Adaptive Random Re-Routing in Sensor Networks

Much of the traffic carried by Sensor Networks will originate from routine measurements or observations by sensors which monitor a particular situation, such as the temperature and humidity in a room or the infrared observation of the perimeter of a house, so that the volume of routine traffic resulting from such observations may be quite high. When important and unusual events occur, such as a sudden fire breaking out or the arrival of an intruder, it will be necessary to convey this new information very urgently through the network to a designated set of sink nodes where this information can be processed and dealt with. This paper addresses the important challenge of avoiding that the volume of routine background traffic creates delays or bottlenecks that impede the rapid delivery of high priority traffic resulting from the unusual events. Specifically we propose a novel technique, the “Randomized Re-Routing Algorithm (RRR)”, which detects the presence of novel events in a distributed manner, and dynamically disperses the background traffic towards secondary paths in the network, while creating a “fast track path” which provides better delay and better QoS for the high priority traffic which is carrying the new information. When the surge of new information has subsided, this is again detected by the nodes and the nodes progressively revert to best QoS or shortest path routing for all the ongoing traffic. The proposed technique is evaluated using a mathematical model as well as simulations.