Predictable Wireless Networking for Real-Time Sensing and Control Hongwei Zhang  Hongwei Zhang, October 18, 2013

Wireless networked, real-time sensing and control Connected vehicles  IEEE p/DSRC, IEEE 1609, SAE J2735 Smart grid  IEEE g, NIST Industrial monitoring and control  WirelessHART, ISA a  Industrial Internet

Call for “predictability” in wireless sensing and control networking Wireless networks as carriers of mission-critical, real-time sensing and control information  Need predictable reliability and real-time in wireless communication Co-design of networked control and wireless networking  Need predictable control of the tradeoffs between reliability, timeliness, and throughput

Complex cyber-physical dynamics/uncertainties Physical-domain dynamics/uncertainties  Multipath signal propagation and power attenuation Spatiotemporally dynamic, anisotropic, and asymmetric wireless communication  Collision of concurrent wireless signals Uncertain communication reliability, timeliness, and throughput  Vehicle mobility Dynamic vehicle spatial distribution and thus wireless channel properties Cyber-domain dynamics/uncertainties  Real-time capacity of wireless networks  Optimal control strategy  Network traffic pattern and QoS requirements

Scheduling for collision avoidance: a basic challenge in wireless communication Open problem for over 40 years  ALOHA protocol considered interference from concurrent transmitters (1970)  Hidden terminal issue first identified by Dr. Leonard Kleinrock (1975) Lack of field-deployable approaches to predictable interference control  Lack of high-fidelity, protocol-design-oriented wireless interference model

Suitable for designing distributed protocols  Both signal strength and link reliability are locally measurable  K is locally controllable  Signal-strength-based definition can deal with wireless channel irregularity High-fidelity Given a transmission from S to R, a concurrent transmitter C does not interfere with the reception at R iff. Physical-Ratio-K (PRK) interference model S R C H. Zhang, X. Che, X. Liu, X. Ju, “Adaptive Instantiation of the Protocol Interference Model in Wireless Networked Sensing and Control”, ACM Transactions on Sensor Networks, to appear

Optimality of PRK-based scheduling Throughput loss is small, and it tends to decrease as the PDR requirement increases.

Challenges of PRK-based scheduling 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 H. Zhang, X. Liu, C. Li, Y. Chen, X. Che, F. Lin, L.Y. Wang, G. Yin, “PRK-Based Scheduling for Predictable Link Reliability in Wireless Networked Sensing and Control”, Technical report WSU-CS-DNC-TR-13-01, Wayne State University, 2013 S R C

Predictable link reliability in distributed, PRK-based scheduling (PRKS) Predictable link PDR through localized PRKS model adaptation Concurrency close to (e.g., 88.94%) the one by state-of-the-art centralized scheduling

Comparison with existing protocols: packet delivery reliability (PDR)

Current practice (1): Improve reliability by retransmission PRKS has significantly less delay than CSMA and RTS-CTS

Current practice (2): Improve reliability by reducing traffic load Lower throughput and larger variability in existing protocols

Concluding remarks PRKS enables predictable link reliability in the presence of uncertainties  Completely eliminates hidden terminals (a 40+ years old open problem)  Serves as a foundation for predictable wireless networking in real-time sensing and control Revisit current practice  Brave initial trials/deployments for proof of application/society benefits  Need for experimental infrastructures for technology evolution and cross- community fertilization

s Hongwei Zhang Wayne State University