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Internet of Things (IoT) technologies enable development of reconfigurable manufacturing systems—a new generation of modularized industrial equipment suitable for highly customized manufacturing. Sequential control in these systems is largely based on discrete events, whereas their formal execution semantics is specified as control interpreted Petri nets (CIPN). Despite industry-wide use of programming languages based on the CIPN formalism, formal verification of such control applications in the presence of adversarial activity is not supported. Consequently, in this article, we introduce security-aware modeling and verification techniques for CIPN-based sequential control applications. Specifically, we show how CIPN models of networked industrial IoT controllers can be transformed into time Petri net (TPN)-based models and composed with plant and security-aware channel models in order to enable system-level verification of safety properties in the presence of network-based attacks. Additionally, we introduce realistic channel-specific attack models that capture adversarial behavior using nondeterminism. Moreover, we show how verification results can be utilized to introduce security patches and facilitate design of attack detectors that improve system resiliency and enable satisfaction of critical safety properties. Finally, we evaluate our framework on an industrial case study. Note to Practitioners—Our main goal is to provide formal security guarantees for distributed sequential controllers. Specifically, we target smart automation controllers geared toward Industrial IoT applications that are typically programed in C/C++ and are running applications originally designed in, for example, GRAFCET (IEC 60848)/SFC (IEC 61131-3) automation programming languages. Since existing tools for the design of distributed automation do not support system-level verification of relevant safety properties, we show how security-aware transceiver and communication models can be developed and composed with distributed controller models. Then, we show how existing tools for verification of time Petri nets can be used to verify relevant properties including safety and liveness of the distributed automation system in the presence of network-based attacks. To provide an end-to-end analysis as well as security patching, results of our analysis can be used to deploy suitable firmware updates during the stage when executable code for target controllers (e.g., in C/C++) is generated based on GRAFCET/SFC control models. We also show that security guarantees can be improved as the relevant safety/liveness properties can be verified after corresponding security patches are deployed. Finally, we show applicability of our framework on a realistic distributed pneumatic manipulator. [ABSTRACT FROM AUTHOR]

A digital microfluidic biochip (DMFB) enables miniaturization of immunoassays, point-of-care clinical diagnostics, and DNA sequencing. A recent generation of DMFBs uses a micro-electrode-dot-array (MEDA) architecture, which provides fine-grained control of droplets and real-time droplet sensing using the CMOS technology. However, microelectrodes in a MEDA biochip degrade when they are charged and discharged frequently during bioassay execution. In this article, we first make the key observation that the droplet-sensing operations contribute up to 94% of all microelectrode actuation in MEDA. Consequently, to reduce the number of droplet-sensing operations, we present a new microelectrode cell (MC) design as well as a selective-sensing method such that only a small fraction of microelectrodes perform droplet sensing during bioassay execution. The selection of microelectrodes that need to perform the droplet sensing is based on an analysis of experimental data. A comprehensive set of simulation results show that the total number of droplet-sensing operations is reduced to only 0.7%, which prolongs the lifespan of a MEDA biochip by $11\times $ without any impact on bioassay time-to-response. [ABSTRACT FROM AUTHOR]

A digital microfluidic biochip (DMFB) enables the miniaturization of immunoassays, point-of-care clinical diagnostics, and DNA sequencing. A recent generation of DMFBs uses a microelectrode-dot-array (MEDA) architecture, which provides fine-grained control of droplets and real-time droplet sensing using CMOS technology. However, microelectrodes in a MEDA biochip can degrade due to charge trapping when they are repeatedly charged and discharged during bioassay execution; such degradation leads to the failure of microelectrodes and erroneous bioassay outcomes. To address this problem, we first introduce a new microelectrode-cell design such that we can obtain the health status of all the microelectrodes in a MEDA biochip by employing the inherent sensing mechanism. Next, we present a stochastic game-based model for droplet manipulation, and a formal synthesis method for droplet routing that can dynamically change droplet transportation routes. This adaptation is based on the real-time health information obtained from microelectrodes. Comprehensive simulation results for four real-life bioassays show that our method increases the likelihood of successful bioassay completion with negligible impact on time-to-results. [ABSTRACT FROM AUTHOR]

A digital microfluidic biochip (DMFB) is an attractive technology platform for automating laboratory procedures in biochemistry. In recent years, DMFBs based on a micro-electrode-dot-array (MEDA) architecture have been proposed. MEDA biochips can provide advantages of better capability of droplet manipulation and real-time sensing ability. However, errors are likely to occur due to defects, chip degradation, and the lack of precision inherent in biochemical experiments. Therefore, an efficient error-recovery strategy is essential to ensure the correctness of assays executed on MEDA biochips. By exploiting MEDA-specific advances in droplet sensing, we present a novel error-recovery technique to dynamically reconfigure the biochip using real-time data provided by on-chip sensors. Local recovery strategies based on probabilistic-timed-automata are presented for various types of errors. An online synthesis technique and a control flow are also proposed to connect local-recovery procedures with global error recovery for the complete bioassay. Moreover, an integer linear programming-based method is also proposed to select the optimal local-recovery time for each operation. Laboratory experiments using a fabricated MEDA chip are used to characterize the outcomes of key droplet operations. The PRISM model checker and three benchmarks are used for an extensive set of simulations. Our results highlight the effectiveness of the proposed error-recovery strategy. [ABSTRACT FROM PUBLISHER]