PRK-Based Scheduling for Predictable Link Reliability in
Wireless Networked Sensing and Control
Hongwei Zhang, Xiaohui Liu , Chuan Li , Yu Chen , Xin Che , Feng Lin*, Le Yi Wang*, George Yin
Department
of Computer Science, Wayne State University, Detroit, Michigan, {hongwei,xiaohui,chuan,yu_chen}@wayne.edu
*Department of Electrical and Computer Engineering, Wayne State University, Detroit, Michigan, {flin,lywang}@wayne.edu
 Department of Mathematics, Wayne State University, Detroit, Michigan, gyin@wayne.edu
From Open-loop Sensing to Closed-loop Sensing and Control
Smart grid:
From centralized generation to distributed generation
Grand societal challenges
 Power grid
 With ~2,459 million metric tons of CO2
emission per year, electricity generation
accounts for ~41% of USA’s total CO2
emission
 Over 60% of today’s energy is wasted
during distribution
 Transportation
 Car accidents cause over 1.4 million
fatalities and 50 million injuries per year
across the world
 Motor vehicles account for >20% of the
world’s energy use and >60% of the
world’s ozone pollution
Connected Vehicles
 From passive to active safety: lane departure warning, collision avoidance
 From single-vehicle control to platoon control & integrated infrastructurevehicle control: networked fuel economy and emission control
 From wired intra-vehicle networks to wireless intra-vehicle networks
 Multiple controller-area-networks (CANs) inside vehicles
•
•
•
50+ kg of wires  increased, reduced fuel efficiency
Lack of scalability: hundreds of sensors, controllers, and actuators
Wiring unreliability: warranty cost, reduced safety
Control-Oriented Wireless Networking: Physical-Ratio-K (PRK) Model
Ratio-K-based scheduling is
highly sensitive to the
choice of K
200
150
Median PDR gain
Median throughput gain
Physical-Ratio-K (PRK) Interference Model
 Key idea: use link reliability requirement as the basis of instantiating the ratio-K model
 Model: given a transmission from node S to node R, a concurrent transmitter C does not
interfere with the reception at R iff.
P( S , R)
P(C , R) 
K S , R ,TS ,R
Optimality of PRK-Based Scheduling
100
25
0
-50
-100
-5
P( S , R)
K S , R ,TS ,R
S
R
C
Challenges of PRK-Based Scheduling
 On-the-fly instantiation of the PRK
model parameter K S ,R,T
 Dynamics and uncertainties in
application requirements as well as
network and environmental
conditions
 Protocol signaling in the presence of
large interference range as well as
anisotropic, asymmetric, and
probabilistic wireless communication
S ,R
50
0
k
5
Highest throughput is usually achieved at a K less than the
minimum K for ensuring a certain min. link reliability, and
this is especially the case when link reliability requirement is
high (e.g., for mission-critical sensing and control)
Throughput loss(%)
Behavior of Ratio-K-Based Scheduling
Possible performance gain (%)
 Co-channel interference as a major obstacle for predictable reliability, real-time, and
throughput in wireless networking
 Reliability as low as ~30% in current wireless scheduling/MAC protocols, thus not
suitable for real-time, safety-critical networked control
 Despite decades of research and practice, high-fidelity interference models that are
suitable for distributed, field-deployable protocol design are still missing
 Ratio-K model (i.e., protocol model) is local but not of high-fidelity
 SINR model (i.e., physical model) is of high-fidelity but non-local
20
15
10
5
0
10
20
30
40 50 60 70 80
PDR requirement(%)
90
95
99
Throughput loss is small, and it tends to
decrease as the PDR requirement increases
Distributed PRK-Based Scheduling for Predictable Link Reliability
Minimum-variance regulation controller
Protocol signaling via local signal maps
PRK model instantiation:
 The control input that minimizes var[y(t  1)]
As minimum-variance regulation control
 Local signal map: maintains wireless
while ensuring E[ y (t  1)]  TS ,R is
signal power attenuation between nodes
 Basic problem formulation
close-by
cy (t )  (1  c)YS , R (t )  TS , R
 Reference input: desired link reliability TS , R
I R (t ) 
 U (t )
(1  c)a (t )
 Simple approach to online estimation of
 Control output: actual link reliability YS , R
wireless signal power attenuation
var
[y(t

1)]
and
the
minimum
value
of
is
 Control input: PRK model parameter K S ,R,T
PC , R  Ptotal  PI
 y2,min (t  1)  (1  c)2 a(t )2  U2 (t )
power loss  Ptx  PC , R
2
 Interference from outside exclusion
where U (t ) and  U (t ) are the mean and variance of the
region treated as disturbance
changes of interferen ce from outside the exclusion region.
PRKS: architecture of PRK-based
scheduling
S ,R
•
Minimize variance of YS , R while ensuring its
mean value of TS , R
Predictable link reliability in PRKS
 Challenge: Difficult to identify closed-form
relation between control input and control
output
Refined control problem formulation
 Leverage communication theory result on
the relation between YS , R and receiver-side
SINR (i.e., PS , R  I R )
YS , R  f PS , R  I R 
Convergence of distributed controllers
From I R (t ) to K S , R,TS ,R (t  1)
 “Desired change in receiver-side
interference I R ” as control input
Comparison with existing protocols
 Linearization of the non-linear f(.)
YS , R (t )  a(t )( PS , R (t )  I R (t ))  b(t )
where
a(t )  f ' ( PS , R (t )  I R (t ))
b(t )  f ( PS , R (t )  I R (t ))  ( PS , R (t )  I R (t )) f ' ( PS , R (t )  I R (t ))
Larger networks