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In this paper, we propose a resilient architecture for network and control co‐design, Wireless‐Simplex (W‐Simplex), which can ensure control performance by adaptively tuning the network and control parameters against wireless channel uncertainty in cyber‐physical systems. To the best of our knowledge, there has been no research into resilient network and control co‐design in response to the unreliable wireless channel. Our key observation is that rate adaptation may cause significant degradation in control performance or even system instability. This performance degradation is contrary to the intuition that rate adaptation provides a reliable link under wireless channel uncertainty. We explain the cause of this phenomenon and resolve the situation by proposing a resilient co‐design algorithm in an optimization framework. Our simulation study with ns‐2 shows the effectiveness of the proposed scheme to provide resilience of cyber‐physical systems against wireless channel uncertainty.

Design of wireless control systems has been extensively studied, which is one of the fundamental issues in cyber-physical systems. In this paper, we empirically investigate heterogeneous sampling rate assignment with a testbed when multiple physical systems are controlled through an IEEE 802.11 network. Among the critical design variables in wireless control systems, we focus on the sampling rates because they are always key control knobs regardless of network protocols. There has been little experimental research on heterogeneous sampling rate optimization for IEEE 802.11 wireless control systems, where the sampling rates of each control loop may have different values. We first formulate the co-design problem in an optimization framework with respect to the heterogeneous sampling rates by explicitly taking into account the relations of the sampling rates with the control cost, network energy consumption, and network delay. We further relax the problem as convex optimization, which is provably solved in polynomial time. Our empirical study ensures that the approximate solution is tightly close to the original optimum. To validate the proposed optimization framework, we build a disk-levitation tube testbed, which wirelessly controls the height of 20 disks at the same time. Our empirical study confirms that our optimization formulation is highly effective in practice. CCS CONCEPTS • Computer systems organization → Embedded and cyberphysical systems; Sensor networks; • Theory of computation → Mathematical optimization.

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