 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 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, *Department of Electrical and Computer Engineering, Wayne State University, Detroit, Michigan,  Department of Mathematics, Wayne State University, Detroit, Michigan, From 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) Model Distributed PRK-Based Scheduling for Predictable Link Reliability Behavior of Ratio-K-Based Scheduling Physical-Ratio-K (PRK) Interference Model Challenges of PRK-Based Scheduling Optimality of PRK-Based Scheduling Throughput loss is small, and it tends to decrease as the PDR requirement increases 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 Grand societal challenges  Power grid With ~2,459 million metric tons of CO 2 emission per year, electricity generation accounts for ~41% of USA’s total CO 2 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 S R C 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 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(.) Minimum-variance regulation controller  The control input that minimizes while ensuring and the minimum value of is 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 PRKS: architecture of PRK-based scheduling Predictable link reliability in PRKS Convergence of distributed controllers Comparison with existing protocols Larger networks