We introduce CapPUF, a Physical Unclonable Function (PUF) that utilizes parasitic capacitances of I/O pins to generate unique identifiers (IDs) for Integrated Circuits (ICs) and the IoT nodes encompassing them. CapPUF is based on the observation that the I/O capacitance of each IC is unique and varies from one IC to another. It utilizes the capacitive sensing hardware commonly found in modern microcontroller ICs with touch-sensing capabilities, to measure I/O capacitances and generate the PUF ID. CapPUF also offers tamper resistance. It is thus challenging to reverse-engineer the PUF ID through physical probing of the I/Os. We evaluated CapPUF using 30 different PSoC-5 microcontroller ICs from Infineon Technologies. By measuring the parasitic capacitances of 18 I/Os, CapPUF generated a 153-bit PUF ID for each IC. Performance evaluation based on standard PUF metrics yielded promising results, including a reliability of 99.79%, uniqueness of 43.11%, and uniformity of 44.64%. Testing across various temperature and voltage settings demonstrated the PUF’s stability. Additionally, we demonstrated CapPUF’s resistance to probing attacks.

We evolve a methodology and define a metric to evaluate Tactile Cyber-Physical Systems (TCPS). Towards this goal, we use the step response analysis, a well-known control-theoretic method. The adoption includes replacing the human operator (or master) with a controller with known characteristics and analyzing its response to slave side step disturbances. The resulting step response curves demonstrate that the Quality of Control (QoC) metric is sensitive to control loop instabilities and serves as a good indicator of potential factors that contribute to operator-side cybersickness. Through experiments, we demonstrate how QoC accounts for network overheads such as the link latency and jitter and non-networking overheads such as the testbed settings and robot performances in a TCPS. We show that there is a one-to-one correlation between QoC and end-to-end latency, jitter, and packet drops of a TCPS implementation. We show through experiments how QoC can be used to estimate positional errors in tactile-visual control applications. Since higher positional errors can result in poor task performance, estimating them is useful in developing a better-performing TCPS. We also evaluate a TCPS using Fitts’ test and compare its results with QoC. We show that QoC is useful in distinguishing TCPS with differences in their specifications that are not detectable using Fitts’ test.

Cybersickness and control-loop instabilities are two main concerns in Tactile Cyber-Physical Systems (TCPS). TCPS applications demand stringent bounds on end-to-end latencies to avoid their occurrences. Traditional best-effort networks cannot guarantee packet latencies in the presence of external traffic. However, emerging deterministic networks such as IEEE 802.1 Time-Sensitive Networking (TSN) can isolate time-critical flows from external traffic using IEEE 802.1Qbv Time-Aware Shaper (TAS) to guarantee bounded end-to-end packet latencies. In this work, we develop eDDSCH-TSN, a decentralized dynamic scheduling protocol to configure non-overlapping gate slots in TAS-enabled TSN switches to support TCPS flows. eDDSCH-TSN supports plug-and-play operation of compatible TCPS terminals with guaranteed minimal end-to-end packet latencies. Compared to the state-of-the-art, eDDSCH-TSN provides three orders lower end-to-end packet latencies for TCPS flows in mid-size networks with 10 hops between source and destination terminals. Further, we also present PYTSN, an open-source discrete-event TSN simulator that we use for evaluating eDDSCH-TSN. In particular, we use PYTSN to show the isolation of TCPS flows from external traffic and plug-and-play operation of TCPS terminals.

Tactile Internet based Cyber-Physical Systems (TCPS) are highly sensitive to component and communication latencies and packet drops. Building a high performing TCPS, thus, necessitates experimenting with different hardware, algorithms, access technologies, and communication protocols. To facilitate such experiments, we have developed TCPSbed, a modular testbed for TCPS. TCPSbed facilitates the integration of different components, both real and simulated, to realize different TCPS applications and evaluate their latency and control performances. TCPSbed’s latency analyzer tool employs a novel method to isolate latencies of individual TCPS components such as the latencies contributed by actuation, sensing, algorithms, and by the network, all in an online fashion. TCPSbed’s method of analyzing stability is also novel. It involves the use of the step response analysis method, a classic control-theoretic method used for analyzing the stability of generic control systems. TCPSbed’s support for edge intelligence modules enables prediction of command and feedback signals at the network’s edge allowing TCPS applications to perform well in adverse network conditions. TCPSbed’s source-code, made available through our GitHub pageTactileInternet, allows developers to extend its features and functionalities further. In this paper, we describe the architecture and implementation details of TCPSbed and demonstrate its features through several proof-of-concept experiments.

In this paper, we evolve a methodology and define a metric to evaluate Tactile Cyber-Physical Systems (TCPS). Towards this goal, we adopt the step response analysis, a well-known control theoretic method. The adoption includes replacing the human operator (or master) with a controller with known characteristics and analyzing its response to slave side haptic sensor step changes. The resulting step response curves demonstrate that the Quality of Control (QoC) metric is sensitive to control loop instabilities and serves as a good indicator of cybersickness experienced by human operators. We demonstrate the efficacy of the proposed methodology and metric through experiments on a TCPS testbed. The experiments include assessing the suitability of several access technologies, intercontinental links and testbed configurations.