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

PRK-Based Scheduling for Predictable Link Reliability in PRK-Based Scheduling for Predictable Link Reliability in Wireless Networked Sensing and ControlWireless 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 ControlFrom Open-loop Sensing to Closed-loop Sensing and Control

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.

Control-Oriented Wireless Networking: Physical-Ratio-K (PRK) ModelControl-Oriented Wireless Networking: Physical-Ratio-K (PRK) Model

Distributed PRK-Based Scheduling for Predictable Link ReliabilityDistributed PRK-Based Scheduling for Predictable Link Reliability

Behavior of Ratio-K-Based Scheduling

Physical-Ratio-K (PRK) Interference Model

Challenges of PRK-Based Scheduling

RSTRSK

RSPRCP

,,,

),(),(

Optimality of PRK-Based Scheduling

10 20 30 40 50 60 70 80 90 95 99

0

5

10

15

20

25

Thr

ough

put l

oss(

%)

PDR requirement(%)

Throughput loss is small, and it tends to decrease as the PDR requirement increases

-5 0 5-100

-50

0

50

100

150

200

k

Pos

sibl

e pe

rfor

man

ce g

ain

(%)

Median PDR gainMedian throughput gain

Ratio-K-based scheduling is highly sensitive to the

choice of K

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)

From passive to active safety: lane departure warning, collision avoidance From single-vehicle control to platoon control & integrated infrastructure-

vehicle 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

Connected VehiclesSmart grid: From centralized generation to distributed generation

Other Controllable

Loads

Washer

Dryer

Water Heater

Uncontrollable Loads

Plug-in Hybrid Electric Vehicle

Home Renewable & Energy

Storage System

A/C

Home Controller(s)

Microgrid Distributed Generator &

Combined Heat and Power System

MicroturbineFC

MicrogridController(s)

MicrogridRenewable & Energy

Storage System

Commercial Microgrid

Other Microgrids

Industrial Microgrid

Residential Microgrid

GridController(s)

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

On-the-fly instantiation of the PRK model parameter

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

RSTRSK

,,,

SR

C

RSTRSK

RSP

,,,

),(

PRK model instantiation: As minimum-variance regulation control Basic problem formulation

Reference input: desired link reliability Control output: actual link reliability Control input: PRK model parameter

Interference from outside exclusion region treated as disturbance

• Minimize variance of while ensuring its mean value of

Challenge: Difficult to identify closed-form relation between control input and control output

RST ,

RSTRSK

,,,

RSY ,

RSY ,

RST ,

Refined control problem formulation

Leverage communication theory result on the relation between and receiver-side SINR (i.e., )

“Desired change in receiver-side interference ” as control input

Linearization of the non-linear f(.)

RRS IP ,

RI

RRSRS IPfY ,,

RSY ,

))()(())()(())()(()(

))()(()(

where

)())()()(()(

,'

,,

,'

,,

tItPftItPtItPftb

tItPfta

tbtItPtatY

RRSRRSRRS

RRS

RRSRS

Minimum-variance regulation controller The control input that minimizes

while ensuring

and the minimum value of is

)()()1(

)()1()()( ,, t

tac

TtYctcytI U

RSRSR

1)][y(tvar is)]1([ ,RSTtyE

)()()1()1( 2222min, ttact Uy

region.exclusion theoutside from ceinterferen of changes

theof varianceandmean theare )( and )( where 2U ttU

1)][y(tvar

)1( to)( From,,, tKtIRSTRSR

Protocol signaling via local signal maps Local signal map: maintains wireless

signal power attenuation between nodes close-by

Simple approach to online estimation of wireless signal power attenuation

RCtx

ItotalRC

PP

PPP

,

,

losspower

PRKS: architecture of PRK-based scheduling

Predictable link reliability in PRKS

Convergence of distributed controllers

Comparison with existing protocols

Larger networks