid paths with high prices and go via low price routes. The low price routes may possibly be longer but require shorter waiting for passage at congested routers making them faster for low priority packets. This enables high priority traffic to get through quickly via shorter paths as they are able to pay high prices after little wait. Thus, our approach distributes traffic flows of different applications in the network and lowers congestion and delays for all applications. We further show that our dynamic path allocation technique ensures robust communication in fully functional as well as partially damaged networks. Our dynamic pricing mechanism quickly adapts routing to the damaged network, increases utilization of the partial network to lower the impact on critical infrastructure and key resources. Moreover, our proposed mechanism is equally applicable to both communication networks and physical infrastructure networks.

[1]  Yuguang Fang,et al.  End-to-end delay differentiation by prioritized multipath routing in wireless sensor networks , 2005, MILCOM 2005 - 2005 IEEE Military Communications Conference.

[2]  Bo Li,et al.  Upstream congestion control in wireless sensor networks through cross-layer optimization , 2007, IEEE Journal on Selected Areas in Communications.

[3]  Erol Gelenbe,et al.  Emergency Cyber-Physical-Human Systems , 2012, 2012 21st International Conference on Computer Communications and Networks (ICCCN).

[4]  Mikkel Thorup,et al.  Optimizing OSPF/IS-IS weights in a changing world , 2002, IEEE J. Sel. Areas Commun..

[5]  Na Yang,et al.  Congestion Avoidance Based on Lightweight Buffer Management in Sensor Networks , 2006 .

[6]  Yantai Shu,et al.  Receiver-Assisted Congestion Control to Achieve High Throughput in Lossy Wireless Networks , 2010, IEEE Transactions on Nuclear Science.

[7]  Erol Gelenbe,et al.  Intelligent Navigation Systems for Building Evacuation , 2011, ISCIS.

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

[9]  Yunhao Liu,et al.  Sensor Network Navigation without Locations , 2009, INFOCOM.

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

[11]  Virginia Murray,et al.  Disasters at Mass Gatherings: Lessons from History , 2012, PLoS currents.

[12]  Inbum Jung,et al.  Speedy Routing Recovery Protocol for Large Failure Tolerance in Wireless Sensor Networks , 2010, Sensors.

[13]  Xu Zhang,et al.  A dynamic traffic distribution strategy for multipath routing , 2009, 2009 7th International Conference on Information, Communications and Signal Processing (ICICS).

[14]  Boleslaw K. Szymanski,et al.  Dynamic Service Execution in Sensor Networks , 2010, Comput. J..