QoS Control For Sensor Networks Iyer, R.; Kleinrock, L. ICC 2003.
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Transcript of QoS Control For Sensor Networks Iyer, R.; Kleinrock, L. ICC 2003.
outlineoutline
►IntroductionIntroduction►Problem descriptionProblem description►The GUR GAMEThe GUR GAME►Network model and algorithmNetwork model and algorithm►SimulationSimulation►ConclusionConclusion
IntroductionIntroduction
►Sensor networks are made up of small devices Sensor networks are made up of small devices equipped withequipped with ProcessorsProcessors MemoryMemory Short-range wireless communicationShort-range wireless communication
►Sensor network have severe energy constraintsSensor network have severe energy constraints Preservation of battery powerPreservation of battery power
IntroductionIntroduction
►What is sensor network QoS?What is sensor network QoS? Sensor network resolutionSensor network resolution An An optimaloptimal number of sensors sending information number of sensors sending information
toward information-collecting sinks, typically base toward information-collecting sinks, typically base stationsstations
Problem descriptionProblem description
►We want to accomplish two thingsWe want to accomplish two things MaximizeMaximize the the lifetimelifetime of the sensor network of the sensor network
►Sensors periodically power-down to conserve their battery Sensors periodically power-down to conserve their battery energyenergy
Have Have enoughenough sensors power-upsensors power-up and sending packets and sending packets toward the information sinks so that enough data is toward the information sinks so that enough data is being collectedbeing collected
Problem descriptionProblem description
►One base station with a broadcast channel to all One base station with a broadcast channel to all the sensors the sensors
►It knows the It knows the optimal numberoptimal number of sensors that of sensors that should be power-on and sending packets at any should be power-on and sending packets at any given timegiven time
►Broadcast the probability Broadcast the probability p*(t)=(optimal number / total number) at time t to all p*(t)=(optimal number / total number) at time t to all
sensorssensors
The GUR GAMEThe GUR GAME
►We have many players and a refereeWe have many players and a referee None of them are aware of the othersNone of them are aware of the others
►Every second, the referee asks each player to vote yes or no, then counts up the yes and no answers
►A reward probability r = r(k) is generated as a function of the number k of players who voted yes
►0 ≤ r(k) ≤ 1
The GUR GAMEThe GUR GAME
►The reward function r = r(kt) =0.2 + 0.8 ev
v = -0.002 (kt – 35)2
kt = the number of yes received at time t
The GUR GAMEThe GUR GAME
►Each player, regardless of how he or she voted, is then independently rewarded (with probability r) penalized (with probability 1-r).
►Assume that at some point the number of players voting yes was k1. The reward probability would be r(k1)
The GUR GAMEThe GUR GAME
►Note that the maximum of the example occurs at k* = 35
►No matter how many players there are, we can “construct” them in such a way that approximately k* of them
The GUR GAMEThe GUR GAME
►Each player has a memory of his previous trials►we associate with each player j, a finite discrete-
time automaton Mj►The player is allowed to be in only one state at
any given time.
The GUR GAMEThe GUR GAME
►The player votes Yes when he is in a positive numbered state No when he is in a negative numbered state.
►When in a negative (positive) numbered state Leftward (rightward ) if he is rewarded Rightward (leftward) when he is punished
Network model and algorithmNetwork model and algorithm
► We associate with the base station a Gur reward function r(k)
► At each time t, the base station counts the number of packets kt it has received from the sensors
► It then calculates the Gur reward probability r(kt )► Finally, it broadcasts this probability to all the sensors.► Each sensor, in turn, independently rewards itself with
probability r(kt ) and punishes itself with probability 1-r(kt )
Network model and algorithmNetwork model and algorithm
►assume that we have a collection of m sensors S1 through Sm and one base station B
►The sensor Si will power-up when it is in a positive numbered state power-down when it is in a negative numbered state
►if a sensor is powered-up, it will send a data packet containing sensor information toward the base station
SimulationSimulation
►Memory size N is equal to 1►100 sensors in the network►Each sensor picks a random state as its initial
state►base station desires a rate of 35 packets received
at each time t
ConclusionConclusion
►quality of service (QoS) for sensor networks remains largely open, and it is a non-trivial problem to specify the optimum number of sensors
►In this paper we presented an algorithm using the Gur Game paradigm that allowed the base station to specify the optimal number of sensors