  Industrial internet of Things (IIoT) are gaining popularity for use in
large-scale applications such as oil-field management (e.g., $74\times 8$km$^2$
East Texas Oil-field), smart farming, smart manufacturing, smart grid, and data
center power management. These applications require the wireless stack to
provide a scalable, reliable, low-power and low-latency communication. To
realize a predictable and reliable communication in a highly unreliable
wireless environment, industrial wireless standards use a centralized wireless
stack design. In a centralized wireless stack design, a central manager
generates routes and a communication schedule for a multi-channel time division
multiple access communication (TDMA) based medium access control (MAC).
However, a centralized wireless stack design is highly energy consuming, not
scalable, and does not support frequent changes to networks or workloads. To
address these challenges, the following contributions are made in this
dissertation: (1) A scalable and distributed routing algorithm for industrial
IoT which generates graph routes, which offer a high degree of redundancy, (2)
A local and online scheduling algorithm that is scalable, energy-efficient, and
supports network/workload dynamics while ensuring reliability and real-time
performance, (3) An approach to minimize latency for in-band integration of
multiple low-power networks, (4) A fast and efficient test of schedulability
that determines if an application meets the real-time performance requirement
for given network topology, and (5) A distributed scheduling and control
co-design that balances the control performance requirement and real-time
performance for industrial IoT.
